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The integration of hydrogen vehicles into citizen-oriented energy communities presents a transformative opportunity to enhance energy resilience, sustainability, and democratization. With zero-emission profiles and rapid refueling capabilities, hydrogen vehicles are pivotal in advancing cleaner transportation solutions. However, uncertainties in driving patterns and refueling behaviors pose challenges to their seamless integration and management. This paper proposes a framework based on information gap decision theory (IGDT) to address these uncertainties within community hydrogen parking lots. These parking lots also function as community energy storage systems, utilizing electrolyzers, fuel cells, and hydrogen storage to manage both hydrogen and electrical energy. The approach facilitates energy sharing among prosumers while ensuring thermal comfort within the community. Results show that under a risk-averse strategy, the system tolerates up to 50% variability in travel distances without exceeding cost limits, while a risk-seeking strategy accommodates up to 60% variability at a 50% deviation factor. Proposed Model for Uncertainty Management in a Hydrogen-Integrated Prosumer-Oriented Citizen Energy Community. [Display omitted] • IGDT framework manages uncertainty in hydrogen vehicle travel behavior in CECs. • CECs model includes energy sharing, thermal comfort, and hydrogen parking. • Risk-averse strategy tolerates 50% travel variability without exceeding costs. • Risk-seeking strategy accommodates 60% variability at a 50% deviation factor. • A one-year case study demonstrates the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This commentary seeks to improve the design and analysis of trials undertaken to obtain approval of drugs for treatment of rare diseases. Methodological analysis reveals that use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs. Hypothesis testing is inattentive to the relative magnitudes of losses to patient welfare when types 1 and 2 errors occur. Requiring the sample size to be large enough to guarantee the specified statistical power particularly inhibits the development of new drugs for treating rare diseases. Rarity of a disease makes it difficult to enroll the number of trial subjects needed to meet the statistical power standards for drug approval. Use of statistical decision theory in drug approval would overcome these serious deficiencies of hypothesis testing. Sample size would remain relevant to drug approval, but the criterion used to evaluate sample size would change. Rather than judging sample size by statistical power, the Food and Drug Administration could require a sample to be large enough to provide a specified nearness to optimality of the approval decision. Using nearness to optimality to set sample size and making approval decisions to minimize distance from optimality would particularly benefit the evaluation of drugs for treatment of rare diseases. It would enable a dramatic reduction in sample size relative to current norms, without compromising the clinical informativeness of trials. • The use of hypothesis testing in the Food and Drug Administration drug approval process is harmful. Conventional asymmetric error probabilities bias the approval process against approval of new drugs. Requiring the sample size to be large enough to guarantee the specified statistical power particularly inhibits the development of new drugs for treating rare diseases. • Use of statistical decision theory in drug approval would overcome these serious deficiencies of hypothesis testing. Sample size would remain relevant to drug approval, but the criterion used to evaluate sample size would change. Rather than judge sample size by statistical power, the Food and Drug Administration could require a sample to be large enough to provide a specified nearness to optimality of the approval decision. [ABSTRACT FROM AUTHOR]

As one of the most promising large-scale de-fossilization technologies currently available, Power-to-Gas (P2G) is expected to promote utilization of renewable sources and mitigate the effects of greenhouse gas. Through the coupling of P2G, Combined Heat and Power (CHP) as well as Carbon Capture and Storage (CCS), the P2G–CCS–CHP microgrid energy system could achieve hierarchical energy transfer and green-powered interconversion of gas, electricity and heat flows. However, the conversion of multiple energy shapes between different facilities is deeply intertwined with renewable energy sources, greatly increasing risk of unbalance operation and impacting the overall robustness and economic viability of system. To address these challenges, this study proposes a decision-making framework based on Information Gap Decision Theory (IGDT) method for a P2G–CCS–CHP system. The real-time balancing of heat & power loads and supply, the mechanism of gas-electricity-heat interaction, as well as recycling pattern of synthetic fuel within the system are carefully addressed to achieve optimal operation. Different scheduling schemes are obtained by setting up two separating decision principles, namely risk-seeking (RS) and risk-avoiding (RA), to solve these uncertainties and ensure robust decision making. Meanwhile, adaptability of decision-making methodology is analyzed based on several criteria including share of recycled green-powered fuel, ecological friendliness, carbon trading benefit, etc. Results show that adding of P2G module enables a 12.6% reduction in carbon emissions and generates 1252.2 kg of green powered gas, highlighting the eco-friendly nature of the multi energy system (MES). About 21.5% of the renewable energy flows participate the interconversion of electric-heat-gas and about 17.7% of total carbon flow is circulated as synthetic fuel within the system. In addition, conservative tendency is witnessed in the case of risk-seeking decision mode, leading to an approximate 13.22% increase in the operation costs, compared with risk-avoiding mode. The proposed IGDT decision strategy has shown significant compatibility in dealing with the impact of multiple uncertainties. • Employing IGDT strategy for decision-making considering energy uncertainty. • Optimization of power and heat balancing for reliable and low-carbon operation. • Recirculation of green-powered fuel within system is enabled via power-to-gas. • Interaction of electricity-heat-gas flows is analyzed under two risk principles. [ABSTRACT FROM AUTHOR]

Safety concerns severely impede industrial adoption of emerging human-robot collaborative manufacturing systems. A human-centric anomaly detection framework rooted in decision theory is proposed for integrated safety and quality assurance—which is a marked departure from earlier, quality- or safety-exclusive process control approaches. The framework adapts deep learning models to track fast robot motions from surveillance cameras and provides real-time, risk-metered alerts of anomalous trajectory deviations with theoretical guarantees. Application to a shared human-robot assembly line suggests that the framework can outperform conventional statistical process control methods in reducing safety risks and allows for straightforward extensions to more involved manufacturing settings. [ABSTRACT FROM AUTHOR]

Frank Ramsey's philosophy of science is considered abstruse due to the incompleteness and difficulty of his paper "Theories". This has not prevented various authors from arguing that Ramsey is committed to meaning holism for scientific theories, and that his philosophy of science is anti-realist but anti-reductionist. However, it is unclear exactly how meaning holism works for Ramsey, and how he can be both anti-realist and anti-reductionist. I argue that clarity can be gained on both issues by examining Ramsey's philosophy of science through a reconstruction of his decision theory compatible with his later philosophical beliefs. I develop an account of how credences can be formed over singular, theoretical propositions despite those propositions being fictions. Credences are ultimately measured by preferences over conditionals whose antecedents are the verification conditions of theoretical propositions and outcomes are elements of a privileged partition on an agent's possibility space induced by the language of the theory. Those verification conditions are the observational elements formed from the unions of this induced partition. Meaning holism is explained as the sensitivity of theoretical propositions to their verification conditions. And anti-realism and anti-reductionism can be maintained due to theoretical propositions forming a finer partition of possibility space than observational propositions, which prevents the former from being truth-functions of the latter. [ABSTRACT FROM AUTHOR]

Hydrogen is called to play a vital role in the future decarbonization of the electricity industry. Among its multiple applications, this energy carrier may improve the energy storage, replacing or complementing the traditional battery banks thanks to its higher energy density. However, the low efficiency and cost of associated devices as well as the difficulty in transport make unfeasible the implantation of hydrogen storage systems at the residential level. However, emerging paradigms like energy communities may change this concept making viable the installation of hydrogen chains in the domestic sector. This paper focuses on day-ahead scheduling of energy communities with integrated collective hydrogen storage system. To this end, a three-stage methodology is developed in which the first level is focused on individual home energy management, the second level handles with peer-to-peer energy trading among prosumers and the last level determines the energy exchanging profile with the utility grid accounting with the hydrogen chain. To handle with uncertainties from renewable sources, demand and energy price, the Information Gap Decision Theory (IGDT) is employed, by which an uncertainty-aware scheduling program can be obtained minimizing the negative effects of uncertain parameters. A case study is performed on a six-prosumer energy community with electrolysis, hydrogen vessel and fuel-cell, allowing both purchasing and selling energy with the grid. The results serve to prove the effectiveness of the developed methodology as well as demonstrate the possible impact of unknowns in energy community operation, and how the hydrogen chain can help to improve the economy and self-sufficiency of the system. • A three-level operating strategy for ECs is proposed. • A multi-stage methodology for day-ahead scheduling of ECs with collective HSS is developed. • Uncertainties from renewable generation and demand are treated using IGDT. • The impact of uncertainties in the operation of ECs is discussed. • The role of HSS in improving the economy of the community is highlighted. [ABSTRACT FROM AUTHOR]

Energy hubs are multi-carrier energy management systems that efficiently distribute various forms of energy, reducing losses and environmental pollution. This paper examines Ontario, Canada, as a major energy hub, using a typical fall day pattern for energy demand. The model includes all power generation sources in Ontario: photovoltaic (PV), wind turbine (WT), nuclear, hydro, biofuel, and natural gas power plants. It also integrates the charging and discharging of electric vehicles (EVs) within the energy distribution framework. Managing the intrinsic uncertainty of the parameters is crucial for efficient operation. This study employs probabilistic functions to account for the arrival and departure hours of EVs, controlled using the Conditional Value at Risk (CVaR) method. Three methods, Information Gap Decision Theory (IGDT) with risk-seeking and risk-averse behaviors, and robust optimization, address uncertainties such as wind and solar electricity production, energy prices, and electrical, heating, and cooling demands. We compare simulation results of three scheduling scenarios for optimal energy production and dispatch. The RS-IGDT method can lead to significant losses during peak hours due to fluctuations. The robust method incurs higher costs by planning for large deviations. The RA-IGDT method balances deviations without the pessimism of the robust method, making it the recommended approach. [Display omitted] • A comprehensive Energy Hub model using all types of Ontario's power generation plants. • Comparing RS-IGDT, RA-IGDT, and robust methods for managing uncertainties. • Examining the cost impact of increasing EVs compared to the current state in Ontario. • Assessing EVs' costs with and without battery depreciation in Ontario's EH model. • Using CVaR to manage EV-related uncertainties. [ABSTRACT FROM AUTHOR]

The gradual increase in the penetration rate of distributed renewable energy has become a new situation. Under the new situation, industrial users with high energy consumption need to make reasonable decisions in electricity and carbon markets while ensuring power quality. This is a real problem that needs to be solved for high energy consumption industrial users. Therefore, we propose a decision model for industrial users participating in electricity and carbon markets considering differentiated power quality based on information gap decision theory (IGDT). Firstly, we analyze the impact of users' installation of distributed renewable energy on their power quality and carbon emissions. Considering the differentiated power quality services in the future electricity market, the carbon emissions when the user chooses different levels of power quality are quantified. Secondly, a decision-making model of industrial users participating in electricity market and carbon market considering differentiated power quality services is established. The objective function is the maximum profit of the user participating in the double market trading performance period. Thirdly, the IGDT theory is used to describe the uncertainty of distributed renewable energy generation, which reduces the decision-making risk of industrial users. Finally, we take a steel production enterprise as an example to analyze. The numerical results show that the proposed model can reduce carbon emissions and obtain maximum benefits for industrial users. The proposed model can realize the coordination and optimization of monthly electricity purchase and carbon quota trading volume. Moreover, it can help industrial users rationally arrange the access capacity of distributed renewable energy devices and select the improved power quality level. • The model takes into account the relationships among distributed renewable energy access, power quality and carbon emissions. • The model in this paper is divided into annual and monthly components, which makes decision-making more flexible. • High-quality power can improve energy efficiency and promote the investment and construction of distributed renewable energy. • Compared with the deterministic model, the IGDT-based model can better reflect industrial users' risk preference. [ABSTRACT FROM AUTHOR]

Multiple uncertainties in the park-level integrated energy system (PIES) can affect the optimal operation of system. Information gap decision theory (IGDT) is a commonly used method of dealing with uncertainty by developing risky strategies to avoid risks or seek risky returns. However, there is no unified method or process for the selection of risk strategies and the setting of related parameters, leading to a certain blindness in the application of IGDT method. A comprehensive risk strategy (CRS)- IGDT approach is proposed for scheduling of PIES considering uncertainties of heat load, photovoltaic output and electric load. Risk averse strategy (RAS) and Risk seek strategy (RSS) scheduling models are constructed. Then an optimized solution method based on adaptive steps ratio (ASR) is proposed to solve the above two models. The CRS and comprehensive risk cost function are proposed from a risk equalization perspective. The target deviation coefficient and steps ratio in the IGDT model are automatically optimized with the objective of minimizing the comprehensive risk cost. Combining the two cases, the average cost reduction is 6.6 % compared to Risk-neutral (RN), 11 % compared to RAS-IGDT, and 4.1 % compared to RSS-IGDT. Moreover, the average costs of CRS-IGDT are lower compared to stochastic programming and robust optimization methods. The experiments verify the generalization and superiority of the proposed method in coping with different information gap situations caused by uncertainty. CRS-IGDT provides new research ideas for dealing with uncertainty problems in PIES and other fields. • A comprehensive risk strategy-information gap decision theory (IGDT) method. • A solution method for IGDT models based on the adaptive steps ratio. • Aggressive and conservative prediction scenarios experiments. • Setting risk strategy and risk parameters automatically. • Analyzing the impact of volatility of different variables more directly. [ABSTRACT FROM AUTHOR]

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. Post-publication, the Editor-in-Chief discovered unauthorized changes in authorship between the original submission and the revised version of this paper. In summary, the authors Ying Xue & Shoujun Huang were removed from the paper and the authors Zhouding Liu & Morteza Nazari-Heris were added to the revised paper without explicit approval by the journal Editor, which is contrary to the journal policy on changes to authorship. Morteza Nazari-Heris stated that he was unaware of the submission and eventual publication of this paper and that his name was added without his permission. The author Zhouding Liu failed to provide a satisfactory explanation to the above points. This breach of the journal's publishing policies caused the editor to lose confidence in the integrity of the article. The Editor-in-Chief regrets the unauthorized use of the name of Morteza Nazari-Heris. [ABSTRACT FROM AUTHOR]

Promoting large-scale transportation electrification and developing new solar energy harvesting and converting technologies are effective ways to achieve 'dual carbon goals'. In this paper, a new road energy harvesting technology, solar road (SR) power generation technology, is introduced. A SR power generation model considering the variation of solar radiation intensity, traffic flow and dynamic shadows of vehicles is constructed. Secondly, considering the uncertainty of traffic network (TN) caused by users' stochastic routing behavior, a model of TN under stochastic user equilibrium (SUE) is constructed. Then the influence of traffic flow on the electric load of charging and battery swapping stations (CBSSs) is analyzed, and the local consumption strategy of SR is established. Meanwhile, information gap decision theory (IGDT) is introduced to reduce the impact of uncertainty on the system. Finally, the coupling system of IEEE-33 node of distribution network (DN) and Nguyen–Dupuis of TN is used to verify the effectiveness of the proposed model and optimal management method, which improves the utilization rate of solar energy, promotes carbon emission reduction, reduces the congestion degree of DN, and ensures the safe and stable operation of the system. • The generation of SR model considering dynamic shadows of vehicles is constructed. • On SUE, the consumption strategy of CBSS is built considered random path selection. • Polyhedral uncertainty set and IGDT are introduced to manage the uncertainty. [ABSTRACT FROM AUTHOR]

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties' impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties. • Characterizations of performance flexibility in hybrid renewable energy systems. • Multi-stage approach for large-scale renewable energy integration into grids. • Integrated consideration of uncertainties and demand response impacts on operation. • Examined energy storage system capabilities for improved performance flexibility. • Time-of-use pricing and risk-averse strategy improved operational performance. [ABSTRACT FROM AUTHOR]

This study addresses the volatility and uncertainty challenges in managing renewable energy within electricity markets, particularly focusing on the role of Virtual Power Plant (VPP) aggregators. Recognizing the risks these uncertainties pose to the revenue and stability of power systems, the paper presents a novel information gap decision theory (IGDT)-Wasserstein metric based distributionally robust chance constraint (WDRCC) approach to devise an optimal bidding strategy for VPP operators. It involves a data-driven distributionally robust optimization framework, leveraging the worst-case scenario from the distributed resource uncertainties, guided by an ambiguity set rooted in the Wasserstein metric. Furthermore, the distributionally robust chance constraint modeling is introduced ensuring that uncertainty constraints of distributed resources meet a predefined risk level. Although this method shows promising out-of-sample performance, it relies on forecasted energy prices, a notable limitation given the price volatility and information inadequacy in the newly-opened market. To address this, the risk-averse bidding strategy, grounded in IGDT, is proposed simulataneously to safeguard the operator's expected returns against price uncertainties, implementing an advanced piecewise linear approximation technique, "nf4l," for linearizing the bi-linear term from IGDT. The effectiveness of this approach is empirically validated through a comprehensive case study and sensitivity analysis. • Proposes IGDT-WDRCC to enhance VPP bidding under market and VRE uncertainty. • Consider multiple uncertainties with distinct and suitable approaches. • Provides an optimal bidding strategy for VPPs in emerging energy markets. • Meets reliability standards while balancing profit and risk efficiently. • Performs sensitivity analysis on parameter changes and linearization precision. [ABSTRACT FROM AUTHOR]

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. [ABSTRACT FROM AUTHOR]

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products. [ABSTRACT FROM AUTHOR]

The bulk of the documents that affect our lives are digital or born digital. Our laborious investigations of layout, script, font and graphics, are turning into mere exercises with little influence on pursuits outside the Document Analysis and Recognition (DAR) community. Recent performance improvements on such tasks, even if based on deep learning and AI, are as much the result of advances in computer hardware as of breakthroughs in document research. It is time to automate tasks beyond transcription. This Commentary addresses our mission, our approach to some technical issues, and the role of AI in DAR. Opportunities for a wider role for document analysis include more pervasive application of statistical decision theory, integrated genre analysis, summarization, interpretation and information extraction, bolder goals in content analysis, and alternative modalities, induced by the open source movement, for sharing research results. Importantly, expanding the scope of our research incurs increased responsibility for retaining human prerogatives in critical decision making and preserving essential human skills like good writing and discriminative reading. [Display omitted] [ABSTRACT FROM AUTHOR]

• A new simulation methodology, intended bounded rationality methodology, is proposed. • Evaluation cost-benefit of different aspects of decision support techniques. • Classification of errors that a decision support technique can generate is proposed. • Consistent erroneous judgments and inconsistent correct judgments are possible. • A case where consistency condition in AHP provides maximum 5% of the expected return. Expert's judgments have been crucial in the development of decision theory; however, what criterion to use in the selection of experts remains an issue to address. Decision support techniques proposed to improve the quality of expert judgment decision making consider a demonstrated inconsistency of the judgments expressed by an expert as a criterion of exclusion in the decision-making process of such expert. Although consistency appears to be a desirable condition to qualify as "expert", little is known about the quality of the decisions made imposing consistency as the expert qualifying condition. This paper proposes a simulation methodology, based on an automaton programmed to make decisions in an intended but bounded rational way, to assess the cost-benefit of different aspects of decision support techniques. Within this methodology, the imposition of the consistency condition in the selection of experts is studied. In particular, the paper shows with a case study example that the Analytical hierarchy process (AHP) decision support technique expected payoff is at most 5% higher when implementing Saaty's consistency criterion of the expert's judgments than when the consistency criterion is not considered. [ABSTRACT FROM AUTHOR]

The aim of this study was to address the problem of weighting disputes in the evaluation of rockburst-induced coal and gas outburst as well as to improve the rationality and accuracy of the risk identification results. Hence, we developed a risk identification method for rockburst-induced coal and gas outburst, considering the influence of multiple factors based on poset decision-making theory. The risk identification index system was established according to stress factors, gas factors, and physical and mechanical properties of coal, including 12 indexes and 4 risk grades. The poset adopted implicit weighting to store the weight information. The proposed method was applied to identify risks in an engineering case study. Thus, we determined the outburst risk grade and compared the case study results with the actual situation of the working surface. The comparison and analyses showed that the outburst risk grade of the case study working surface under gas factors was grade Ⅱ, the outburst risk grade under stress factors and the physical and mechanical properties of coal was grade Ⅲ, and the outburst risk grade under comprehensive factors was grade Ⅲ. The results of the risk identification of rockburst-induced coal and gas outburst based on poset were consistent with the results obtained by the method used in the case study and the actual situation of the working surface. This study extends the methods for accurately identifying risks of rockburst-induced coal and gas outburst and facilitates the formulation of effective preventive measures. [ABSTRACT FROM AUTHOR]

Multi-attribute decision-making problem (MADM) is a kind of multi-criteria decision-making (MCDM), which is often used to solve decision-making problems where the decision variables are discrete and the number of decision-making schemes is limited. This paper reviews the basic definition of multi-attribute decision making (MADM) through the performance evaluation of science and technology funds in a city, and then deduces the TODIM decision making method and TOPSIS decision making method in detail. Finally, the clue case is decided by combining these two decision making methods. This paper specifically shows the usage scenarios and limitations of the TODIM decision method and the TOPSIS decision method through the clue case, analyzes the advantages and disadvantages of the two methods through the comparison of the two methods, and puts forward suggestions for the clue case based on the results of the two schemes. [ABSTRACT FROM AUTHOR]

Group decision-making is one of the most important topics in decision-making theory. In today's complex world, when there are a large number of conflicting criteria, it is difficult for a decision maker (DM) to make decisions considering all aspects of a problem. The quality of the decision is determined by the level of knowledge and experience of the DM. In order to incorporate as much experience and knowledge as possible, it was passed from the decision made by one DM to a decision made by a group of DMs. This passage has increased the complexity of Multi-Criteria Decision-Making (MCDM) methods that use groups of DMs. In the present paper a Hybrid Group Weighting Method (HGWM) based on the Borda Method and the Group Best Worst Method (GBWM) for determining weights of a set of criteria is proposed. For the choice of the best and the weakest criteria, by a group of experts/DMs, Borda Method is applied. These criteria (best and worst) are used in GBWM, by every expert from the group, to calculate the individual criteria weights. The final criteria weights are computed as a combination of the individual criteria weights. An application of the HGWM is made for obtaining the weights of indicators from a set of Digital Development Indicators (DDIs). The proposed hybrid method may provide support for group decision making in multi-criteria problems. [ABSTRACT FROM AUTHOR]

[Display omitted] • Modeling of optimal operation in an unbalanced MG including CASE and EVs. • Reformulating the nonlinear and non-convex day-ahead optimal operation problem of unbalanced MG to a linear and convex problem via SDP and hence, guaranteeing the global optimum. • Considering shiftable load ToU as DRPs. • Applying the IGDT method with risk averse strategy in order to involve uncertainties in unbalanced MG. This paper proposes a mathematical optimization approach based on semidefinite programing (SDP) for optimal operation of an unbalanced microgrid (MG) equipped with renewable energy resources (RERs) as well as energy storage systems (ESSs) including compressed air energy storage (CAES) and electric vehicles (EVs). The SDP converts nonlinear and non-convex models proposed for day-ahead optimal operation into a convex approximation, which is easily implemented by commercial software. An optimization problem is formulated such that it minimizes the costs related to operation and environmental effects limited to several technical limitations. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in MG. This paper presents a stochastic optimal operation model based on Information Gap Decision Theory (IGDT) with risk averse strategy to overcome this information gap and to help Microgrid Operator (MGO). As demand response programs (DRPs), the shiftable load and time of use (ToU) are considered for enhancing the flexibility of MG. Employing the presented model in the 21-bus test system shows that CAES and EVs, as novel ESSs, have good capability to reduce the costs pertaining to operation and emissions in the day-ahead optimal operation. [ABSTRACT FROM AUTHOR]

The marketization and deregulation of the power sector are the development directions in various countries. Operators with multiple fast charging stations can participate in the electricity market as large users to reduce costs. However, operators face uncertainties in electricity prices and renewable energy output and procurement risks in multi-time scale markets. Therefore, this paper proposes a risk-based procurement strategy for the charging station operator in electricity markets under multiple uncertainties. Considering the medium- and long-term market, day-ahead market, real-time market, and tradable green certificate market, the electricity decision model in the multi-time scale electricity market is established. Additionally, this model combines information gap decision theory with conditional value at risk to handle multiple uncertain characteristics. The proposed problem is linearly transformed to accelerate the solution. Case analysis is carried out on real electricity prices and charging load datasets. The results show that the procurement cost of 230784.10 CNY can withstand 55.22% of PV fluctuations, while meeting the electricity demand of charging stations. At this time, the proportion of electricity procurement in the medium- and long-term market and green electricity is 72.16% and 14.81%, respectively. Operators can develop risk procurement strategies based on their risk preferences and budgets. [Display omitted] • The risk-based procurement strategy of the charging station operator is proposed. • The uncertainties of spot prices and renewable energy output are considered. • The electricity procurement models for multiple time-scale markets are established. • The proposed strategy reduces the operator's electricity procurement cost and risk. [ABSTRACT FROM AUTHOR]

Probabilistic linguistic term set (PLTS) provides a flexible and comprehensive approach to reflecting qualitative information about decision makers (DMs) by fusing linguistic terms and probability distributions. This fusion makes PLTS an important focus of fuzzy decision theory. Dealing with uncertainty and ambiguity has always been a major challenge in the group decision-making (GDM) process, and PLTS provides a versatile and effective approach to address these issues. PLTS is able to more accurately represent the preferences and opinions of the DMs, thus improving the accuracy and consistency of decision-making, thereby improving the accuracy and consistency of decision-making. Therefore, the application of PLTSs in GDM (PLTS-GDM) has attracted more and more attention and shown great potential. In this paper, we provide a comprehensive overview of the underlying theories of PLTS-GDM, the existing approaches and the challenges they face. Specifically, we explore how the PLTS utilizes fuzzy information systems to manage imprecise and ambiguous data to enhance the effectiveness of decision-making. In addition, through an extensive review and analysis of the current literature, we summarize the major advances in the field and identify important gaps in the existing research. Finally, we point out future research directions aimed at addressing these challenges and further advancing the application and development of PLTS-GDM. In summary, this paper provides a valuable resource for scholars and practitioners to help them understand and promote the practical applications of PLTS-GDM. • Describing the evolution of linguistic terms and their use in GDM. • Analyzing the theoretical basis for integrating PLTSs into GDM and reviewing literature. • Using bibliometrics to identify research trends and status in PLTS-based GDM. • Identifying research gaps and future directions in PLTS-based GDM. [ABSTRACT FROM AUTHOR]

• Developed a new circular Quintic Fuzzy Sets (cQuFSs). • An extension of the FWZIC is formulated under cQuFSs which is named as FWZICbIP Method. • Developed 3WD is by extending CPOS technique under cQuFSs. • Developed robust 3WD based-HQP for grading agricultural swarm robotics technologies. Robotics is expected to play a crucial role in agriculture, with swarm robotics technologies frequently highlighted as solutions to enhance the efficiency and resilience of mechanised agricultural operations, particularly in precision farming and large-scale applications. While many technologies have been proposed and evaluated based on seven key aspects in academic literature, previous evaluations have not considered the interrelationships among these aspects or provided a simultaneous ranking and grading of the technologies, leaving a significant research gap. Identifying the most and least efficient technologies presents a complex multi-aspect decision-making (MADM) problem due to diverse evaluation aspects, prioritisation challenges, and data variability. Although MADM methods based on Three-Way Decisions (3WD) approach can address this, they often yield conflicting results due to the use of multiple divergence measures. Furthermore, uncertainty remains an open challenge, highlighting a significant theoretical gap. Thus, the main objective of this study is to propose a robust 3WD approach based on ensembled multi-divergence measures, with the introduction of a new Circular Quintic Fuzzy Sets (cQuFS) to provide a robust mechanism for ranking and grading swarm robotics technologies. This study's contributions encompass: (1) the reformulation of Fuzzy Weighted Zero Inconsistency-Based Interrelationship Process (FWZICbIP) utilising the proposed cQuFS, referred to as the cQuFS–FWZICbIP method to prioritize evaluation aspects and address uncertainty in the weighting process; (2) the extension of the 3WD approach within Conditional Probabilities by Opinion Scores (CPOS) method to determine decision thresholds in the context of the proposed cQuFS, referred to as the cQuFS–CPOS method, for determining the conditional probability of each technology. Furthermore, the proposed cQuFS reformulates Bayesian decision theory to establish decision thresholds for all technologies; and (3) reconciling varied outcomes through an ensemble 3WD (E-3WD) approach based on Half-Quadratic Programming (HQP). The proposed MADM method based on E-3WD is applied to rank and grade 34 technologies on the basis of seven evaluation aspects, with a consensus index and trust level calculated through the E-3WD approach, providing a robust mechanism for ranking and grading. The results revealed that T 29 , T 26 , T 24 , T 12 , and T 1 exhibited a notable degree of efficacy, as indicated by their placement in the positive region throughout eight σ values. However, T 33 consistently belonged to the NEG region across all σ values. Sensitivity and comparison analyses confirmed the robustness and stability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Rational decision theory assumes that individuals have perfect knowledge of the consequences of their choices and actions. However, this assumption often fails to align with reality, particularly in the context of environmental degradation, where the impacts of actions can be distant in both time and space. Will an enhanced perception of those impacts encourage pro-environmental choices? To explore this question, we designed and conducted an experiment in an immersive virtual reality environment (IVE). After an initial training phase, participants were asked to choose between using a tumble dryer or a clothesline to dry their laundry. Participants in the treatment group received exaggerated feedback during the training phase, experiencing a simulated sudden outbreak of a thunderstorm when they used the dryer. In contrast, participants in the control group did not receive any feedback. The experiment was conducted at two Italian universities, Siena and Pisa, with a total of 270 subjects. The methodological finding is that even less elaborated IVEs can still be effective as experimental tools. The substantive finding is that exposure to exaggerated feedback in an IVE significantly increased the likelihood of choosing a more environmentally friendly action, such as using a clothesline, which involves lower energy consumption. [ABSTRACT FROM AUTHOR]

A method for analyzing the uncertain adjustability of charging stations (CSs) under the influence of multi-attribute group decision-makings of electric vehicles (EVs) is proposed. Firstly, considering physical constraints of parameters such as charging power and energy stored, a model for characterizing the schedulability of a single EV under different charging modes including rated power charging, adjustable charging, and flexible charging and discharging is constructed. Secondly, since the charging mode selection of EVs is a comprehensive decision considering charging cost, charging time and other attributes, and individual decision-making is often affected by group users, an EV charging mode selection probability model is developed based on the multi-attribute group decision-making theory. Further, scheduling potential boundary parameters of CSs affected by the scheduling potential of all EVs in the station are derived based on the Minkowski sum theory, and their probability characteristics are calculated by combining the charging mode selection probability of single EVs and the schedulability under each mode. Finally, case studies prove the superiority of the proposed theory. • A method for analyzing the uncertain adjustability of charging stations is proposed. • A charging mode selection probability model under effects of group users is studied. • The schedulability of electric vehicles under different charging mode is constructed. • Scheduling potential of a charging station is derived based on the Minkowski sum. • The evaluation of uncertain scheduling potential is much more complete and practical. [ABSTRACT FROM AUTHOR]

• Projects play a key role in navigating sustainability transitions in societies. • A conceptual framework for sustainable project decision-making has been developed. • The butterfly effects of project decisions on overall sustainability are examined. • The importance of decision-makers' knowledge about sustainability is investigated. • Sustainable decision-making in different scenarios is elaborated upon and compared. Sustainable development and sustainability transitions are becoming increasingly significant in research and practice due to immense challenges that social, economic, and environmental ecosystems are grappling with, such as climate change. Projects as interventions are game-changers in addressing these challenges, as decisions made in projects impact both project success and sustainability transition trajectories in societies. This study developed a conceptual framework through which the cruciality of decisions in different scenarios is evaluated to showcase how the priority of project managers' decisions at the project level (i.e., micro-level) not only impacts the same level but also has butterfly effects on overall sustainability transitions at the broader societal levels (i.e., meso-level and macro-level). To reflect real-world complexities, we drew on various perspectives and theories, namely projects-as-interventions perspective, project-as-practice perspective, socio-technical perspective, actor-network theory , and decision theory , along with comparative analysis. The findings underscored that the project managers' awareness of sustainability transitions throughout the project life cycle (PLC) may change the prioritization and cruciality of decisions, and those can subsequently trigger societal sustainability transitions. Besides, the sensitivity of decision-making in line with sustainability in international and regional projects is more than in national and local projects. Therefore, this study primarily contributes to making sustainable decisions within projects while navigating sustainability transitions at the broader societal levels. [ABSTRACT FROM AUTHOR]

• Established a hierarchical cluster regulation framework for 5G BSs. • Proposed an IGDT-based PV-load uncertainty modeling method for 5G BSs. • Proposed an ACSP-based multi-agent distributed coordination regulating algorithm. Photovoltaic (PV)-storage integrated 5G base station (BS) can participate in demand response on a large scale, conduct electricity transaction and provide auxiliary services, thus reducing the high electricity consumption of 5G BSs and increasing the flexibility resource capacity of the distribution network. However, the flexible resource regulation of PV-storage integrated 5G BSs still faces problems such as many regulators, ignored PV-load uncertainty, and poor adaptability of existing regulatory frameworks and scheduling methods, which leads to greater security operation risks in resource regulation decisions and affects the exploitation of the 5G BSs scheduling potential. Aiming at the above problems, this paper proposes an aggregated regulation and coordinated scheduling method of PV-storage integrated 5G BSs considering PV-load uncertainty. Firstly, a hierarchical cluster-cooperative aggregated regulation framework for the scale PV-storage integrated 5G BSs is established, and a regional communication operator (RCO) schedulable capability model and an information gap decision theory (IGDT) based PV-load uncertainty model are built. Next, a two-stage joint optimization problem is proposed for maximizing the RCO income while reducing the BS cluster operation cost. Then, the first-stage day-ahead transaction optimization problem in the electricity market is solved, and the reliable operation planning and economic operation planning strategies are proposed based on IGDT to adapt to the regulation demand under different uncertainty risks; the second-stage BS cluster real-time operation optimization problem is solved based on the adaptive consensus algorithm considering scheduling preferences (ACSP), achieving distributed real-time coordinated scheduling of multiple agents in the BS cluster. Finally, the effectiveness of the proposed method is verified by simulation examples, which show that the aggregated regulation and coordinated scheduling of PV-storage integrated 5G BSs can achieve mutual benefits for the distribution network and communication operators. [ABSTRACT FROM AUTHOR]

In this paper, we provide a theoretical framework that separates the control and learning tasks in a linear system. This separation allows us to combine offline model-based control with online learning approaches and thus circumvent current challenges in deriving optimal control strategies in applications where a large volume of data is added to the system gradually in real time and not altogether in advance. We provide an analytical example to illustrate the framework. [ABSTRACT FROM AUTHOR]

Nowadays, power sector is faced with the challenge of low-carbon transition by reducing carbon emissions while ensuring sufficient electricity supply. However, the prolonged transition process may present long-term uncertainties related to the concurrent tasks of regulating total carbon emissions and providing abundant electricity. The variation of these tasks poses a potential threat to the success of low-carbon transition. To confront this challenge, this study proposes a power system expansion planning model which integrates transmission expansion, renewable generation expansion, energy storage systems deployment coordinated with the retirement of coal-fired power plants and retrofit of coal-fired power plants to carbon capture power plants in compliance with transition targets. Then, a risk-averse planning strategy countering the long-term uncertainties of transition tasks is adopted to constitute a two-level multi-objective planning framework based on information gap decision theory. And the second-level risk-averse model is reformulated with an augmented normalized normal constraint method to obtain the Pareto frontier. Numerical results indicate that integrating the retirement and retrofit of coal-fired power plants yields a significant reduction in total cost (4.71 %), compared to a case without such integration. And the transition tasks can be accomplished in a robust way by preferred risk-averse approach. • Propose a holistic low-carbon oriented power system expansion planning model. • Address the uncertainties of transition tasks by multi-objective IGDT-based model. • Address the conflicting objectives by augmented normalized normal constraints. • Adjust the risk-hedging level according to the planner's preference. • Implement the cost-effective analysis and sensitivity analysis on case studies. [ABSTRACT FROM AUTHOR]

• Modeling wind, PV, EES, and TES simultaneously, in several EH with a different structure. • Considering the optimal cost factor for the EDRP and TDRP in a MCES with several different and diverse EHs. • Applying a new method of SA and trade-off between the cost and the number of program operations. • Using a two-stage, hybrid scenario-based/IGDT, uncertainty to examine the impact of different parameters. The optimal operation of multi-carrier energy systems (MCESs) has opened new horizons for energy network management and the satisfaction of consumers. In this paper, the optimization of the MCES's operation cost is considered by combining several energy hubs (EHs). To make optimal use of thermal and electrical demand response programs (TDRPs and EDRPs), the cost factors are extracted using a new sensitivity analysis (SA) method. Then, taking into account the optimized cost coefficients in EDRP and TDRP, the optimal operation of MCES is investigated in the presence of uncertain parameters. In this paper, a two-stage uncertainty method is used for solving the uncertain problem. First, the uncertainty of wind, photovoltaic (PV), and electrical and thermal loads is modeled with a scenario-based method, in the second step, the price uncertainty of generated electricity is added to the previous model using the information gap decision theory (IGDT). A hybrid scenario-based/IGDT uncertainty has been performed simultaneously at the GAMS platform. The results show the impact of each uncertainty parameter and system resilience due to the optimization done in the problem and the EDRP and TDRP programs. The results show about 135$ difference between the lowest and highest scenarios at a total operation cost. [ABSTRACT FROM AUTHOR]

Bayesian optimization has proven effective for optimizing expensive-to-evaluate functions in Chemical Engineering. However, valuable physical insights from domain experts are often overlooked. This article introduces a collaborative Bayesian optimization approach that re-integrates human input into the data-driven decision-making process. By combining high-throughput Bayesian optimization with discrete decision theory, experts can influence the selection of experiments via a discrete choice. We propose a multi-objective approach togenerate a set of high-utility and distinct solutions, from which the expert selects the desired solution for evaluation at each iteration. Our methodology maintains the advantages of Bayesian optimization while incorporating expert knowledge and improving accountability. The approach is demonstrated across various case studies, including bioprocess optimization and reactor geometry design, demonstrating that even with an uninformed practitioner, the algorithm recovers the regret of standard Bayesian optimization. By including continuous expert opinion, the proposed method enables faster convergence and improved accountability for Bayesian optimization in engineering systems. • New Bayesian optimization approach applying human knowledge in discrete choices. • Multi-objective high-throughput method balances solution utility and diversity. • Benchmarking shows performance gains with expert input across various scenarios. • Improves knowledge integration & accountability in decisions. • Case studies showcase real-world use in bioprocess optimization & reactor design. [ABSTRACT FROM AUTHOR]

In this paper, a microgrid groups with shared hybrid energy storage (MGs-SHESS) operation optimization and cost allocation strategy considering flexible ramping capacity (FRC) is proposed. Firstly, a joint system containing MGs with SHESS is constructed and its operation modes are analyzed. Secondly, Gaussian mixture model (GMM) and Latin Hypercubic Sampling (LHS) are used to obtain the confidence intervals of prediction errors for wind power, PV, and load. And then the FRC demand and supply of MGs-SHESS are quantified to construct the FRC sufficiency index. Furthermore, a multi-objective optimization model considering FRC sufficiency and operating costs is constructed, and the range of uncertain variables is described by confidence intervals, which transform the model into confidence gap decision theory (CGDT) model form. Further, the basic Shapley method is improved by considering the electricity interaction factor, carbon reduction factor, and FRC supply factor. Then use improved Shapley method to construct the MGs-SHESS two- layer cost allocation model. Finally, the case study results show that: (1) The total costs of SHESS are reduced by 5.89% and the FRC sufficiency is increased by 8.43% compared with decentralized energy storage system (DESS), which indicates that SHESS is able to achieve the co-growth of economy and flexibility. (2) Multi-objective dispatch plan considering FRC sufficiency is able to significantly improve FRC sufficiency by 12.56% at an additional cost of only 1.66% compared to conventional economic dispatch. It is worthwhile because it reduces the risk of power curtailment and load shedding during actual operation. (3) The costs of the CGDT model are reduced by 1.26% and the FRC sufficiency is increased by 1.20% compared with the IGDT method. Moreover, the total costs of the CGDT model are only increased by 1.84% compared with the day-ahead stage after substituting multiple random scenarios. It indicates that the CGDT is more robust in dealing with uncertainty. (4) The costs allocation method considering multi-factors improvement can satisfy the interests of multi-subjects. Even if the decision maker has a slight or moderate tendency to a certain factor, it can maintain the satisfaction of each subject to the allocation results, which improve the stability of the joint operation. [Display omitted] • Constructed a joint operation system of MGs and SHESS. • Incorporating FRC sufficiency into the optimization objective of the MGs-SHESS operation plan. • A CGDT method is used to deal with uncertainty. • Constructed an improved Shapley value cost allocation strategy. [ABSTRACT FROM AUTHOR]

The study introduces a novel method for optimizing surge arrester placement in the distribution line to mitigate lightning-induced overvoltages, employing a single-objective optimization algorithm. The study assesses the effectiveness of two types of surge arresters concerning discharge energy in reducing lightning overvoltage. Moreover, the comparative analysis takes into account the transfer of overvoltage performance from protected to unprotected towers, highlighting its significance in estimating flashover rates under different protection alternatives. Decision theory analysis is employed in the present study to identify optimal arrester locations. The study aims to minimize the expected flashover rate and identify the most effective protection strategy by considering futures/scenarios associated with different values of peak current and lightning locations. The results of numerical applications also showed the substantial impact of lightning surge transfer within the network, emphasizing the imperative to incorporate this phenomenon into lightning protection modelling. • Focuses on indirect lightning protection for medium-voltage networks. • Analyses common surge arrester types for network protection. • Studied transfer voltage effects on adjacent towers. • Studies transfer voltage effects on adjacent towers. • Evaluates effectiveness of combining surge arresters and overhead shieldwire. • Considers optimal surge arrester placement using decision theory for cost-effectiveness. [ABSTRACT FROM AUTHOR]

Electric vehicle battery swapping stations (BSS) have significant potential in power system frequency regulation. However, uncertainties of swapping demand and regulation signals introduce risks to operational benefits and regulation performances. Aming to enhance overall profitability, this study proposes day-ahead bidding and real-time scheduling strategies for BSS to participate in frequency regulation. In the day-ahead stage, the BSS scheduling model is established. Then, the information-gap decision theory (IGDT) is employed to determine the optimal reserve capacities. The randomness of regulation signals is captured by designing a new uncertainty set based on statistical analysis. Additionally, a boundary autonomous selection (BAS) method is developed to solve the IGDT model, making the selected scenarios more consistent with real-world situations. In the intra-day stage, a real-time response strategy is proposed to track the regulation signals considering the BSS operation economy. Simulations are conducted using historical data from Pennsylvania, Jersey, and Maryland (PJM). The results demonstrate that the reserve capacities and response ratios obtained by the proposed strategies yield higher profits for BSS while ensuring regulation performance. • Modeling for the battery swapping station providing frequency regulation services. • A new uncertainty set of the regulation signal based on statistics. • IGDT based day-ahead bidding strategy and BAS solving method. • A real-time response strategy that can selectively track regulation signals. [ABSTRACT FROM AUTHOR]

This study investigates the key factors that impact the digitalized-education , and what those mean for the new and emerging educational models of learning. The research is based on state-of-the-art education and assessment models for learning such as Descriptive decision theory, Futures Literacy (FLF), and 8-Affordances framework that theoretically addresses both the need to use multidisciplinary components in identifying the several challenges and opportunities to the use of digital technologies in education (digitized-education) and improvement of the student's learning outcomes. Through a quasi-experimental study design and comparative analysis of the students' evaluation of teaching (SET) dataset (n = 3178) collected in a higher education setting; the study applies a multivariate analysis of covariance (MANCOVA) and multiple comparisons (post-hoc) tests to determine the impact or association that the evaluation period (between 2019–2021) and type of school or discipline have on the students' learning performance and evaluation. The results show that students' satisfaction and experiences with digitized-education have exponentially grown in recent times, and mainly varied across the years, and marginally by discipline or school. In addition, the study empirically sheds light on the importance of the key findings and results by considering their pedagogical, technological, economic infrastructure, and ethical implications for the future of education and sustainable educational practice. • Didactic and socio-technical factors that impact the wide adoption of the digitized-education are discussed in this study. • Defines a data-structure (data-driven) approach used to understand the implications of digitalized-education and assessment. • Results show that students' satisfaction and experience with digitized-education has exponentially grown across the years. • More situational studies on institutional specifics is needed to help shape the global adoption of digitalized-education. • It empirically sheds light on implications of digitized-education toward its future and sustainable educational practice. [ABSTRACT FROM AUTHOR]

Community multi-energy systems (CMESs) are emerging as a pivotal solution to address the pressing issues of the current energy crisis and environmental pollution. This paper introduces a comprehensive multi-horizon risk-averse scheduling strategy for CMESs, targeting participation in energy and flexible ramp markets while meeting the demands of electric, natural gas, and hydrogen vehicle charging stations, data centers, and residential and commercial buildings. The proposed CMES incorporates power-to-x vector coupling storage (PtX-VCS) systems, enabling the storage of electricity in various forms to cater to multi-energy demands. Additionally, an integrated demand response (IDR) program, incorporating incentive and load-shifting strategies, is applied to enhance operational efficiency and system flexibility. To manage uncertainties effectively, a hybrid stochastic/robust/fuzzy information gap decision theory (IGDT) method is employed. This approach, formulated as a tri-level optimization model, is transformed into a single-level model using strong duality theory. The results demonstrate a notable reduction in daily operational costs by approximately 5% and 4% through the implementation of the IDR program and PtX-VCS technologies, respectively. Moreover, the CMES's potential to participate in the flexible ramping market leads to an approximately 7% reduction in total operational costs. [ABSTRACT FROM AUTHOR]

In practice, there are methods such as EN 15450 designing a heat pump system consisting of a heat pump, auxiliary heater, and thermal energy storage. In heat pumps, the refrigerant has a significant impact on the overall efficiency and thus on its sustainability. However, refrigerant selection is not considered in standard design procedures. Moreover, the refrigerant selection is complex due to many selection criteria, boundary conditions, and stakeholder dependencies. Therefore, a guided procedure is promising to support the selection process. Applying prescriptive decision theory and the PROMETHEE method, a seven-phase selection process is presented, focusing on refrigerant selection for residential applications. Compared to standard selection processes, we integrate a stakeholder dependency into the evaluation to capture process-relevant boundary conditions. We carry out four phases of the decision process and present a reduced refrigerant list for residential heat pumps. Based on stakeholder-dependent and process-relevant boundary conditions, mainly hydrocarbons and their mixtures represent a sustainable selection. However, hydrocarbons are flammable and safety significantly impacts different stakeholders. Hence, a recommendation for further and especially safety measures, which should be proven in living labs, is deduced to increase the quality of the refrigerant selection towards sustainable heat pump systems. [ABSTRACT FROM AUTHOR]