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Open Access

Peer-reviewed

Research Article

Does time management work? A meta-analysis

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Concordia University, Sir George Williams Campus, Montreal, Quebec, Canada

Roles Methodology, Validation

Affiliation FSA Ulaval, Laval University, Quebec City, Quebec, Canada

Roles Validation, Writing – review & editing

• Brad Aeon, 
• Aïda Faber, 
• Alexandra Panaccio

• Published: January 11, 2021
• https://doi.org/10.1371/journal.pone.0245066
• Reader Comments

Does time management work? We conducted a meta-analysis to assess the impact of time management on performance and well-being. Results show that time management is moderately related to job performance, academic achievement, and wellbeing. Time management also shows a moderate, negative relationship with distress. Interestingly, individual differences and contextual factors have a much weaker association with time management, with the notable exception of conscientiousness. The extremely weak correlation with gender was unexpected: women seem to manage time better than men, but the difference is very slight. Further, we found that the link between time management and job performance seems to increase over the years: time management is more likely to get people a positive performance review at work today than in the early 1990s. The link between time management and gender, too, seems to intensify: women’s time management scores have been on the rise for the past few decades. We also note that time management seems to enhance wellbeing—in particular, life satisfaction—to a greater extent than it does performance. This challenges the common perception that time management first and foremost enhances work performance, and that wellbeing is simply a byproduct.

Citation: Aeon B, Faber A, Panaccio A (2021) Does time management work? A meta-analysis. PLoS ONE 16(1): e0245066. https://doi.org/10.1371/journal.pone.0245066

Editor: Juan-Carlos Pérez-González, Universidad Nacional de Educacion a Distancia (UNED), SPAIN

Received: October 27, 2020; Accepted: December 21, 2020; Published: January 11, 2021

Copyright: © 2021 Aeon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist

Introduction

Stand-up comedian George Carlin once quipped that in the future a “time machine will be built, but no one will have time to use it” [ 1 ]. Portentously, booksellers now carry one-minute bedtime stories for time-starved parents [ 2 ] and people increasingly speed-watch videos and speed-listen to audio books [ 3 – 5 ]. These behaviors are symptomatic of an increasingly harried society suffering from chronic time poverty [ 6 ]. Work is intensifying—in 1965 about 50% of workers took breaks; in 2003, less than 2% [ 7 ]. Leisure, too, is intensifying: people strive to consume music, social media, vacations, and other leisure activities ever more efficiently [ 8 – 11 ].

In this frantic context, time management is often touted as a panacea for time pressure. Media outlets routinely extol the virtues of time management. Employers, educators, parents, and politicians exhort employees, students, children, and citizens to embrace more efficient ways to use time [ 12 – 16 ]. In light of this, it is not surprising that from 1960 to 2008 the frequency of books mentioning time management shot up by more than 2,700% [ 17 ].

Time management is defined as “a form of decision making used by individuals to structure, protect, and adapt their time to changing conditions” [ 18 ]. This means time management, as it is generally portrayed in the literature, comprises three components: structuring, protecting, and adapting time. Well-established time management measures reflect these concepts. Structuring time, for instance, is captured in such items as “Do you have a daily routine which you follow?” and “Do your main activities during the day fit together in a structured way?” [ 19 ]. Protecting time is reflected in items such as “Do you often find yourself doing things which interfere with your schoolwork simply because you hate to say ‘No’ to people?” [ 20 ]. And adapting time to changing conditions is seen in such items as “Uses waiting time” and “Evaluates daily schedule” [ 21 ].

Research has, furthermore, addressed several important aspects of time management, such as its relationship with work-life balance [ 22 ], whether gender differences in time management ability develop in early childhood [ 23 ], and whether organizations that encourage employees to manage their time experience less stress and turnover [ 24 ]. Despite the phenomenal popularity of this topic, however, academic research has yet to address some fundamental questions [ 25 – 27 ].

A critical gap in time management research is the question of whether time management works [ 28 , 29 ]. For instance, studies on the relationship between time management and job performance reveal mixed findings [ 30 , 31 ]. Furthermore, scholars’ attempts to synthesize the literature have so far been qualitative, precluding a quantitative overall assessment [ 18 , 32 , 33 ]. To tackle this gap in our understanding of time management, we conducted a meta-analysis. In addressing the question of whether time management works, we first clarify the criteria for effectiveness. In line with previous reviews, we find that virtually all studies focus on two broad outcomes: performance and wellbeing [ 32 ].

Overall, results suggest that time management enhances job performance, academic achievement, and wellbeing. Interestingly, individual differences (e.g., gender, age) and contextual factors (e.g., job autonomy, workload) were much less related to time management ability, with the notable exception of personality and, in particular, conscientiousness. Furthermore, the link between time management and job performance seems to grow stronger over the years, perhaps reflecting the growing need to manage time in increasingly autonomous and flexible jobs [ 34 – 37 ].

Overall, our findings provide academics, policymakers, and the general audience with better information to assess the value of time management. This information is all the more useful amid the growing doubts about the effectiveness of time management [ 38 ]. We elaborate on the contributions and implications of our findings in the discussion section.

What does it mean to say that time management works?

In the din of current debates over productivity, reduced workweeks, and flexible hours, time management comes to the fore as a major talking point. Given its popularity, it would seem rather pointless to question its effectiveness. Indeed, time management’s effectiveness is often taken for granted, presumably because time management offers a seemingly logical solution to a lifestyle that increasingly requires coordination and prioritization skills [ 39 , 40 ].

Yet, popular media outlets increasingly voice concern and frustration over time management, reflecting at least part of the population’s growing disenchantment [ 38 ]. This questioning of time management practices is becoming more common among academics as well [ 41 ]. As some have noted, the issue is not just whether time management works. Rather, the question is whether the techniques championed by time management gurus can be actually counterproductive or even harmful [ 26 , 42 ]. Other scholars have raised concerns that time management may foster an individualistic, quantitative, profit-oriented view of time that perpetuates social inequalities [ 43 , 44 ]. For instance, time management manuals beguile readers with promises of boundless productivity that may not be accessible to women, whose disproportionate share in care work, such as tending to young children, may not fit with typically male-oriented time management advice [ 45 ]. Similarly, bestselling time management books at times offer advice that reinforce global inequities. Some manuals, for instance, recommend delegating trivial tasks to private virtual assistants, who often work out of developing countries for measly wages [ 46 ]. Furthermore, time management manuals often ascribe a financial value to time—the most famous time management adage is that time is money. But recent studies show that thinking of time as money leads to a slew of negative outcomes, including time pressure, stress, impatience, inability to enjoy the moment, unwillingness to help others, and less concern with the environment [ 47 – 51 ]. What’s more, the pressure induced by thinking of time as money may ultimately undermine psychological and physical health [ 52 ].

Concerns over ethics and safety notwithstanding, a more prosaic question researchers have grappled with is whether time management works. Countless general-audience books and training programs have claimed that time management improves people’s lives in many ways, such as boosting performance at work [ 53 – 55 ]. Initial academic forays into addressing this question challenged those claims: time management didn’t seem to improve job performance [ 29 , 30 ]. Studies used a variety of research approaches, running the gamut from lab experiments, field experiments, longitudinal studies, and cross-sectional surveys to experience sampling [ 28 , 56 – 58 ]. Such studies occasionally did find an association between time management and performance, but only in highly motivated workers [ 59 ]; instances establishing a more straightforward link with performance were comparatively rare [ 31 ]. Summarizing these insights, reviews of the literature concluded that the link between time management and job performance is unclear; the link with wellbeing, however, seemed more compelling although not conclusive [ 18 , 32 ].

It is interesting to note that scholars often assess the effectiveness time management by its ability to influence some aspect of performance, wellbeing, or both. In other words, the question of whether time management works comes down to asking whether time management influences performance and wellbeing. The link between time management and performance at work can be traced historically to scientific management [ 60 ]. Nevertheless, even though modern time management can be traced to scientific management in male-dominated work settings, a feminist reading of time management history reveals that our modern idea of time management also descends from female time management thinkers of the same era, such as Lillian Gilbreth, who wrote treatises on efficient household management [ 43 , 61 , 62 ]. As the link between work output and time efficiency became clearer, industrialists went to great lengths to encourage workers to use their time more rationally [ 63 – 65 ]. Over time, people have internalized a duty to be productive and now see time management as a personal responsibility at work [ 43 , 66 , 67 ]. The link between time management and academic performance can be traced to schools’ historical emphasis on punctuality and timeliness. In more recent decades, however, homework expectations have soared [ 68 ] and parents, especially well-educated ones, have been spending more time preparing children for increasingly competitive college admissions [ 69 , 70 ]. In this context, time management is seen as a necessary skill for students to thrive in an increasingly cut-throat academic world. Finally, the link between time management and wellbeing harks back to ancient scholars, who emphasized that organizing one’s time was necessary to a life well-lived [ 71 , 72 ]. More recently, empirical studies in the 1980s examined the effect of time management on depressive symptoms that often plague unemployed people [ 19 , 73 ]. Subsequent studies surmised that the effective use of time might prevent a host of ills, such as work-life conflict and job stress [ 22 , 74 ].

Overall, then, various studies have looked into the effectiveness of time management. Yet, individual studies remain narrow in scope and reviews of the literature offer only a qualitative—and often inconclusive—assessment. To provide a more quantifiable answer to the question of whether time management works, we performed a meta-analysis, the methods of which we outline in what follows.

Literature search and inclusion criteria

We performed a comprehensive search using the keywords “time management” across the EBSCO databases Academic Search Complete , Business Source Complete , Computers & Applied Sciences Complete , Gender Studies Database , MEDLINE , Psychology and Behavioral Sciences Collection , PsycINFO , SocINDEX , and Education Source . The search had no restrictions regarding country and year of publication and included peer-reviewed articles up to 2019. To enhance comprehensiveness, we also ran a forward search on the three main time management measures: the Time Management Behavior Scale [ 21 ], the Time Structure Questionnaire [ 19 ], and the Time Management Questionnaire [ 20 ]. (A forward search tracks all the papers that have cited a particular work. In our case the forward search located all the papers citing the three time management scales available on Web of Science .)

Time management measures typically capture three aspects of time management: structuring, protecting, and adapting time to changing conditions. Structuring refers to how people map their activities to time using a schedule, a planner, or other devices that represent time in a systematic way [ 75 – 77 ]. Protecting refers to how people set boundaries around their time to repel intruders [ 78 , 79 ]. Examples include people saying no to time-consuming requests from colleagues or friends as well as turning off one’s work phone during family dinners. Finally, adapting one’s time to changing conditions means, simply put, to be responsive and flexible with one’s time structure [ 80 , 81 ]. Furthermore, time management measures typically probe behaviors related to these three dimensions (e.g., using a schedule to structure one’s day, making use of downtime), although they sometimes also capture people’s attitudes (e.g., whether people feel in control of their time).

As shown in Fig 1 , the initial search yielded 10,933 hits, excluding duplicates.

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https://doi.org/10.1371/journal.pone.0245066.g001

The search included no terms other than “time management” to afford the broadest possible coverage of time management correlates. Nevertheless, as shown in Table 1 , we focused exclusively on quantitative, empirical studies of time management in non-clinical samples. Successive rounds of screening, first by assessing paper titles and abstracts and then by perusing full-text articles, whittled down the number of eligible studies to 158 (see Fig 1 ).

https://doi.org/10.1371/journal.pone.0245066.t001

Data extraction and coding

We extracted eligible effect sizes from the final pool of studies; effect sizes were mostly based on means and correlations. In our initial data extraction, we coded time management correlates using the exact variable names found in each paper. For instance, “work-life imbalance” was initially coded in those exact terms, rather than “work-life conflict.” Virtually all time management correlates we extracted fell under the category of performance and/or wellbeing. This pattern tallies with previous reviews of the literature [ 18 , 32 ]. A sizable number of variables also fell under the category of individual differences and contextual factors, such as age, personality, and job autonomy. After careful assessment of the extracted variables, we developed a coding scheme using a nested structure shown in Table 2 .

https://doi.org/10.1371/journal.pone.0245066.t002

Aeon and Aguinis suggested that time management influences performance, although the strength of that relationship may depend on how performance is defined [ 18 ]. Specifically, they proposed that time management may have a stronger impact on behaviors conducive to performance (e.g., motivation, proactiveness) compared to assessments of performance (e.g., supervisor rankings). For this reason, we distinguish between results- and behavior-based performance in our coding scheme, both in professional and academic settings. Furthermore, wellbeing indicators can be positive (e.g., life satisfaction) or negative (e.g., anxiety). We expect time management to influence these variables in opposite ways; it would thus make little sense to analyze them jointly. Accordingly, we differentiate between wellbeing (positive) and distress (negative).

In our second round of coding, we used the scheme shown in Table 2 to cluster together kindred variables. For instance, we grouped “work-life imbalance,” “work-life conflict” and “work-family conflict” under an overarching “work-life conflict” category. The authors reviewed each variable code and resolved rare discrepancies to ultimately agree on all coded variables. Note that certain variables, such as self-actualization, covered only one study (i.e., one effect size). While one or two effect sizes is not enough to conduct a meta-analysis, they can nonetheless be grouped with other effect sizes belonging to the same category (e.g., self-actualization and sense of purpose belong the broader category of overall wellbeing). For this reason, we included variables with one or two effect sizes for comprehensiveness.

Meta-analytic procedures

We conducted all meta-analyses following the variables and cluster of variables outlined in Table 2 . We opted to run all analyses with a random effects model. The alternative—a fixed effects model—assumes that all studies share a common true effect size (i.e., linking time management and a given outcome) which they approximate. This assumption is unrealistic because it implies that the factors influencing the effect size are the same in all studies [ 83 ]. In other words, a fixed effects model assumes that the factors affecting time management are similar across all studies—the fallacy underlying this assumption was the main theme of Aeon and Aguinis’s review [ 18 ]. To perform our analyses, we used Comprehensive Meta-Analysis v.3 [ 84 ], a program considered highly reliable and valid in various systematic assessments [ 85 , 86 ].

In many cases, studies reported how variables correlated with an overall time management score. In some cases, however, studies reported only correlations with discrete time management subscales (e.g., short-range planning, attitudes toward time, use of time management tools), leaving out the overall effect. In such cases, we averaged out the effect sizes of the subscales to compute a summary effect [ 83 ]. This was necessary not only because meta-analyses admit only one effect size per study, but also because our focus is on time management as a whole rather than on subscales. Similarly, when we analyzed the link between time management and a high-level cluster of variables (e.g., overall wellbeing rather than specific variables such as life satisfaction), there were studies with more than one relevant outcome (e.g., a study that captured both life satisfaction and job satisfaction). Again, because meta-analyses allow for only one effect size (i.e., variable) per study, we used the mean of different variables to compute an overall effect sizes in studies that featured more than one outcome [ 83 ].

Overall description of the literature

We analyzed 158 studies for a total number of 490 effect sizes. 21 studies explored performance in a professional context, 76 performance in an academic context, 30 investigated wellbeing (positive), and 58 distress. Interestingly, studies did not systematically report individual differences, as evidenced by the fact that only 21 studies reported correlations with age, and only between 10 and 15 studies measured personality (depending on the personality trait). Studies that measured contextual factors were fewer still—between 3 and 7 (depending on the contextual factor). These figures fit with Aeon and Aguinis’s observation that the time management literature often overlooks internal and external factors that can influence the way people manage time [ 18 ].

With one exception, we found no papers fitting our inclusion criteria before the mid-1980s. Publication trends also indicate an uptick in time management studies around the turn of the millennium, with an even higher number around the 2010s. This trend is consistent with the one Shipp and Cole identified, revealing a surge in time-related papers in organizational behavior around the end of the 1980s [ 87 ].

It is also interesting to note that the first modern time management books came out in the early 1970s, including the The Time Trap (1972), by Alec MacKenzie and How to Get Control of your Time and your Life (1973), by Alan Lakein. These books inspired early modern time management research [ 21 , 58 , 88 ]. It is thus very likely that the impetus for modern time management research came from popular practitioner manuals.

To assess potential bias in our sample of studies, we computed different estimates of publication bias (see Table 3 ). Overall, publication bias remains relatively low (see funnel plots in S1). Publication bias occurs when there is a bias against nonsignificant or even negative results because such results are seen as unsurprising and not counterintuitive. In this case, however, the fact that time management is generally expected to lead to positive outcomes offers an incentive to publish nonsignificant or negative results, which would be counterintuitive [ 89 ]. By the same token, the fact that some people feel that time management is ineffective [ 38 ] provides an incentive to publish papers that link time management with positive outcomes. In other words, opposite social expectations surrounding time management might reduce publication bias.

https://doi.org/10.1371/journal.pone.0245066.t003

Finally, we note that the link between time management and virtually all outcomes studied is highly heterogeneous (as measured, for instance, by Cochran’s Q and Higgins & Thompson’s I 2 ; see tables below). This high level of heterogeneity suggests that future research should pay more attention to moderating factors (e.g., individual differences).

Time management and performance in professional settings

Overall, time management has a moderate impact on performance at work, with correlations hovering around r = .25. We distinguish between results-based and behavior-based performance. The former measures performance as an outcome (e.g., performance appraisals by supervisors) whereas the latter measures performance as behavioral contributions (e.g., motivation, job involvement). Time management seems related to both types of performance. Although the effect size for results-based performance is lower than that of behavior-based performance, moderation analysis reveals the difference is not significant (p > .05), challenging Aeon and Aguinis’s conclusions [ 18 ].

Interestingly, the link between time management and performance displays much less heterogeneity (see Q and I 2 statistics in Table 4 ) than the link between time management and other outcomes (see tables below). The studies we summarize in Table 4 include both experimental and non-experimental designs; they also use different time management measures. As such, we can discount, to a certain extent, the effect of methodological diversity. We can perhaps explain the lower heterogeneity by the fact that when people hold a full-time job, they usually are at a relatively stable stage in life. In school, by contrast, a constellation of factors (e.g., financial stability and marital status, to name a few) conspire to affect time management outcomes. Furthermore, work contexts are a typically more closed system than life in general. For this reason, fewer factors stand to disrupt the link between time management and job performance than that between time management and, say, life satisfaction. Corroborating this, note how, in Table 6 below, the link between time management and job satisfaction ( I 2 = 58.70) is much less heterogeneous than the one between time management and life satisfaction ( I 2 = 95.45).

https://doi.org/10.1371/journal.pone.0245066.t004

Moreover, we note that the relationship between time management and job performance (see Fig 2 ) significantly increases over the years ( B = .0106, p < .01, Q model = 8.52(1), Q residual = 15.54(9), I 2 = 42.08, R 2 analog = .75).

https://doi.org/10.1371/journal.pone.0245066.g002

Time management and performance in academic settings

Overall, the effect of time management on performance seems to be slightly higher in academic settings compared to work settings, although the magnitude of the effect remains moderate (see Table 5 ). Here again, we distinguish between results- and behavior-based performance. Time management’s impact on behavior-based performance seems much higher than on results-based performance—a much wider difference than the one we observed in professional settings. This suggests than results-based performance in academic settings depends less on time management than results-based performance in professional settings. This means that time management is more likely to get people a good performance review at work than a strong GPA in school.

https://doi.org/10.1371/journal.pone.0245066.t005

In particular, time management seems to be much more negatively related to procrastination in school than at work. Although we cannot establish causation in all studies, we note that some of them featured experimental designs that established a causal effect of time management on reducing procrastination [ 90 ].

Interestingly, time management was linked to all types of results-based performance except for standardized tests. This is perhaps due to the fact that standardized tests tap more into fluid intelligence, a measure of intelligence independent of acquired knowledge [ 91 ]. GPA and regular exam scores, in contrast, tap more into crystallized intelligence, which depends mostly on accumulated knowledge. Time management can thus assist students in organizing their time to acquire the knowledge necessary to ace a regular exam; for standardized exams that depend less on knowledge and more on intelligence, however, time management may be less helpful. Evidence from other studies bears this out: middle school students’ IQ predicts standardized achievement tests scores better than self-control while self-control predicts report card grades better than IQ [ 92 ]. (For our purposes, we can use self-control as a very rough proxy for time management.) Relatedly, we found no significant relationship between time management and cognitive ability in our meta-analysis (see Table 8 ).

Time management and wellbeing

On the whole, time management has a slightly stronger impact on wellbeing than on performance. This is unexpected, considering how the dominant discourse points to time management as a skill for professional career development. Of course, the dominant discourse also frames time management as necessary for wellbeing and stress reduction, but to a much lesser extent. Our finding that time management has a stronger influence on wellbeing in no way negates the importance of time management as a work skill. Rather, this finding challenges the intuitive notion that time management is more effective for work than for other life domains. As further evidence, notice how in Table 6 the effect of time management on life satisfaction is 72% stronger than that on job satisfaction.

https://doi.org/10.1371/journal.pone.0245066.t006

Time management and distress

Time management seems to allay various forms of distress, although to a lesser extent than it enhances wellbeing. The alleviating effect on psychological distress is particularly strong ( r = -0.358; see Table 7 ).

https://doi.org/10.1371/journal.pone.0245066.t007

That time management has a weaker effect on distress should not be surprising. First, wellbeing and distress are not two poles on opposite ends of a spectrum. Although related, wellbeing and distress are distinct [ 93 ]. Thus, there is no reason to expect time management to have a symmetrical effect on wellbeing and distress. Second, and relatedly, the factors that influence wellbeing and distress are also distinct. Specifically, self-efficacy (i.e., seeing oneself as capable) is a distinct predictor of wellbeing while neuroticism and life events in general are distinct predictors of distress [ 94 ]. It stands to reason that time management can enhance self-efficacy. (Or, alternatively, that people high in self-efficacy would be more likely to engage in time management, although experimental evidence suggests that time management training makes people feel more in control of their time [ 89 ]; it is thus plausible that time management may have a causal effect on self-efficacy. Relatedly, note how time management ability is strongly related to internal locus of control in Table 8 ) In contrast, time management can do considerably less in the way of tackling neuroticism and dampening the emotional impact of tragic life events. In other words, the factors that affect wellbeing may be much more within the purview of time management than the factors that affect distress. For this reason, time management may be less effective in alleviating distress than in improving wellbeing.

https://doi.org/10.1371/journal.pone.0245066.t008

Time management and individual differences

Time management is, overall, less related to individual differences than to other variables.

Age, for instance, hardly correlates with time management (with a relatively high consistency between studies, I 2 = 55.79, see Table 8 above).

Similarly, gender only tenuously correlates with time management, although in the expected direction: women seem to have stronger time management abilities than men. The very weak association with gender ( r = -0.087) is particularly surprising given women’s well-documented superior self-regulation skills [ 95 ]. That being said, women’s time management abilities seem to grow stronger over the years ( N = 37, B = -.0049, p < .05, Q model = 3.89(1), Q residual = 218.42(35), I 2 = 83.98, R 2 analog = .03; also see Fig 3 below). More realistically, this increase may not be due to women’s time management abilities getting stronger per se but, rather, to the fact that women now have more freedom to manage their time [ 96 ].

https://doi.org/10.1371/journal.pone.0245066.g003

Other demographic indicators, such as education and number of children, were nonsignificant. Similarly, the relationships between time management and personal attributes and attitudes were either weak or nonsignificant, save for two notable exceptions. First, the link between time management and internal locus of control (i.e., the extent to which people perceive they’re in control of their lives) is quite substantial. This is not surprising, because time management presupposes that people believe they can change their lives. Alternatively, it may be that time management helps people strengthen their internal locus of control, as experimental evidence suggests [ 89 ]. Second, the link between time management and self-esteem is equally substantial. Here again, one can make the argument either way: people with high self-esteem might be confident enough to manage their time or, conversely, time management may boost self-esteem. The two options are not mutually exclusive: people with internal loci of control and high self-esteem levels can feel even more in control of their lives and better about themselves through time management.

We also note a very weak but statistically significant negative association between time management and multitasking. It has almost become commonsense that multitasking does not lead to performance [ 97 ]. As a result, people with stronger time management skills might deliberately steer clear of this notoriously ineffective strategy.

In addition, time management was mildly related to hours spent studying but not hours spent working. (These variables cover only student samples working part- or full-time and thus do not apply to non-student populations.) This is consistent with time-use studies revealing that teenagers and young adults spend less time working and more time studying [ 98 ]. Students who manage their time likely have well-defined intentions, and trends suggest those intentions will target education over work because, it is hoped, education offers larger payoffs over the long-term [ 99 ].

In terms of contextual factors, time management does not correlate significantly with job autonomy. This is surprising, as we expected autonomy to be a prerequisite for time management (i.e., you can’t manage time if you don’t have the freedom to). Nevertheless, qualitative studies have shown how even in environments that afford little autonomy (e.g., restaurants), workers can carve out pockets of time freedom to momentarily cut loose [ 100 ]. Thus, time management behaviors may flourish even in the most stymying settings. In addition, the fact that time management is associated with less role overload and previous attendance of time management training programs makes sense: time management can mitigate the effect of heavy workloads and time management training, presumably, improves time management skills.

Finally, time management is linked to all personality traits. Moreover, previous reviews of the literature have commented on the link between time management and conscientiousness in particular [ 32 ]. What our study reveals is the substantial magnitude of the effect ( r = 0.451). The relationship is not surprising: conscientiousness entails orderliness and organization, which overlap significantly with time management. That time management correlates so strongly with personality (and so little with other individual differences) lends credence to the dispositional view of time management [ 101 – 103 ]. However, this finding should not be taken to mean that time management is a highly inheritable, fixed ability. Having a “you either have it or you don’t” view of time management is not only counterproductive [ 104 ] but also runs counter to evidence showing that time management training does, in fact, help people manage their time better.

Does time management work? It seems so. Time management has a moderate influence on job performance, academic achievement, and wellbeing. These three outcomes play an important role in people’s lives. Doing a good job at work, getting top grades in school, and nurturing psychological wellbeing contribute to a life well lived. Widespread exhortations to get better at time management are thus not unfounded: the importance of time management is hard to overstate.

Contributions

Beyond answering the question of whether time management works, this study contributes to the literature in three major ways. First, we quantify the impact of time management on several outcomes. We thus not only address the question of whether time management works, but also, and importantly, gauge to what extent time management works. Indeed, our meta-analysis covers 53,957 participants, which allows for a much more precise, quantified assessment of time management effectiveness compared to qualitative reviews.

Second, this meta-analysis systematically assesses relationships between time management and a host of individual differences and contextual factors. This helps us draw a more accurate portrait of potential antecedents of higher (or lower) scores on time management measures.

Third, our findings challenge intuitive ideas concerning what time management is for. Specifically, we found that time management enhances wellbeing—and in particular life satisfaction—to a greater extent than it does various types of performance. This runs against the popular belief that time management primarily helps people perform better and that wellbeing is simply a byproduct of better performance. Of course, it may be that wellbeing gains, even if higher than performance gains, hinge on performance; that is to say, people may need to perform better as a prerequisite to feeling happier. But this argument doesn’t jibe with experiments showing that even in the absence of performance gains, time management interventions do increase wellbeing [ 89 ]. This argument also founders in the face of evidence linking time management with wellbeing among the unemployed [ 105 ], unemployment being an environment where performance plays a negligible role, if any. As such, this meta-analysis lends support to definitions of time management that are not work- or performance-centric.

Future research and limitations

This meta-analysis questions whether time management should be seen chiefly as a performance device. Our questioning is neither novel nor subversive: historically people have managed time for other reasons than efficiency, such as spiritual devotion and philosophical contemplation [ 72 , 106 , 107 ]. It is only with relatively recent events, such as the Industrial Revolution and waves of corporate downsizing, that time management has become synonymous with productivity [ 43 , 65 ]. We hope future research will widen its scope and look more into outcomes other than performance, such as developing a sense of meaning in life [ 108 ]. One of the earliest time management studies, for instance, explored how time management relates to having a sense of purpose [ 73 ]. However, very few studies followed suit since. Time management thus stands to become a richer, more inclusive research area by investigating a wider array of outcomes.

In addition, despite the encouraging findings of this meta-analysis we must refrain from seeing time management as a panacea. Though time management can make people’s lives better, it is not clear how easy it is for people to learn how to manage their time adequately. More importantly, being “good” at time management is often a function of income, education, and various types of privilege [ 42 , 43 , 46 , 109 ]. The hackneyed maxim that “you have as many hours in a day as Beyoncé,” for instance, blames people for their “poor” time management in pointing out that successful people have just as much time but still manage to get ahead. Yet this ill-conceived maxim glosses over the fact that Beyoncé and her ilk do, in a sense, have more hours in a day than average people who can’t afford a nanny, chauffeur, in-house chefs, and a bevy of personal assistants. Future research should thus look into ways to make time management more accessible.

Furthermore, this meta-analysis rests on the assumption that time management training programs do enhance people’s time management skills. Previous reviews have noted the opacity surrounding time management interventions—studies often don’t explain what, exactly, is taught in time management training seminars [ 18 ]. As a result, comparing the effect of different interventions might come down to comparing apples and oranges. (This might partly account for the high heterogeneity between studies.) We hope that our definition of time management will spur future research into crafting more consistent, valid, and generalizable interventions that will allow for more meaningful comparisons.

Finally, most time management studies are cross-sectional. Yet it is very likely that the effect of time management compounds over time. If time management can help students get better grades, for instance, those grades can lead to better jobs down the line [ 110 ]. Crucially, learning a skill takes time, and if time management helps people make the time to learn a skill, then time management stands to dramatically enrich people’s lives. For this reason, longitudinal studies can track different cohorts to see how time management affects people’s lives over time. We expect that developing time management skills early on in life can create a compound effect whereby people acquire a variety of other skills thanks to their ability to make time.

Overall, this study offers the most comprehensive, precise, and fine-grained assessment of time management to date. We address the longstanding debate over whether time management influences job performance in revealing a positive, albeit moderate effect. Interestingly, we found that time management impacts wellbeing—and in particular life satisfaction—to a greater extent than performance. That means time management may be primarily a wellbeing enhancer, rather than a performance booster. Furthermore, individual and external factors played a minor role in time management, although this does not necessarily mean that time management’s effectiveness is universal. Rather, we need more research that focuses on the internal and external variables that affect time management outcomes. We hope this study will tantalize future research and guide practitioners in their attempt to make better use of their time.

Supporting information

S1 checklist. prisma 2009 checklist..

https://doi.org/10.1371/journal.pone.0245066.s001

S1 File. Funnel plots.

https://doi.org/10.1371/journal.pone.0245066.s002

S2 File. Dataset.

https://doi.org/10.1371/journal.pone.0245066.s003

Acknowledgments

We would like to take this opportunity to acknowledge our colleagues for their invaluable help: Mengchan Gao, Talha Aziz, Elizabeth Eley, Robert Nason, Andrew Ryder, Tracy Hecht, and Caroline Aubé.

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Relation between stress, time management, and academic achievement in preclinical medical education: A systematic review and meta-analysis

Soleiman ahmady.

Department Medical Education, Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Nasrin Khajeali

1 Deprtment of Medical Education, Fasa University of Medical Sciences, Fasa, Iran

Masomeh Kalantarion

Farshad sharifi.

2 Elderly Health Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

Mehdi Yaseri

3 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of medical Sciences, Tehran, Iran

Identifying the learners' problems is important. Besides, many factors are associated with academic failure, among which time management and stress are more important than any others based on evidence. By using a systematic review and meta-analysis, this study aims to synthesize the findings of studies about the correlation of time management and stress with academic failure to suggest a more in-depth insight into the effect of these two factors on academic failure. Four databases were searched from the inception of January 2018. Publication bias was evaluated visually using funnel plots and sized up by Egger's test. Ninety-four articles were found to be qualified for inclusion after full-text review and additional manual reference made. Of these, 8 were studies of educational interventions that were reviewed in this paper. Regarding the relation of stress and academic performance, the Funnel plot (results not shown) and Egger's test showed no publication bias in the studies ( P = 0.719). Based on this result, the estimated pooled correlation (reverted by hyperbolic tangent transformation) between stress and academic performance was found to be -0.32 (95% confidence interval: -0.38–-0.25). In conclusion, the review recognized a series of potentially mutable medium-to-large correlates of academic achievement, time management, and stress. It would be essential to have experimental data on how easily such self-regulatory capacities can be altered, and these interventions could help students enhance their potential, providing empirical tests for offered process models of academic achievement.

Introduction

Identifying the learners' issues early and offering advice from the start is an essential investment in the training and progress of future practitioners.[ 1 ] The National Committee on Internal Medicine (1999) has described the learner as a trainee who identifies the underlying problems that required to be addressed by a program leader or manager.[ 2 ] Some educators have expressed their concern about difficult learners in case they negatively affect educational programs and other students. Although studies may predict different elements, medical educators would like to be able to predict merely.[ 3 ]

Academic failure is a problem that has turned out to be a central concern for countries in different parts of the world. In order to find the different causes of academic failure, several research projects in this field have been performed. Typically, students experience academic issues with academic and nonacademic characteristics, and the various combinations of reasons for academic failure result in different types of student profiles, suggesting different strategies of intervention.[ 4 ]

The evidence indicates that when intervention techniques are applied for failed students, their performance improves in the subsequent academic year.[ 5 ] Ahmady et al . indicate that failed students can be assisted in becoming successful in the classroom when appropriate intervention techniques are applied. Usually, in research concerning student learning and behavioral outcomes, certain personal attributes of the students are measured, which are then related to some outcome measure. Among these, study skills, such as time management, is one of the factors affecting academic achievement and also stress.[ 6 ]

Personal characteristics are personality, motivation, self-concept, cognitive style, intelligence, and locus of control. Nevertheless, some environmental and contextual difficulties, which lead to unsuccessful learning, are not considered. The purpose of this study is to identify the factors related to the failure of college students.[ 4 ]

Many factors have been related to academic failure.[ 1 ] Ahmady et al . indicate that 21 factors related to academic failure in preclinical medical students, and study skill and stress is reported to be more important among other factors. We have found several studies[ 7 , 8 ] that suggest time management is perhaps more important than any other study strategies.[ 6 ]

West et al . (2011) show that study skills (time management) are usually powerful predictors of first-semester academic performance in medical school and other higher education disciplines.[ 7 ] Practical time management skills are essential. Students who do not plan their time effectively run out of time before running out of the content. Relatively, few studies have investigated the joint contribution of academic performance and study skills.[ 9 , 10 , 11 , 12 ]

Another reason is that medical education is inherently stressful and demanding. An ideal level of stress can increase the level of learning, while over-stress can cause health problems, leading to a decrease in students' self-esteem and failure in their academic competence. A high level of stress can affect the students' learning process in medical school negatively.[ 13 ] Sources of stress include curriculum, personal competence, tolerance, and time outside of medical school. Increased anxiety is associated with increased depression and anxiety.[ 14 , 15 ]

Knowledge about the effective size of these factors (time management and stress) can help policymakers, managers, medical teachers, and counselors track the students' academic failure. It is essential to integrate the evidence produced through all studies to obtain useful information, help medical students, and provide directions for future studies. To the best of the authors' knowledge, this is the first systematic review and meta-analysis of the findings of studies concerning time management and stress associated with academic failure. It suggests a more in-depth insight into the effect of these two factors on the students' academic failure.

Materials and Methods

This systematic review was carried out following PRISMA guidelines.[ 16 ]

Search strategy

PubMed, Web of Knowledge Educational Resources, and Information Center, and Scopus databases were searched.

Using the search No., time limitation was set for searching the resources. For comprehensiveness of the search, the following keywords were used in the abstract, title, and keyword sections: “academic performance” and “academic failure” or “academic achievement” and “drop out;” “medical student” and “struggle student;” “time management” and “stress.” Hand searching was also done in Medical Teacher and Medical Education journals. Furthermore, reference lists of many articles were reviewed to identify the relevant papers. The most celebrated authors in this area were contacted for “gray literature:” conference proceedings, unpublished studies, and internal reports. The obtained data were included in the study. The inclusion criteria for the articles were as follows: being a correlation between study skill and stress with academic performance, observational study design, preclinical medical students, without any language, or time limitation from January 1987 to January 2018.

Inclusion and exclusion criteria

The exclusion criteria for the search were being secondary research or not being a preclinical medical student. All the databases were searched by one reviewer, and Endnote X8 was applied for data management. The articles were imported into Endnote X8 to remove the duplicate data before importing the data into Excel. The imported data were the list of authors, titles, journals, and years of publication. Two team members (N Kh and SA) screened the titles and abstracts to determine the potentially relevant articles. The full-text version of the study was then reviewed if the study met the selection criteria or if there was any doubt concerning the study's eligibility. Furthermore, a third independent researcher was requested to resolve any disagreements.

Quality assessment

The study quality was rated on STROBE guidelines. Over 100 journals have endorsed STROBE guidelines ( http://www.strobe-statement.org ).[ 17 , 18 , 19 , 20 ] Studies were rated for each of the following: title and abstract, introduction, methods, results, discussion, data collection methods, and other information. This yielded a quality rating with a range from 8 to 22.

Data extraction and analysis

As several different variables were tested in each article, thus the article names were repeated. Studies were coded according to author (publication year), effective factors in academic performance, measurement method, type of R, type of analysis, location, and type of study [ Table 1 ]. Two reviewers extracted data from the included articles. They compared extractions and resolved differences through discussion or with a third nonauthors.

Data extraction of articles related to study skill (time management) and stress

SMART=Study management and academic results test

This meta-analysis was conducted via Stata 15.0 software (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). As the distribution of the correlation was highly skewed, the inverse hyperbolic tangent transformation (z = tangh-1(rho) =1/2 ln ((rho + 1)/(rho - 1))) was applied. All the calculations were based on the transformed values. The Cochran's Q test and The I 2 statistic were used to assess and characterize the extent of the heterogeneity, respectively. I 2 -50% was indicated as considerable heterogeneity. Given the high heterogeneity of the data, the random-effects model was used. We used hyperbolic tangent transformation (rho = tangh (z) = [e 2 z - 1]/[e 2 z + 1]) to change the pooled estimates (and its 95% confidence intervals [CI]) to the pooled correlation. All the individual studies results were reported with 95% CIs and demonstrated in a forest plot. Publication bias was evaluated visually using funnel plots and sized up by Egger's test. A P < 0.05 was statistically significant.

The study selection initial database searches retrieved 13,123 articles. After exclusion of duplicate references, conference abstracts, screening titles and abstracts, 6305 articles were selected for further review (title and abstract). A total of 100 articles were found eligible for inclusion after full-text review and additional manual reference screening. Five articles, including the studies of educational interventions, were reviewed in this paper [ Figure 1 ].

Study flowchart demonstrates the inclusion-exclusion process

Study characteristics

Study setting and populations.

Most of the studies were completed in Europe (50%), 2 (25%) USA, and 2 (25%) Asia.

Type of design

The majority design in the articles was prospective, followed by correlational [ Table 1 ].

Aims of studies

The purpose of the studies was to report the effect level of the study skill (time management) and stress on academic performance.

Regarding the relation of stress and academic performance, the Egger' test and Funnel plot (results not shown) indicated that there was no publication bias in the studies ( P = 0.719). The same was obtained when we evaluated the relation of the study skill (time management) and academic performance, not statistically significant ( P = 0.833).

The individual studies transformed between stress and academic performance were shown in a forest plot [ Figure 2 ]; based on this result, pulled correlation (result from hyperbolic tangent transformation) between stress and academic performance was found to be – 0.32 (95% CI [-0.38, -0.25]).

Correlation between stress and academic failure

The individual studies transformed between study skill (time management) and academic performance were demonstrated in a forest plot [ Figure 3 ]; based on this result, pulled correlation (result from hyperbolic tangent transformation) between stress and academic performance was found to be 0.39 (95% CI [0.29, 0.47]).

Correlation between study skill (time management) and academic failure

To the authors' knowledge, this is the first systematic review and meta-analysis of the evidence concerning the effect of study skill (time management) and stress on academic performance.

Overall, with this review, we found medium to high-quality evidence from a modest number of studies, suggesting that study skills (time management) and stress significantly affect academic achievement: study skill (time management) (ES: 0.39) and stress (ES: -0.32).

However, research suggests that study skills (time management) are also significant factors affecting academic achievement in medical schools.[ 8 , 21 , 22 , 23 , 24 , 25 ]

Study skills are one of the more reliable predictors of first-semester total grades.[ 7 ] The predictive strength of first-semester final average is accounted for by scores on time management,

Teaching time management rules, such as preventing postponement, previewing data, reviewing material shortly right after presented, prioritizing items, handling study periods, reviewing repeatedly, and making time for other commitments, is an essential component.[ 26 ]

For instance, sometimes, students procrastinate studying material they have problem with or do not see the applicability of. In this instance, seminars or counseling, which concentrate on arranging these projects for one's optimum time of day such that it will be simpler to focus on the material and reduce procrastination, may be offered.[ 27 ]

Time management aims to improve the nature of activities that require a limited time. The inability to use time in the learning process is the main problem for the students. Previous studies have shown that the excessive intensity of courses affects productivity negatively. In this situation, medical students, who have to cope with an intensive training curriculum, may inevitably but efficiently make the most of their time. To succeed in the education process, medical students must set goals for their education and plan for appropriate academic progress. They, therefore, have to follow course schedules, be prepared for examinations, and use the time available for other activities.[ 28 ]

Another significant issue is that there is a substantial increase in stress levels during study times, in the 1 st year in particular.[ 29 ] Perceived stress is a key factor in discriminating among students with low versus high academic performance.[ 30 ] First-year students face different challenges that can be seen as potential stressors. They have to get familiar with a new environment, get into contact with other students, choose their lectures and seminars, participate in extracurricular activities, and manage their first tests. Another source of students' perceived stress is time-related demands, such as an increasing workload, time pressure, and regulation of their self-study.[ 31 ]

Pfeiffer notes that too much stress is negatively associated with students' readiness, focus, and performance, while positive stress helps the student achieve maximum performance.[ 32 ] It should also be recommended that this situation is the first exam in which students are exposed to a significant amount of integrated curriculum. Often, students are suggested by their seniors to pursue an education in the coming years; thus, they can lower the stress levels, control stress in a better way, and enhance their academic performance.

Managing self-efficacy, flexibility, and social support also are related to academic achievement; thus, intervening to enhance self-efficacy, resilience, and social support may lessen the perception that stress is affecting performance.

Limitations

The limitation of this review is that statistically significant time management and stress have not been reported in all studies.

Conclusions

This review of 31 years of research on the correlation of stress, time management, and academic failure has been devoted to the understanding of the effect of time management and stress on academic achievement of medical students. This systematic review and meta-analysis are the first in the field. We wish that this work provides a base for more focused research and intervention. Finally, our review and others have identified a series of potentially modifiable medium-to-large correlates of academic achievement, time management and stress in particular. It would be worthful to have experimental data on how easily such self-regulatory capacities can be altered, as well as for whom, over what period, and to what extent do such changes to be effective academic performance. These interventions could help students develop their potential and would provide empirical tests for proposed process models of academic achievement.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgments

The authors would like to thank all of authorities and students at Medical School in Shahid Beheshti University of Medical Sciences for their assistance.

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Personnel Review

ISSN : 0048-3486

Article publication date: 13 February 2007

The purpose of this article is to provide an overview for those interested in the current state‐of‐the‐art in time management research.

Design/methodology/approach

This review includes 32 empirical studies on time management conducted between 1982 and 2004.

The review demonstrates that time management behaviours relate positively to perceived control of time, job satisfaction, and health, and negatively to stress. The relationship with work and academic performance is not clear. Time management training seems to enhance time management skills, but this does not automatically transfer to better performance.

Research limitations/implications

The reviewed research displays several limitations. First, time management has been defined and operationalised in a variety of ways. Some instruments were not reliable or valid, which could account for unstable findings. Second, many of the studies were based on cross‐sectional surveys and used self‐reports only. Third, very little attention was given to job and organizational factors. There is a need for more rigorous research into the mechanisms of time management and the factors that contribute to its effectiveness. The ways in which stable time management behaviours can be established also deserves further investigation.

Practical implications

This review makes clear which effects may be expected of time management, which aspects may be most useful for which individuals, and which work characteristics would enhance or hinder positive effects. Its outcomes may help to develop more effective time management practices.

Originality/value

This review is the first to offer an overview of empirical research on time management. Both practice and scientific research may benefit from the description of previous attempts to measure and test the popular notions of time management.

• Time measurement
• Job satisfaction
• Performance management

Claessens, B.J.C. , van Eerde, W. , Rutte, C.G. and Roe, R.A. (2007), "A review of the time management literature", Personnel Review , Vol. 36 No. 2, pp. 255-276. https://doi.org/10.1108/00483480710726136

Emerald Group Publishing Limited

Copyright © 2007, Emerald Group Publishing Limited

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Time Management Case Studies: Two Examples for Non-Profit Organizations (From My Time Management Workshop)

Deanna went through her pile of "to-do" lists, checking off item after item. Done, done, done, done, done, done. With each flick of the pen, one more task was lifted from her shoulders. Then she copied the tasks that remained on to a new list:

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7 Essential Time Management Skills

Take control of your time with these seven key time management skills.

Learning how to effectively manage your time enables you to meet deadlines, explore new ideas, and find a healthy work-life balance. If you feel overwhelmed and overworked, learning a few time management tools may help you reduce stress and plan how to meet your goals.

In this article, we'll outline seven skills that, when developed, can give a big boost to your productivity.

What is time management?

Time management is the art of effectively planning your time. This allows you to efficiently and productively complete the activities and tasks you need to in the appropriate amount of time. Time management also involves prioritizing your to-do list so that you complete urgent or important tasks before others. This helps to avoid missing important deadlines or rushing through important tasks.

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7 time management skills

If you're ready to take control of your time, work on developing these seven time management skills.

1. Prioritization.

To effectively manage your time, you will need to decide in which order you should complete your tasks. Reviewing your schedule each day and labeling your to-do list with whether tasks are urgent, important, or neither can help you decide when and how to manage your time throughout the day. In general, you will want to prioritize your urgent tasks in the order of their importance. Following this, you can complete your non-urgent tasks in the same manner. This makes sure you complete critical tasks with the needed attention and time.

One key difference when prioritizing tasks will be between “urgent” and “important” tasks. Urgent tasks are ones that you must complete as soon as possible, while important tasks are those that matter, and not doing them may lead to negative consequences, but there is more flexibility on when they get done.

For example, picking up your prescription from the pharmacy is an important task because you likely need the medication, and waiting too long could have negative health consequences. However, you may have freedom over the next couple of days as to when you pick up the prescription. If you are expecting an important phone call and your phone rings, then picking up the phone call is an urgent task. This is because it is a task that demands your immediate attention.

2. Goal setting

Goals give a measurable way to determine progress toward the end product. Setting goals can help you organize your to-do list and determine the priority of your tasks. If you have a goal set for the end of each week or month, then you can create a priority list specifically for each goal. This can reduce the feeling of being stressed or overwhelmed when working toward larger goals.

Read more: What Are Your Career Goals? Tips for Setting Your Goals

3. Planning

Writing down your schedule can give you a realistic idea of how much time you have to allocate to different tasks. For example, you may have a standard 9-to-5 work day and assume you have eight hours to complete your five project-based tasks of the day. Let’s say you then write down your schedule, and see you have an hour-long lunch meeting, a 30-minute internal meeting on a different floor, and have to leave 30 minutes early to pick up your kids from school. When you write this down, you see you actually have six hours outside of meetings.

By breaking down your schedule, you’ll realize each meeting is a 10-minute walk from your office. In addition to this, you know it takes you at least 10 minutes to get organized at your desk before beginning work each time you return, and you will need a 30-minute break in the day to recharge. You now realize you have to factor in 40 minutes of walking time and 50 minutes of non-working time at your desk. This leaves you 4.5 hours of working time for your project-based tasks.

By writing down your schedule, you can better allocate time for each task and make reasonable plans for your day.

Tip: Analyzing your daily and weekly patterns can help you find which times in the day you are typically most productive. Some people may find they tend to have hours of uninterrupted time before lunch, while others may work their best in the afternoons or evenings. By understanding when you are able to focus best, you can schedule more complex tasks during these hours. For mindless and simple tasks, you can schedule these assignments during less focused hours.

4. Delegation

Delegating tasks is an important skill to avoid being overwhelmed. If you have the capacity to delegate tasks within your workplace, consider assigning certain projects to team members that have the capacity to take them on. This gives you time to focus on more challenging tasks.

5. Setting boundaries and saying "no"

If your supervisor or colleague asks you to complete a task and you do not have the time, practice being assertively honest about your work capacity and current workload. Taking on too many responsibilities can prevent us from completing important work and contributes to missing deadlines. If you are repeatedly assigned more tasks than you are able to effectively complete, consider scheduling a meeting with your supervisor to discuss the limits of your role and how you can best perform in your current position.

Read more: How to Set Boundaries at Work

Creating an organized workspace can help you focus on your assignments and prevent wasting time on distractions. To work productively, make sure you are able to find needed materials, a place where nobody will interrupt you, and a comfortable space.

Instead of trying to complete several tasks at once, focus on one task at a time. This may improve the quality of completed tasks and allow you to reduce distractions.

7. Automation

Many technologies exist to automate common workplace tasks. Depending on your profession, using project management software, human resource software, email templates, or scheduling software may be able to help streamline your workload.

Benefits of good time management

There are many benefits of managing your time well in the workplace. In general, those with good time management skills will experience the following benefits compared to professionals with poor time management skills:

Lower levels of stress

Lower anxiety levels

Improved reputation in the workplace

Better focus

More productivity

Improved decision-making

Attainment of goals

Increased ability to meet deadlines

Improved work quality

Better work-life balance

Increase professional confidence

More free time

More energy for personal activities

Empower yourself to reach your goals at home and at work with Achieving Personal and Professional Success from the University of Pennsylvania's The Wharton School. Learn how to define success, communicate more effectively, and use your influence through hands-on exercises, surveys, and case studies, all at your own pace.

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This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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Teaching Time Management – A Case Study

Can time management skills be taught.

I love a good case study. I enjoy reading through a problem and then seeing the solution with actual results. Case studies actually happened. They are not theoretical. Events took place, challenges were presented and were overcome. Fun stuff.

Ok I’m a nerd who spends any tv time she has watching real life crime dramas (ID channel is my favorite). I like the real stuff, really real, not Kardashian real.

But I digress.

I have a case study for you today from my own career. In 2008 I was the Director of People Services (HR) for a call center company. Anytime we opened a new center I was the HR liaison. This year we opened a call center in Kingston Jamaica. While this was not our first international call center, this one was different than any others. First, it was the first that the company owned rather than outsourcing. Second, it is the first built from the ground up on international soil. Everyone except the General Manager was hired in Jamaica. Third, it was the first where we owned ingraining American culture in an environment that was very different.

I spent a ton of time in Jamaica and learned to love the culture and people. That experience taught me more than possibly any other experience in my career. Not only did I learn about conducting international business, but I learned about diversity, stretching boundaries and perceptions that others have of Americans. It was eye opening.

One of the major challenges that presented itself initially with the center in Jamaica was time management. The story you hear about Jamaicans being so relaxed is partially true. They are a hard working culture that does not indulge in daily naps as is often told, but they do enjoy a more relaxed nature when it comes to managing their time.

Being an American company with American clients and a call center at that, time management is essential to success. It was a challenge to overcome and quickly.

Click on presentation below to read a quick case study on how we overcame the time management crisis and was able to actually teach it as a skill.

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Case Study on Time Management

Time management case study:.

Time management is the process of the effective organization of time for the production of goods and services. time is the most valuable thing, that is why everybody should praise it and use effectively.

Most often the value of time can be seen at work. A successful manager should be able and skillful enough to predict and count how much time is required to produce certain goods or services, how much time it is required to transport these goods or supply the company with resources and materials.If he manages to organize this process effectively, the time will be divided wisely among every employee and the company will manage to improve its production and even the quality of work. It is important to set certain goals and plan the working process in such a way to complete the work on time. Every organization, no matter whether it is a school or a great business corporation, has its schedule and every employee knows when he is expected to come to work and what amount of duties he has to fulfill during the day.

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If the manager makes a proper schedule, the work of the company will be improved greatly.Every manager should be aware of the principles of time management in the workplace, so every student who plans to be a professional in future has to prepare a good case study on time management system. A well-organized case study should be a skillfully composed and present interesting facts. First of all a student should choose some problem which has been discussed at class and to brainstorm his own case for the case study. Of course one can be offered a ready case but if he manages to offer his own variant, it will be an advantage, because the teacher will be student’s motivation and ambition. A case study is the research of a problem which happened in the particular case site and one should devote much time to analyze the reason and the consequences of the problem and to draw the right conclusions.

Writing a case study one will have a great number of problems connected with it. It is not quite easy to collect data for the research of the case and one will have to interview the people connected with the case or spend too much time at the library looking through the old periodicals. There is another way to improve ones knowledge of the topic, to read free sample case studies on time management for school students in the web and see how professional writers complete papers of this kind. If you have problems with the composition of the paper, logical presentation of information, formatting or methodology section, take advantage of the free example case study on time management is the key to success and you will learn how to write a good paper yourself.

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What the Next Generation of Project Management Will Look Like

• Rachel Longhurst
• Woojin Choi

Research identifies 10 skills that will have a disproportionate impact on performance.

Traditional project management skills, such as project governance or project management methodology, aren’t sufficient to meet changing organizational needs.  Gartner recently surveyed 373 project management leaders to identify the “next generation” skills — from organizational awareness to financial acumen — that have a disproportionate impact on performance. They also identified three future-focused project manager roles: the teacher, the fixer, and the orchestrator — all of which highlight the uniquely human aspects of project management that go beyond performing discrete, repetitive tasks.

The future of the project manager role has been hotly debated as a number of trends shift organizational dynamics:

• RL Rachel Longhurst is a director within the Gartner IT Leaders and Tech Professionals research practice advising clients on strategic portfolio management, including project and portfolio management and application portfolio management.
• WC Woojin Choi is a senior principal within the Gartner IT Leaders and Tech Professionals research practice advising clients on strategic portfolio management.

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• Online First
• Enhancing title and abstract screening for systematic reviews with GPT-3.5 turbo
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• http://orcid.org/0000-0002-8182-0582 Omid Kohandel Gargari 1 ,
• Mohammad Hossein Mahmoudi 2 ,
• Mahsa Hajisafarali 1 ,
• http://orcid.org/0009-0006-4862-1131 Reza Samiee 3 , 4
• 1 Alborz Artificial Intelligence Association , Alborz University of Medical Sciences , Karaj , Alborz , Iran (the Islamic Republic of)
• 2 Industrial Engineering Department , Sharif University of Technology , Tehran , Iran (the Islamic Republic of)
• 3 NCWEB Association , Tehran University of Medical Sciences , Tehran , Iran (the Islamic Republic of)
• 4 Students’ Scientific Research Center , Tehran University of Medical Sciences , Tehran , Iran (the Islamic Republic of)
• Correspondence to Dr Omid Kohandel Gargari, Alborz Artificial Intelligence Association, Alborz University of Medical Sciences, Karaj, Alborz, Iran (the Islamic Republic of); kohandelgargar{at}gmail.com

http://dx.doi.org/10.1136/bmjebm-2023-112678

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• Systematic Reviews as Topic
• Health Services Research

After conducting a database search, the subsequent phase in the execution of systematic reviews (SRs) involves title and abstract screening. 1 This stage bears significant importance and necessitates the involvement of dedicated and experienced researchers who can exhibit precision and accuracy, particularly when the search yields a substantial number of studies. Besides the qualities of experience and dedication demonstrated by the screeners, several other factors influence the quality of the screening process, such as effective team management, the adoption of a double-screening approach and, notably, the implementation of a well-structured screening design. A screening tool comprises a set of questions that must be addressed by the screeners, and these questions should adhere to the following criteria: (1) they must be objective, (2) they should be single-barrelled and (3) they should encompass questions answerable with ‘yes’, ‘no’ or ‘unsure’ responses. 2

The domain of large language and transformer models has showcased a promising trajectory of advancement, consistently improving day by day. These models are trained on a vast corpora of text and possess the capability to comprehend and generate human-like text. 3 A prominent example within this realm is the Generative Pre-Trained Transformer (GPT) developed by OpenAI, with the latest iteration being GPT-4 at the time of composing this discourse. GPT-4 has exhibited commendable performance across a range of human-related tasks and has surpassed its predecessor, GPT-3.5, in evaluations conducted by the company. 4

This single-case study was conceived to assess the performance of GPT 3.5 in the context of title and abstract screening for SRs. To execute this task, a recently published SR titled ‘Light Therapy in Insomnia Disorder: A Systematic Review and Meta-Analysis’ was selected, and the databases were queried using the keywords stipulated in the original paper. 5 Two key rationales underpinned the selection of this review: first, it yielded a relatively moderate number of studies, and second, its eligibility criteria were somewhat subjective, and challenging to discern during the screening process, making it a suitable testbed to evaluate GPT-3.5’s capabilities. For instance, this study enrolled patients experiencing sleep difficulties but did not specify the particular types of sleep disorders although reviewers did not face much trouble but models had difficulties with studies that included patients with secondary sleeping troubles like patients with cancer. Furthermore, it was unclear which specific light therapy was chosen for inclusion.

The initial search yielded 330 citations, which were subsequently imported into EndNote X20. An RTF file containing titles and abstracts was generated, followed by its conversion into a more compatible TXT format, thus facilitating further data processing. This transformation laid the foundation for our experimental data set, comprising the research paper titles, abstracts and accompanying metadata. The screening team consisted of three researchers: (1) an expert with screening experience from over 20 SRs, (2) a senior researcher with screening experience from 10 SRs and (3) a junior researcher without any prior screening experience. All researchers possessed a strong command of the English language and a thorough understanding of SR principles.

Supplemental material

To compare the performance of eligibility screening of the human screeners with the performance of GPT-3.5, a range of prompts were devised for GPT by two of the authors (OKG and MHM), these prompts were carefully chosen during several discussion sessions. A prompt is a specific input or instruction given to a language model, to generate a desired output or response. The integration of the OpenAI GPT 3.5 Turbo API played a pivotal role in advancing our research. This powerful tool enabled us to initiate requests to evaluate the pertinence of prompts to individual papers. The binary relevancy results of this interaction were recorded in a structured Pandas data frame that had been prepared in advance. The code for this process is available at the provided link: https://github.com/mamishere/Article-Relevancy-Extraction-GPT3.5-Turbo .

GPT evaluated the eligibility of studies based on the provided prompts, resulting in the creation of numpy arrays containing binary outcomes for each prompt response. These numpy arrays, along with the numpy arrays generated by the researchers, were employed to compute sensitivity, specificity, accuracy and the F1 score for both the researchers and the prompts. The labels used as the ‘gold standard’ were the studies included in the selected SR. 5

Prompt 1, which replicated the criteria from the original paper, demonstrated 80% accuracy and 62% sensitivity. In contrast, Prompt 2 broadened the population by focusing on patients with ‘sleep troubles’ instead of providing a specific definition, leading to reduced accuracy, sensitivity, and specificity.

For subsequent prompts, GPT was assigned the role of an ‘Experienced Systematic Researcher’ (Prompt 3), which increased accuracy while decreasing sensitivity. Prompt 4 introduced an ‘inclusivity sentence’ to instruct the model to include studies it was uncertain about, prioritising inclusivity, leading to increased sensitivity and reduced specificity.

Prompt 5, which combined the original criteria with the inclusion of an ‘inclusivity sentence’, demonstrated the highest sensitivity, similar to the junior researcher and surpassing the senior researcher. Prompt 6, which omitted segmented criteria in favour of a more descriptive sentence, resulted in a significant reduction in sensitivity, suggesting that GPT responded better to segmented and classic criteria.

Prompt 7 assessed the impact of phrasing by modifying the language of the previous prompt and providing a more detailed description of the inclusivity phrase. This modification substantially increased the model’s sensitivity.

Lastly, Prompt 8 introduced a screening tool with four questions, requiring the model to include a study if the answer to all four questions was ‘Yes’ or ‘Unsure’. Surprisingly, the model performed poorly in this format, potentially due to the technical structure of GPT.

GPT is a potent tool, and we propose its usage in title and abstract screening for SRs, following the method we have delineated in this case report, alongside other researchers. 6 However, it is imperative to recognise that GPT is not yet fully capable of independently completing this task and should be employed as an assistant to mitigate the risk of overlooking potential studies.

Notably, even the human researchers did not attain exemplary performance, attributable to the inherent challenges posed by the subjective criteria and the absence of clear, objective definitions. We advise researchers wanting to deploy GPT to manually screen a proportion of titles and abstracts, experiment with different prompts and consider the combination of findings, a method unexplored in our study. It is crucial to first establish a clear study objective before designing prompts.

The most formidable challenge encountered in this study pertained to defining the population for the model. For instance, the lack of clarity in the criteria for ‘sleep troubles’ resulted in substantial bias in the model’s performance and significant disparities among researchers. It needs to be acknowledged that the findings of this single-case study are not generalisable, and each study objective necessitates its distinct format. This study serves as an illustrative example and offers guidance for replication with more cases and further research on the topic.

The prompt texts are available in the online supplemental table 1 .

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

• Higgins JPT ,
• Chandler J , et al
• Polanin JR ,
• Pigott TD ,
• Espelage DL , et al
• Vaswani A ,
• Shazeer N ,
• Parmar N , et al
• ↵ GPT-4 technical report ; 2023 . Available : https://ui.adsabs.harvard.edu/abs/2023arXiv230308774O
• Reynaud E ,
• Maruani J , et al
• Deng J , et al

Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2

Twitter @Omidkohandelg

Contributors OKG: Designed the research, wrote the manuscript, data analysis, title and abstract screening. MHM: Wrote Python codes. MH and RS: Title and abstract screening.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; internally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Original research article, enhanced control strategy and energy management for a photovoltaic system with hybrid energy storage based on self-adaptive bonobo optimization.

• 1 Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria, Egypt
• 2 Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, United Arab Emirates
• 3 Engineering Mathematics Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt
• 4 Electrical Engineering Department, University of Business and Technology, Jeddah, Saudi Arabia

Large-scale energy storage systems (ESSs) that can react quickly to energy fluctuations and store excess energy are required to increase the reliability of electricity grids that rely heavily on renewable energy sources (RESs). Hybrid systems, which combine different energy storage technologies such as batteries and supercapacitors, are becoming increasingly popular because no single technology can satisfy all requirements. In this study, a supercapacitor is used to stabilize quickly shifting bursts of power, while a battery is used to stabilize gradually fluctuating power flow. This paper proposes a robust controller for managing the direct current (DC) bus voltage to optimize the performance of ESS. The proposed controller combines a fractional-order proportional integral (FOPI) with a classical PI controller for the first time in the DC microgrid area. The hybrid (FOPI-PI) controller achieves an outstanding and superior performance in all transient and dynamic response specifications compared to other traditional controllers. The parameters of the suggested controller are incorporated with the self-adaptive bonobo optimizer (SaBO) to determine the optimal values. Furthermore, various optimization techniques are applied to the model and the SaBO’s output outperforms other techniques by minimizing the best objective function. In addition, the current study has utilized a novel power management strategy that includes two closed current loops for both batteries and supercapacitors. By using this method, batteries’ lifespans may be increased while still retaining optimal system performance. The suggested controller is implemented in MATLAB/Simulink 2022b, and the outcomes are reported for several case studies. The findings demonstrate that the control technique remarkably improves the transient response, such as transient duration, overshoot/undershoot, and the settling time. The proposed controller (FOPI-PI) with the SaBO optimizer is effective in maintaining the DC bus voltage under load and solar system variation.

1 Introduction

1.1 background and motivation.

DC microgrid technology is becoming more popular since it is able to link renewable sources of energy, electrical needs, and ESSs. Due to its ability to create a decentralized, sustainable power system grid with minimal carbon emissions, distributed power generating research has grown quickly as a result ( Gu et al., 2023 ). The inherent unpredictability of renewable energy sources (RES) and the shifting patterns of energy demand over time make it difficult to guarantee a dependable power supply. The intermittent nature of RES, like solar and wind energy, adds unpredictability to the process of producing electricity ( Song et al., 2022 ; Liao et al., 2023 ). ESSs can help solve problems caused by the fact that green energy sources don’t always work. Finding a single solution that can satisfy all unique criteria is difficult due to the vast range of characteristics and features of ESSs. Considerations for power-energy ratings, drainage, and load are crucial for choosing the best ESS for a given application. Utilize a mix of technologies and tailored approaches to satisfy a range of functional needs and guarantee the effectiveness of the energy storage system ( Chen, 2022 ; Shao et al., 2023 ). As a feasible resolution to this problem, systems that store energy using a mix of electrical technologies have been put forward. These technologies integrate several EES components, relying on the strengths of each while effectively addressing their limitations through comprehensive system management ( Kim et al., 2017 ). Moreover, there are several innovative EES configurations utilized, such as Flywheel Energy Storage (FES), Superconducting Magnetic Energy Storage (SMES), Supercapacitors, and Batteries. This technology provides excellent efficiency, a quick response time, a longer cycle life, high power density, fast charging and draining, and low self-discharge ( Kim et al., 2020 ). Batteries are frequently used as energy storage units in microgrid applications, despite their restricted potential to deliver high power during transient events due to their extended charging and discharging periods ( Zuo et al., 2017 ). In situations that involve elevated levels of demand, the battery’s response time declines, resulting in increased load and diminished lifespan of the battery. In order to address this matter, supercapacitors (Sup-Cs) have been developed. Sup-Cs provide useful features for ESS performance optimization and power surge management. They are appropriate for high-power applications because of their quick charging and discharging rates for huge amounts of energy, and because they act as an energy buffer to control power flow and prolong battery life ( Raghavendra et al., 2020a ). Hence, the integration of the ESS technologies with favorable attributes, such as a hybrid configuration comprising both batteries and Sup-Cs, can yield advantages in terms of both fast dynamic responsiveness and prolonged power delivery ( Jing et al., 2018a ).

1.2 Literature review

A thorough examination of the various topologies and contemporary uses of battery and Sup-C combo systems was conducted by ( Jing et al., 2018b ; Uloom et al., 2022 ). In addition, several hybrid electrical energy storage (HEES) approaches and control strategies have been presented to mitigate HEES defects to achieve a better dynamic response in many applications ( Koohi-Fayegh and Rosen, 2020 ). Ref ( Hacini et al., 2023 ) discussed a controller using a mechanism that incorporates a reversible chopper between the batteries and Sup-C with the DC bus to provide a steady voltage on the DC bus to provide improved energy management and supply continuity. A conventional PI controller was implemented for the incorporation between Sup-Cs and batteries so as to store solar power ( Kollimalla et al., 2014 ). Furthermore, the authors of ( Chong et al., 2017 ) proposed a strategy for managing energy dynamically in HESS that applied to renewable power generation. For better transient characteristics, the authors of ( P et al., 2020 ) put together power management features with a classic PI controller. The Self-Organizing Map-Particle Swarm Optimization (SOM-PSO) method being suggested for the Battery-SCs HEES involves utilizing a power distribution algorithm that incorporates both charging and discharging thresholds. This approach aims to enhance the system’s versatility and flexibility ( Chong et al., 2018 ). The system covered in ( Laldin et al., 2013 ; Liu et al., 2023 ) concentrates on energy management and EV charging control inside a station linked to a hybrid power system. The system intends to maximize energy usage, improve renewable energy integration, and achieve efficient operation by merging various RES and efficiently controlling the solid oxide fuel cell with electrolyzer subsystem. Due to fluctuations in electrical properties and unidentified external disturbances, which unavoidably worsen the control performance of the HESS, uncertainty is inescapable in system modeling. In order to mitigate the negative impacts and increase control precision, this work adopts a novel adaptive dynamic surface control with disturbance observers underlying tracking in the HESS ( Xiao et al., 2021 ; Zhang et al., 2022 ; Min et al., 2023 ). The authors of ( Pa et al., 2014 ) introduced a technique for examining the dynamic reaction of the PV power-generating system that consists of a bidirectional charger/discharger and a standalone battery. In addition, the complete power conditioning architecture of the stand-alone solar system includes a DC-link voltage regulator scheme that takes advantage of storage current during operation without connecting to an external power source. Moreover, The scheduling of a battery storage system in a hybrid wind-PV plant has been optimized using a mixed receding horizon control strategy ( Alramlawi et al., 2018 ; Yang et al., 2019 ). In order to achieve the optimum charge/discharge rates of the battery with SC, an EMS for DC standalone has been presented in ( Gugulothu et al., 2023a ). The charge/discharge rate constants of HESS were determined using a Jaya-based optimization method for the given fluctuations of the PV/load power. The EMS approach that has been suggested in ( Gugulothu et al., 2023b ) is intended to increase the dependability and lifespan of the batteries while lowering hydrogen consumption. The deep charging of the battery under low demand is avoided in the suggested EMS by using the PV system de-rating approach. Utilizing a reverse sigmoidal function of the battery’s state of charge (SoC), the fuel cell power supply is modulated. This increases the hydrogen fuel’s efficiency and lessens the battery’s deep discharge in situations of high loading. The aim of ( Ongaro et al., 2012 ; Şahin and Blaabjerg, 2020 ) was to investigate the energy management system of a wireless sensor network using photovoltaic energy. The system comprises the integration of Sup-Cs and a lithium-ion battery as a hybrid electrical energy storage (HEES) system. A method for controlling the voltage magnitude of the load bus in a DC power system that consists of a standalone PV panel and batteries, with a constant power load is introduced by ( Bhagiya and Patel, 2019 ). The proposed approach uses a two-loop proportional-integral (PI) control strategy based on pulse width modulation (PWM). The inner loop controller is responsible for managing the inductor current. Conversely, the outer loop controller is responsible for controlling the load bus voltage. Furthermore ( Al-Saadi et al., 2021 ; Guentri et al., 2021 ), contributes to the development of a control strategy that integrates PV and ESS in DC micro-grids while also accounting for fluctuating loads and solar radiation. The control strategy of the Grid Side Voltage Source Converter ensures a constant DC voltage in both grid-connected and isolated modes, allowing for optimal utilization of PV power under various operating conditions. Following this trend, the Enhancement of management of a DC microgrid that includes solar panels as RES and batteries for energy storage by utilizing a voltage source converter (VSC) control system is discussed in ( Emara et al., 2021 ; Al et al., 2023 ). The work has been developed in ( Prathikantham and Somlal, 2023 ) using a meta-heuristic optimization approach to suggest a probabilistic neural network-based EMS design for a freestanding microgrid. The design’s main objective was to balance the demand while controlling the start-up and shutdown of the diesel generator, maximizing the use of solar resources, and maintaining the State of Charge (SOC) of the batteries and super capacitor within safe limits. The study ( Boumediene et al., 2023 ) has suggested that an electric scooter powered by a combination of battery and supercapacitor sources. In order to fulfill load needs during braking and counter-loading, maintain the battery and supercapacitor at their ideal states of charge (SOC), and increase the independence of the energy system, a fuzzy logic-based control method for energy management is created.

1.3 Contribution

In this study, a novel control architecture that is a combination of Fractional-order proportional integral (FOPI) and PI controller is implemented to maintain the stability and resilience of the interconnected DC microgrids. The proposed controller parameters can be tuned by the self-adaptive Bonobo Optimization algorithm (SaBO). The distinct contributions of this study can be clearly highlighted in the following important points when compared to prior research:

• An innovative control strategy is introduced, merging the fractional-order Pi with classical PI, for regulating the power of the solar panels, batteries, and the Sup-C. As a result, better stability, steady-state performance, robustness, and enhanced transient response could be achieved.

• The proposed (FOPI-PI) controller is incorporated with a recent optimization algorithm called the self-adaptive bonobo optimizer (SaBO) to tune the parameters of the controller. This proposed technique is utilized for the first time in DC microgrids.

• The performance of SaBO is influenced by memory, previous experiences, and a novel repulsion-based learning technique for adjusting parameters. Additionally, the algorithm also incorporates four different mating techniques: promiscuous, restrictive mating, consortship, and extra-group mating.

• Furthermore, compared to the classical PI controller established in ( Guentri et al., 2021 ), the proposed (FOPI-PI) controller’s performance obviously overcomes the PI controller in the major aspects including the transient repose characteristics such as transient time, steady-state error and overshoot/undershoot.

• This research comprehensively compares three controllers and four optimization techniques. The simulation part also runs three cases including load fluctuation and solar system variation (Temperature and Solar irradiance). Thus, the existing investigations show the improved performance of the suggested control methodology.

The paper is structured as follows: Section 2 describes the overall system’s configuration and modeling. Section 3 presents the proposed control strategy, which includes the DC bus scheme, proposed controller, and PMS strategy. Moreover, Section 4 introduces the developed optimization technique (SaBO) and its different strategies. Section 5 discusses the simulation results in detail, covering solar irradiance and load variations. Finally, Section 6 provides the conclusions and the outcomes of the study.

2 Structure and modeling of the PV panels with hybrid batteries and Sup-Cs system

A solar PV system coupled to the DC bus via a bidirectional DC-DC converter (BDC) is shown in Figure 1 as the studied PV system proposal. The EES is composed of batteries and SCs, with each of them coupled to the DC bus through a BDC. The PV system provides the main source of DC power to a load, while the battery is utilized during instances of power surplus or deficit. Additionally, the charge controller (SC) is employed to regulate fluctuations in either the PV system or the load. The Maximum Power Point Tracking (MPPT) is utilized to harvest the maximum power from the PV via the BDC, while each BDC is regulated by a FOPI-PI controller. The entire system is managed through a highly effective power management strategy (PMS). PMS regulates the power flow between the HEES system and the solar system to meet the load power.

FIGURE 1 . Block diagram of the proposed PV model with hybrid ESS.

2.1 Modeling of PV panel

The calculated I-V and P-V characteristics of a PV cell are determined by means of exact formulas in the proposed model. Various models have been applied by researchers, utilizing a range of parameters ranging from one to five. The five-parameter model is well recognized and esteemed, particularly when applied in outdoor settings ( Humada et al., 2018 ). Figure 2 illustrates the implementation of the PV model based on the single-diode representation ( Humada et al., 2020 ). The model for a PV cell includes several components, where I ph represents the current generated by sunlight, I D is the diode current, and I sh stands for the shunt-leakage current. Additionally, I pv is the output current provided by the panel, and R s is the series resistance that is affected by the depth of the p-n junction ( Zaouche et al., 2017 ).

FIGURE 2 . Single-diode model of a PV module.

Applying Kirchhoff’s Current Law for the above circuit results in:

The final relationship of a solar panel output current can be expressed by Eq. 2 ( Kumar and Kumar, 2017 ):

where I s represents the saturation current, V t represents the thermal voltage, q represents the electron charge (q = 1.60217 × 10 −19  C), k is the Boltzmann’s constant (k = 1.38065 × 10 −23  J/K), T c indicates the operating temperature, and A represents a factor which range typically varies from 1 to 2 ( Mendalek and Al-Haddad, 2017 ).

T ref refers to the reference temperature of the cell, and G r represents the solar insulation. A PV array is created by connecting many PV modules in series and parallel combinations to reach the required current and voltage levels ( Cherukuri et al., 2022 ). The resulting output current (I pv ) can be calculated by Eq. 4 ( Aidoud et al., 2019 ).

The parameters of the PV module utilized in this model are presented briefly in Table 1 .

TABLE 1 . Parameters of PV module WU-120.

2.2 Characteristics of the batteries

In PV applications, the battery model shown in Figure 3 is thought to be the most widely used. The configuration consists of an ideal battery with a terminal voltage V t , an internal resistance R in , the open circuit voltage V o , and an internal resistance R in ( Thakkar, 2021 ).

FIGURE 3 . Equivalent circuit of Lithium-ion battery.

The Ampere-hour (Ah) counting method could be considered a simple and computationally efficient technique to estimate the battery’s state of charge (B SOC ) and could be mathematically explained as shown in Eq. 5 .

B SOC 0 denotes the battery’s starting state of charge, I (t) refers to the current flow at a given time t, C n is the nominal battery capacity, η is the coulomb efficiency, and S r is the battery’s discharge rate ( Zhang et al., 2018 ).

2.3 Configuration of the bidirectional DC-DC converter (BDC)

The basic BDC is depicted in Figure 4 . The typical buck and boost converter’s unidirectional switches are swapped out for bidirectional power switches to create this converter. The end product is a BDC that works as a boost converter from V s to V dc and as a buck converter in the opposite way ( Gorji et al., 2019 ).

FIGURE 4 . Bidirectional DC-DC converter Model.

Q 1 is always turned off in the buck converter mode, and current flows from the DC bus to the batteries and Sup-Cs system. To charge the HEES, lower the voltage across the DC bus by changing Q 2 according to Eq. 6 .

By contrast, Q2 is turned off in the boost mode, allowing current to flow from the HEES to the DC bus only in one direction. The converter may increase the potential of the HESS by adjusting the duty cycle of Q 1 as shown in Eq. 7 ( Raghavendra et al., 2020b ).

3 Architecture of the proposed control strategy

This section illustrates a detailed representation of the overall proposed control system, used in this paper, including the DC bus control, the suggested hybrid controller, the current control loops, and the power management strategy (PMS).

3.1 DC bus control

The DC bus is an essential component of a PV system because it transfers power from the PV array to the load. The DC bus control system has the responsibility of managing the power flow between the PV array, HEESS, and the load to optimize HEESS charging and discharging.

The control block diagram utilized for the regulation of the DC bus voltage, denoted as Vdc, is presented in Figure 5 . To create the current I dc_ref , a hybrid controller (FOPI-PI) is employed. The PMS then utilizes this reference current to compute the reference currents for the batteries and Sup-C control loop, represented by I Bat_ref and I Sup-C_ref , respectively. It is necessary to control current references of BDC to achieve this goal ( Zeraati et al., 2018 ).

FIGURE 5 . Block diagram of the proposed DC bus control strategy.

3.2 Proposed hybrid (FOPI-PI) controller

The controller that is being discussed uses both fractional-order PI and standard PI words. This makes it different from the typical FOPI controller because it combines the best parts of both. Enhanced control performance is achieved using mixed fractional-order PI and PI controllers as they accurately capture the process dynamics leading to precise control. Moreover, the mixed controller offers increased stability and robustness against modeling errors and system disturbances. Rapid and smooth system response is also attained due to the mixed controller’s ability to reduce settling times and minimize overshot and oscillations. Additionally, the proposed controller is less sensitive to changes in process parameters compared to traditional PID controllers ( Ram Babu and Chandra Saikia, 2019 ; Altbawi et al., 2023 ). The schematic diagram of the proposed controller is represented in Figure 6 .

FIGURE 6 . Structure of the proposed hybrid controller.

The transfer function (TF) of the FOPI-PI controller can be described by Eq. 8 .

where K p1 , K i1, and λ represent the proportional gain, Integral gain, and the integrator order value of the FOPI controller, respectively ( Nacer et al., 2022 ). Furthermore, K p2 , K i2 represent the proportional and Integral gain of the PI regulator ( Deželak et al., 2021 ).

3.3 Current controllers design of the HEES system

The control loop for the DBC of the Sup-Cs is shown in Figure 7A . Sup-C reference current I Sup-C_ref is generated by the controller’s voltage control loop ( Abdullah et al., 2013 ). G Sup-C represents the TF of the Sup-C as in Eq. 9 .

FIGURE 7 . The Proposed FOPI-PI Control loop of the (A) Sup-C and (B) the batteriers.

G C_Sup-C represents the TF of the (FOPI-PI) that controls the SCs’ current as described by:

As presented, the TF for controlling the Sup-C current is dependent on several parameters, including the DC bus capacitor (C), the DC bus resistance (R), the BDC inductance (L sc ), the Sup-C voltage (V sc ), and the duty ratio (D sc ) ( P et al., 2020 ).

The TF of the DC bus control loop compensator can be expressed as in Eq. 11 .

The voltage control loop which transfers the Sup-C current to the voltage signal can be obtained by:

Similar to the SCs, the control loop of the BDC of the battery is depicted in Figure 7B .

G Bat represents the Batteries’ transfer function as described by:

G C_Bat represents the (FOPI-PI) transfer function that controls the Batteries’ current as given by:

As previously stated, the transfer function for controlling the batteries’ current relies on several parameters, including the DC bus capacitor (C), the DC bus resistance (R), the BDC inductance (L Bat ), the potential of the batteries (V Bat ), and the duty ratio (D Bat ).

In general, the batteries’ reference current is given by:

These standard currents are used to keep the DC bus voltage stable when the load on the solar panels or the power taken from them changes.

3.4 Power management strategy

A new and straightforward control strategy has been developed and evaluated for HESS ( Kumar Kollimalla et al., 2014 ). The suggested methodology utilizes batteries to handle gradual changes in power surges, while Sup-Cs are used to manage rapid changes. By transitioning power surges into Sup-Cs, this method can overcome the obstacle of the slow battery time response. Additionally, this approach improves the lifespan of the batteries by implementing charge/discharge rate control to reduce current stresses. The main concept of this strategy is the achievement of coordination between the batteries and the SCs. In case the power extracted from by the PV system is less than the power required by the load (P load ), and at the same time, the batteries are unable to compensate for the difference, then Sup-Cs will discharge. Conversely, when the power developed by the solar system is greater than the required by the load, and the batteries are unable to quickly store the excess power, the Sup-Cs will start to charge. Figure 8 shows the overall strategy of all possible cases for the regulation process of the DC bus voltage.

FIGURE 8 . Strategy of power management of the DC bus.

The proposed control strategy is illustrated in Figure 9 . This algorithm aims to minimize the pressure on the batteries during charging and discharging cycles, thereby increasing the battery’s lifespan. It is assumed that the SOC of the batteries remains within an acceptable range at all times. The algorithm works by comparing the average value of V dc with a reference voltage (V ref ), and afterward, the error has proceeded to a proposed (FOPI-PI) controller. The total current (ΔI) represents the output signal of the proposed controller. Eq. 6 introduces the combined current that needs to be provided by the (HESS), which includes both the batteries and SCs.

FIGURE 9 . Hybrid energy storage system control scheme.

According to the frequency, the reference current I tot_ref is split into a low-frequency component (I LF_ref ) and a high-frequency component (I HF_ref ). On one hand, the (I LF_ref ) is fulfilled by the batteries, after the rate-limiting process, could be achieved using a low-pass filter. On the other hand, (I HF_ref ) could be satisfied by the SCs. The low-frequency component could be defined as:

where f LPF is the low-pass filter TF. The batteries’ reference current can be expressed as Eq. 13 .

where f RL is the rate limiter TF.

The proposed control strategy involves comparing the IBat _ ref with the actual battery current IBat and feeding the error signal IBat _ err into the proposed (FOPI-PI) controller. The FOPI-PI computes the appropriate duty ratio (D Bat ) based on the error signal, which is then sent to the PWM. The switching pulses that correspond to the battery switches (SW 1 and SW 2 ) could be produced by the PWM to regulate the amount of power flowing into or out of the batteries.

Meanwhile, the high-frequency component of the total current can be given by:

Due to the battery’s slightly poor response time, it may not instantaneously align with the reference current ( IBat _ ref ). As a result, the control strategy accounts for this delay by calculating the uncompensated battery power that is expressed as:

To balance the uncompensated battery power, the control strategy sets a reference current for the Sup-C using Eq. 21 as follows:

The control strategy involves comparing the reference current for the Sup-Cs ( I sup ⁡ - C _ ref ) with the actual current flowing through the Sup-Cs ( I sup ⁡ - C ). Any difference between these two signals is then sent to the (FOPI-PI) controller, which uses the error signal to make the right D Sup-C , which is then sent to the PWM generator. The PWM generator initiates switching pulses that are synchronized to the SCs switches (SW 3 and SW 4 ), regulating the flow of power to or from the SCs. By adjusting the duty ratio based on the error signal, the control strategy can ensure that the actual SCs current matches the reference current and maintains a balanced power follow to the load.

4 Self-adaptive bonobo optimization algorithm

The Self-Adaptive Bonobo Optimization Algorithm (SaBO) is an evolutionary optimization algorithm. It is based on how bonobos mate and behave in social situations. SaBO is characterized by its ability to adaptively adjust its search parameters based on the performance of the population, enabling it to successfully strike an equilibrium between exploration and execution. To produce descendants, bonobos utilize four fundamental mating strategies: consortship, restricted, promiscuous, and extra-group mating. SaBO is especially useful for tackling issues involving complex, nonlinear search zones that are challenging for conventional optimization methods to explore ( Das and Pratihar, 2019 ). The SaBO is a population-based method that uses random population initialization and a fixed population size. The fitness values of all bonobos are computed based on the solution, which is referred to as a bonobo in the population. The alpha bonobo (α bon ), which holds the highest rank within a bonobo community’s social structure, is selected based on its superior fitness value relative to other bonobos in the population. Consequently, it is presently regarded as the optimal choice. The SaBO parameters are also set to their default values contemporaneously with this procedure. Moreover, bonobos go between the positive and negative phases of their phase probability ( p p ), indicating either population diversity or selective pressure ( Das and Pratihar, 2019 ). SaBO makes the major two controlling parameters: p p and sharing co-efficient (β). The aforementioned parameters have the ability to adjust autonomously and undergo updates during iterations through the utilization of repulsion-based learning. This approach is entirely dependent on the values collected from the search process. Both the variables p p and β can take values within the interval of 0–1. In each cycle, we give N, the number of solutions, to p p or β from the current population. These parameters’ values are anticipated to vary from one value to another. First, we generate N of these parameters, each with a mean (μ) and standard deviation σ equal to 0.5 from a normal distribution. The parameters’ maximum and lowest values may be changed. Initially, they are set to their maximum value. Nevertheless, they are afterward adjusted within their stated range based on the needs of the search process ( Farh et al., 2022 ).

Figure 10 illustrates the process of updating the phase probability ( p p ) based on the repulsion technique according to Eq. 22 .

FIGURE 10 . Repulsion-based learning method for p p update.

4.1 Creating new bonobos using various mating techniques

The phase probability ( p p ) parameter determines the mating behavior of bonobos. The value of ( p p ) is initially set to 0.5 and is modified after each iteration.

4.1.1 Promiscuous and restrictive mating strategies

The new generation of bonobos by promiscuous and restrictive mating approaches are created according to Eq. 23 .

where the new_bon i represents the ith-new solution. Moreover, bon i , bon p , and bon k1 are the present population’s i th, p th, and k 1 th solutions, respectively. Similarly, oldpop k2 and oldpop k3 are k 2 th and k 3 th solutions, respectively. Badpop k4 is the k 4 th solution of the badpop population, which is the third memorized population. The value of β i is the ith-sharing coefficient. It should be noticed that k 1 , k 2 , k 3 , and k 4 are four separate values chosen at random from the range (1, N) ( Das and Pratihar, 2019 ).

4.1.2 Extra-group mating strategy

Extra-group mating is used to update the solution if the random number is equal to or less than the probability of extra-group mating (p xgm ) through Eqs 24 – 26 .

C o is an intermediary parameter. R 1 and r 2 are two distinct random numbers generated in the range (0 upto1). new_bon j i and new_bon j i are the j th variables of the given solution and the existing population’s i th bonobo, respectively ( Farh et al., 2022 ).

4.1.3 Consortship mating strategy

When the value of r 2 is larger than the value of (p xgm ), the consortship mating strategy is used to generate new offspring. Eqs 27 – 31 demonstrate this process.

C 1 ,C 2 , C 3 and d v are intermediary parameters while tsgsmax stands for the maximum size of group ( Abdelghany et al., 2021 ).

4.2 Modified boundary handling technique

The generated bonobo is given a value equal to the maximum bound when it crosses the top variable limit with a probability of occurrence of 0.5. If not, it is changed with a 50% chance using Eqs 32 , 33 .

Similarly, the same Equations are used to adjust a new bonobo if it is determined that it exceeds the lower variable boundaries.

The steps of the Implementation of the SaBO can be summarized as follow:

• Initialization of parameters and population size of the SaBO.

• Comparison of all bonobos’ fitness levels.

• Identification of the α bon .

• Choose the p th bonobo randomly from good solutions.

• Is a random number (0, 1) ≤ p p ?

• If this is the case, use the promiscuous or restricted mating technique to make a new bonobo.

• If false, use the consortship or extra group mating technique to make a new generation.

• Calculate the fitness values of new bonobos, as well as the alpha bonobo.

• Update the memorized parameters of populations using the repulsion-based learning method.

• Sorting of the mixed population based on fitness.

• Calculate and display the objective function.

Figure 11 depicts the flowchart outlining SaBO’s solution procedures.

FIGURE 11 . Flowchart of the proposed Self-adaptive Bonobo Optimizer (SaBO).

5 Simulation results and discussion

This section presents the performance of the proposed controller for different cases, such as solar irradiance, temperature variation, and load fluctuation using SaBO and other competitive controllers.

5.1 Case (1): variation of solar irradiance

In this scenario, the load is maintained constant at 500 W, at the same time the PV system employs a changing solar radiation profile. The particular profile adheres to the same trend as real solar radiation by being low at the start of the day and climbing to 200 (W/m 2 ) before a substantial increase in the middle of the day to 800 (W/m 2 ). Then it is followed by a significant dip to 500 (W/m 2 ) before sunset as shown in Figure 12 .

FIGURE 12 . Solar irradiance variation.

5.1.1 Performance evaluation of the SaBO compared to other optimization algorithms

In order to assess the reliability, effectiveness, and validity of the SaBO, a range of Optimization Algorithms are employed. The proposed model employs Chimp Optimization Algorithm (ChOA), Gradient-Based Optimizer (GBO), and Wild Horse Optimizer (WHO) for optimization, based on 30 populations and 100 iterations. These techniques could be applied to tune the parameters of the proposed (FOPI-PI) controller using MATLAB/Simulink 2022b software. Table 2 illustrates the various parameters of the abovementioned optimization algorithms in a comprehensive and detailed manner. The fitness function is calculated based on the integral square error method (ISE). It measures the cumulative squared error between the setpoint and the actual signal of a system over a given period.

TABLE 2 . Parameters of the proposed (FOPI-PI) controller with different Optimization Algorithms.

Figure 13A shows the optimization convergence curves for the GBO, ChOA, WHO, and SaBO methods so that their convergence rates can be compared and understood. The optimization technique (SaBO) proposed in this study succeeded in mastering the list of the four selected algorithms.

FIGURE 13 . (A) Convergence rates of ChOA, GBO, WHO and SaBO algorithms, and (B) The P Load response with the proposed (FOPI-PI) controller combined with different optimizers.

Moreover, a comprehensive analysis of the load power response is carried out in order to assess and contrast the efficacy of the selected optimization approaches. The SaBo optimization method has demonstrated superior performance in comparison to other approaches. For instance, at 2 s, the nearest overshot (GBO) is more than twice the value of the (SaBO). The transient response is also ameliorated with SaBO over other optimization techniques as illustrated in Figure 13B .

Figure 3 The Response of P load of the proposed (FOPI-PI) controller with different optimization techniquesTechniques.

5.1.2 Implementation of the proposed optimizer (SaBO) with the proposed (FOPI-PI) controller

In this part, the proposed optimizer (SaBO) is incorporated with the proposed controller (FOPI-PI) and applied into the model to analyze the effectiveness, steadiness, and robustness of the proposed system (The optimizer and the controller). Figure 14 exhibits the power response of PV, battery, and the load of the proposed system. During the initial second, the load is fully supplied with the battery as the PV is off. Between the time interval of 1-2 s, it can be shown that the solar irradiance curve indicates a rise so the solar power reaches around 200 W, while the battery power reaches around 300 W. This increase is necessary to fulfill the power need of 500 W. During the third second, the battery is recharged due to the excess of the PV power over the load.

FIGURE 14 . Responses of P pv , P load and, P Bat of the proposed (FOPI-PI) Controller with Variable Solar Irradiance.

5.1.3 Comprehensive comparison between the proposed controller (FOPI-PI) and other controllers using SaBO optimizer

To investigate the strength and durability level of the proposed system (the controller and the optimization technique), a detailed comparison is conducted with different controllers including PI ( P et al., 2020 ; Guentri et al., 2021 ) and FOPI. Using the model, each controller with SaBO optimizer has been simulated using MATLAB/Simulink 2022b. Figure 15 displays the voltage of the DC bus with the different controllers PI, FOPI, and FOPI-PI.

FIGURE 15 . Response of V bus with different controllers.

Table 3 demonstrates the results of each controller transient response including the Maximum overshoot, the transient time, and the steady state error of the system. The response seen at the 2-s mark serves as a distinct illustration of the claimed advantage of the suggested (FOPI-PI) approach in every aspect of comparison.

TABLE 3 . Summary of transient response specifications for each controller.

The comprehensive comparison can be obtained by inclusive results that are attained through the ESS including the power response of the load, the Photovoltaic system, the battery, and the supercapacitor with variations in solar irradiance. Consequently, it is clearly noticeable that the proposed controller overcomes the other controllers in transient response specifications for the power of the load and the solar system as indicated in Figures 16A, B .

FIGURE 16 . Responses of (A) P load , (B) P pv , (C) P Sup-C , and (D) P bat of different controllers under solar irradiance variation.

The productivity and effectiveness of the hybrid EES is supported by the results presented in Figures 16C, D . The results demonstrate the harmony between the battery and supercapacitor to provide the load power in case of power shortage from the PV system. The proposed PMS regulates the process of coordination between the battery and supercapacitor by enhancing the response of the hybrid electric ESS during charging or release of power to stabilize the power of the load. For instance, at 2 s, the supercapacitor quickly releases a significant amount of power while simultaneously switching the battery’s mode from draining to charging in order to lessen the load on the battery. To give a clear example of that process, at 3 s, the battery switches from the storage process into the release power process according to the PMS to maintain the load power. At the same time, the supercapacitor provides a power spike in order to maintain the battery power at an appropriate level. It is evident that the proposed (FOPI-PI) controller performance surpasses other mentioned controllers like PI ( Guentri et al., 2021 ) and FOPI to sustain the system’s stability and reliability. For example, at time 1 s, the maximum undershot of the power of the Sup-C is reduced to about 50 W for the proposed FOPI-PI compared to about 55 W and 95 W for PI and FOPI, respectively.

5.2 Case (2): load power variation

The primary objective of the second simulation scenario is to determine and verify the effectiveness of the system’s suggested control approach across various loading circumstances. The power of the solar panel is fixed at 800 W and the variation of the load power is shown in Figure 17 .

FIGURE 17 . Variable load power.

5.2.1 Implementation of the (SaBO) with the proposed (FOPI-PI) controller

Figure 18 demonstrates the variation of the load, the response of the PMS, and the proposed control strategy to accommodate this variation. The battery runs in charging mode to saves the excess power because the power demanded by the load is less than the power developed by the solar system.

FIGURE 18 . Responses of P pv , P load and, P Bat of the proposed (FOPI-PI) Controller with Variable Load Power.

5.2.2 Comprehensive comparison between the proposed controller (FOPI-PI) and other controllers using the (SaBO)

Numerous controllers including PI ( Guentri et al., 2021 ), FOPI, and FOPI-PI are integrated with the discussed model to test the effectiveness and stability of the proposed controller (FOPI-PI) with the SaBO optimization technique. Figures 19A, B show the load and PV response with the selected control schemes. The proposed control strategy presents a significantly enhanced performance regarding the qualities of transient response such as shorter transient time, lower overshoot or undershoot, and better steady state error with a variation of the load power. For example, at the beginning of the operation, the (FOPI-PI) peak overshot in the load power is reduced to about (6.25%) compared to about (7.875%) and (9.375%) for FOPI and PI, respectively.

FIGURE 19 . Responses of (A) Pload, (B) Ppv, (C) PSup-C, and (D) Pbat of different controllers with variable load power.

By using various controllers to regulate the Sup-C response during the charge and discharge processes, numerous potential advantages of the hybrid EES application can easily be realized. Additionally, the appropriate integration of supercapacitor power at appropriate times will significantly alleviate the strain on the battery, resulting in reduced stress. At t = 1 s, the power load (P load ) experiences a decrease from 800 W to 300 W, prompting the battery to transition into a charging state, reaching around 450 W. At the moment, the Sup-C power is actively engaged in maintaining the load at its designated goal value. The power demand increases from 500 W to 700 W during a time span of 2 s. During this brief period, the battery runs in the discharge state. There is a paucity of power provided by the Sup-C during this period as illustrated in Figures 19C, D . The proposed controller (FOPI-PI) exhibits better performance for the PMS to maintain stability and battery life in the long run than the abovementioned controllers. For instance, at the beginning of the operation, the maximum overshot of the power of the battery is reduced to about 200 W for the proposed FOPI-PI, compared to about 252 W and 255 W for the PI and FOPI, respectively.

5.3 Case (3): temperature variation

In this scenario, the load is maintained constant at 500 W, and at the same time, the PV system employs a changing temperature profile. Figure 20 illustrates the variation in the temperature of the solar panels, which causes a variation in the output power of the PV system. Hence, the output power of the battery also changes to recover the load demand, and the participation of the Sup-C is also needed in times of transient.

FIGURE 20 . Variable temperature profile of the solar system.

5.3.1 Implementation of the (SaBO) with the proposed (FOPI-PI) controller

Figure 21 illustrates the changing output power of the PV system according to the variation in the atmospheric temperature. The effective performance of the PMS system is demonstrated by the cooperation between the battery and the Sup-C to supply the power demanded by the load. For example, from 2 to 3 s, the battery is in discharging mode, and output power is developed to accommodate the required power.

FIGURE 21 . Responses of P pv , P load and, P Bat of the proposed (FOPI-PI) Controller with Temperature Variation.

5.3.2 Comprehensive comparison between the proposed controller (FOPI-PI) and other controllers using the (SaBO)

As shown in Figure 22A , the proposed FOPI-PI controller beats the other controllers (PI) and (FOPI) in terms of transient response and stability. For example, at t = 3 s, the FOPI-PI exposed the least overshot of the PV power (about 0.7%) from PI (about 1%) and FOPI (about 1.2%). Additionally, the steady state error and the settling time are also improved by the proposed controller. Enhancing these specifications is reflected in the performance of the hybrid EES, as shown in Figures 22B, C . At the instant of 1, the stress on the battery is reduced by about (10%) of the FOPI-PI, less than (22.5%) and (27.5%) of the PI and FOPI, respectively. Due to the FOPI-PI controller’s greater performance, the battery’s lifespan is extended.

FIGURE 22 . Responses of (A) P pv , (B) P Bat , and (C) P Sup-C of different controllers with temperature variation.

6 Conclusion

This study introduces an innovative control technique to regulate the power flow between the solar system and the hybrid ESS. The proposed approach separates the low- and high-frequency components of the power and employs the battery’s error current to manage the Sup-C, distinguishing it from traditional techniques. The low frequency power component is regulated by the battery storage system, while the Sup-C takes responsibility for the high frequency component. Furthermore, the proposed cascaded regulator comprises a First Order Plus Integral (FOPI) controller and a traditional Proportional-Integral (PI) controller, which exhibit many substantial advantages. The proposed (FOPI-PI) controller has a vital role in enhancing the transient response, including the settling time, overshoot/undershoot, and transient duration. As a result, the DC bus voltage is maintained constant during load and solar variation to reduce the stress on the battery and increase its lifespan. In order to enhance the robustness of our studies, we employ a novel optimization approach known as SaBO to finely adjust the parameters of the controller. The effectiveness of the incorporated system between the controller and the optimizer has obviously improved the performance of the system. Compared to the other (PI) and (FOPI) controllers, The proposed controller shows great superiority in most cases and aspects of comparison. Subsequently, the transient and dynamic response specifications of the proposed system have been substantially enhanced, as shown in the wealth and extensive results throughout the research.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

AK: Conceptualization, Investigation, Writing–original draft. HK: Supervision, Writing–original draft, Validation. KA: Resources, Software, Supervision, Writing–review and editing. MR: Software, Writing–original draft. HE: Supervision, Writing–review and editing. YG: Investigation, Writing–review and editing. AY: Funding acquisition, Writing–review and editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abdelghany, R. Y., Kamel, S., Sultan, H. M., Khorasy, A., Elsayed, S. K., and Ahmed, M. (2021). Development of an improved bonobo optimizer and its application for solar cell parameter estimation. Sustainability 13 (7), 3863. doi:10.3390/su13073863

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PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: photovoltaic, energy management, energy storage, enhanced control, FOPI-PI, SaBO, optimization

Citation: Khairalla AG, Kotb H, AboRas KM, Ragab M, ElRefaie HB, Ghadi YY and Yousef A (2023) Enhanced control strategy and energy management for a photovoltaic system with hybrid energy storage based on self-adaptive bonobo optimization. Front. Energy Res. 11:1283348. doi: 10.3389/fenrg.2023.1283348

Received: 25 August 2023; Accepted: 31 October 2023; Published: 20 November 2023.

Reviewed by:

Copyright © 2023 Khairalla, Kotb, AboRas, Ragab, ElRefaie, Ghadi and Yousef. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Hossam Kotb, [email protected] ; Amr Yousef, [email protected]

This article is part of the Research Topic

Fuel Cell: Technology Advancements

Education Africa launched its case study at the Gordan Institute of Business Science

The case study details the journey that james urdang, has taken to ensure that the south african non-profit organisation continues to grow..

The Education Africa: Networks and Coalitions for Securing Non-Profit Funding case study was launched at the Gordan Institute of Business Science on November 14 and discussed the state of education in the country.

The event in Illovo hosted panellists: Dr Tracey Toefy, Founder and CEO of Education Africa, James Urdang, Professor Jonathan Jansen and Miles Japhet.

The case study was researched and written by Dr Tracey Toefy and Abdullah Verachia and details the journey that Urdang has taken to ensure that the South African non-profit organisation continues to grow and impact lives. The case overviews the various programmes that Education Africa runs, highlighting their importance in providing holistic educational offerings to children in various communities.

To help unpack the question regarding the state of South Africa’s education system, Professor Jonathon Jansen explained that in many ways South Africans have been numb to the crisis of the state of education in the country.

“Think about it, if 81% of Grade 4 learners cannot read for understanding, in any self-respecting democracy the president would make sure everything comes to a halt and put a time frame on when the reading problem must be solved. Children not being able to read does not mean they cannot only read a book, but it means they cannot read generally for understanding the world around them.”

Urdang agreed with Jansen that the education system was concerning. He said, “Unless we fix the state of our education our country will not go anywhere. I find it troubling that 30 years into our democracy, for a black child to receive a quality education is still about beating the system which should not be happening.”

The Education Africa founder added that he was also worried about the early childhood development realm, due to the foundation phase of a child being the most important and if it was not nurtured we would have another lost generation.

Answering the question about how and what it would take to better the education system, Miles Japhet noted that it had to start with having a value system and finding people who have a servant leadership culture. “Great leadership determines the nature of any organisation which also applies to the education system.”

The case has been published through Ivey Publishing, the publishing house of Ivey Business School in Canada. The case is also available on the Harvard Business Publishing Education site.

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