以区域能效优化为目标计及不确定因素的分布式能源综合优化配置

分布式能源的大规模应用,可以有效缓解化石能源引起的环境污染和能源危机。针对如何确定分布式能源位置和容量的问题,建立了考虑不确定因素影响的分布式能源综合优化配置模型。模型以规划期内总成本最小为目标,以系统安全运行要求为约束,考虑区域能效优化、低碳目标及分布式能源的建设成本、运行成本和维护成本,并以机会约束规划解决风机、光伏等出力的不确定性及负荷的波动性问题。在确定目标函数的基础上,采用基于蒙特卡洛模拟嵌入量子粒子群算法来求解该NP难问题,较粒子群算法在搜索效率和搜索能力上均有较大提升。通过对IEEE 37-bus配电系统的分析,表明所建模型能够较好地解决不同资源水平、经济发展程度地区分布式能源的选址与定容问题。

Comprehensive Optimal Allocation of Distributed Energy Resources by Considering Uncertainties Factors with the Objectives of Regional Energy Efficiency Optimization

The utilization of large-scale distributed energy resources (DER) can effectively alleviate the environmental pollution and energy crisis brought by fossil energy. As to how to determine the location and capacity size of DER, a comprehensive optimal allocation model of DER is built by considering uncertainties factor. In this model, the minimum total programming cost is taken as the objective, the system security and operation requirements are regarded as constraints, and such factors as the construction cost of low-carbon and DER, running cost, maintenance cost and carbon emission cost as well as the optimization of regional energy efficiency are considered, in which chance constrained programming is used to handle such uncertainties as uncertain load growth, output of wind turbines and photovoltaic cells. On the basis of determining objective function, a Monte Carlo simulation based embedded quantum particle swarm optimization algorithm approach is developed to solve the NP-hard problem, which greatly improve the searching efficiency and ability by comparing with particle swarm algorithm. Finally, through analysis on the IEEE 37-bus distribution test system, results show that proposed model can solve the locating and sizing problems of regional DER.