考虑污染气体排放的热电联供型微电网多目标运行优化  

Multi-objective Optimization of Combined Heat and Power Microgrid Considering Pollutant Emission

作　　者：顾伟[1,2] 吴志[1,2] 王锐 

机构地区：[1]东南大学电气工程学院,江苏省南京市210096 [2]江苏省智能电网技术与装备重点实验室,江苏省镇江市212009 [3]江苏省电力公司泰州供电公司,江苏省泰州市225300

出　　处：《电力系统自动化》2012年第14期177-185,共9页Automation of Electric Power Systems

基　　金：国家高技术研究发展计划(863计划)资助项目(2011AA05A107);国家自然科学基金资助项目(50907008);江苏省自然科学基金资助项目(SBK201122387);东南大学优秀青年教师基金资助项目;东南大学重大基础科研基金资助项目~~

摘　　要：热电联供系统由于高效、环保、节能等优点,具有广泛的应用前景。文中针对由光伏电池、蓄电池、蓄热槽、微型燃气轮机、余热锅炉以及热电负荷组成的热电联供系统的运行优化问题进行研究。综合考虑了微型燃气轮机的发电效率、制热效率和污染排放特性,蓄电池采用KiBaM模型并考虑其最低荷电状态,最终建立了计及光伏、热电负荷随机性,综合考虑运行费用、CO排放量以及NOx排放量最小的多目标机会规划模型。采用随机模拟技术和Pareto最优解集处理多目标机会约束规划问题,并提出采用局部和全局记忆体的改进粒子群优化算法进行求解。实例分析结果表明,文中方法可以为微电网优化配置提供建议,实现微源动态调度,有效减少运行费用及污染气体排放量。With an eye on the broad vistas of extensive application of the combined heat and power （CHP） system for its environment friendliness and energy-saving, an investigation has been made on the operation optimization of a CHP system composed of PV batteries, storage batteries, heat storage tank, mieroturbine（MT）, heat recovery boiler, and thermo-electric loads. The characteristics of MT＇s electric power generation efficiency, exhaust heat recovery efficiency and pollutant gas emission are comprehensively considered. The kinetic battery model （KiBaM） is used to consider the minimum state of charge （SOC）. Finally a multi-objective chance constrained programming （CCP） model is developed with PV and stochastic thermo- electric load, operation cost, CO emission and minimum NOn emission all taken into consideration, The stochastic simulation and Pareto set are used to solve the CCP problem. A revised multi-objective particle swarm optimization （MOPSO） based on local and global memory is proposed to solve the model. Simulation results show that, with this method, suggestions can be made to optimize microgrid configuration, realize micro-sources dynamic dispatching and effectively reduce the operation cost and pollution gas emission.

关 键 词：微电网 节能减排 经济运行 热电联供 多目标优化 机会约束规划 粒子群优化算法 

分 类 号：TM714.3[电气工程—电力系统及自动化]