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multi-objective reactive power optimization Based on Improved Particle Swarm optimization With ε-Greedy Strategy and Pareto Archive Algorithm

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IEEE ACCESS 2021年 9卷 65650-65659页

作者： Liu, Xiaofei Zhang, Pei Fang, Hui Zhou, Yinglu Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China State Grid Chongqing Elect Power Res Inst Chongqing 401123 Peoples R China State Grid Chongqing Elect Power Co Chongqing 401123 Peoples R China

This paper proposes combining an improved particle swarm optimization and Pareto archive algorithm to solve the multi-objective reactive power optimization problem. The idea of epsilon-greedy strategy is adopted and designed to improve particle swarm optimization algorithm. It makes some particles have stronger global search capability, meanwhile, others have stronger local search capability during the whole iteration process. Henceforth, the strategy significantly explores the possibility of optimal solution in local space at the early stage of the iteration, in addition, it mitigates the tendency to fall into the local optimal solution at the later stage of the iteration. The Pareto optimal solution selection problem is solved by minimizing the sum of the difference between each objective function and its optimal solution. The proposed approach is tested on IEEE39-bus and IEEE118-bus system, and it is demonstrated that the proposed approach not only restores the nodes voltage to the normal range and achieves better value for each objective function, but also outperforms other algorithms including standard particle swarm optimization and non-dominated sorting genetic algorithm II(NSGA-II).

关键词： multi-objective reactive power optimization particle swarm optimization epsilon-greedy strategy Pareto archive algorithm voltage control

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multi-objective reactive power optimization Based on Improved Artificial Searching Swarm Algorithm

Multi-objective Reactive Power Optimization Based on Improve...

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China International Conference on Electricity Distribution (CICED)

作者： Liu, Chao Chen, Tanggong Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300130 Peoples R China Hebei Univ Technol Key Lab Electromagnet Field & Elect Apparat Relia Tianjin 300130 Peoples R China

ISBN: (纸本)9781538667750

In order to solve the problem of multi-objective reactive power optimization, firstly the defects of current algorithms are analyzed, then an improved chaos artificial searching swarm algorithm based on artificial search is proposed. The optimization performance of the algorithm is improved by introducing chaos theory and the dynamic improvement of algorithm step. This algorithm was tested with classical test functions. The results show that the optimization ability of the artificial search swarm algorithm is significantly improved. In the MATLAB 2014b environment, ICASSA is used to optimize the simulation of the IEEE14 and IEEE30 buses of the standard power system. Comparing the performance of ICASSA, IPSO and IDE, the results show the ICASSA has better search ability. Finally, the reactive power optimization of a real power network is simulated, and the results show that the ICASSA algorithm has potential application value.

关键词： multi-objective reactive power optimization artificial searching swarm algorithm MATLAB power systems

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Application of Global Best Imperialist Competition Algorithm for multi-objective reactive power optimization

Application of Global Best Imperialist Competition Algorithm...

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Chinese Automation Congress (CAC)

作者： Chen, Gonggui Cao, Jia Zhang, Zhizhong Chongqing Univ Posts & Telecommun Coll Automat Chongqing Peoples R China Chongqing Univ Posts & Telecommun Key Lab Commun Network & Testing Technol Chongqing Peoples R China

ISBN: (纸本)9781728113128

This study proposes a multi-objective global best imperialist competition algorithm (MOGBICA) for solving two dual-objective reactive power optimization problems. Previous research has shown that imperialist competition algorithm (ICA) has certain advantages in solving optimization problems. However, the original ICA often converges to local optima. The global best imperialist country solution is proposed to enhance the searching capability of ICA. In addition, the non-dominated sorting and crowding distance methods are employed to solve multi-objective optimization problems (MOPs). The proposed approaches is studied and evaluated on the IEEE 30 bus system. The obtained results show that the MOGBICA provides better results than MOPSO and MOICA algorithm.

关键词： multi-objective global best imperialist competition algorithm multi-objective reactive power optimization Pareto optimal solutions

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Application of Global Best Imperialist Competition Algorithm for multi-objective reactive power optimization

Application of Global Best Imperialist Competition Algorithm...

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Chinese Automation Congress

作者： Gonggui Chen Jia Cao Zhizhong Zhang College of Automation Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Communication Network and Testing Technology Chongqing University of Posts and Telecommunications Chongqing China

关键词： multi-objective global best imperialist competition algorithm multi-objective reactive power optimization Pareto optimal solutions

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multi-objective reactive power optimization by Modified Artificial Fish Swarm Algorithm in IEEE 57-bus power System 6

Multi-objective Reactive Power Optimization by Modified Arti...

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IEEE PES Asia-Pacific power and Energy Engineering Conference (IEEE PES APPEEC)

作者： Liu, Shukui Han, Ying Ouyang, Yubo Li, Qi Chengdu Elect Power Bur Chengdu 610000 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

ISBN: (纸本)9781479975372

Modified Artificial Fish Swarm Algorithm (MAFSA) was proposed to optimize the reactive power optimization, which is evaluated on an IEEE 57-bus power system. MAFSA based on the global search characteristic of Artificial Fish Swarm Algorithm (AFSA) and combined with the local search of chaos optimization algorithm(COA) can avoid trapping into local minimal value and decrease the iteration numbers, which was a swarm intelligence optimization algorithm applied to continuous space. The models of multi-objective reactive power optimization were established taking the minimum active power losses, the best voltage level and the biggest static voltage stability margin as the objects, using fuzzy set theory to transform multi-objective optimization problems into a single objective optimization problem. The simulation results and the comparison results with AFSA proved that the MAFSA was able to heighten power system voltage stability during the economical operation and the algorithm can make effectively use in multi-objective reactive power optimization.

关键词： power System Modified Artificial Fish Swarm Algorithm multi-objective reactive power optimization Voltage Stability Fuzzy Set Theory Shrinked Region

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A Systematic Investigation into the optimization of reactive power in Distribution Networks Using the Improved Sparrow Search Algorithm-Particle Swarm optimization Algorithm

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ENERGIES 2024年第9期17卷 2001页

作者： Wang, Yonggang Li, Fuxian Xiao, Ruimin Zhang, Nannan Shenyang Agr Univ Coll Informat & Elect Engn Shenyang 110866 Peoples R China State Grid Huludao Elect Power Supply Co Huludao 125000 Peoples R China

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm-particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

关键词： multi-objective reactive power optimization network loss sparrow search algorithm particle swarm optimization

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reactive power optimization of Renewable Energy Base Considering reactive power Adjustment Capacity of Grid-forming Energy Storage Station 6

Reactive Power Optimization of Renewable Energy Base Conside...

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6th International Conference on Renewable Energy and power Engineering (REPE)

作者： Lin, Li Ma, Xiaohan Ding, Wenmin Cui, Hao Xu, Ning North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

ISBN: (纸本)9798350306194;9798350306187

In the renewable energy base without synchronous power support, it is difficult to meet the demand of voltage level and dynamic reactive power margin by using conventional reactive power regulation, while the grid-forming battery energy storage station (BESS) has the grid support capability similar to synchronous generator and can participate in the reactive power optimization of the new energy base as a high-quality regulating resource. This paper investigates the reactive power regulation capability of grid-forming BESS based on its control principle and grid-connected characteristics, and proposes a reactive power optimization strategy for renewable energy base, which can dynamically adjust the upper limit of reactive power regulation capacity of BESS according to the change of voltage level and BESS operation status. On this basis, a multi-objective reactive power optimization model considering the operational constraints of grid-forming BESS is constructed, and the effectiveness of the proposed model is verified based on the calculation cases.

关键词： grid-forming reactive power regulation energy storage power station renewable energy base multi-objective reactive power optimization

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optimization of reactive power using dragonfly algorithm in DG integrated distribution system

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ELECTRIC power SYSTEMS RESEARCH 2023年 220卷

作者： Singh, Himmat Sawle, Yashwant Dixit, Shishir Malik, Hasmat Marquez, Fausto Pedro Garcia MITS Gwalior Dept Elect Engn Gwalior India Univ Technol Malaysia UTM Fac Elect Engn Dept Elect Power Engn Johor Baharu 81310 Malaysia Univ Castilla La Mancha Ingenium Res Grp Ciudad Real 13071 Spain Graphic Era Deemed Be Univ Dehradun Dept Elect Engn Dehra Dun India

This study proposes, a swarm intelligence Memory based new multi-objective Dragonfly (MMOD) algorithm. Analyze to optimize active power loss, total investment on reactive power sources and total voltage variations in distribution systems. MMOD algorithm is implemented for a number of cycles repeatedly and in each cycle dragonflies are made to memorize available Pareto-optimal solutions. The memorized Pareto-optimal solutions are used as initial solutions and only the remaining swarm is reinitialized. Usefulness of the MMODA algorithm is established by solving MORPD problem in the two cases. Cases are standard IEEE-30 bus test system and another IEEE-69 bus radial distribution systems integrated with DGs and RPS units system. Comparing MORPD results for IEEE 33 bus are more suitable for power loss 11.42986 kW, voltage profile 0.094375pu and reactive power capacity $599.8718k with respective other algorithm like NSGA-II, MODE, MODA, and MDE algorithm. Similarly for IEEE-69 bus radial distribution system found power loss minimum 4.3964 kW, voltage profile 0.05474pu reactive power capacity $553.061k.

关键词： Active power loss minimization Distributed generation Memory-based multi-objective dragonfly algorithm multi-objective reactive power optimization Total voltage variations Total investment on RPS units

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Solution for multi-objective reactive power optimization Using Fuzzy Guided Tabu Search

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2012年第8期37卷 2231-2241页

作者： Stephen, D. Silas Somasundaram, P. Anna Univ Coll Engn Guindy Dept Elect & Elect Engn Madras 600025 Tamil Nadu India

A fuzzy guided tabu search (FGTS) algorithm for solving multi-objective reactive power optimization problem is presented. The problem is formulated as a non-linear constrained multi-objective problem with three competing objectives, viz., minimization of losses, cost of VAR sources, and improvement of voltage profile. The proposed algorithm is based on the combination of fuzzy logic strategy incorporated in tabu search (TS). The standard IEEE 30-bus system and practical Indian 76-bus system are used as the test systems. The results are encouraging and indicate the viability of the proposed technique. The proposed method is capable of generating diverse and well-distributed pareto-optimal solutions.

关键词： multi-objective reactive power optimization Tabu search reactive power control multi-objective optimization

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The Operation Mechanism and Benefit Analysis of Diverse Emerging Energy Storage Participating in the Electricity Market 7

The Operation Mechanism and Benefit Analysis of Diverse Emer...

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Wen, Tang Li, Yuzhong Lei, Xu Lin, Wang Li, Zhengyu State Grid Gansu Electric Power Company Lanzhou730050 China

ISBN: (纸本)9798350370782

Emerging energy storage is a critical technology for achieving carbon peak and neutrality goals, serving as a vital support for establishing a new power system predominantly based on renewable energy sources. It also facilitates the creation of a competitive and flexible resource market. With a variety of emerging energy storage technologies available, their participation in electricity markets entails diverse operational mechanisms and economic benefits. This paper explores the operational mechanisms of three typical energy storage technologies in the electricity market. Based on this exploration, it conducts a comparative analysis of the economic benefits of different energy storage technologies participating in the electricity energy market, while further considering the impact of capacity markets on the economic viability of various energy storage technologies. The study reveals that, in the absence of capacity market participation, economically mature energy storage technologies demonstrate significantly higher economic viability compared to other types of technologies. With the inclusion of capacity markets, energy storage technologies with longer duration capabilities can achieve higher economic benefits, providing valuable insights for the future development of energy storage technologies. © 2024 IEEE.

关键词： high share of new energy multi-objective reactive power optimization NSGA-III algorithm Pareto optimal solution

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