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Historically, economic models of memory have neglected a pivotal aspect: decay, the established notion that memories deteriorate over time, resulting in a loss of fidelity. In this paper we show that a framework of rational memory with decay can produce the recency effect and other intuitively appealing memory phenomena. We apply the framework to both finite time and dynamic programming settings, deriving a wide range of results with a focus on comparative statics and asymptotic behaviors. Many of our results can also be applied to multi-dimensional rational inattention settings where individuals have to split their attention across several information sources. [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]

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]

The multiple-probability decision-making method for earthquake hazards considers multiple performance indicators and can be used to effectively estimate the seismic risk of structures. The seismic vulnerability model directly affects the development of the earthquake risk distribution in regional structures. This paper innovatively developed a bivariate intensity measurement method based on macroscopic and instrumental seismic intensities. An optimized instrument intensity calculation model was established considering the actual monitoring data of historical earthquakes in China. A hybrid seismic vulnerability quantification method based on empirical, expert judgement and analysis has been proposed and applied to evaluate the vulnerability levels of three types of building clusters in two typical earthquakes in Qinghai Province, China (the 2008 Dachaidan (DCD) earthquake and the 2009 Haixi (HX) earthquake). A structural hybrid vulnerability matrix and surface for nine zones based on bivariate intensity measures were developed. A synthetic vulnerability membership index considering fuzzy failure criteria and decision theory was proposed, and vulnerability membership curves for different vulnerability levels were established. An improved earthquake economic loss prediction function is proposed, and synthetic economic vulnerability index prediction models for three typical buildings in different regions are established. The estimated and calculated results of the developed multiscale variable parameter synthetic vulnerability model are highly consistent with the actual earthquake loss observations. • This paper proposes a bivariate intensity measure to estimate the seismic fragility of buildings. • An optimized hybrid vulnerability matrix and surface of typical buildings were established. • The 2008 and 2009 Dachaidan and Haixi earthquakes in Qinghai Province, China, were reported. [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]

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]

Partial data is a common challenge in many applied problems, where we can measure only the part of the solution. This paper considers the poroelasticity problem in fractured media, where only partial measurements of fracture pressures are available. The poroelasticity models have applications in many engineering and scientific fields, such as geomechanics, hydrology, and reservoir simulation. This paper aims to develop a Bayesian decision making approach using partial data and consider its application to the poroelasticity problem in fractured media. The methodology of the proposed approach combines Proper Orthogonal Decomposition, Discrete Empirical Interpolation Method, Bayesian Inversion Approach, and Bayesian Decision Theory. It allows us to compute posterior probabilities and decide according to the optimal decision rule based on the partial data. Moreover, we apply partially explicit temporal discretization to solve forward problems efficiently. For testing the proposed approach, we consider a two-dimensional model problem. We simulate the cases when we need to make a single decision about the reservoir or decisions about the elements of the set of reservoirs. The results show that the proposed approach can efficiently compute the posterior probabilities and make a decision in the presence of only partial data for both cases. [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]

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]

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]

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 increasing uncertainty and volatility of net load caused by the high penetration of renewable energy leads to higher demand tariffs for industrial park and potentially impacts their economic benefits. To tackle these issues, this paper develops a novel business mode to enable rental energy storage sharing among multiple users within an industrial park, and propose a robust optimization and demand defense-based iterative bi-layer planning framework. The upper layer focuses on the maximization of the investment profitability of shared rental energy storage by developing a robust information gap decision theory optimization. Meanwhile, the lower layer is dedicated to enhancing the demand defense ability of shared rental energy storage in real-time operation through the formulation of a distributed model predictive control. After that, the synchronous alternating direction multiplier method with consistency theory is derived for solving the distributed optimization. Numerical results demonstrate that the proposed shared rental energy storage is 6.391% and 7.714% more economical than shared and self-built energy storage, respectively. Moreover, the iterative bi-layer planning enables flexible energy storage capacity configuration, reduces the impact of net load uncertainty, improves the ability of demand defense, and enhances the system's overall economy. • A novel business mode to enable rental energy storage sharing is proposed. • An iterative bi-layer optimization for shared rental energy storage is developed. • The model leverages both robust optimization and demand defense. • A fully distributed control in conjunction with consistency theory is derived. [ABSTRACT FROM AUTHOR]

Hydrogen energy has the advantages of being clean and carbon-free, convenient storage, and easy conversion to electrical energy. The use of renewable energy electrolysis for hydrogen production is an important solution to promote clean energy utilization and decarburization in the power and transportation industries. However, hydrogen has a low hydrogen storage density and is prone to explosion. The high cost and risk of hydrogen transportation pose challenges to the promotion and utilization of hydrogen. To address these difficulties, this paper proposes a day-ahead dispatch of electricity-hydrogen systems (EHS) under the solid-state transportation mode of hydrogen energy. Firstly, based on the Van't Hoff equation, the relationship between gas pressure and the reaction temperature is established during the gas-solid conversion process, and a hydrogen energy solid-state transport model based on a magnesium-based hydrogen transport vehicle (MHTV) is proposed. Secondly, a renewable energy uncertainty set is established based on the information-gap decision theory (IGDT) envelope constraint for addressing the uncertainty of renewable energy and a day-ahead bi-level dispatch model of the EHS is constructed based on the solid-state transportation mode of hydrogen energy. Finally, the IGDT approach considering fuzzy variables (FV-IGDT) is presented by using a fuzzy variable membership function, and the proposed bi-level model is transformed into a single-level model for a solution based on this approach. The effectiveness of the proposed model and solution method is verified through simulation on an EHS consisting of a modified IEEE-118 power system, 2 hydrogen production stations (HPS), and 10 hydrogen refueling stations (HRS). • Hydrogen pressure & temperature during gas-solid conversion established by Van't Hoff equation. • Hydrogen energy solid-state transport model based MHTV is proposed using magnesium. • MHTV hydrogen transportation time, energy balance, time-coupling constraints are considered. • FV-IGDT model is proposed to address issue of traditional IGDT models unable to solve optimal value. • Optimize fluctuation range of uncertain variables in RSOS and RARS models and optimal values. [ABSTRACT FROM AUTHOR]

The selection of mining equipment is one of the most important parts for decision-makers, which can affect the mine and the entire planning. Underground mines are usually divided into two categories; hard rock and soft rock, each of which has its equipment according to the type of exploitation method used. There are different ways to make decisions and choose mining equipment. One of the practical approaches can be the use of the Multi-Criteria-Decision-Making models (MCDM). The main aim of this research is to develop a decision support system based on MCDM methods and use FUZZY logic to resolve uncertainties in the Underground-Hard Rock Mining Equipment Selection (U-HRMES). In this paper, the U-HRMES model has been used in Anguran underground lead and zinc mine as a case study. At present, the drilling and loading-hauling equipment of the Angoran mine does not perform properly. Therefore, to improve the performance and speed up the mining operation, it was tried to suggest suitable equipment using the U-HRMES. For this purpose, effective criteria were obtained based on a literature review, and according to the conditions of the Anguran underground lead and zinc mine, these criteria were filtered, and then, Twenty-two subcriteria with four groups of main criteria; technical specifications of the machine, economic and environmental issues, stope conditions and finally management and operational issues were obtained. It should be noted that the types of preference functions in the selection process of underground hard rock mining equipment were determined based on expert groups. Using the U-HRMES, two series of decisions were evaluated for the options of drilling equipment and loading and hauling equipment for the Anguran underground stopes. Finally, single-arm drilling with a score of 0.870 was chosen as the appropriate choice for drilling equipment, and LHD WX04 with a score of 0.252 was selected for loading and hauling equipment. Also, by analyzing the sensibility of the model results based on the change of criteria weights from ±10 % to ±20 %, there was no change in the results. The observations were that the drilling equipment selection has more sensitivity and reaction to changes in weight of the stope conditions. At the same time, the loading and transport equipment selection has this reaction and this sensitivity to changes in the weight of technical specifications of the machine. [ABSTRACT FROM AUTHOR]

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of continuous-time stochastic systems subject to non-convex input constraints. The input is designed to minimize the probability of misdiagnosis. Since the misdiagnosis probability cannot be expressed in the closed form, its Chernoff upper bound likely tighter than its widely used Bhattacharyya upper bound is exploited as an alternative. The closed-form expression of the Chernoff distance under Gaussian distributions is correctly deduced. For calculation simplification and diagnosis performance enhancement purposes, a novel input design criterion is constructed for AFD based on the Chernoff distance. The optimization problem for AFD is formulated as a bilevel optimization, where the outer and inner layers optimize the Chernoff distance parameter and the input respectively. A convex relaxation is proposed to relax non-convex input constraints into convex ones, and the optimal solution to the relaxed inner input design problem is proven to be also optimal for the original inner problem whose objective function and input constraints are both non-convex under certain conditions, which is referred to as lossless relaxation. The relaxed inner input design problem under the constructed design criterion can be solved to global optimums, and the relaxed outer optimization is solved by a grid search method. Given the optimal input, faults are diagnosed based on the minimum misdiagnosis probability decision rule. The effectiveness of the proposed AFD approach is demonstrated by utilizing a planetary landing example. [ABSTRACT FROM AUTHOR]

In real-life dynamic scheduling systems, the release times of some important and/or urgent jobs are usually known deterministically or predictably in advance. When making dynamic scheduling decisions, we are expected to get better scheduling solutions if these jobs are considered before they actually arrive. However, to the best of our knowledge, we have not yet found corresponding research to date. This paper addresses a dynamic single machine scheduling problem with predicted arriving jobs (DSMSP/PRJs). We give a detailed description and the corresponding mathematical programming model with the objective of minimizing total weighted tardiness (TWT). With respect to the inclusion strategy of PRJs, which is one of the key technologies affecting the solution quality, we propose a job-based inclusion (JBI) procedure that focuses on the influence on the tardiness of PRJs rather than on a certain length of lookahead window. The novel JBI procedure shows obvious superior performance to the time-based method in the literature with respect to TWT. The discrete event simulation based on dispatching rules/heuristics (RHs) is adopted to address the DSMSP/PRJs. Six weighted dispatching rules and two improved heuristics based on the DT-TD (Decision Theory-Tactically Delayed) are proposed. Large-scale and systematic experiments show that the proposed RHs perform well. Especially, the DT-TD2 heuristic has overwhelming superior performance to the others. For the huge volumes of experimental raw data produced by 1350 simulation runs under 135 problem scenarios, we innovatively put forward the overall procedure and methods of data processing and analysis oriented to the different purposes. The systematic data analyses show that, under the various problem scenarios, considering PRJs in advance can significantly improve the scheduling solutions as long as appropriate RHs are used. The analysis results also reveal that, in the scenarios with lower shop utilization, lower percentage of PRJ, higher weight of PRJ, and looser due date of normal job (NJ), it is more obvious in the improvement of solutions, implying that it is more necessary to consider PRJs in advance. [ABSTRACT FROM AUTHOR]

This article studies the use of asymmetric loss functions for the optimal prediction of positive-valued spatial processes. We focus on the family of power-divergence loss functions with properties such as continuity, convexity, connections to well known divergence measures, and the ability to control the asymmetry and behaviour of the loss function via a power parameter. The properties of power-divergence loss functions, optimal power-divergence (OPD) spatial predictors, and related measures of uncertainty quantification are studied. In addition, we examine in general the notion of asymmetry in loss functions defined for positive-valued spatial processes and define an asymmetry measure, which we apply to the family of power-divergence loss functions and other common loss functions. The paper concludes with a simulation study comparing the optimal power-divergence predictor to predictors derived from other common loss functions. Finally, we illustrate OPD spatial prediction on a dataset of zinc measurements in the soil of a floodplain of the Meuse River, Netherlands. • Spatial prediction is examined through a decision-theoretic lens. • The automatic use of squared-error loss for spatial prediction is questioned. • The family of power-divergence loss functions is examined as an alternative. • Optimal power-divergence prediction and uncertainty quantification are studied. • A measure of asymmetry in loss functions for positive-valued processes is proposed. [ABSTRACT FROM AUTHOR]

To cope with the risk from the uncertain power output of wind turbines (WTs), energy storage system (ESS) is employed to coordinate with WTs as a combined agent to participate in power market. Due to the limitation of conventional ESSs, gravity energy storage (GES) is invented and developed recently. This paper establishes a day-ahead scheduling model for an economic and environment-friendly GES and WTs. The studied GES is charged when bricks are lifted up by cranes and discharged when bricks fall down to drive generators. Information-gap decision theory (IGDT) is employed to attain the profit against uncertainties from market price and wind power, and a risk-constrained IGDT-based day-ahead scheduling model of the GES and WT is proposed. According to the risk preference of the decision maker, a robustness model for the risk-averse strategy and an opportunity model for the risk-seek strategy are proposed to obtain the minimum and windfall profits, respectively. Finally, the effectiveness of the IGDT-based model is verified by numerical simulations. Simulation results indicate that the expectation of decision makers with a risk-averse or risk-seek strategy could be satisfied by the proposed model. Additionally, the market price has a vital impact on achieving a windfall profit. [ABSTRACT FROM AUTHOR]

In order to explore far offshore wind resource, it is an eloquent pathway to develop a far offshore wind-hydrogen project. The size optimization of electrolysis plant is beneficial and conducive to upgrade wind-hydrogen projects' cost-effectiveness. This paper aims to perform size optimization as well as a techno-economic assessment to contribute to investors' decision-making. Firstly, the efficiency methodology of Proton Exchange Membrane (PEM) electrolyzer is deduced in the presence of aging and changing operation powers since it directly impacts on the productivity of hydrogen. Secondly, a chance constrained programming (CCP) model of size optimization is established to reach a benchmark maximum net present value (NPV) for the wind-hydrogen project, taking into account random fluctuations of offshore wind power output and replacement price of electrolyzer which is the key device of the hydrogen system, and the aging of electrolyzer. Further, for the pursuit of evaluating the influence of hydrogen sale price's irregular fluctuations on the economic feasibility, information gap decision theory (IGDT) is exerted to assess acceptable hydrogen price ranges from investors' point based on acceptable NPVs, taking maximum hydrogen price uncertainty as the upper layer objective and minimum acceptable NPV as the lower layer objective, and the delicate IGDT-CCP prototype is evolved with CCP constraint conditions. The particle swarm optimization of stochastic simulation is adopted to solve both CCP and IGDT-CCP models. Finally, detailed results are demonstrated and compared through a study case, including optimum size in view of multi scenarios, economic viability and the acceptable hydrogen prices. • Size optimization for a far offshore wind-hydrogen system is developed by IGDT-CCP. • Dynamic efficiency of electrolyzer considering the aging factors is derived. • Fluctuations of hydrogen sale price based on expected NPVs are quantified by IGDT. • Probabilistic wind power output and eletrolyzer replacement costs are considered. • Compare the differences of two efficiency features of electrolyzer on NPVs. [ABSTRACT FROM AUTHOR]

• This paper employs IGDT to model the uncertain factors in the day-ahead scheduling plan to reduce the large deviation between the day-ahead scheduling plan and the actual scheduling caused by source and load prediction errors. • To support IGDT in dealing with multiple uncertainties, this work implements a two-point estimation method and Cornish-Fisher series expansion to transform the multiple uncertainties (in terms of the source and the load) into a single power shortage uncertainty, thereby improving the solving efficiency of the IGDT model. • the Nataf inverse transformation and singular value decomposition techniques are introduced so that the 2PEM-Cornish-Fisher method could handle input variables that are correlated, thereby improving the applicability of the IGDT model. • This paper uses MPC rolling optimization within a day to correct the day-ahead scheduling deviation and compensate for the shortcomings of IGDT open-loop control. As the capacity of new energy sources connected to the regional integrated energy system increases, it is necessary to consider (i) the energy imbalance caused by multiple uncertainty factors and (ii) the correlation between the power output of those uncertainty sources in the overall system. This paper proposes a bi-level optimal energy scheduling strategy, which combines information gap decision theory (IGDT) with model predictive control (MPC). In order to reduce the large deviation between the day-ahead scheduling plan and the actual scheduling caused by prediction errors of source and load, the IGDT is adopted in the upper layer strategy to model the uncertain factors in the day-ahead scheduling plan. To support IGDT in dealing with multiple uncertainties, the two-point estimation method and Cornish-Fisher series expansion is used to transform the uncertainties of the sources and the loads into uncertainties of power unbalance. Moreover, the Nataf transformation and singular value decomposition (SVD) technique are introduced, so that the two-point estimation and Cornish-Fisher method could handle correlative variables, thereby the solving efficiency of the IGDT model can be improved much more. The MPC rolling optimization is adopted within a day to correct the day-ahead scheduling deviation and compensate for the shortcomings of IGDT open-loop control. Finally, the proposed method is applied to a numerical example to verify its effectiveness. [ABSTRACT FROM AUTHOR]

• Presenting a MILP framework to reduce computation time and achieve the optimal solution. • Using advanced technologies (EVs, BDGs, BESSs, and DRP) simultaneously to enhance SDN resilience. • Utilizing a hybrid stochastic-IGDT method to model uncertainties of RESs, electricity price, EVs, and load. • Providing RA and RT strategies to face the uncertainty of load. Due to climate changes, the occurrence of low-probability but high-impact disasters such as hurricanes, earthquakes, thunders and etc are increasing. So in the occurrence of these disasters, the resilient operation of smart distribution networks (SDNs) needs to be more investigated by researchers. To address this challenge, in this paper, the optimal resilient operation of the SDN in the presence of renewable energy resources (RERs) and intelligent parking lots (IPLs) is investigated. A hybrid stochastic-IGDT approach is developed to handle the uncertainties of the proposed model. So to do this, the stochastic method is used to modeling of RERs generation, electricity price, and EVs (arrival time, departure time, and initial energy) uncertainties. Also, due to the high impact of load uncertainties on the resilient operation of SDN, the information gap decision theory (IGDT) approach is applied to mitigate the uncertainties of load. In this paper, backup diesel generators (BDGs), battery energy storage systems (BESSs), and demand response programs (DRPs) are suggested to improve the resilient operation of SDN. In order to show the efficacy of the proposed model, four case studies are evaluated on a modified IEEE-33 bus test system. The numerical results indicate, in the risk-averse strategy, the SDN is robust against 7.2% increasing active load by having 10% more operation cost, and the resiliency of the system decrease 3.05% for the worst scenario. On the other side, in the risk-taker strategy, with decreasing 7.4% in active load, the operation cost decrease 10%, and the resiliency of the system increase 4.93%. [ABSTRACT FROM AUTHOR]

• A theoretical framework of individual wayfinding decisions in indoor emergency situations is proposed. • Existing literature is synthesized with reference to different components of the theoretical framework. • Two meta -analysis are conducted on two factors influential to people's exit choices for validating the proposed framework. • Implications of the proposed theoretical framework and directions for future research are discussed in the paper. Individual wayfinding decisions are critical for evacuation efficiency in building emergencies. By synthesizing the increasing volume of relevant literature, this study proposes a theoretical framework for individual wayfinding decisions in building emergencies. Consisting of four modules of cognitive processes and a module of acute stress, the framework formalizes the relationships between different aspects contributing to observed evacuation wayfinding behavior by integrating the impacts of acute stress with the knowledge about indoor wayfinding in normal conditions. The existing literature on individual wayfinding decisions in indoor emergencies is organized with reference to the theoretical framework to identify the overlaps, contradictions and refinements. In addition, two meta -analyses are conducted to validate this framework. The meta -analyses results indicate that the likelihood of people choosing the exit indicated by (enhanced) signage or choice of the majority of surrounding people is over 18 times higher than that of other exits. The results also show that the controlled conflicting information provided for other exits has a negative moderator effect. The proposed theoretical framework is the first step towards developing an individual wayfinding decision theory in indoor emergencies. Implications of the proposed theoretical framework, and directions for future research, particularly related to wayfinding goals, assessment of wayfinding progress, and wayfinding strategies within the proposed framework, are also discussed in the paper. [ABSTRACT FROM AUTHOR]

• Developing the flexibility metrics for the electrical and thermal networks; • Developing the flexibility and economic criteria as the objective functions; • Employing the NNC method to effectively solve the proposed multiobjective problem; • Utilizing a linear DR model to formulate the shiftable loads; • Employing the IGDT technique to model the uncertain parameters; This paper addressed the optimal operation of the microgrids (MGs) containing both the electrical and thermal loads. Combined Heat and Power (CHP) units, boilers, wind turbines, storage devices, Demand Response Resources (DRRs), as well as the power exchange possibility with the upstream wholesale market are the energy resources that have been considered as the portfolio of the decision maker. The aim of the MG operator is to provide the electrical and thermal loads of the network in the most economic and flexible way. Therefore, suitable metrics are developed in this paper in order to evaluate the flexibility of the system for both the electrical and thermal units. Then, considering both the flexibility and financial concerns, a multiobjective framework is proposed. The Information Gap Decision Theory (IGDT) method is employed to handle the uncertainties of the proposed problem. Since the objectives of the MG operator might be conflicting with each other, an efficient optimization approach should be employed to properly satisfy the operator's policies. The Normalized Normal Constraint (NNC) approach is employed here as an efficient method that is able to find the evenly distributed Pareto solutions. The IEEE 33-bus test system is utilized to simulate the proposed problem and analyze the results. The performance of the proposed problem is approved using different scenarios. The simulation results justify the advantages and necessities of the proposed problem. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) is a popular branch of decision theory in which many methods have been proposed to solve MCDM real-world problems. However, in spite of the multiple methods, it is common that different of them provide the same solutions. Therefore, this paper analyzes the causes of the same solutions from different MCDM methods, studies why different normalization methods can achieve the same solutions in MCDM methods through the theory of MCDM methods, and discusses the relationship between different MCDM methods by category. To achieve such goals, MCDM methods are classified into three categories according to their ranking methods. Some traditional MCDM methods are then extended by considering general normalization methods and distance measures. Finally, the characteristics of various MCDM methods are discussed by category and their evaluation indexes are also compared. The comparison results are represented by tables and flowchart models. The relationship between different categories of MCDM methods is shown through the comparison of the evaluation indexes. The aim of the proposed method is not to replace existing MCDM methods, but to fill some present research gaps about MCDM methods. • General normalization methods and distance measures are considered in MCDM methods. • The causes of the same solutions from different MCDM methods are examined. • The characteristics of various MCDM methods are discussed by category. • The relationship between different MCDM methods is summarized. [ABSTRACT FROM AUTHOR]

As distributed generation (DG) systems continue reshaping power networks, optimizing pricing strategies at DG buses using the distribution locational marginal price (DLMP) concept becomes critical for economic efficiency and the optimal operation of the network. This paper proposes a novel approach that combines Information Gap Decision Theory (IGDT) and Particle Swarm Optimization (PSO) to address these challenges. IGDT offers a robust framework for optimizing pricing in the presence of uncertainties and incomplete information in the distribution network. It quantifies and models these uncertainties, enabling the creation of adaptable pricing strategies that respond effectively to changes in DG output and market conditions. To further refine pricing strategies, PSO is integrated into the IGDT framework. PSO's iterative approach allows for the discovery of optimal pricing parameters at DG buses, striking a balance between maximizing DG owner profits and ensuring system optimality. Extensive simulations on two realistic distribution network models validate the effectiveness of the proposed IGDT-PSO approach. It demonstrates that DG owners can reliably achieve their predefined profit targets while also adhering to the objectives of the distribution network. • Combination of information gap decision theory and particle swarm optimization for DLMP calculation. • Achieving a delicate balance between maximizing DG owner profits and ensuring overall system optimality. • Presenting an accurate and realistic solution for DLMP calculation. [ABSTRACT FROM AUTHOR]

• 1. To propose a quasi-quantum model, which can reasonably divide and guide the planning period to reduce uncertainty. • 2. To propose an evaluation model, which can analyze the uncertainty potential energy function before and after the planning to assist the quasi quantum model, so as to reasonably divide the planning cycle. • 3. To propose a three-layer planning framework, including planning cycle division, stochastic planning, and rolling planning. With the rapid development in social informatization, more and more factors, such as regional economy, technological development, and people's living needs, will affect the supply–demand relationship of regional integrated energy systems (RIES), which involve multiple energy forms. These factors turn the supply–demand relationship of an energy system into a nonlinear and time-varying chaotic system. This makes it difficult to predict and balance these relationships, which poses a huge challenge to the prediction, planning, operation, etc. Existing conventional methods attempt to quantify the prediction bias caused by various external environmental factors of energy systems and to weaken the uncertainty caused by multiple energy loads through random programming. However, uncertainty factors increase with the development of an energy system, thereby inreasing the requirements of such uncertainty quantification methods. Furthermore, in conventional methods, the prediction or planning decisions are often made by an observer while neglecting the impact of the observer's decision-making behavior on prediction and planning. Therefore, this paper proposes a three-layer planning framework for to solve the above problems. This architecture includes quasi-quantum uncertainty periodic evaluation, stochastic planning based on information gap decision theory, and rolling planning based on model predictive control. First, we establish a quasi-quantum model of multi-energy system prediction error based on the quasi-quantum wave function model to qualitatively analyze prediction errors affected by uncertainty before and after planning. Simultaneously, combined with the evaluation model of the quasi-quantum potential energy function, a quasi-quantum uncertainty period evaluation model is proposed. Based on the minimum equivalent planning entropy, the planning cycle of multistage rolling planning is divided to minimize uncertainty. Second, the information gap decision theory is used to quantify the influence range of source and load uncertainty in the planning process and the multistage planning cycle is randomly planned. Then, the model predictive control method is used to control the actual error, and the planning strategy is changed in time to reduce the planning deviation caused by the prediction error. Finally, adopting a Beichen demonstration area in Tianjin, China, as a case study, the effectiveness of the proposed method in uncertainty analysis and long-term planning improvement is verified. The three-layer planning framework can improve the adaptability of the regional integrated energy system in the long-term planning process, and can timely adjust the planning scheme to cope with the impact of unpredictable uncertainties in the planning process. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• Studying a stochastic based model for CAESs in day-ahead energy and reserve markets. • Constraints formulation of CAES's reserve provision capability at every hour. • Modeling operation modes of CAESs in six different modes to provide reserve. • Introducing the CAESs reserve deliverability constraints during various hours. • Considering demand response program as curtailed demands. Regarding numerous benefits of the compressed air energy storage (CAES) in the utility level, these devices can be taken into account in energy and reserve markets. One of the most important features of CAES is its fast response ability, which makes it an attractive option to alleviate the uncertainties of renewable energy resources and demands. This paper proposes a two-stage mathematical optimization model for optimally day ahead operation of generation units as well as CAESs in energy and reserve markets in a stochastic way. The features of presented reserve model of CAESs are as follows: (a) considering two constraints in order to model the reserve of CAES for providing capability at each hour by six operation modes; (b) considering the limitations related to the state of charge in the CAESs. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in a power system. This paper presents a stochastic optimal operation model on the basis of information gap decision theory together with risk averse strategy in order to overcome this information gap and to help independent system operator. As demand response program, the curtailed demand is considered for enhancing the market flexibility. The proposed model is formulated as a mixed-integer nonlinear problem, which is solved by CPLEX solver of the GAMS software. Employing the presented model in the 6-bus test system demonstrates the efficacy of the proposed model. Simulation results show that considering restrictions on reserve deliverability across multiple hours, lessens the total reserve by 22.75 MW and increases the operation cost by $438.26. [ABSTRACT FROM AUTHOR]

• Proposed a novel MCC-IGDT approach to determine the extreme N-k contingencies and extreme realization of wind power uncertainty in a co-operation integrated EES&NGS problem for enhancing the EES resilience under extreme windstorm events. • A decentralized three-level hierarchical solution method has been presented for the co-optimization of the resilience of integrated EES&NGS. This paper explores a decentralized collaboration strategy aimed at enhancing the resilience of integrated electricity and natural gas energy systems in the face of natural disasters. In real-world scenarios, when confronted with natural disasters, operators of the electric energy system (EES) and natural gas system (NGS) must independently manage their respective systems due to physical limitations, the need for information privacy preservation, and uncertainties. Recent attention has been focused on developing decentralized approaches to bolster the resilience of integrated EES&NGES during extreme natural disasters (ENDs). To address these challenges, a novel three-level hierarchical iterative algorithm is proposed in this study, ensuring the privacy of operational data shared between EES&NGES operators during ENDs. Moreover, a multi-stage extreme windstorm model is considered to capture the spatial dynamics of ENDs. Consequently, decision-making for the EES operator becomes more intricate in the presence of multi-stage extreme windstorm events. In order to address the aforementioned challenge, the research presents an innovative approach known as the Multi-Stage Contingency-Constrained Information Gap Decision Theory (MCC-IGDT), tailored to effectively handle the impact of severe windstorm occurrences. This novel approach plays a crucial role in managing the impact of extreme windstorm events on the EES operator. Finally, case studies are conducted using a modified IEEE-30 bus system and a modified ten-node gas system. The numerical results underscore the effectiveness of the proposed decentralized MCC-IGDT model in enhancing the resilience of integrated EES&NGES. [ABSTRACT FROM AUTHOR]

• A novel DG hosting capacity evaluation method is proposed for multi-feeders system interconnected by external tie lines and SOPs. • An improved-IGDT model is presented to assess DG hosting capacity considering unpredictable integrated locations and uncertainty in outputs simultaneously. • A hybrid solution procedure integrated with genetic algorithm and model reformulation is developed to determine DG hosting capacity. Unknown integrated locations caused by unpredictable consumers' willingness and uncertainty in outputs induced by variable natural conditions are acknowledged challenges in the evaluation process of distributed generation (DG) hosting capacity (DGHC). For the former factor, this paper first proposes a novel assessment model to obtain the maximum feasible capacity no matter consumers in which locations have the possibility to stall DG. The decision variables of max operator and min operator are DG capacities and integrated locations respectively. Notably, unlike the previous works for one radial feeder, the interplay of DGHC in multi-feeders interconnected by tie lines and soft open points is also taken into consideration. After that, to combine uncertainties in integrated locations and outputs simultaneously, the above DGHC assessment model is further transformed into an improved information gap decision theory (IGDT) model with worst-case constraints for DG integrated locations. The specific solution procedure is also developed to solve this nonlinear IGDT model effectively. Case studies demonstrate that the evaluation results of DGHC based on this method always have the applicability and feasibility for any nodes in distribution system. [ABSTRACT FROM AUTHOR]

In multi-label classification, the expansion of output dimension seriously interferes learning performance, and even fails to build a joint prediction model. In order to restrain the proliferation of multi-label classifier's hypothesis space, the current works focus on the application of global positive label correlation. However, the "black or white" mechanism ignore other possible forms of label correlation, such as negative or neutral correlation. By introducing the doctrine of the mean, three-way decision (3WD) theory provides a solution for in-depth research on local label correlation, and aims to handle the uncertainty of multi-label learning tasks. In this paper, a novel learning algorithm for multi-label joint classification, namely ML-3WD, is proposed by considering the 3WD label correlation from the perspective of samples. According to the weights of different features on any label, the comprehensive loss of each sample to three action strategies can be measured. Obviously, the 3WD rules for any label variable in multi-label output space is obtained. By aggregating the cutting thresholds between different labels, the division principles of 3WD label correlation are further established. Given any multi-label sample, the local fuzzy membership to co-occurrence or mutual state for label pair is examined based on kernelized fuzzy rough sets. The 3WD local label relevance of each sample is confirmed, that is, positive, negative or neutral. The global application strategy for multi-label classification is utilized to avoid over-fitting induced by local mining strategy. Based on the integral mean of the distribution of 3WD local label relevance in multi-label sample space, two different versions of empirical label relevance are constructed. By constraining the relative position between sub-separation hyperplanes, the 3WD label correlation distribution-based model for multi-label joint classification is designed. The experiment results on fifteen real world multi-label datasets reflect that our algorithm achieves good classification ability and versatility. The impact of core parameters on learning performance is also dissected. • The doctrine of the mean in three-way decision theory, which accords with human behavior cognition, inspires us to enrich the "black or white" mining mechanism on label correlation. By adding buffer processing to the determined correlation, three possible forms for label correlation are first considered, they are positive, negative or neutral. A multi-label classification algorithm is designed according to 3WD label correlation from the perspective of samples, where global empirical label relevance is explicitly applied to restrict the sub-separation hyperplanes of different labels. [ABSTRACT FROM AUTHOR]

• Proposing optimal management of a local operator under the concept of P2P trading. • Assessing the performance of the local operator in different uncertainty attitudes. • Several sources of uncertainty considered such as load, price, and PV generation. • Risk-averse decision-maker tends to purchase from the grid to reduce potential risks. • Risk-seeking decision-maker targeting demand supply through the P2P trading scheme. Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed. [ABSTRACT FROM AUTHOR]

• A novel generator start-up sequence optimization strategy is proposed in this work, taking the integration time of wind-solar hybrid systems into account. • The proposed model requires fewer variables compared with other models, therefore the computational efficiency can be greatly improved. • The confidence gap decision theory based generator start-up sequence strategy is proposed to address the generation uncertainty of wind-solar hybrid systems. • Better generator start-up sequence solutions with higher computational efficiency and less conservatism can be attained by employing the proposed strategy. A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The major objective of this paper is to introduce a novel multi-criteria decision making method (MCDM) called Root Assessment Method (RAM) which is easy to understand, straightforward to use and comparable with other well-known MCDM methods. RAM is motivated by the shortcomings of existing MCDM methods to obtain a reliable result in a decision problem and also the fact that decision maker (DM) should understand the whole decision making process to be able to avoid possible computational pitfalls. To do so, we define an aggregating function to rank the alternatives based on a radical expression which its radicand and index are the sums of benefit and cost criteria of each alternative, respectively. Accordingly, RAM allows a different compensation degree between beneficial and non-beneficial criteria which is rare in existing MCDM methods. As the advantages of RAM, the ranking result is not influenced by additional parameter, beneficial and non-beneficial criteria can be simultaneously considered without any transformation and pairwise comparisons are not used. On the other hand, it is observed that RAM can provide the advantage of preventing rank reversal in two case studies, whereas TOPSIS and VIKOR are subject to this problem in the same cases. As another contribution, a new heuristic procedure is suggested to compare the ranking results obtained by RAM with other methods. Ten numerical examples demonstrate that RAM results are consistent with its presumed function and are easily interpretable for a DM who may not be well acquainted with theoretical aspects of MCDM methods. Examples are positioned in the field of sustainability research as the area with wide application of MCDM methods. The results provided by RAM are encouraging and we hope this method contributes to enrich decision theory. [ABSTRACT FROM AUTHOR]

Regarding to group decision-making, decision makers may express different decision preferences for the same target. Since existing decision-making methods only consider a single decision preference at most, it is difficult to integrate the multi-decision preferences of multiple decision makers effectively. In this paper, we explore a class of heterogeneous multi-attribute group decision-making (MAGDM) problems with multiple decisions. Over the framework of granular computing, we present a new MAGDM method with the aid of the valued outranking relations in the ELECTRE III method. Considering the non-compensability among attributes, we first define a special valued outranking relation on a heterogeneous attribute multi-decision information system, and then present the notion of valued outranking classes. Subsequently, given the fact that the fault tolerance of probability approximations can reduce the vulnerability of decision-making methods, we construct multi-decision multigranulation probabilistic rough sets (MD-MGPRSs) form different perspectives, which extend existing rough set models to a more general situation. After that, we establish a novel MAGDM method, and apply the method to a real medical case to illustrate its applicability. Lastly, the experimental results on seven datasets demonstrate the effectiveness, robustness, and superiority of the method. Our study not only enriches the theoretical research of rough sets, but also extends the application scope of decision-making theory. • A novel concept of valued outranking classes is presented. • Novel MD-MGRSs and MD-MGPRSs are constructed. • An MAGDM method based on MD-MGPRSs is established. • The robustness, validity and superiority of the established method are verified. [ABSTRACT FROM AUTHOR]

The multiple uncertainties in renewable energy generation and load pose challenges for the operation of integrated energy systems (IES) with robustness. To achieve robust scheduling of IES, this paper proposes a multi-objective bi-level confidence gap decision theory robust model (MOCGDT). First, a neural network-based surrogate model is used to obtain the inverse cumulative distribution function of a Gaussian mixture model for uncertain parameters, constructing a confidence interval uncertainty set. Then, the upper-level optimization objective is to maximize the fluctuations of wind, solar, and load, while the lower-level objective is to minimize the total operating cost, resulting in an IES MOCGDT robust model. Finally, an effective bi-level solution algorithm is developed for the model's characteristics. The case study results demonstrate that the proposed method improves economic costs by 20.69% and 3.87% compared to robust optimization and stochastic optimization, respectively, and reduces computation time by approximately 601 s compared to stochastic optimization. This suggests that the proposed MOCGDT model not only reduces the conservatism of conventional robust decision-making but also overcomes the coarseness of uncertain sets in the information gap decision theory model, achieving more reasonable robust scheduling with uncertainty. The effectiveness and superiority of the proposed method are thus verified. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization framework based on information gap decision theory (IGDT) for strategic participation of multi-carrier systems (MCS) in the electricity market under price uncertainty. The proposed framework helps the operator of MCS to take advantage of flexible generation units such as combined heat and power (CHP) systems, boilers, and battery energy storage systems (BESS) and participate in the electricity market considering the strategies presented by IGDT against the uncertainty in the market clearing price. The proposed optimization framework is developed as a bilevel problem, in which the risk-based bidding strategy of the MCS operator is modeled in the upper-level problem, and the energy market is cleared by the bulk power system operator in the lower-level problem. Karush–Kuhn–Tucker conditions are used to transform the bilevel problem into a single-level optimization problem. Duality theory is later used to approximate the product of power and market clearing price in the MCS operation problem, and the non-linearity enforced by the implementation of IGDT is handled by a linearization method called Triangle method. The studied optimization framework is modeled as a single-level mixed-integer linear programming problem, and CPLEX is used to solve it. The simulations are carried out on the modified IEEE 6-bus transmission network, and the results are analyzed to verify the economic and computation efficiency of the studied model. According to the simulation results, by taking a risk-averse bidding strategy provided by the IGDT, the operation cost of MCS increases 16.6% in order to increase the robustness level of MCS up to 36%, indicating that MCS becomes robust against 36% of the increase in the electricity market price, compared to the case with a risk-neutral bidding strategy being taken. • Proposing a novel IGDT-based model for strategic participation of MCS in power market. • A bilevel model for risk-based bidding strategy of the MCS and clearing energy market. • Considering strategies of IGDT against the uncertainty in the market clearing price. • Approximating product of power and market clearing price in MCS operation problem. • Analysis of economic and computation efficiency of the proposed model by simulations. [ABSTRACT FROM AUTHOR]

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of linear stochastic systems subject to chance constraints, where the input signals are specifically designed to enhance diagnosis performance by discriminating between multi-fault modes. As the misdiagnosis probability cannot be expressed in the closed form, a reachable upper bound on it is derived to construct a novel input design criterion for AFD based on the Cauchy–Schwarz divergence. A sufficient condition is provided to decompose the multivariate chance constraints into a set of univariate ones. A three-stage optimization approach is proposed to solve the input design problem incorporating chance constraints for AFD. In the upper stage, the best iteration parameter is determined for subsequent iterative optimization to obtain the least conservative risk allocation scheme. In the middle stage, the total risk of violating the multivariate constraints is optimally allocated to a set of univariate ones through iterative optimization, which can improve diagnosis performance. In the lower stage, a set of univariate chance constraints is equivalently converted into expectation constraints under the allocated risk, and then the input design problem under the constructed design criterion is solved to global optimality. The optimal input signals are injected, and the AFD decision is made based on real-time measurement information according to the minimum probability of misdiagnosis decision principle. Finally, simulation results on a four-tank system demonstrate the effectiveness and superiority of the proposed AFD method. [ABSTRACT FROM AUTHOR]

• PV hosting capability for multiple locations is modeled as a feasible region. • The uncertainty of PV output is addressed by data-driven WDRCC. • Active management with limited budget is used to improve the hosting capability. • A systematic solution procedure is proposed to observe the feasible region. • Each PV integration request in the feasible region is a feasible request. The increasing integration scale of photovoltaic (PV) systems brings enormous challenges on distribution networks (DNs). To provide an explicit boundary of feasible PV integration capacity (PVIC) associated with each integration location, this paper proposes a novel depiction of PV hosting capability, which is the feasible region for PVIC in high-dimensional space. Next, a multi-objective optimization model based on information gap decision theory (IGDT) is proposed to observe the feasible region, where active distribution network management (ADNM) schemes with a limited budget are deployed to improve PV hosting capability. The impact of PV output uncertainty on security constraints is addressed by data-driven Wasserstein-distance-based distributionally robust chance constraints (WDRCCs). Finally, a systematic procedure is developed to solve the proposed model. Note that the exact power flow model associated with a new equivalent WDRCC reformulation method is deployed so as to guarantee the accuracy of the assessment. The effectiveness, solution accuracy, computational efficiency, and scalability of the proposed model and method are verified with the 4-bus system, the IEEE 33-bus system, and the IEEE 123-bus system. The output of the common method based on the linearized power flow deviates by more than 10% from the output of the proposed method based on the exact power flow in the 33-bus system. Each multi-location PV integration request is feasible as its locations and capacities belong to the proposed region. It implies that this region can provide guidance for PV allocation. [ABSTRACT FROM AUTHOR]