独立微电网系统电源容量优化配置研究
摘要:微电网将多种发电形式进行整合和利用，其诞生有效缓解了现阶段大电网的扩张需求。电源容量配置是微电网建设的基础，可在规划设计阶段为微电网建设的经济性、环保性和供电可靠性提供参考。微电网系统电源容量优化配置的结果在很大程度上取决于优化模型的选择，不同优化模型会对容量配置结果造成差异。因此，将重点放在不同优化模型对容量配置结果的影响上，对不同优化模型在独立型水－光－柴－储微电网系统电源容量配置结果中造成的差异性及其原因进行分析。在综合考虑经济性、供电可靠性、环境效益等评价指标的基础上，通过考虑不同的优化目标，建立不同导向的优化模型。以一个实际建设的微电网项目为例，使用自适应权重粒子群算法对模型进行求解，得到不同模型下电源容量配置情况的最优解；通过对实例求解结果的分析，研究微电网电源容量优化配置结果的差异性及其原因，并验证所提出模型的正确性及可靠性。
Abstract：Microgrid, which can combine different forms of renewable energy generation, effectively retards the expansion needs of the grid. Power capacity design is the basis of microgrid planning and construction, which can also provide reference for the economy, environmental friendliness and power supply reliability of microgrid construction in the planning and design period. The results of the optimal capacity design of microgrid largely depend on the choice of the optimization model, different optimization models will result in different capacity allocation results. Therefore, this paper focuses on the influence of different optimization models on the results of capacity allocation, and analyses the differences and causes of photovoltaic, hydropower, diesel power and battery based standalone microgrid capacity allocation results. Optimization models with different optimization objectives are established with comprehensive consideration of economy, environmental friendliness and power supply reliability. An actual microgrid project is used as an example to solve the models by adaptive weighted particle swarm algorithm, and optimum solutions of capacity allocation are obtained in different models. The differences of different optimal capacity design results are analyzed, as well as the reason, and the correctness and reliability of the proposed model are verified.
标题：独立微电网系统电源容量优化配置研究
title:Power Capacity Optimization Design of Standalone Microgrid
作者：程苒,李峰,常湧
authors:CHENG Ran,LI Feng,CHANG Yong
关键词：微电网,分布式电源,容量配置,多目标优化,自适应权重粒子群算法,
keywords:microgrid,distributed generator,capacity configuration,multi-objective optimization,adaptive weight particle swarm algorithm,
发表日期：2019-07-01
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