Suboptimal Decisions Research Articles

The Role of Age-Related Changes to Memory in Social Judgment and Decision Making Involving Artificial Social Agents

Abstract: Research links a progressive decrement in episodic memory to deficits in judgment and decision making in aging. In social decision making, decrements in episodic memory contribute to suboptimal decisions among older adults. As artificial agents – such as humanoid robots, artificial intelligence (AI)-powered deepfakes, and chatbots – become increasingly present in human society, they can both benefit (e.g., combat loneliness) and harm (e.g., defraud) older adults. Understanding how older adults make social judgments and decisions involving artificial agents is crucial for research and policy making. Nevertheless, it remains poorly understood how episodic memory deficits influence social judgments and decisions involving artificial agents, and how this relation changes with age. This review will bridge this gap by applying a developmental model to explain how memory influences social judgments and decisions involving artificial agents in late adulthood. Evidence suggests that older adults increasingly rely on gist-based processing, which may explain their greater preference for humanoid robots compared with younger adults. We will also discuss the distinct challenges deepfakes pose for older adults’ trust-related decision making. In closing, we will discuss the implications of age-related memory changes for social judgments and decisions involving disembodied artificial agents, such as chatbots.

Conducting Pairwise and Network Meta-analyses in Updated and Living Systematic Reviews: a Scoping Review Protocol.

The objective of this scoping review will be to describe existing guidance documents or studies reporting on the conduct of meta-analyses in updated systematic reviews (USRs) or living systematic reviews (LSRs). The rapid increase in the medical literature poses a substantial challenge in keeping systematic reviews up to date. In LSRs, a review is updated with a pre-specified frequency or when some other signalling criterion is triggered. While the LSR framework is well-established, there is uncertainty regarding the most appropriate methods for conducting repeated meta-analyses over time, which may result in sub-optimal decision-making. Studies of any design (including commentaries, books, manuals) providing guidance on conducting meta-analysis in USRs or LSRs. We will use the JBI methodology for scoping reviews. We will search multiple medical bibliographic databases (Cochrane Library, Embase, ERIC, MEDLINE, JBI Evidence Synthesis, and PsycINFO), statistical and mathematics databases (COBRA, Current Index to Statistics, MathSciNet, Project Euclid Complete, and zbMATH), pre-print archives (Arvix, BioRxiv, and MedRxiv), as well as difficult to locate/unpublished (or gray) literature. Two reviewers will independently screen titles, abstracts, and full-text documents, and extract data. Characteristics of recommendations for meta-analysis in USRs and LSRs will be presented using descriptive statistics and categorized concepts.Details of this review project can be found in Open Science Framework: https://osf.io/9c27g.

Cultural uncertainty avoidance predicts consumers' affective reactions to chemicals.

Chemicals in general often evoke negative emotions (e.g., worry or fear) in consumers. This can cause consumers to avoid beneficial products and may even lead to suboptimal public policy decisions. It is, therefore, important to better understand how affective reactions to chemicals in general (ARC) form in order to be able to counteract these undesirable effects. The present research contributes to the literature on ARC by studying between-country differences in ARC. While ARC were negative in all countries in our dataset, there were practically relevant between-country differences in how negative they were. We predicted and found that consumers in higher uncertainty avoidance (UA) societies reported more negative ARC than their counterparts in lower UA societies. This effect was due to the rule orientation component rather than the anxiety component of UA. Importantly, while we found evidence for several alternative explanations for between-country variation in ARC (i.e., differences in affluence, individualism, prevalence of chemicals, and trust in consumer protection), the UA effect remained statistically significant when we controlled for other country characteristics. The present research contributes to a better understanding of how contextual factors on the society level influence consumers' ARC and in doing so advances our understanding of ARC. It also has implications for practitioners who wish to educate consumers on the risks and benefits of chemicals.

Loan selection, attribute substitution and information effect

PurposeBorrowers are usually presented with a menu of similar-looking options when selecting a loan plan. However, it remains unclear whether they are able to choose the most cost-effective plan. This study aims to investigate whether people select loan plans optimally, whether their choices are influenced by the informational cues provided to them and how their selections change with the provision of additional loan-related information.Design/methodology/approachThe study uses a within-participant experimental design, where participants are presented with similar-looking loan plans but with different present discounted costs. Subjects are asked to rank those plans, with the minimum present discounted cost plan maximizing their experimental payoff. The loan plans across different rounds are presented with different informational cues to see whether that influences participants' ability to select the cost-minimizing loan plans.FindingsThe author finds that participants were 78% likely to state a suboptimal preference ordering and 68% likely to select a loan plan that does not minimize cost. There is suggestive evidence regarding the role of attribute substitution in the decision-making process, wherein participants substituted annual percentage rate (APR) and total cost attributes for the present discounted cost of a loan plan. Additionally, the author presents causal evidence demonstrating how providing additional information can influence the choice of the substituted attribute.Originality/valueResearch has indicated that individuals often make suboptimal financial decisions, particularly in complex decision contexts. Examples include selecting medical insurance, a retirement savings plan, a student loan or borrowing on a credit card. This paper demonstrates that similar biases are present when individuals borrow using a loan plan, wherein they tend to substitute the APR or total cost of the loan plan for the present discounted cost of the loan plan. Furthermore, the study reveals that the impact of providing more information to borrowers can vary, depending on the combination of loan plans being considered.

Design and Implementation of a Virtual Hoisting System: A Digital Twin Approach for Early Warning and Fault Detection

Mine hoists are vital for efficiently transporting materials and personnel between surface and underground mining operations, making their safety and reliability paramount. However, conventional monitoring approaches, which rely on distributed sensors or video surveillance, are limited in their ability to capture the global dynamics of the system, leading to suboptimal decision-making and potential safety risks. This research introduces the integration of digital twin technology into the monitoring and management of mine hoists to address these challenges. This study uses Unity-based modeling to create a virtual system capable of real-time synchronization with its physical counterpart. The digital twin simulates dynamic load changes, predicts potential faults, and optimizes operational parameters through intelligent algorithms. A robust monitoring framework was developed, consisting of a multi-layered architecture that integrates physical systems, real-time data collection, and virtual simulations. Field tests on a multi-rope friction hoist verified the system’s performance, with results demonstrating improved stability, accuracy, and predictive capabilities. A health evaluation model further enhances safety by categorizing the hoist’s operational state into health levels such as ‘healthy,’ ‘sub-healthy,’ ‘warning,’ and ‘fault.’ This model identifies critical risks, such as wire rope tension anomalies, and provides early warnings, ensuring timely interventions.

Epistocracy and populism: second-order ideologies challenging democracy

ABSTRACT Epistocracy and populism are usually seen as opposites. The first finds error in democracy’s reliance on the sub-optimal decisions by the supposedly incompetent masses, and argues that political decisions should be tied to epistemic merit, not popularity. The populist critique of democracy, contrarily, finds that there is not enough political confrontation in standard representative democracies where the ‘real people’ are not properly embodied, and thus pits an imagined direct will of the unified and virtuous people against a self-serving establishment. This article demonstrates that these ideologies have surprising underlying similarities concerning their categorization, political ontology, epistemology and a conception of political authority. Firstly, they both are second-order political ideologies that are not directly tied with substantive political content but rather with the interpretation of how to govern assuming disagreement concerning first-order political ideologies. Secondly, their political ontology divides citizens into two constitutively differing parts, one of which they (partly) exclude from political membership. Thirdly, their political epistemology assumes the existence of political truths which makes their conception of authority anti-proceduralist, either instrumentalist or moralist. Fourthly, they downplay the value of pluralism, deliberation and dissent. Uncovering these commonalities helps us detect and understand the dynamics of some anti-democratic tendencies better.

The Effects of Adverse Events and Associated Costs on Value-Based Care for Metastatic Pancreatic Ductal Adenocarcinoma

Background: Rising oncology healthcare costs have led to value-based care reimbursement models that coordinate care and improve quality while reducing overall spending. These models are increasingly important for traditional Medicare and other payers. Objectives: To compare the incidence of adverse events (AEs), AE-associated excess costs, and total cost of care (TCOC) of 3 cohorts receiving first-line treatment for metastatic pancreatic ductal adenocarcinoma (mPDAC). Methods: We conducted a retrospective analysis of administrative claims data from 2018 to 2022 using the Medicare 100% Research Identifiable Files. We examined 3 cohorts receiving mPDAC treatment: FOLFIRINOX (FFX) (oxaliplatin, irinotecan, leucovorin, 5-FU bolus and infusion); modified FFX, (5-FU infusion only); and gemcitabine/nab-paclitaxel (gem/abrax). We compared the incidence of clinically significant AEs, TCOC, components of TCOC, and costs related to AEs/treatment toxicity. Results: Patient AE rates ranged from 6.2% to 51.7%. AEs occurred more frequently in patients receiving FFX with all 4 components. Patients receiving brand name gem/abrax had lower rates of febrile neutropenia (6.2%) and neutropenia (22.2%) than those receiving FFX with no 5-FU bolus (febrile neutropenia, 9.9%; neutropenia, 36.9%) and FFX with all 4 components (febrile neutropenia, 6.9%; neutropenia, 30.4%). Rates of most nonhematologic AEs were higher in patients receiving FFX with all 4 components, with diarrhea occurring in 28.3%, abdominal pain in 31.5%, and nausea/vomiting in 41.5% of patients. TCOC was lower in the gem/abrax cohort: $6505 vs FFX with no 5-FU bolus ($6995) and FFX with all 4 components ($7142) per administration. The development of any studied hematologic AE was associated with a mean excess cost of $5993 per administration, while the development of any studied nonhematological AE was associated with a mean per-administration excess cost of $3665. Discussion: Treatment decisions intended to minimize chemotherapy costs may lead to suboptimal decisions if the goal is to reduce TCOC. Our research suggests FFX is more costly than gem/abrax (TCOC per administration). Patients receiving gem/abrax were older and had higher baseline Charlson Comorbidity Index scores; however, other factors may be important in driving cost differences. Conclusions: Irrespective of drug cost, chemotherapy leading to a significant increase in AEs is associated with higher TCOC.

Experience Minimizes the Pull-to-Center Effect in Newsvendor Decisions

Problem definition: The asymmetric pull-to-center effect for newsvendors is a robust finding in operations, and understanding why newsvendors make suboptimal decisions is key for identifying ways to improve decision-making quality in this critical task. Although prior studies have indicated that experience does not substantially mitigate the pull-to-center effect, the experience levels achieved in those previous studies are limited in comparison with the experience level that a near-continuous time environment can provide. Methodology/results: We conduct a set of laboratory experiments using a near-continuous time environment to determine the effect that extensive experience has on newsvendor behavior and the extent to which resultant learning is transferable across conditions. Observed behavior clearly demonstrates that the pull-to-center effect is substantially reduced, if not eliminated, with sufficient experience and that this learning can have positive spillovers to more traditional settings. Further, the experiments suggest that it is the repeated feedback regarding a given inventory decision rather than the ability to explore many strategies that drives improved decision making. Managerial implications: Experience can mitigate the pull-to-center effect, and near-continuous time environments can be an effective training tool for gaining such experience. Funding: This work was supported by the Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky [Grants VEGA 1/0660/23 and VEGA 1/0721/24] and the Slovak Research and Development Agency [Grant APVV-19-0573]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0439 .

The Dynamics of Informational Black Holes in Financial Markets: Implications for Cost of Capital and Project Screening

The phenomenon of informational black holes—areas in financial markets where critical information is inaccessible, misinterpreted, or entirely absent—poses significant challenges for decision-making, capital allocation, and market efficiency. This research explores the dynamics of informational black holes and their implications for the cost of capital and project screening processes. By examining theoretical models and empirical evidence, the study identifies key factors contributing to the formation of these informational voids, including asymmetric information, opaque reporting practices, and behavioral biases. The analysis highlights how informational black holes distort risk perception, inflate financing costs, and lead to suboptimal investment decisions. Furthermore, this research investigates mechanisms to address these challenges, such as regulatory interventions, enhanced transparency, and advancements in financial technologies. The findings provide actionable insights for market participants, policymakers, and corporate leaders, emphasizing the importance of mitigating informational asymmetries to foster fair and efficient capital markets. By bridging gaps in understanding, this study contributes to a nuanced perspective on the interplay between information dynamics and financial decision-making.

Herding Bias and Investment Performance in Kenya's Dynamic Real Estate Sector.

One fundamental question remain unanswered, are investments free from behavioral biases? This study explores the impact of herding bias on investment performance, with a focus on real estate investors in Kenya. Behavioral finance highlights how psychological factors influence investment decisions, challenging the assumptions of rationality and market efficiency. Herding, characterized by individuals imitating the decisions of others, emerges as a key bias affecting financial markets. It creates inefficiencies by distorting prices and returns, driven by social influences and noise trading. This research employed both descriptive and inferential analyses to assess the relationship between herding behavior and investment outcomes. Respondents were evaluated on their reliance on personal judgment, external opinions, and group influence when making investment decisions. Findings indicate a statistically significant effect of herding bias on investment performance, with 26.1% of performance variation attributable to herding. Regression analysis demonstrated a moderate correlation (R = 0.511) between herding and performance, underscoring the influence of behavioral biases. The study aligns with previous findings that herding can diminish return dispersion and, under extreme conditions, lead to negative dispersion. Notably, investors' reliance on group trends and noise rather than fundamental data often results in suboptimal decisions, adversely affecting financial outcomes. The results emphasize the need for strategies to mitigate the negative impacts of behavioral biases. Investors should evaluate investments based on portfolio performance rather than isolated returns and risks. Furthermore, the establishment of regulatory frameworks could guide investors toward informed decision-making and market stability. This study contributes to the understanding of behavioral finance in emerging markets, providing insights into the interplay between herding bias and investment performance in Kenya's dynamic real estate sector

Predicting Subject Traits From Brain Spectral Signatures: An Application to Brain Ageing.

The prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous prediction work has predominantly been done on neuroimaging data, our focus is on electroencephalography (EEG), a relatively inexpensive, widely available and non-invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is sometimes done manually, risking biases and suboptimal decisions. Here we investigate the use of data-driven Kernel methods for prediction from single channels using the EEG spectrogram, which reflects macro-scale neural oscillations in the brain. Specifically, we introduce the idea of reinterpreting the spectrogram of each channel as a probability distribution, so that we can leverage advanced machine learning techniques that can handle probability distributions with mathematical rigour and without the need for manual feature extraction. We explore how the resulting technique, Kernel mean embedding regression, compares to a standard application of Kernel ridge regression as well as to a non-Kernelised approach. Overall, we found that the Kernel methods exhibit improved performance thanks to their capacity to handle nonlinearities in the relation between the EEG spectrogram and the trait of interest. We leveraged this method to predict biological age in a multinational EEG data set, HarMNqEEG, showing the method's capacity to generalise across experiments and acquisition setups.

Recurrent Neural Network for Quantitative Time Series Predictions of Bridge Condition Ratings

Traditional forecasting models for bridge conditions, such as ARIMA and Markov chains, often fail to adequately capture nonlinear and dynamic relationships among critical variables like age, traffic patterns, and environmental factors, leading to suboptimal maintenance decisions, increased long-term maintenance costs, and heightened safety risks. This study addresses these limitations by developing recurrent neural network (RNN) models utilizing Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures with a TimeDistributed output layer. This novel approach enables accurate forecasting of the Bridge Health Index (BHI) and condition ratings for key components—deck, superstructure, and substructure—while effectively modeling temporal dependencies. Applied to bridge data from Georgia, USA, the regression models (BHI) achieved R2 values exceeding 0.84, while the classification models (components condition ratings) demonstrated accuracy between 84.78% and 87.54%. By modeling complex temporal trends in bridge deterioration, our method processes time-dependent data from multiple bridges simultaneously, revealing intricate relationships that influence bridge performance within a state’s inventory. These results provide actionable insights for maintenance planning, optimized resource allocation, and reduced risks of unexpected failures. This research establishes a robust framework for bridge performance prediction, ensuring improved infrastructure safety and resilience amid aging assets and constrained maintenance budgets.

A systems perspective on gaps in the person-centered sick leave and rehabilitation process: a Swedish interview study

Background Consensus on priorities to optimize the sick leave and rehabilitation process (SRP) is lacking. Objective To explore perspectives of stakeholders in the SRP on bridging the gap between desired process scope, and actual practice, from a multi-professional, multi-organizational, and interdisciplinary approach. Design and setting Focus group interviews were conducted with various SRP frontline professionals in Region Västra Götaland, Sweden, using purposive sampling to capture a range of experiences. Participants discussed their perceptions of critical changes and priorities needed to meet patients’ SRP needs in a primary care context. All interviews were analyzed using systematic text condensation, as described by Malterud. Subjects General practitioners (n = 6), rehabilitation coordinators and licensed healthcare professionals from primary healthcare (n = 13), administrators from the Social Insurance Agency, the Employment Agency and Social Services (n = 12). Results Through data analysis, the following themes emerged: 1) The need for rules and regulations to enable coherent process governance 2) Challenges and opportunities in person-centered SRP: Professional collaboration, organizational priorities, and the need for enhanced leadership, and 3) Balancing resources and patient needs in the SRP: How to improve care quality and accessibility. In summary, participants mainly discussed how to improve process efficiency and quality of care while balancing available resources and a heavy workload. A main goal was to prevent negative spirals of suboptimal decision-making in individual cases, which could lead to increased work, unfortunate outcomes, and patient suffering. Conclusions This qualitative study indicated that gaps between a desired process scope and actual practice might be bridged by enabling coherent cross-organizational process governance, prioritizing person-centered ways of working, and balancing available resources and workload. The above changes were believed to improve process quality and overall efficiency. Trial registration The study project plan was pre-registered on September 21st, 2020, in the database FOU i VGR (researchweb.org), project number 274941.

Dorsolateral prefrontal cortex drives strategic aborting by optimizing long-run policy extraction.

Real world choices often involve balancing decisions that are optimized for the short-vs. long-term. Here, we reason that apparently sub-optimal single trial decisions in macaques may in fact reflect long-term, strategic planning. We demonstrate that macaques freely navigating in VR for sequentially presented targets will strategically abort offers, forgoing more immediate rewards on individual trials to maximize session-long returns. This behavior is highly specific to the individual, demonstrating that macaques reason about their own long-run performance. Reinforcement-learning (RL) models suggest this behavior is algorithmically supported by modular actor-critic networks with a policy module not only optimizing long-term value functions, but also informed of specific state-action values allowing for rapid policy optimization. The behavior of artificial networks suggests that changes in policy for a matched offer ought to be evident as soon as offers are made, even if the aborting behavior occurs much later. We confirm this prediction by demonstrating that single units and population dynamics in macaque dorsolateral prefrontal cortex (dlPFC), but not parietal area 7a or dorsomedial superior temporal area (MSTd), reflect the upcoming reward-maximizing aborting behavior upon offer presentation. These results cast dlPFC as a specialized policy module, and stand in contrast to recent work demonstrating the distributed and recurrent nature of belief-networks.

Food for thought: The impact of short term fasting on cognitive ability.

Growing evidence suggests that resource scarcity can severely impede individuals' cognitive capacity, resulting in sub-optimal decision making. Few experimental studies investigate whether food deprivation as a form of resource scarcity influences decisions in other non-hunger related domains. We examine the effect of short term fasting on cognitive capacity by exogenously manipulating individuals' fasting time in a laboratory experiment. Participants were randomly assigned to one of three treatments: 1) 3-hour fast; 2) 12-hour fast; and 3) control, in which participants were not required to fast and consumed a protein shake upon arriving to the lab. Following the manipulation, participants completed the Raven's Progressive Matrices test which measures cognitive function. Although we find null treatment effects on cognitive ability, our results provide evidence that short term fasting does not directly inhibit cognition.

Dynamic Resource Aggregation Method Based on Statistical Capacity Distribution

The computing network is a novel architecture that enables resource matching through the network. In distributed computing networks, computing resource management devices collect resource information and report it to network nodes. These nodes then broadcast the information to guide resource matching. One challenge is efficiently aggregating and disseminating computing resource information, as directly reporting fully multi-dimensional data can cause excessive overhead, while overly simplified aggregation may reduce matching accuracy. Existing aggregation methods typically rely on static resource information, overlooking the heterogeneity and dynamics of computing resources that arise from variations in resource capabilities and fluctuations over time, leading to suboptimal matching decisions. In response, this study proposes a dynamic resource aggregation method based on statistical capacity distribution. By modeling the capacity distribution of computing nodes, this method captures dynamic resource information, enabling more precise resource matching. Additionally, constructing resource groups and calculating representative distributions effectively compress the volume of data announcements. Experiments and data analysis demonstrate that, compared to static resource matching methods, the proposed method improves matching accuracy by 48%. Furthermore, it reduces announcement overhead by approximately 77.1% compared to existing dynamic resource allocation methods. These findings provide an efficient solution for resource aggregation in distributed computing networks.

The Role of Cognitive Bias in Principal Decision Making: A Narrative Analysis of the Literature

This study aims to critically examine the role of cognitive biases in principal decision-making processes by synthesizing and analysing existing literature. It seeks to identify key cognitive biases affecting principal decisions, their impact on organizational outcomes, and potential mitigation strategies. A comprehensive narrative analysis of the literature was conducted. Relevant peer-reviewed articles, books, and book chapters published between 1970 and 2023 were systematically identified and reviewed using major academic databases such as Web of Science, JSTOR, and Google Scholar. The analysis revealed that cognitive biases, particularly confirmation bias, overconfidence, anchoring, and availability bias, significantly influence principal decision-making. These biases can lead to suboptimal decisions, affecting strategic planning, risk assessment, and overall organizational performance. The study also identified several debiasing techniques, including awareness training and structured decision-making frameworks, which show promise in mitigating the impact of cognitive biases. This study provides a comprehensive and up-to-date synthesis of research on cognitive biases in principal decision-making, offering a holistic view of the field’s current state. It uniquely combines insights from cognitive psychology, behavioural economics, and management science to provide a multidisciplinary perspective on the topic. The study contributes to both theory and practice by offering a structured understanding of how cognitive biases influence principal decision-making. It provides valuable insights for principals and organizations to improve their decision-making processes and highlights areas for future research in this critical field. The findings can inform the development of more effective principal training programs and decision support systems.

Empowering Investors: Reducing Herd Bias through Financial Literacy and Self-reflective Thinking

Herd behavior, where investors mimic the trades of others rather than relying on their own information or analysis, is a widespread phenomenon that can lead to suboptimal investment decisions and market inefficiencies. Hence, understanding how to reduce this behavior is crucial for improving individual investment outcomes and sustainable market performance. Though the literature suggests that self-reflection and financial literacy have potential to mitigate herd bias, it is unclear as to how these two perspectives interact to minimize this irrational behavior. By drawing on insights from the adaptive market hypothesis, transformative learning theory, concept of bounded rationality and dual process theory, this study attempts to enhance the understanding of this phenomenon by introducing a conditional mediation model that promotes self-directed learning. It predicts that investors can reduce their herd bias through self-reflection on their past stock trading experiences, which they can practice by engaging intuitive logical thinking and strengthen further through their financial literacy. Since herding is generally predicted to be more prevalent in frontier stock markets, the study was conducted in a frontier market - the Colombo Stock Exchange. The data was obtained through a self-administered questionnaire from 253 active individual investors, and analyzed by applying the PROCESS procedure. The findings reveal the significance of self-reflection as a mediating variable across its antecedents, which provides strong evidence for its central role in reducing herd bias. Notably, the findings indicate that self-reflection has a stronger effect on reducing herd bias of investors with lower level of financial literacy. It implies that investors with low financial literacy are more susceptible to herd bias, and self-reflection facilitates them to recognize and minimize their herd bias. Accordingly, the study concludes that promoting self-reflection can empower individual investors to become more financially literate and thereby mitigate their herd bias.

Multi-fidelity optimization for the day-ahead scheduling of Pumped Hydro Energy Storage

Optimizing the operation of Pumped-Hydro Energy Storage (PHES) requires accurately representing nonlinearities, such as reservoir geometry and water-power conversion efficiency. While traditional methods like Mixed-Integer Linear Programming (MILP) offer theoretical guarantees, they rely on approximations that can lead to suboptimal decisions and costly redispatch or penalties. Because of its inherent approximations, MILP is a low-fidelity optimization model. In this paper, we propose a multi-fidelity approach that combines MILP with a Surrogate-Based Optimization Algorithm (SBOA). MILP solutions are used as warm-starts for the SBOA, which refines the solutions using a high-fidelity simulator of PHES dynamics and redispatch costs. This allows the SBOA to handle nonlinearities and improve the initial MILP solution by exploring areas with higher expected value. Our approach is tested on a PHES unit that participates in the energy and reserve markets in Belgium. The results show that, despite the extensive efforts made in MILP modeling, decisions can still be improved through smart integration with SBOAs.

Hierarchical optimal operation for bipolar DC distribution networks with remote residential communities

To facilitate the seamless integration of renewable energy, bipolar DC distribution networks (Bi-DCDNs) have been widely adopted in various applications, including the Shenzhen Future Building, the Boeing 787 aircraft, and DC LED lighting systems in Singapore. Bi-DCDNs incorporate diverse flexible devices to improve both economic efficiency and security. However, a comprehensive coordination framework considering the regulatory heterogeneity among these flexible devices remains absent. Hence, this paper proposes a hierarchical coordination framework for flexible devices in Bi-DCDNs. More specifically, the upper-level model considers the operational differences of the DC transformer (DCT) in various modes to determine the optimal switching scheme for reducing losses; In the lower-level model, the control parameters of the DCT, energy storage systems (ESSs), and DC electrical springs (DCESs) are coordinated to enhance voltage quality. Furthermore, to accurately capture the steady-state behavior of flexible devices, the hierarchical framework incorporates the Newton-Raphson power flow method. This method formulates a steady-state model for multiple flexible devices and demonstrates the impact of different control modes of DCT on power flow. Subsequently, a genetic algorithm is used to solve the proposed model, ensuring that suboptimal decisions made at the upper level are rectified at the lower level, and vice versa. The numerical results indicate that the proposed framework achieves the optimal operation for both reduced losses and enhanced voltage quality in Bi-DCDNs. Furthermore, it exhibits advantageous applications for Bi-DCDNs with additional DCTs for remote residential communities.