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Energy markets are crucial for integrating Distributed Energy Resources (DER) into modern power grids. However, this integration presents challenges due to the inherent variability and decentralized nature of DERs, as well as poorly adapted regulatory environments. This paper proposes a medium-term decision-making approach based on a comprehensive suite of computational tools for integrating DERs into Colombian energy markets. The proposed framework consists of modular tools that are aligned with the operation of a Commercial Virtual Power Plant (CVPP). The tools aim to optimize participation in bilateral contracts and short-term energy markets. They use forecasting, uncertainty management, and decision-making modules to create an optimal portfolio of DER assets. The suite's effectiveness and applicability are demonstrated and analyzed through its implementation with heterogeneous DER assets across various operational scenarios. [ABSTRACT FROM AUTHOR]

During the energy transition, new types of electrical equipment, especially power electronic devices, are proposed to increase the flexibility of electricity distribution grids. One type is the solid-state transformer (SST), which offers excellent possibilities to improve the voltage quality in electricity distribution grids and integrate hybrid AC/DC grids. This paper compares SST to conventional copper-based power transformers (CPT) with and without an on-load tap changer (OLTC) and with additional downstream converters. For this purpose, a corresponding electricity distribution grid is set up in the power system analysis tool DIgSILENT PowerFactory 2022. A DC generator like a photovoltaic system, a DC load like an electric vehicle fast charging station, and an AC load are connected. Based on load flow simulations, the four power transformers are compared concerning voltage stability during a generator-based and a load-based scenario. The results of load flow simulations show that SSTs are most valuable when additional generators and loads are to be connected to the infrastructure, which would overload the existing grid equipment. The efficiency of using SSTs also depends on the parameters of the electrical grid, especially the lengths of the low-voltage (LV) lines. In addition, a flowchart-based decision process is proposed to support the decision-making process for the appropriate power transformer from an electrical perspective. Beyond these electrical properties, an evaluation matrix lists other relevant criteria like characteristics of the installation site, noise level, expected lifetime, and economic criteria that must be considered. [ABSTRACT FROM AUTHOR]

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs' automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to addressing critical global challenges in long-term energy planning for low- and middle-income countries (LMICs). The paper proposes and tests an international enabling environment, a delivery ecosystem, and a community of practice. These components are integrated into workflows that yield four self-sustaining capacity-development outcomes. Planning long-term energy strategies in LMICs is particularly challenging due to limited national agency and poor international coordination. While outsourcing energy planning to foreign experts may appear to be a viable solution, it can lead to a reduction in government agency (the ability of a government to make its own informed analysis and decisions). Additionally, studies commissioned by external experts may have conflicting terms of reference, and a lack of familiarity with local conditions can result in misrepresentations of on-the-ground realities. It is argued here that enhancing national agency and analytical capacity can improve coordination and lead to more robust planning across line ministries and technical assistance (TA) providers. Moreover, the prevailing consulting model hampers the release and accessibility of underlying analytics, making it difficult to retrieve, reuse, and reconstruct consultant outputs. The absence of interoperability among outputs from various consultants hinders the ability to combine and audit the insights they provide. To overcome these challenges, five strategic principles for energy planning in LMICs have been introduced and developed in collaboration with 21 international and research organizations, including the AfDB, IEA, IRENA, IAEA, UNDP, UNECA, the World Bank, and WRI. These principles prioritize national ownership, coherence and inclusivity, capacity, robustness, transparency and accessibility. In this enabling environment, a unique delivery ecosystem consisting of knowledge products and activities is established. The paper focuses on two key knowledge products as examples of this ecosystem: the open-source energy modeling system (OSeMOSYS) and the power system flexibility tool (IRENA FlexTool). These ecosystem elements are designed to meet user-friendliness, retrievability, reusability, reconstructability, repeatability, interoperability, and audibility (U4RIA) goals. To ensure the sustainability of this ecosystem, OpTIMUS is introduced—a community of practice dedicated to maintaining, supporting, expanding, and nurturing the elements within the ecosystem. Among other ecosystem elements, training and research initiatives are introduced, namely the Energy Modelling Platform for Africa, Latin America and the Caribbean, and Asia-Pacific as well as the ICTP Joint Summer School on Modelling Tools for Sustainable Development. Once deployed via workflows, the preliminary outcomes of these capacity-development learning pathways show promise. Further investigation is necessary to evaluate their long-term impacts, scalability, replication, and deployment costs. [ABSTRACT FROM AUTHOR]

While biogas production offers promising solutions for waste management, energy diversification, and sustainable development, effective project implementation requires comprehensive evaluation criteria that encompass diverse aspects, such as the problem to be addressed, biodigester technology selection, business model development, investment considerations, and final product utilization. A preliminary study involving an integrative review of 58 articles yielded 499 unique criteria. These criteria were categorized into four groups: economic, environmental, social, and technical, encompassing a total of 39 subcriteria. Six stages of the biogas production cycle were considered in the analysis: project, initiation, biodigester type selection, location determination, operational cycle definition, and final product utilization. The analysis revealed that existing decision-making models often prioritize technical and economic considerations while neglecting broader social and environmental perspectives. This paper addresses this gap by proposing, for the first time, stage-specific, multicriteria decision-making (MDCA) models tailored to each phase of a biogas production cycle. These models empower project managers and policymakers to optimize resource allocation, minimize the environmental impact, maximize social benefits, and ensure project viability and profitability. The models' adaptability allows for tailored prioritization based on specific project requirements and contexts. This groundbreaking research fills a critical void in biogas decision making by bridging the gap between existing technical and economic model limitations and the growing need for truly sustainable project development. [ABSTRACT FROM AUTHOR]

This paper explores business models for community energy storage (CES) and examines their potential and feasibility at the local level. By leveraging Multi Criteria Decision Making (MCDM) approaches and real-world case studies in Europe and India, it presents insights into CES deployment opportunities, challenges, and best practices. Different business models, including community energy cooperatives, utility–community partnerships, demand response, energy services, and market mechanisms, are analyzed. The proposed method combines the MCDM method PROMETHEE II with the fuzzy set theory to obtain a complete CES business model ranking, addressing project uncertainties. The analysis emphasizes CES's role in balancing local renewable energy supply and demand, facilitating energy sharing, and achieving energy independence. Findings prioritize models like Community Cooperative, Energy Arbitrage, and Energy Arbitrage Peak Shaving for CES with renewables. Environmental benefits include reduced diesel use and greenhouse gas emissions. Efficient cooperatives are advocated to recover costs and enable competitive energy prices. The paper highlights the need for novel value propositions to boost the energy transition in local communities. This research contributes to the discourse on CES business models, fostering knowledge exchange and promoting effective strategies for sustainable energy systems. [ABSTRACT FROM AUTHOR]

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in multiple modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all of these applications lead to considerable computational time and computer resource-associated costs, rendering reservoir simulators as not fast and robust enough, and thus introducing the need for more time-efficient and intelligent tools, such as ML models which are able to adapt and provide fast and competent results that mimic the simulator's performance within an acceptable error margin. In a recent paper, the developed ML applications in a subsurface reservoir simulation were reviewed, focusing on improving the speed and accuracy of individual reservoir simulation runs and history matching. This paper consists of the second part of that study, offering a detailed review of ML-based Production Forecast Optimization (PFO). This review can assist engineers as a complete source for applied ML techniques in reservoir simulation since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is now used in various fields, such as product development, design, manufacturing, planning, and quality management services, as a planning tool to achieve customer requirements and needs while improving performance and sustainability concerns. This paper presents a systematic literature review of multi-criteria decision-making (MCDM) methodologies integrated into QFD over the last year. In 2022, 56 research papers on planning strategies, the supply chain, and product development using QFD were published. Other fields such as energy, academia, and environment have also shown considerable interest in the integration of MCDM methodologies in QFD to improve decision-making processes. This research shows that the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solutions (TOPSIS) methodologies are mainly used to rank customer requirements and weigh their importance in the house of quality (HoQ) structure. The use of fuzzy logic has been incorporated into the correlation matrix to evaluate the relationships between customer requirements and technical requirements. Methodologies such as decision-making trial and evaluation laboratory (DEMATEL) and fuzzy cognitive maps are implemented to deal with contradictions, and they have also been used to rank engineering characteristics. In the field of energy and renewable technologies, only few studies related to the integration of MCDM methodologies in QFD were found, but it is forecasted that their use will be used more often as they offer improvements and benefits in the ocean energy sector. [ABSTRACT FROM AUTHOR]

In an era of big data, organizations increasingly aim to adopt data-driven decision-making processes to enhance their performance. This paper investigates the data-driven decision-making process by developing a framework tailored for application in the energy sector. The proposed framework integrates interdisciplinary approaches to comprehensively address the "data, information, knowledge" triad, applying it to both operational and maintenance decision-making. Designed to be managerially focused rather than technically oriented, the framework aims to engage all employees, including those without technical backgrounds, enabling them to effectively contribute to the decision-making process from their respective roles. To demonstrate the practical application of the proposed framework, this paper presents a case study of an energy organization managing a wind farm project, which implemented the framework to improve its decision-making process. The case study examines how the organization identified its objectives and information needs, formulated key performance questions for each stakeholder, explicitly defined and measured the key performance indicators, employed data collection and organization methods, managed the progression from data to information to knowledge, and transformed the acquired knowledge into informed decisions. By adopting this pragmatic framework, energy organizations are anticipated to solve problems, predict trends, and discover new opportunities, thereby enhancing their efficiency and predictability. [ABSTRACT FROM AUTHOR]

This paper proposes the method pELECTRE Tri, a novel probabilistic Multi-Criteria Decision Making (MCDM) method using the traditional ELECTRE Tri, probability distribution, and Monte Carlo simulation to support informed decision-making in complex and uncertain decision environments. The proposed method is illustrated through a case study involving the renovation of three social housing buildings. The paper provides new insights into the application of probabilistic sorting MCDM in the context of energy efficiency in buildings and highlights the benefits of using probabilities rather than crisp values to categorize alternatives enabling stakeholders to make better use of available resources, especially when dealing with a large dataset of energy measures with different features. The methodology implemented in Python (DOI: 10.5281/zenodo.7967655) is available as an open source. [ABSTRACT FROM AUTHOR]

In power systems with high penetration of renewable energy, traditional physical model-based optimal dispatch methods suffer from modeling difficulties and poor adaptability, while data-driven dispatch methods, represented by reinforcement learning, have the advantage of fast decision making and reflecting long-term benefits. However, the performances of data-driven methods are much limited by the problem of distribution shift under insufficient power system scenario samples in the training. To address this issue, this paper proposes an improved scenario division method by integrating the power system's key physical features and the data-driven variational autoencoder (VAE)-generated features. Next, based on the scenario division results, a multi-scenario data-driven dispatch model is established. The effectiveness of the proposed method is verified by a simulation conducted on a real power system model in a province of China. [ABSTRACT FROM AUTHOR]

All long-term hydropower planning problems require a forecast of the inflow during the planning period. However, it is challenging to accurately forecast inflows for a year or more. Therefore, it is common to use stochastic models considering the uncertainties of the inflow. This paper compares deterministic and stochastic models in a weekly rolling horizon framework considering inflow uncertainty. The stochastic model is tested in both a risk-neutral and a risk-averse version. The rolling horizon framework helps make periodic decisions and update the information in each rolling week, which minimizes the errors in prolonged forecasts. The models aim to utilize the water stored in the rainy season throughout the year with minimum load shedding while storing as much water as possible at the end of the planning horizon. The Conditional Value at Risk ( C V a R ) risk measure is used to develop the risk-averse stochastic model. Three different risk measures are investigated to choose the risk measure that yields the best outcome in the risk-averse problem, and the two best measures are compared to a deterministic and risk-neutral model in a weekly rolling horizon framework. The results show that the risk-neutral and best risk-averse models perform almost equally and are better than the deterministic model. Hence, using a stochastic model would be an improvement to the actual planning performed in the Ethiopian and other African countries' power systems. [ABSTRACT FROM AUTHOR]

This study aims to achieve intelligent decision making in HVDC systems in the framework of knowledge graphs (KGs). First, the whole life cycle KG of an HVDC system was established by combining intelligent decision making. Then, fault diagnosis was studied as a typical case study, and an intelligent decision-making method for HVDC systems based on XGBoost that significantly improved the speed, accuracy, and robustness of fault diagnosis was designed. It is noteworthy that the dataset used in this study was extracted in the framework of KGs, and the intelligent decision making of KG and HVDC systems was accordingly combined. Four kinds of fault data extracted from KGs were firstly preprocessed, and their features were simultaneously trained. Then, sensitive weights were set, and the pre-computed sample weights were put into the XGBoost model for training. Finally, the trained test set was substituted into the XGBoost classification model after training to obtain the classification results, and the recognition accuracy was calculated by means of a comparison with the standard labels. To further verify the effectiveness of the proposed method, back propagation (BP) neural network, probabilistic neural network (PNN), and classification tree were adopted for validation on the same fault dataset. The experimental results show that the XGBoost used in this paper could achieve accuracy of over 87% in multiple groups of tests, with recognition accuracy and robustness being higher than those of its competitors. Therefore, the method proposed in this paper can effectively identify and diagnose faults in HVDC systems under different operation conditions. [ABSTRACT FROM AUTHOR]

This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data's behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector. [ABSTRACT FROM AUTHOR]

Decision-making in energy retrofit of existing buildings needs to consider the economic and technical criteria, as well as social and environmental ones. The main problems are to define and to aggregate these contradicting issues to support a measurable and verifiable decision process. The working hypothesis of this paper is that the ELECTRE Tri method would respond to the problem. The paper proposes and tests in a real professional environment the ELECTRE Tri method to help choose a retrofit program for collective residential buildings. The evaluation criteria used as performance indicators are defined in consultation with the decision maker and weighted by using the revised Simos' cards method. The paper proposes to construct the renovation scenarios by combining individual actions that are mutually compatible. The methodology, the proposed criteria and the scenarios were successfully applied on a renovation project of a residential assembly composed of three buildings (67 apartments). A new solution to the problem of defining reference scenarios specific to the ELECTRE Tri method is also proposed. The main advantages of the proposed methodology are the formalization of the decision-making process within the framework of energy renovation strategies and the application of a robust multi-criteria analysis method, considering the complexity of the problem. The main drawbacks are the cost (in time and resources) for obtaining the information needed to construct the decision matrix and the precision of the weights of the criteria. The methodology implemented in Python is available in open source. [ABSTRACT FROM AUTHOR]

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system's stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction results. To verify the validity and accuracy of the proposed model, this paper uses data from three electricity markets, Australia, Spain, and France, for validation analysis. The experimental results show that the proposed model has MAPE of 0.39%, 1.58%, and 0.95%, RMSE of 0.25, 0.9, and 0.3, and MAE of 0.19, 0.68, and 0.31 in three different cases, indicating that the proposed model can well handle the nonlinear and non-stationarity characteristics of the electricity price series and has superior forecasting performance. [ABSTRACT FROM AUTHOR]

Photovoltaic installations (PVs) are currently one of the fastest-growing sources of renewable energy. Expanded forms of financial support and higher electricity prices have resulted in a large increase in its installed capacity. PV installations are increasingly being ordered by industry and privates, often for installations capacity of several hundred kilowatts. In addition to the advantages, photovoltaic installations also have drawbacks. One of these is that the increase in the voltage in the power grid leads to the exclusion of individual installations from the grid. An important issue in the operation of photovoltaic installations is also their reliability during their lifetime. The reliability of photovoltaic installations depends on the random nature of the cloud cover as well as the material's mechanical degradations. This paper presents a literature analysis using Citespace software in terms of reliability. A detailed bibliometric analysis has been performed to outline the main drawbacks of the PV installations cited by researchers. This literature review forms the basis for further analysis. The paper also presents a new approach to implementing the Multiple-Criteria Decision Analysis (MCDA) method for assessing the risk of failure of PV panels. The obtained results showed the main interests of scientists in the field of failure analysis of photovoltaic installations and countries having the largest share in research on this issue. The applied Analytic Hierarchy Process (AHP) analysis enables supporting the process of managing photovoltaic installations by analyzing installation operations in terms of reliability as reliability impacts the profitability of investments and operating costs. The proposed method can be used by the operators of photovoltaic installations or farms. [ABSTRACT FROM AUTHOR]

The roles and benefits of pumped storage are reflected in different stakeholders of the power system. The multi-dimensionality and non-linearity of pumped storage multi-stakeholder decision-making make pumped storage benefit realization a hot research topic with challenges. This paper takes pumped storage benefit sharing as the breakthrough. It adopts multiple regulation strategies and multi-level decision-making measures based on the multiple objectives of different stakeholders. A method and a framework for pumped storage benefit realization are proposed. This paper proposes the objective function system with the fairness objective at the leader level, the participation objective at the secondary leader level, and the efficiency objective at the follower level. It also constructs a three-level Leader-Follower objective model with multi-dimensional regulation and multiple values, and it develops the corresponding solution method based on Stackelberg dynamic game and interactive algorithms. The results reveal that the values of fairness, participation, and efficiency indicators of the pumped storage plant are 1.75%, 0.25%, and 9.75%, respectively, and all the indicators meet the requirements. The research results are conducive to promoting the realization of a win-win situation for different stakeholders of pumped storage, and they highlight how pumped storage plants can fulfill their functions in the power system as well as ensure its survival and development. [ABSTRACT FROM AUTHOR]

Well construction operations require continuous complex decision-making and multi-step action planning. Action selection at every step demands a careful evaluation of the vast action space, while guided by long-term objectives and desired outcomes. Current human-centric decision-making introduces a degree of bias, which can result in reactive rather than proactive decisions. This can lead from minor operational inefficiencies all the way to catastrophic health and safety issues. This paper details the steps in structuring unbiased purpose-built sequential decision-making systems. Setting up such systems entails representing the operation as a Markov decision process (MDP). This requires explicitly defining states and action values, defining goal states, building a digital twin to model the process, and appropriately shaping reward functions to measure feedback. The digital twin, in conjunction with the reward function, is utilized for simulating and quantifying the different action sequences. A finite-horizon sequential decision-making system, with discrete state and action space, was set up to advise on hole cleaning during well construction. The state was quantified by the cuttings bed height and the equivalent circulation density values, and the action set was defined using a combination of controllable drilling parameters (including mud density and rheology, drillstring rotation speed, etc.). A non-sparse normalized reward structure was formulated as a function of the state and action values. Hydraulics, cuttings transport, and rig state detection models were integrated to build the hole cleaning digital twin. This system was then used for performance tracking and scenario simulations (with each scenario defined as a finite-horizon action sequence) on real-world oil wells. The different scenarios were compared by monitoring state–action transitions and the evolution of the reward with actions. This paper presents a novel method for setting up well construction operations as long-term finite-horizon sequential decision-making systems, and defines a way to quantify and compare different scenarios. The proper construction of such systems is a crucial step towards automating intelligent decision-making. [ABSTRACT FROM AUTHOR]

Multifamily buildings account for about half of all residential buildings in Poland and about 70% of the urban population live in them. Most of them require thermal refurbishment. Evaluation of a building's energy performance with the use of correlation models has been very rarely used in relation to buildings in Poland. This method is simple, fast and can improve the decision process on thermal modernisation. It is especially important at the pre-design stage of planned investments. This paper presents an attempt to develop correlations that could be used in the energy assessment of multifamily buildings. For this purpose a dataset containing of 29 audits of multifamily buildings was used. The statistical analysis was performed for buildings before and after planned refurbishment. Obtained relationships showed strong correlation between thermal demand for space heating or domestic hot water and ventilation airflow and thermal transmittance of windows (R2 > 0.6). After designed modernisation the number of strong correlations decreased. Energy for domestic hot water was correlated with the number of inhabitants (R2 = 0.6). Energy for space heating was correlated with the number of users and heated volume of a building. [ABSTRACT FROM AUTHOR]

The integration and management of distributed energy resources (DERs), including residential photovoltaic (PV) production, coupled with the widespread use of enabling technologies such as artificial intelligence, have led to the emergence of new tools, market models, and business opportunities. The accurate forecasting of these resources has become crucial to decision making, despite data availability and reliability issues in some parts of the world. To address these challenges, this paper proposes a deep and machine learning-based methodology for PV power forecasting, which includes XGBoost, random forest, support vector regressor, multi-layer perceptron, and LSTM-based tuned models, and introduces the ConvLSTM1D approach for this task. These models were evaluated on the univariate time-series prediction of low-volume residential PV production data across various forecast horizons. The proposed benchmarking and analysis approach considers technical and economic impacts, which can provide valuable insights for decision-making tools with these resources. The results indicate that the random forest and ConvLSTM1D model approaches yielded the most accurate forecasting performance, as demonstrated by the lowest RMSE, MAPE, and MAE across the different scenarios proposed. [ABSTRACT FROM AUTHOR]

This paper examines the issues of designing optimization tasks with the objective of ensuring the safety and continuation of transportation processes. Modelling the processes that are a consequence of a breakdown is a crucial issue enabling an increase of safety at selected stages of transport. This paper elaborates on the matter of modelling hazardous situations resulting from an uncontrolled LNG release due to a crash or damage to a ship's hull. This paper demonstrates subsequent stages of modelling along with theoretical assumptions and finally it presents the results of simulation calculations for various scenarios of LNG releases. The article shows the complexity of modelling at a time when variable atmospheric conditions occur, which hinder the planning of rescue operations in the event of an uncontrolled LNG release into the atmosphere. It needs to be remembered that making decisions in critical situations and developing proper procedures at a time when people's lives are at risk or in the face of an environmental pollution incident constitutes one of the most significant components of effective management in transport. Two LNG release scenarios are presented in this article: catastrophic rupture and leak, for which danger zone dimensions were calculated. Simulations were conducted with the use of a tool called Phast ver. 8.23 for LNG. Calculations were made in a function of variable weather conditions and for two values of Pasquill coefficients. [ABSTRACT FROM AUTHOR]

This paper proposes a new strategy for modeling predictability uncertainty in a stochastic context for decision making within a Virtual Power Plant (VPP). Modeling variable renewable energy generation is an essential step for effective VPP planning and operation. However, it is also a challenging task due to the uncertain nature of its sources. Therefore, developing tools to effectively predict these uncertainties is essential for the optimal participation of VPPs in the electricity market. The purpose of this paper is to present a novel method to model the uncertainties associated with energy dispatching in a VPP using the Unscented Transform (UT) method. The proposed algorithm minimizes the risks associated with the VPP operation in a computationally efficient and simple manner, and can be used in real-time on a power system. The proposed framework was evaluated based on an Electric Power System (EPS) model with historical data. Case studies have been performed to demonstrate the effectiveness of the proposed framework in minimizing power demand and renewable-energy-forecasting uncertainty for a VPP. [ABSTRACT FROM AUTHOR]

The high volatility of commodity prices and various problems that the energy sector has to deal with in the era of COVID-19 have significantly increased the risk of oil price changes. These changes are of the main concern of companies for which oil is the main input in the production process, and therefore oil price determines the production costs. The main goal of this paper is to discover decision rules for a buyer of American WTI (West Texas Intermediate) crude oil call options. The presented research uses factors characterizing the option price, such as implied volatility and option sensitivity factors (delta, gamma, vega, and theta, known as "Greeks"). The performed analysis covers the years 2008–2022 and options with an exercise period up to three months. The decision rules are discovered using association analysis and are evaluated in terms of the three investment efficiency indicators: total payoff, average payoff, and return on investment. The results show the existence of certain ranges of the analyzed parameters for which the mentioned efficiency indicators reached particularly high values. The relationships discovered and recorded in the form of decision rules can be effectively used or adapted by practitioners to support their decisions in oil price risk management. [ABSTRACT FROM AUTHOR]

In the present paper, an approach for updating the continuous stress range distribution of a welded connection of a wind turbine support structure with predicted information from strain measurements is presented. Environmental conditions, such as wind or, in offshore fields, waves and currents, in combination with rotor excitations generate cyclic stresses affecting the reliability of welded joints of the support structure over the service life. Using strain measurements, these conditions can be monitored, and the resulting stress ranges, under consideration of measurement, mechanical and material uncertainties, can be reconstructed. These stress ranges can be used as an input for updating the prior probability density function (PDF) of the stress ranges predicted by the overall dynamics and a detailed design analysis. Applying Bayesian probability theory and decision theoretical implications, the predicted posterior probability density of the stress ranges is calculated based on the design information and uncertainties. This approach is exemplified, and it is shown how the predicted stress ranges and the design stress ranges are distributed. The prior and the predicted posterior stress ranges are used for a reliability calculation for potentially entering a pre-posterior decision analysis. [ABSTRACT FROM AUTHOR]

The sorting problem in the Multi-criteria Decision Analysis (MCDA) has been used to address issues whose solutions involve the allocation of alternatives in classes. Traditional multi-criteria methods are commonly used for this task, such as ELECTRE TRI, AHP-Sort, UTADIS, PROMETHEE, GAYA, etc. While using these approaches to perform the sorting procedure, the decision-makers define profiles (thresholds) for classes to compare the alternatives within these profiles. However, most such applications are based on subjective tasks, i.e., decision-makers' expertise, which sometimes might be imprecise. To fill that gap, in this paper, a comparative analysis using the multi-criteria method ELECTRE TRI and clustering algorithms is performed to obtain an auxiliary procedure to define initial thresholds for the ELECTRE TRI method. In this proposed methodology, K-Means, K-Medoids, Fuzzy C-Means algorithms, and Bio-Inspired metaheuristics such as PSO, Differential Evolution, and Genetic algorithm for clustering are tested considering a dataset from a fundamental problem of sorting in Water Distribution Networks. The computational performances indicate that Fuzzy C-Means was more suitable for achieving the desired response. The practical contributions show a relevant procedure to provide an initial view of boundaries in multi-criteria sorting methods based on the datasets from specific applications. Theoretically, it is a new development to pre-define the initial limits of classes for the sorting problem in multi-criteria approach. [ABSTRACT FROM AUTHOR]

Low-permeability reservoirs have strong heterogeneity, and the production prediction based on traditional seepage model is not accurate enough. The dynamic capillary-force seepage model can characterize the dynamic heterogeneity of seepage and more accurately describe the oil–water flow process. In this paper, the calculation formula of the dynamic capillary force is obtained through a real low-permeability core experiment, and the seepage model of dynamic capillary force is established. Based on the model, the authors quantitatively study the effects of formation pressure, heterogeneity and production speed on dynamic capillary force through numerical solutions. It is found that compared with the traditional static capillary-force seepage model, the dynamic capillary-force seepage model makes the predicted water cut increase and the recovery factor decrease. With the increase in development time, formation pressure and production rate will make the effect of dynamic capillary force more obvious. According to the comparison of heterogeneous reservoir models, results show that the horizontal heterogeneity will strengthen the dynamic capillary-force effect, while the vertical heterogeneity will weaken the dynamic capillary-force effect. In the range of research parameters, the recovery ratio predicted by the dynamic capillary-force seepage model can be reduced by 4.7%. A new oil–water seepage model is proposed, which can characterize the spatial difference and dynamic change of low-permeability reservoirs with time. It is of great significance for describing the remaining oil distribution of low-permeability reservoirs in detail and making decisions on efficient EOR measures. [ABSTRACT FROM AUTHOR]

Fuzzy control is widely used in linear motor servo systems. However, simple fuzzy rules reduce the control accuracy of the servo system, while complex fuzzy rules reduce the speed of its decision making. This paper proposes a fitted adaptive fuzzy controller (FAFC) to improve the speed tracking performance of a coreless linear motor servo system. The FAFC took a planned curve as a motion target. The planned curve is compounded by multiple performance curves of fuzzy control under the same given input. These multiple performance curves cover the variation range of motor parameters, which are offline-obtained. The performance of the planned curve is ensured by the multiple offline performance curves. The FAFC only needs simple fuzzy rules to fit the planned curve, and achieves high control accuracy without affecting the decision speed. The experimental results verified the feasibility of an FAFC. This research shows that an FAFC can effectively shorten the online calculation time of complex algorithms and keep the consistency of performance. [ABSTRACT FROM AUTHOR]

Enterprises operating in the energy and heating (E&H) sector play a particularly important role in the economy of each country. At the same time, the conditions in which they currently operate mean that the managers of these organizations have many decision-making problems that they have to deal with. They can be supported by the introduction of well-functioning controlling. This forces scientists to conduct extended research aimed at determining the current and future directions of development of controlling in E&H sector enterprises. At the moment, this is not a frequent field of research exploration. The area of research concerns issues related to the use of controlling in E&H sector enterprises. The objective of this paper is to present the results of the research obtaining knowledge on to what extent companies in the energy and heating sector use controlling tools helping them to increase efficiency of enterprises and effectiveness of the decisions made by managers. The scientific problem is looking for a way in which controlling can increase the efficiency of enterprises in the E&H sector and how it can improve the effectiveness of decisions made by managers. The general conclusion of the research is that it seems necessary to strengthen the role of controlling aimed at its transformation from reporting controlling to management controlling. In view of the challenges of the global economy related to the energy crisis, controlling should be used to a greater extent in the E&H industry to increase the efficiency of basic processes and to effectively implement modern management tools. [ABSTRACT FROM AUTHOR]

Nowadays, the integration of multi-energy carriers is one of the most critical matters in smart energy systems with the aim of meeting sustainable energy development indicators. Hydrogen is referred to as one of the main energy carriers in the future energy industry, but its integration into the energy system faces different open challenges which have not yet been comprehensively studied. In this paper, a novel day-ahead scheduling is presented to reach the optimal operation of a hydrogen-based energy hub, based on a stochastic multi-attribute decision-making approach. In this way, the energy hub model is first developed by providing a detailed model of Power-to-Hydrogen (P2H) facilities. Then, a new multi-objective problem is given by considering the prosumer's role in the proposed energy hub model as well as the integrated demand response program (IDRP). The proposed model introduces a comprehensive approach from the analysis of the historical data to the final decision-making with the aim of minimizing the system operation cost and carbon emission. Moreover, to deal with system uncertainty, the scenario-based method is applied to model the renewable energy resources fluctuation. The proposed problem is defined as mixed-integer non-linear programming (MINLP), and to solve this problem, a simple augmented e-constrained (SAUGMECON) method is employed. Finally, the simulation of the proposed model is performed on a case study and the obtained results show the effectiveness and benefits of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper proposes a new peer-to-peer (P2P) energy trading method between energy sellers and consumers in a community based on multi-agent reinforcement learning (MARL). Each user of the community is treated as a smart agent who can choose the amount and the price of the electric energy to sell/buy. There are two aspects we need to examine: the profits for the individual user and the utility for the community. For a single user, we consider that they want to realise both a comfortable living environment to enhance happiness and satisfaction by adjusting usage loads and certain economic benefits by selling the surplus electric energy. Taking the whole community into account, we care about the balance between energy sellers and consumers so that the surplus electric energy can be locally absorbed and consumed within the community. To this end, MARL is applied to solve the problem, where the decision making of each user in the community not only focuses on their own interests but also takes into account the entire community's welfare. The experimental results prove that our method is profitable both both the sellers and buyers in the community. [ABSTRACT FROM AUTHOR]

This paper presents the application of multi-criteria decision-making (MCDM) for evaluating what technologies using renewable energy sources (RES) for electricity production have the chance to develop in Poland under the current socio-economic conditions. First, the Analytical Hierarchy Process (AHP) method was used to determine the weights of the optimization criteria. Five main criteria and 30 sub-criteria were identified. Next, the authors modified numerical taxonomy (NT) to rank eight RES technologies (such as onshore and offshore wind farms, photovoltaics, or biogas plants). The results show that offshore wind farms are the RES technology with the greatest development opportunities in Poland. The following three technologies: distributed photovoltaic energy, biogas plants, and biomass power plants, respectively, received a similar rating in the ranking. Hydropower and geothermal were the lowest-ranked technologies. The ranking, which is the result of multi-criteria analysis, in several respects, is significantly different from the directions of activities indicated in the state energy policy. [ABSTRACT FROM AUTHOR]

The energy storage is an important character for sustainable energy structures and the prospective future economy. This paper aims to propose a multi-attribute decision analysis (MADA) approach to prioritize and choose the energy storage system (ESS) alternatives in terms of the different technical, economic, environmental and social aspects of them. In this line, an integrated approach is developed with the combination of intuitionistic fuzzy sets (IFSs), a method using the removal effects of criteria (MEREC), rank sum (RS) and the measurement of the alternatives and ranking based on compromise solution (MARCOS) methods for prioritizing the ESSs. The IF-MEREC-RS was used to find the integrated weight by combining the objective and subjective weights of the different indicators for prioritizing the ESSs. The MARCOS method was implemented to rank the various ESSs over several crucial indicators of sustainability. The practical outcome illustrates that the Li-ion battery (LIB) is the best ESS among all of the options, and this is followed by NaSB and NiMHB. A sensitivity investigation with the diverse weights of the indicators shows the impact of the risk preferences on an alternative prioritization. A comparison is discussed with the outcomes of the different presented, extant approaches to certify the superiority of the presented approach. [ABSTRACT FROM AUTHOR]

Car-sharing systems, i.e., automatic, short-time car rentals, are among the solutions of the new mobility concept, which in recent years has gained popularity around the world. With the growing interest in services in society, their demands for the services offered to them have also increased. Since cars play a key role in car-sharing services, the fleet of vehicles should be properly adapted to the needs of customers using the systems. Due to the literature gap related to the procedure of proper selection of vehicles for car sharing and the market need for car-sharing service operators, this work has been devoted to the selection of car models for car sharing from the perspective of users constantly using the systems (regular users). This paper considered the case of the Polish who are constantly using car-sharing service systems. Vehicle selection was classified as a multi-faceted, complex problem, which is why one of the ELECTRE III multi-criteria decision support methods was used for this study. This study focused on the classification of vehicles from the user's perspective. Twelve modern and most popular car models in 2021 with internal combustion, electric and hybrid engines were considered. The results indicate that the best choice from the point of view of regular customers is large cars (representing vehicle classes C and D), with a large luggage compartment capacity, the highest possible ratio of engine power to vehicle weight, and the ratio of engine power to energy consumption. Importantly, small urban vehicles, which ideologically should be associated with car-sharing services due to occupying as little urban space as possible, were classified as the worst in the ranking. The results support car-sharing operators during the process of completing or upgrading their vehicle fleets. [ABSTRACT FROM AUTHOR]

The classification of hidden dangers in urban gas pipelines plays a vital role in the smooth operation of urban gas pipelines and in solving the problem of hidden safety dangers in urban gas pipelines. In recent years, the number and proportion of polyethylene (PE) pipelines in urban gas pipelines are increasing day by day, but the current classification of hidden dangers in urban gas pipelines is still based on steel pipelines, and the classification method is highly subjective. Therefore, this paper proposes an improved PLC method that integrates the use of a risk matrix and compensation coefficient to solve the problem of grading the hidden dangers of PE pipelines of urban gas. The improved PLC method is based on the failure database of urban gas PE pipelines to obtain the vulnerability and severity of consequences when determining the initial level of hidden dangers, and the compensation coefficient is modified according to regional vulnerability, ease of rectification, condition around the pipeline, positioning technology, leak detection technology, and emergency ability, which can effectively reduce the subjectivity of hidden danger classification. Using the improved PLC method to classify urban gas pipelines for hidden dangers can provide pipeline operating companies with a basis for decision making in the process of hidden danger disposal and effectively reduce pipeline safety risks. [ABSTRACT FROM AUTHOR]

Strategic Environmental Assessment (SEA) is one of the most important instruments for directing the strategic planning process toward the sustainable development goals in various areas of human activity. This also applies to the field of waste management. By applying SEA in waste management planning, it is possible to see the benefits and consequences of the proposed changes in space that will occur during the implementation of strategic planning concepts and based on that make appropriate decisions respecting the capacity of the space where the planned activities are implemented. The paper presents the application of SEA for the National Waste Management Program with all its spatial, organizational, energy, environmental, and other solutions, and the way they are included in the specific method of multicriteria evaluation in SEA. The specificity of the methodological approach indicates the need for equal consideration of environmental and socio-economic aspects of development and a clear presentation of the results obtained in order to make optimal decisions in waste management planning at the national level. The National Waste Management Program in Serbia, which legislation in the field of environmental protection and waste management is harmonized with EU legislation and directives, was chosen for the case study. The obtained results indicate the importance of an interdisciplinary approach in the evaluation of strategic solutions in the field of waste management, which is achieved by specific choice of environmental and socio-economic SEA goals and indicators as a basis for valorization of the proposed concept of waste management. [ABSTRACT FROM AUTHOR]

This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. Then, the analytical solutions to sequential optimization for production-sales-stock decision-making issues are presented by using the inverse inference method in dynamic programming. Finally, the impact of price and demand prediction of refined oil products on sequential optimization for production-sales-stock decision-making are analyzed using a numerical analysis method. Numerical results demonstrated the significant impact of forecasting results of price and demand of refined oil products on sequential optimization decision-making, indicating that the prediction-driven sequential optimization methodology can be used as an effective tool for joint decision-making of production-sales-stock. [ABSTRACT FROM AUTHOR]

In the building operation phase, the Heating, Ventilation, and Air-Conditioning (HVAC) equipment are the main contributors to excessive energy consumption unless proper design and maintenance is carried out. Moreover, HVAC problems might have an impact on occupants' discomfort in thermal comfort. Hence, the identification of the root cause of HVAC problems is imperative for facility managers to plan preventive and corrective maintenance actions. However, due to the complex interaction between various equipment and the lack of data integration among Facility Management (FM) systems, they fail to provide necessary information to identify the root cause of HVAC problems. Building Information Modelling (BIM) is a potential solution for maintenance activities to address the challenges of information reliability and interoperability. Therefore, this paper presents a novel conceptual model and user-centric framework to determine the causes of HVAC problems implemented in BIM for its visualization. CMMS and BMS data were integrated into BIM and utilized by the framework to analyze the root cause of HVAC problems. A case study in a university building was used to demonstrate the applicability of the approach. This framework assists the FM team to determine the most probable cause of an HVAC problem, reducing the time to detect equipment faults, and providing potential actions to solve them. [ABSTRACT FROM AUTHOR]

In order to make reasonable production-sales-stock decision-making for gasoline production enterprises, it is necessary to make an accurate prediction of the gasoline demand. However, gasoline demand is often affected by many factors, which makes it very difficult to predict. Therefore, this paper tries to construct a trend- and periodicity-trait-driven decomposition-ensemble forecasting model in terms of trend and periodicity characteristics of gasoline demand data. In order to verify the effectiveness of the proposed model, the demand data of a typical gasoline product-93# gasoline in China, is used. The empirical results show that the proposed trend- and periodicity-trait-driven decomposition-ensemble forecasting model can achieve better prediction results than the single models, indicating that the proposed methodology can be used as a feasible solution to predict the gasoline demand series with trend and periodicity traits. [ABSTRACT FROM AUTHOR]

To make a correct decision during normal and transient states, the signal processing for relay protection must be completed and designated the correct task within the shortest given duration. This paper proposes to solve a dc offset fault current phasor with harmonics and noise based on a Deep Neural Network (DNN) autoencoder stack. The size of the data window was reduced to less than one cycle to ensure that the correct offset is rapidly computed. The effects of different numbers of the data samples per cycle are discussed. The simulations revealed that the DNN autoencoder stack reduced the size of the data window to approximately 90% of a cycle waveform, and that DNN performance accuracy depended on the number of samples per cycle (32, 64, or 128) and the training dataset used. The fewer the samples per cycle of the training dataset, the more training was required. After training using an adequate dataset, the delay in the correct magnitude prediction was better than that of the partial sums (PSs) method without an additional filter. Similarly, the proposed DNN outperformed the DNN-based full decay cycle dc offset in the case of converging time. Taking advantage of the smaller DNN size and rapid converging time, the proposed DNN could be launched for real-time relay protection and centralized backup protection. [ABSTRACT FROM AUTHOR]

This paper summarizes the experience gained in the application of multi-criteria decision making and uncertainty treatment methods to a comparative assessment of nuclear energy systems and related nuclear fuel cycles. These judgment measures provide a means for comprehensive evaluation according to different conflicting criteria, such as costs, benefits and risks, which are inevitably associated with the deployment of advanced technologies. Major findings and recommendations elaborated in international and national projects and studies are reviewed and discussed. A careful analysis is performed for multi-criteria comparative assessment of nuclear energy systems and nuclear fuel cycles on the basis of various evaluation and screening results. The purpose of this paper is to discuss the lessons learned, to share the identified solutions, and indicate promising future directions. [ABSTRACT FROM AUTHOR]

The exploitation potential of shallow geothermal energy is usually defined in terms of site-specific ground thermal characteristics. While true, this assumption limits the complexity of the analysis, since feasibility studies involve many other components that must be taken into account when calculating the effective market viability of a geothermal technology or the economic value of a shallow geothermal project. In addition, the results of a feasibility study are not simply the sum of the various factors since some components may be conflicting while others will be of a qualitative nature only. Different approaches are therefore needed to evaluate the suitability of an area for shallow geothermal installation. This paper introduces a new GIS platform-based multicriteria decision analysis method aimed at comparing as many different shallow geothermal relevant factors as possible. Using the Analytic Hierarchic Process Tool, a geolocalized Suitability Index was obtained for a specific technological case: the integrated technologies developed within the GEOTeCH Project. A suitability map for the technologies in question was drawn up for Europe. [ABSTRACT FROM AUTHOR]

The paper presents a decision-making algorithm that has been developed for the optimum size and placement of distributed generation (DG) units in distribution networks. The algorithm that is very flexible to changes and modifications can define the optimal location for a DG unit (of any type) and can estimate the optimum DG size to be installed, based on the improvement of voltage profiles and the reduction of the network's total real and reactive power losses. The proposed algorithm has been tested on the IEEE 33-bus radial distribution system. The obtained results are compared with those of earlier studies, proving that the decision-making algorithm is working well with an acceptable accuracy. The algorithm can assist engineers, electric utilities, and distribution network operators with more efficient integration of new DG units in the current distribution networks. [ABSTRACT FROM AUTHOR]

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices. [ABSTRACT FROM AUTHOR]

Given the important role of the carbon market in fighting against global warming, the impact of information on the efficiency of the scheme is a critical issue for both its designers and the central authority. At least two aspects of information are worthy of attention. First is the incompleteness of information, with which the firms make decisions to minimize their abatement cost and maximize their profit. Second is the mechanism of information transmission. Based on an agent-based framework we established in our previous work, we explicitly depicted the first aspect and analysed its impact on firms' decision-making and consequent market results. In this paper, we focus on the second aspect, transmission mechanism of information, which is depicted as an observation network among firms. The basis in reality is that the firms in the carbon market are usually from different industries or areas, and it is relatively easier to observe the conditions of firms from the same industry or area, corresponding to neighbours in the network. Four scenarios are considered, including no network, regular network, random network, and small-world network. We find that the existence of an observation network has a significant influence on the market results. [ABSTRACT FROM AUTHOR]

The effects of urban transport are highly concerning. The rapid urbanization and motorization in smart cities have a huge impact on sustainability. The goal of the paper is to analyse the smart cities selected, in terms of the urban transport. This paper presents an overview of research works published between 1991 and 2020 concerning urban transport and MCDM (multi-criteria decision making). The author highlights the importance of decision-making criteria and their weight, as well as techniques. Seven criteria and forty-four objects were used as the input of the approach. The entropy weight method was used to compute the weight of each criterion. The TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) was applied to calculate the assessment and ranking of transport performance for each smart city. Portland was found to be the best location for transport enterprises and projects; Tbilisi was ranked last. The values of the relative closeness coefficient ranged from 0.03504 to 0.921402. Finally, some suggestions for future research are discussed. [ABSTRACT FROM AUTHOR]

The long-term impact of high-energy consumption in the manufacturing sector results in adverse environmental effects. Energy consumption and carbon emission prediction in the production environment is an essential requirement to mitigate climate change. The aim of this paper is to evaluate, model, construct, and validate the electricity generated data errors of an automotive component manufacturing company in South Africa for prediction of future transport manufacturing energy consumption and carbon emissions. The energy consumption and carbon emission data of an automotive component manufacturing company were explored for decision making, using data from 2016 to 2018 for prediction of future transport manufacturing energy consumption. The result is an ARIMA model with regression-correlated error fittings in the generalized least squares estimation of future forecast values for five years. The result is validated with RSS, showing an improvement of 89.61% in AR and 99.1% in MA when combined and an RMSE value of 449.8932 at a confidence level of 95%. This paper proposes a model for efficient prediction of energy consumption and carbon emissions for better decision making and utilize appropriate precautions to improve eco-friendly operation. [ABSTRACT FROM AUTHOR]

This paper suggests that the efficiency of a system (decision-making unit) and its subsystem cannot be properly measured using a two-stage data envelopment analysis (DEA) model either in cooperative or non-cooperative evaluation. Indeed, the existing methods subjectively determine the status of the subsystems in the whole system. The two-stage DEA models, either cooperative game or non-cooperative game, are used to analyze the environmental efficiency. However, when the actual relationship between the two subsystems is inconsistent with the subjective relationship assumptions, the overall efficiency of the system and the efficiency of each subsystem will be biased. The conventional two-stage DEA models require predetermining the relationship between the subsystems within the system based on the subjective judgment of the decision-maker. Based on this, this paper proposes a three-step method to solve the two-stage DEA. First, the position relation among subsystems is determined according to the optimal weights through the model. According to the status relationship among subsystems, the decision units are grouped, and the two-stage DEA model of cooperative game or non-cooperative game is used to analyze the efficiency in each group. This method reduces the subjectivity of decision making and analyzes the efficiency of each decision unit applying the most appropriate two-stage DEA model to find the source of inefficiency. Finally, this paper verifies the rationality and validity of the method by analyzing the water use efficiency of industrial systems in China. It is found that most regions in China value economic development more than environmental protection (as evidenced by the DEA weights). What is more, the method proposed by the paper can be generalized for any two-stage DEA problem. [ABSTRACT FROM AUTHOR]

Microgrids are autonomous low-voltage power distribution systems that contain multiple distributed energy resources (DERs) and smart loads that can provide power system operation flexibility. To effectively control and coordinate multiple DERs and loads of microgrids, this paper proposes a distributed intelligent management system that employs a multi-agent-based control system so that delicate decision-making functions can be distributed to local intelligent agents. This paper presents the development of a hardware-in-the-loop simulation (HILS) system for distributed intelligent management system for microgrids and its promising application to an emergency demand response program. In the developed HILS system, intelligent agents are developed using microcontrollers and ZigBee wireless communication technology. Power system dynamic models are implemented in real-time simulation environments using the Opal-RT system. This paper presents key features of the data communication and management schemes based on multi-agent concepts. The performance of the developed system is tested for emergency demand response program applications. [ABSTRACT FROM AUTHOR]

Based on Game Theory and Multi-objective optimization problems (MOP), Game Optimization Theory (GOT) is discussed in this paper. Optimization Stability Analysis (OSA), Distance Entropy Multi-Objective Particle Swarm Optimization (DEMPSO) and Fuzzy Multi-weights Decision-making Method (FMW) are proposed as well. Game Optimization Theory, which is a comprehensive system, could not only handle multi-objective optimization problems effectively, but also could offset the disadvantages of traditional optimization theories, such as lack of framework and the insufficient consideration of relevant elements. In this paper GOT is used for the first time in solving the distribution systems planning (DSP) issue by implementing distributed generation. The proposed model integrates costs, losses, and voltage index to achieve optimal size and site of distributed generation. The model allows minimizing total system costs, system power losses and maximizing voltage improvement. A detailed DSP example is used for verifying the effectiveness and reasonableness of GOT in this context. [ABSTRACT FROM AUTHOR]