Distributed generation planning in distribution network based on hybrid intelligent algorithm by SVM-MOPSO
Distributed generation planning in distribution network based on hybrid intelligent algorithm by SVM-MOPSO
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DOI：
10.1109/appeec.2013.6837134
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
R. Cai;Haoyong Chen;Runge Lu;X. Jin;Huanghuang Liu
中科院分区：
文献类型：
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作者：
R. Cai;Haoyong Chen;Runge Lu;X. Jin;Huanghuang Liu
针对分布式电源并网给电力系统带来的随机扰动，综合考虑系统运行效率，以经济性、电能质量和环境效率为目标，建立了随机机会约束规划优化模型，并采用混合智能算法，该方法基于支持向量机（SVM）模拟不确定性函数，采用多目标粒子群优化算法（MOPSO）求解模型，然后得到Pareto非劣决策集。仿真结果表明，该规划模型能充分考虑DG的随机性、并网概率分布，提高了算法的效率，验证了所提方法的合理性和有效性。此外，Pareto前沿的引入为操作者提供了充分的选择空间，具有更高的工程应用价值.
Regarding stochastic disturbance in power system brought by grid-connected distributed generation(DG), generally considering operational effectiveness, aiming at economy, power quality and environmental efficiency, the optimization model of stochastic chance-constrained programming is built, while using hybrid intelligent algorithm, which simulates the uncertainty functions based on support vector machine(SVM) and solves the model by multi-objective particle swarm optimization (MOPSO), then the Pareto noninferior decision set is obtained. Simulation results show that the planning model can fully take into account randomness, grid-connected probability distribution of DG, and improve the efficiency of the algorithm, then verify the rationality and validity of the proposed approach. Moreover, the introduction of Pareto front gives fully choices to operators and possesses more engineering value.