An improved particle swarm optimization algorithm for optimal reactive power dispatch

Zhao, Bo; Guo, Chuangxin; Cao, Yuchen

doi:10.1109/pes.2005.1489159

Cited by 27 publications

(21 citation statements)

References 16 publications

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“…To solve the optimization problems, an improved particle swarm optimization algorithm (IPSO) [29,30] is adopted to conduct the day-ahead optimal scheduling for OLTC, PVs and distributed SCs. The IPSO method can be described as follows:…”

Section: Objective Function and Problem Solvingmentioning

confidence: 99%

Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs

Xiao

Zhao²,

Ding

et al. 2017

Energies

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Considering the possible overvoltage caused by the high proportion of photovoltaic systems (PVs) accessing distribution networks in the future, traditional centralized control methods will be too complex to satisfy the control response time demands. To solve this problem this paper presents a two-level voltage control method. At the day-ahead level, based on the PV-output and load-demand forecast, a community detection algorithm using an improved modularity index is introduced to divide the distribution network into clusters; a day-ahead optimal scheduling is drawn up on the basis of the network partition, and the objective is to minimize the operation costs of the distribution networks. At the real-time level, under the day-ahead optimal scheduling and network partition of the upper level, a real-time optimal voltage control algorithm is proposed based on the real-time operation data of the distribution networks, and the objective is to correct the day-ahead optimal scheduling through modifications. Thus, the algorithm realizes the combination of day-ahead scheduling and real-time control and achieves complete zonal voltage control for future distribution networks with high proportion of PVs. The proposed method can not only optimize the tap operation of an on-load tap changer (OLTC), improving the PV hosting capacity of the distribution network for a high proportion of PVs, but can also reduce the number of control nodes and simplify the control process to reduce the optimization time. The proposed approach is applied to a real, practical, 10 kV, 62-node feeder in Zhejiang Province of China to verify its feasibility and effectiveness.

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Section: Objective Function and Problem Solvingmentioning

confidence: 99%

Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs

Xiao

Zhao²,

Ding

et al. 2017

Energies

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“…Besides the application domains previously highlighted, in the last few years parallel implementations of metaheuristics have been also successfully applied in many other areas such as energy and power network optimization (Peng et al., ; Zhao et al., ), health and medicine (Karnan and Gopal, ), strategic and military applications (Gao et al., ), economy and finance (Liu et al., ), workforce planning (Alba et al., ), and image processing (Cardenas et al., ; Harding and Banzhaf, ; Peng et al., ) and many other optimization problems (Alba et al., ; Crainic et al., ). This shows the growing research in parallel metaheuristics, and therefore we can conclude that the near future will witness many more real‐life situations and problems tackled using parallel metaheuristic algorithms.…”

Section: Modern Applications Solved By Parallel Metaheuristicsmentioning

confidence: 99%

Parallel metaheuristics: recent advances and new trends

Alba

Luque

Nesmachnow

2012

Int Trans Operational Res

268

118

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The field of parallel metaheuristics is continuously evolving as a result of new technologies and needs that researchers have been encountering. In the last decade, new models of algorithms, new hardware for parallel execution/communication, and new challenges in solving complex problems have been making advances in a fast manner. We aim to discuss here on the state of the art, in a summarized manner, to provide a solution to deal with some of the growing topics. These topics include the utilization of classic parallel models in recent platforms (such as grid/cloud architectures and GPU/APU). However, porting existing algorithms to new hardware is not enough as a scientific goal, therefore researchers are looking for new parallel optimization and learning models that are targeted to these new architectures. Also, parallel metaheuristics, such as dynamic optimization and multiobjective problem resolution, have been applied to solve new problem domains in past years. In this article, we review these recent research areas in connection to parallel metaheuristics, as well as we identify future trends and possible open research lines for groups and PhD students.

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“…When solving practical optimization problem, global search is usually performed at first, which can make the search space quickly converge to a particular range. And then local fine search is applied to obtain solutions with high precision [11] . This paper introduces an inertia weight parameter w into equation (7).…”

Section: B Particle Swarm With Weighted Factorsmentioning

confidence: 99%

“…Linear weighting coefficient W in formula (9) can get the minimum power loss as 1.112MW after 130 times of iterations, non-linear weighting coefficient W1 in formula (10) can get the minimum power loss as 0.9834MW after 121 times of iterations and non-linear weighting coefficient W2 in formula (11) can get the minimum power loss as 0.4137MW after 174 times of iterations.…”

Section: Simulated Annealing and Particle Swarm Coevolutionary Algormentioning

confidence: 99%

Reactive Power Optimization Based on SA-NLWPSO Algorithm

Suang-Ye

Ren

2012

2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics

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Particle swarm optimization algorithm was applied to reduce power loss and to prevent the decline of the power supply quality caused by the imbalance of reactive power, but reactive power optimization is a mixed non-linear programming problem with lots of variables and uncertain parameters, PSO algorithm also has some limitations such as premature convergence, which causes the bad accuracy of convergence. And then the coevolution of Particle Swarm Optimization (PSO) with nonlinear inertia weight factor (w) and Simulated Annealing algorithm (SA) is established to improve the original algorithm which is named as SA-NLWPSO. Compared with the algorithms such as PSO, SA-PSO and SA-WPSO, SA-NLWPSO is better for global convergence and higher accuracy of reactive power optimization by using IEEE-10 bus system as a model for the simulation.

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An improved particle swarm optimization algorithm for optimal reactive power dispatch

Cited by 27 publications

References 16 publications

Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs

Zonal Voltage Control Combined Day-Ahead Scheduling and Real-Time Control for Distribution Networks with High Proportion of PVs

Parallel metaheuristics: recent advances and new trends

Reactive Power Optimization Based on SA-NLWPSO Algorithm

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