MOEA/D-Based Probabilistic PBI Approach for Risk-Based Optimal Operation of Hybrid Energy System With Intermittent Power Uncertainty

Zhang, Huifeng; Yue, Dong; Yue, Wenbin; Li, Kang; Yin, Mingjia

doi:10.1109/tsmc.2019.2931636

Cited by 24 publications

(8 citation statements)

References 35 publications

(33 reference statements)

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“…(4) Economic cost: Since renewable energy can be considered with no power generation cost, economic cost can be made merely by CHP generators. The economic cost can be described as [26]:…”

Section: A Multiple Indexes Of Interconnected Micro-gridsmentioning

confidence: 99%

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Resilient Optimal Defensive Strategy of TSK Fuzzy-Model-Based Microgrids’ System via a Novel Reinforcement Learning Approach

Zhang

Yue

Dou

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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With consideration of false data injection (FDI) on demand side, it brings great challenge for optimal defensive strategy with security issue, voltage stability, power flow and economic cost indexes. This paper proposes a Takagi-Sugeuo-Kang (TSK) fuzzy system based reinforcement learning approach for resilient optimal defensive strategy of interconnected micro-grids. Due to FDI uncertainty of system load, TSK based deep deterministic policy gradient (DDPG) is proposed to learn actor network and critic network, where multiple indexes assessment occurs in critic network and security switching control strategy is made in actor network. Alternating direction method of multipliers (ADMM) method is improved for policy gradient with on-line coordination between actor network and critic network learning, and its convergence and optimality are proved properly. On the basis of security switching control strategy, penalty-based boundary intersection (PBI) based multi-objective optimization method is utilized to solve economic cost and emission issue simultaneously with considering voltage stability, rate-of-change of frequency (RoCoF) limits. According to simulation results, it reveals that the proposed resilient optimal defensive strategy can be a viable and promising alternative for tackling uncertain attack problem on inter-connected micro-grids.

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“…(4) Economic cost: Since renewable energy can be considered with no power generation cost, economic cost can be made merely by CHP generators. The economic cost can be described as [26]:…”

Section: A Multiple Indexes Of Interconnected Micro-gridsmentioning

confidence: 99%

“…(5) Emission rate: Since power generation of CHP generator can produce emission pollutant, which can affect social lives. Hence, emission rate is taken as another index as [26]:…”

Section: A Multiple Indexes Of Interconnected Micro-gridsmentioning

confidence: 99%

Resilient Optimal Defensive Strategy of TSK Fuzzy-Model-Based Microgrids’ System via a Novel Reinforcement Learning Approach

Zhang

Yue

Dou

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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“…• Power systems: The planning, operation, and control problems in power systems are challenging optimization problems given their large-scale, complex and geographically widely distributed characteristics. MOEA/D and its variants has been applied to the planning problem of electric vehicle charging stations and power distribution system [268], electric motor design [269], optimal operation of hybrid energy systems [270], power system voltage stability design [271] and optimal power flow optimization [272].…”

Section: Applications To Engineering Optimizationmentioning

confidence: 99%

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

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Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including weight vector settings, subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments.

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“…The nature of multi-objective evolutionary algorithms (MOEAs) enable them to achieve excellent performance when dealing with multi-objective optimization problems. Common examples of real-world multiobjective optimization problems include multi-objective op-timal control of urban wastewater treatment processes [12], [13], teaching manipulator [14], barrier coverage with wireless sensors [15], image feature extraction in the presence of noise [16], the design of a trauma system [17], and risk-based optimal operation of a hybrid energy system [18].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

Xie

Yang

Wang

et al. 2022

IEEE Trans. Evol. Computat.

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The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has attained excellent performance in solving optimization problems involving multiple conflicting objectives. However, the Pareto optimal front (POF) of many multi-objective optimization problems (MOPs) has irregular properties, which weakens the performance of MOEA/D. To address this issue, we devise a dynamic transfer reference point oriented MOEA/D with local objective-space knowledge (DTR-MOEA/D). The design principle is based on three original and rigorous mechanisms. First, the individuals are projected onto a line segment (two-objective case) or a three-dimensional plane (three-objective case) after being normalized in the objective space. The line segment or the plane is divided into three different regions: the central region, the middle region, and the edge region. Second, a dynamic transfer criterion of reference point is developed based on population density relationships in different regions. Third, a strategy of population diversity enhancement guided by local objective-space knowledge is adopted to improve the diversity of the population. Finally, the experimental results conducted on sixteen benchmark MOPs and eight modified MOPs with irregular POF shapes verify that the proposed DTR-MOEA/D has attained a strong competitiveness compared with other representative algorithms.

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MOEA/D-Based Probabilistic PBI Approach for Risk-Based Optimal Operation of Hybrid Energy System With Intermittent Power Uncertainty

Cited by 24 publications

References 35 publications

Resilient Optimal Defensive Strategy of TSK Fuzzy-Model-Based Microgrids’ System via a Novel Reinforcement Learning Approach

Resilient Optimal Defensive Strategy of TSK Fuzzy-Model-Based Microgrids’ System via a Novel Reinforcement Learning Approach

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

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