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Intelligent Business Process Optimization for the Service Industry

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ISBN: 9783866444546 Year: Pages: 310 p. DOI: 10.5445/KSP/1000014466 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Business and Management
Added to DOAB on : 2019-07-30 20:01:57
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The company's sustainable competitive advantage derives from its capacity to create value for customers and to adapt the operational practices to changing situations. Business processes are the heart of each company. Therefore process excellence has become a key issue. This book introduces a novel approach focusing on the autonomous optimization of business processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm Optimization.

Complexity, Criticality and Computation (C³)

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ISBN: 9783038425144 9783038425151 Year: Pages: VI, 262 DOI: 10.3390/books978-3-03842-515-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Physics (General)
Added to DOAB on : 2017-10-02 11:37:22
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Complex systems is a new approach to science, engineering, health and management that studies how relationships between parts give rise to the collective emergent behaviours of the entire system, and how the system interacts with its environment.A system can be thought of as complex if its dynamics cannot be easily predicted, or explained, as a linear summation of the individual dynamics of its components. In other words, the many constituent microscopic parts bring about macroscopic phenomena that cannot be understood by considering a single part alone (“the whole is more than the sum of the parts”). There is a growing awareness that complexity is strongly related to criticality: the behaviour of dynamical spatiotemporal systems at an order/disorder phase transition where scale invariance prevails.Complex systems can also be viewed as distributed information-processing systems. Consciousness emerging from neuronal activity and interactions, cell behaviour resultant from gene regulatory networks and swarming behaviour are all examples of global system behaviour emerging as a result of the local interactions of the individuals (neurons, genes, animals). Can these interactions be seen as a generic computational process? This question shapes the special issue, linking computation to complexity and criticality.

Applications of Computational Intelligence to Power Systems

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ISBN: 9783039217601 9783039217618 Year: Pages: 116 DOI: 10.3390/books978-3-03921-761-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-11-08 11:31:56
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Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.

Optimization Methods Applied to Power Systems: Volume 1

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ISBN: 9783039211302 9783039211319 Year: Pages: 382 DOI: 10.3390/books978-3-03921-131-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and Mixed-Integer Programming to the most modern methods based on bio-inspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.

Keywords

Cable joint --- internal defect --- thermal probability density --- power system optimization --- optimal power flow --- developed grew wolf optimizer --- energy internet --- prosumer --- energy management --- consensus --- demand response --- day-ahead load forecasting --- modular predictor --- feature selection --- micro-phasor measurement unit --- mutual information theory --- stochastic state estimation --- two-point estimation method --- JAYA algorithm --- multi-population method (MP) --- chaos optimization algorithm (COA) --- economic load dispatch problem (ELD) --- optimization methods --- constrained parameter estimation --- extended Kalman filter --- power systems --- C&I particle swarm optimization --- ringdown detection --- optimal reactive power dispatch --- loss minimization --- voltage deviation --- hybrid method --- tabu search --- particle swarm optimization --- artificial lighting --- simulation --- calibration --- radiance --- GenOpt --- street light points --- DC optimal power flow --- power transfer distribution factors --- generalized generation distribution factors --- unit commitment --- adaptive consensus algorithm --- distributed heat-electricity energy management --- eight searching sub-regions --- islanded microgrid --- dragonfly algorithm --- metaheuristic --- optimal power flow --- particle swarm optimization --- CCHP system --- energy storage --- off-design performance --- dynamic solving framework --- battery energy storage system --- micro grid --- MILP --- PCS efficiency --- piecewise linear techniques --- renewable energy sources --- optimal operation --- UC --- demand bidding --- demand response --- genetic algorithm --- load curtailment --- optimization --- hybrid renewable energy system --- pumped-hydro energy storage --- off-grid --- optimization --- HOMER software --- rural electrification --- sub-Saharan Africa --- Cameroon --- building energy management system --- HVAC system --- energy storage system --- energy flow model --- dependability --- sustainability --- data center --- power architectures --- optimization --- AC/DC hybrid active distribution --- hierarchical scheduling --- multi-stakeholders --- discrete wind driven optimization --- multiobjective optimization --- optimal power flow --- metaheuristic --- wind energy --- photovoltaic --- smart grid --- transformer-fault diagnosis --- principal component analysis --- particle swarm optimization --- support vector machine --- wind power --- integration assessment --- interactive load --- considerable decomposition --- controllable response --- SOCP relaxations --- optimal power flow --- current margins --- affine arithmetic --- interval variables --- optimizing-scenarios method --- power flow --- wind power --- active distribution system --- virtual power plant --- stochastic optimization --- decentralized and collaborative optimization --- genetic algorithm --- multi-objective particle swarm optimization algorithm --- artificial bee colony --- IEEE Std. 80-2000 --- Schwarz’s equation --- fuzzy algorithm --- radial basis function --- neural network --- ETAP --- distributed generations (DGs) --- distribution network reconfiguration --- runner-root algorithm (RRA) --- inter-turn shorted-circuit fault (ISCF) --- strong track filter (STF) --- linear discriminant analysis (LDA) --- switched reluctance machine (SRM) --- charging/discharging --- electric vehicle --- energy management --- genetic algorithm --- intelligent scatter search --- electric vehicles --- heterogeneous networks --- demand uncertainty --- power optimization --- Stackelberg game --- power system unit commitment --- hybrid membrane computing --- cross-entropy --- the genetic algorithm based P system --- the biomimetic membrane computing --- transient stability --- two-stage feature selection --- particle encoding method --- fitness function --- power factor compensation --- non-sinusoidal circuits --- geometric algebra --- evolutionary algorithms --- electric power contracts --- electric energy costs --- cost minimization --- evolutionary computation --- bio-inspired algorithms --- congestion management --- low-voltage networks --- multi-objective particle swarm optimization --- affinity propagation clustering --- optimal congestion threshold --- optimization --- magnetic field mitigation --- overhead --- underground --- passive shielding --- active shielding --- MV/LV substation --- n/a

Optimization Methods Applied to Power Systems: Volume 2

Authors: ---
ISBN: 9783039211562 9783039211579 Year: Pages: 306 DOI: 10.3390/books978-3-03921-157-9 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-08-28 11:21:27
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This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and Mixed-Integer Programming to the most modern methods based on bio-inspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.

Keywords

Cable joint --- internal defect --- thermal probability density --- power system optimization --- optimal power flow --- developed grew wolf optimizer --- energy internet --- prosumer --- energy management --- consensus --- demand response --- day-ahead load forecasting --- modular predictor --- feature selection --- micro-phasor measurement unit --- mutual information theory --- stochastic state estimation --- two-point estimation method --- JAYA algorithm --- multi-population method (MP) --- chaos optimization algorithm (COA) --- economic load dispatch problem (ELD) --- optimization methods --- constrained parameter estimation --- extended Kalman filter --- power systems --- C&I particle swarm optimization --- ringdown detection --- optimal reactive power dispatch --- loss minimization --- voltage deviation --- hybrid method --- tabu search --- particle swarm optimization --- artificial lighting --- simulation --- calibration --- radiance --- GenOpt --- street light points --- DC optimal power flow --- power transfer distribution factors --- generalized generation distribution factors --- unit commitment --- adaptive consensus algorithm --- distributed heat-electricity energy management --- eight searching sub-regions --- islanded microgrid --- dragonfly algorithm --- metaheuristic --- optimal power flow --- particle swarm optimization --- CCHP system --- energy storage --- off-design performance --- dynamic solving framework --- battery energy storage system --- micro grid --- MILP --- PCS efficiency --- piecewise linear techniques --- renewable energy sources --- optimal operation --- UC --- demand bidding --- demand response --- genetic algorithm --- load curtailment --- optimization --- hybrid renewable energy system --- pumped-hydro energy storage --- off-grid --- optimization --- HOMER software --- rural electrification --- sub-Saharan Africa --- Cameroon --- building energy management system --- HVAC system --- energy storage system --- energy flow model --- dependability --- sustainability --- data center --- power architectures --- optimization --- AC/DC hybrid active distribution --- hierarchical scheduling --- multi-stakeholders --- discrete wind driven optimization --- multiobjective optimization --- optimal power flow --- metaheuristic --- wind energy --- photovoltaic --- smart grid --- transformer-fault diagnosis --- principal component analysis --- particle swarm optimization --- support vector machine --- wind power --- integration assessment --- interactive load --- considerable decomposition --- controllable response --- SOCP relaxations --- optimal power flow --- current margins --- affine arithmetic --- interval variables --- optimizing-scenarios method --- power flow --- wind power --- active distribution system --- virtual power plant --- stochastic optimization --- decentralized and collaborative optimization --- genetic algorithm --- multi-objective particle swarm optimization algorithm --- artificial bee colony --- IEEE Std. 80-2000 --- Schwarz’s equation --- fuzzy algorithm --- radial basis function --- neural network --- ETAP --- distributed generations (DGs) --- distribution network reconfiguration --- runner-root algorithm (RRA) --- inter-turn shorted-circuit fault (ISCF) --- strong track filter (STF) --- linear discriminant analysis (LDA) --- switched reluctance machine (SRM) --- charging/discharging --- electric vehicle --- energy management --- genetic algorithm --- intelligent scatter search --- electric vehicles --- heterogeneous networks --- demand uncertainty --- power optimization --- Stackelberg game --- power system unit commitment --- hybrid membrane computing --- cross-entropy --- the genetic algorithm based P system --- the biomimetic membrane computing --- transient stability --- two-stage feature selection --- particle encoding method --- fitness function --- power factor compensation --- non-sinusoidal circuits --- geometric algebra --- evolutionary algorithms --- electric power contracts --- electric energy costs --- cost minimization --- evolutionary computation --- bio-inspired algorithms --- congestion management --- low-voltage networks --- multi-objective particle swarm optimization --- affinity propagation clustering --- optimal congestion threshold --- optimization --- magnetic field mitigation --- overhead --- underground --- passive shielding --- active shielding --- MV/LV substation --- n/a

Swarm Robotics

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ISBN: 9783038979227 9783038979234 Year: Pages: 310 DOI: 10.3390/books978-3-03897-923-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-06-26 08:44:06
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Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties:

Keywords

3D model identification --- shape normalization --- weighted implicit shape representation --- panoramic view --- scale-invariant feature transform --- optimization --- meta-heuristic --- parallel technique --- Swarm intelligence algorithm --- artificial flora (AF) algorithm --- bionic intelligent algorithm --- particle swarm optimization --- artificial bee colony algorithm --- swarm robotics --- search --- surveillance --- behaviors --- patterns --- comparison --- swarm behavior --- Swarm Chemistry --- self-organization --- asymmetrical interaction --- genetic algorithm --- cooperative target hunting --- multi-AUV --- improved potential field --- surface-water environment --- signal source localization --- multi-robot system --- event-triggered communication --- consensus control --- time-difference-of-arrival (TDOA) --- Cramer–Rao low bound (CRLB) --- optimal configuration --- UAV swarms --- path optimization --- multiple robots --- formation --- sliding mode controller --- nonlinear disturbance observer --- system stability --- formation control --- virtual structure --- formation reconfiguration --- multi-agents --- robotics --- unmanned aerial vehicle --- swarm intelligence --- particle swarm optimization --- search algorithm --- underwater environment --- sensor deployment --- event-driven coverage --- fish swarm optimization --- congestion control --- modular robots --- self-assembly robots --- environmental perception --- target recognition --- autonomous docking --- formation control --- virtual linkage --- virtual structure --- formation reconfiguration --- mobile robots --- robotics --- swarm robotics --- formation control --- coordinate motion --- obstacle avoidance --- n/a

Evolutionary Computation

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ISBN: 9783039219285 9783039219292 Year: Pages: 424 DOI: 10.3390/books978-3-03921-929-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,

Keywords

artificial bee colony algorithm (ABC) --- cloud model --- normal cloud model --- Y conditional cloud generator --- global optimum --- evolution --- computation --- urban design --- biology --- shape grammar --- architecture --- SPEA 2 --- energy-efficient job shop scheduling --- dispatching rule --- nonlinear convergence factor --- mutation operation --- whale optimization algorithm --- particle swarm optimization --- confidence term --- random weight --- benchmark functions --- t-test --- success rates --- average iteration times --- set-union knapsack problem --- moth search algorithm --- transfer function --- discrete algorithm --- evolutionary multi-objective optimization --- convergence point --- acceleration search --- evolutionary computation --- optimization --- bat algorithm (BA) --- bat algorithm with multiple strategy coupling (mixBA) --- CEC2013 benchmarks --- Wilcoxon test --- Friedman test --- facility layout design --- single loop --- monarch butterfly optimization --- slicing tree structure --- material handling path --- integrated design --- wireless sensor networks (WSNs) --- DV-Hop algorithm --- multi-objective DV-Hop localization algorithm --- NSGA-II-DV-Hop --- first-arrival picking --- fuzzy c-means --- particle swarm optimization --- range detection --- minimum total dominating set --- evolutionary algorithm --- genetic algorithm --- local search --- constrained optimization problems (COPs) --- evolutionary algorithms (EAs) --- firefly algorithm (FA) --- stochastic ranking (SR) --- Artificial bee colony --- swarm intelligence --- elite strategy --- dimension learning --- global optimization --- DE algorithm --- ?-Hilbert space --- topology structure --- quantum uncertainty property --- numerical simulation --- whale optimization algorithm --- flexible job shop scheduling problem --- nonlinear convergence factor --- adaptive weight --- variable neighborhood search --- elephant herding optimization --- EHO --- swarm intelligence --- individual updating strategy --- large-scale --- benchmark --- diversity maintenance --- particle swarm optimizer --- entropy --- large scale optimization --- minimum load coloring --- memetic algorithm --- evolutionary --- local search --- particle swarm optimization --- large-scale optimization --- adaptive multi-swarm --- diversity maintenance --- deep learning --- convolutional neural network --- rock types --- automatic identification --- monarch butterfly optimization --- greedy optimization algorithm --- global position updating operator --- 0-1 knapsack problems

Computational Intelligence in Photovoltaic Systems

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ISBN: 9783039210985 9783039210992 Year: Pages: 180 DOI: 10.3390/books978-3-03921-099-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 16:10:12
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Photovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems. Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems. This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields: Forecasting techniques (deterministic, stochastic, etc.); DC/AC converter control and maximum power point tracking techniques; Sizing and optimization of photovoltaic system components; Photovoltaics modeling and parameter estimation; Maintenance and reliability modeling; Decision processes for grid operators.

Distributed Energy Resources Management 2018

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ISBN: 9783039281701 9783039281718 Year: Pages: 286 DOI: 10.3390/books978-3-03928-171-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering --- Technology (General)
Added to DOAB on : 2020-01-30 16:39:46
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The Special Issue Distributed Energy Resources Management 2018 includes 13 papers, and is a continuation of the Special Issue Distributed Energy Resources Management. The success of the previous edition shows the unquestionable relevance of distributed energy resources in the operation of power and energy systems at both the distribution level and at the wider power system level. Improving the management of distributed energy resources makes it possible to accommodate the higher penetration of intermittent distributed generation and electric vehicle charging. Demand response programs, namely the ones with a distributed nature, allow the consumers to contribute to the increased system efficiency while receiving benefits. This book addresses the management of distributed energy resources, with a focus on methods and techniques to achieve an optimized operation, in order to aggregate the resources namely in the scope of virtual power players and other types of aggregators, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as an enabler for their increased and efficient use.

Keywords

clustering --- demand Response --- distributed generation --- smart grids --- demand-side management --- multi-agent system --- distributed coordination --- distributed energy resources --- swarm intelligence --- virtual power plant --- distributed energy resources --- multi-agent technology --- bidding strategy --- stackelberg dynamic game --- aggregator --- distribution system operator --- distributed energy resources --- local flexibility market --- flexibility service --- distributed energy --- comprehensive benefits --- multi-agent synergetic estimation --- synergistic optimization strategy --- control system --- fault-tolerant control --- algorithm design and analysis --- IoT (Internet of Things) --- nonlinear control --- optimization --- DSM --- microgrid --- solar --- wind --- teaching-learning --- microgrid --- energy storage system --- distributed generator --- frequency control --- active power control --- autonomous control --- droop control --- frequency bus-signaling --- batteries --- energy storage --- microgrids --- optimal scheduling --- particle swarm optimization --- power system management --- smart grid --- supply and demand --- trade agreements --- low voltage networks --- multi-period optimal power flow --- multi-temporal optimal power flow --- active distribution networks --- unbalanced networks --- indoor environment quality --- occupant comfort --- building climate control --- healthy building --- energy efficiency --- adaptability --- decentralized energy management system --- local energy trading --- multi-agent system --- optimization --- smart grid --- demand response --- distributed generation --- particle swarm optimization --- prosumer --- n/a

Distributed Energy Resources Management

Author:
ISBN: 9783038977186 Year: Pages: 236 DOI: 10.3390/books978-3-03897-719-3 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Physics (General) --- Science (General)
Added to DOAB on : 2019-03-21 15:50:41
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At present, the impact of distributed energy resources in the operation of power and energy systems is unquestionable at the distribution level, but also at the whole power system management level. Increased flexibility is required to accommodate intermittent distributed generation and electric vehicle charging. Demand response has already been proven to have a great potential to contribute to an increased system efficiency while bringing additional benefits, especially to the consumers. Distributed storage is also promising, e.g., when jointly used with the currently increasing use of photovoltaic panels. This book addresses the management of distributed energy resources. The focus includes methods and techniques to achieve an optimized operation, to aggregate the resources, namely, by virtual power players, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as a main drive for their efficient use.

Keywords

ac/dc hybrid microgrid --- adaptive droop control --- autonomous operation --- distributed generation --- energy management system --- aggregator --- optimal bidding --- electricity markets --- probabilistic programming --- microgrid --- uncertainty --- hierarchical game --- non-cooperative game (NCG) --- energy trading --- pricing strategy --- demand response --- distributed generation --- microgrid --- real-time simulation --- consensus algorithm --- diffusion strategy --- distributed system --- energy management system --- microgrid operation --- optimal operation --- microgrids --- renewable energy --- storage --- scheduling --- co-generation --- decision-making under uncertainty --- domestic energy management system --- energy flexibility --- interval optimization --- stochastic programming --- Unit Commitment (UC) --- Demand Response (DR) --- Demand Response Unit Commitment (DRUC) --- Cat Swarm Optimization (CSO) --- average consensus algorithm (ACA) --- black start --- local controller --- microgrid (MG) --- multi-agent system (MAS) --- power system restoration (PSR) --- demand-side energy management --- multiplier method --- Powell direction acceleration method --- advance and retreat method --- thermal comfort --- transmission line --- fault localization --- time series --- ARIMA --- discrete wavelet transformer --- demand response --- virtual power plant --- energy flexibility potential --- aggregators --- business model --- building energy flexibility --- aggregator --- clustering --- demand response --- distributed generation

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