【摘 要】
：
针对分布式电源(DistributedGeneration,DG)并网给电力系统带来的随机扰动,综合考虑配电网运行效益,计及风光时序特性,以经济性、电能质量及环保性为目标,搭建了机会约束规划模型，做好配电网规划.采用混合智能算法求解,即基于支持向量机(SupportVectorMachine,SVM)算法模拟优化变量到目标函数以及约束条件映射的不确定性函数,运用多目标粒子群算法(Multi-Obj
【机 构】
：
国网泉州供电公司,福建泉州362000 南方电网科学研究院有限责任公司,广东广州 510080
【出 处】
：
福建省电机工程学会第十六届学术年会
论文部分内容阅读
针对分布式电源(DistributedGeneration,DG)并网给电力系统带来的随机扰动,综合考虑配电网运行效益,计及风光时序特性,以经济性、电能质量及环保性为目标,搭建了机会约束规划模型，做好配电网规划.采用混合智能算法求解,即基于支持向量机(SupportVectorMachine,SVM)算法模拟优化变量到目标函数以及约束条件映射的不确定性函数,运用多目标粒子群算法(Multi-ObjectiveParticleSwarmOptimization,MOPSO)求解模型,得出Pareto非劣决策集并给出典型解及理想解.算例结果表明,该规划方法考虑到DG的随机性特征、时序特性和并网概率分布,能提高算法执行效率,证明了所提方法的合理性和有效性,且Pareto前沿的引入,给决策者充分选择空间,更具有工程性.
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