基于混杂Petri网的微电网需求侧能量管理在线优化方法研究

项目名称： 基于混杂Petri网的微电网需求侧能量管理在线优化方法研究

项目编号： No.51307074

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 电工技术

项目作者： 李春华

作者单位： 江苏科技大学

项目金额： 25万元

中文摘要： 微电网在充分发挥可再生能源分布式发电优势的同时，也是解决其无缝接入大电网的发展趋势。对微电网的能量进行在线优化管理，制定系统未来一段时间内的最优运行方案，最大程度地降低系统运行成本和提高系统能源利用效率，是当前微电网研究领域一个急待解决的关键问题。本项目从研究和分析微电网的混杂行为特征入手，建立由事件驱动的连续型混杂Petri网，描述微电网状态的动态变迁，为微电网能源优化管理提供优化模型；以保证未来一段时间内能源供需平衡和系统元件安全运行为约束，以微电源和储能装置未来一段时间内的功率输出设定值序列为优化变量，以运行成本最小化和能源利用效率最大化为目标，在不断变化的环境下，采用粒子群优化算法PSO实现微电网需求侧能源管理的动态约束多目标优化，并探究不同能源管理策略下的能源利用效率和各类成本因素对优化结果的影响程度。本项目的研究可有力促进实现微电网经济且有效的运行，顺应了微电网技术的发展趋势。

中文关键词： 微电网；混杂Petri网；能量管理；粒子群算法；动态约束多目标优化

英文摘要： Microgrids give full play to the renewable energy distributed generation advantages, and are the developing trends to realize its seamless access to grid. Online optimization of the microgrid energy management needs to develop a optimization system running program in the next period of time to minimize the system operating costs and improve system energy use efficiency. This is a key issue need to be solved urgently in the current microgird research areas. The project begins from the analysis of the hybrid behavior characteristics of the microgrid. A event-driven continuous hybrid Petri nets are developed to describe the dynamic changes of the microgrid state, and are used as the optimization model for the microgrid energy optimization management. Ensuring the future energy balance between supply and demand and the safe operation of the system components are treated as the constraint conditions. Micro-power sources and energy storage devices' power output setting sequence in the next period of time are treated as optimization variables.The optimization objective functions are the system running cost minimization and energy efficiency maximization. In changing environment, particle Swarm optimization algorithm is used to realize the microgrid demand side energy management dynamic constrained multi-objective opti

英文关键词： Microgrids；Hybrid Petri net；Energy management；Particle swarm optimization；Dynamic constrained multi-objective optimization