大规模电动汽车(plug-in electric vehicle,PEV)和风光等可再生能源发电并网使配电网分布式电源(distributed generation,DG)定容选址需考虑更多的不确定因素,为此,利用机会约束规划方法建立了以环境效益、供电可靠性、DG总费用和有功...大规模电动汽车(plug-in electric vehicle,PEV)和风光等可再生能源发电并网使配电网分布式电源(distributed generation,DG)定容选址需考虑更多的不确定因素,为此,利用机会约束规划方法建立了以环境效益、供电可靠性、DG总费用和有功损耗最优为目标的DG优化配置模型,并提出蒙特卡洛模拟嵌入改进量子粒子群(improved quantum particle swarm optimization algorithm-Monte Carlo simulation,IQPSO-MCS)的方法进行求解。在优化配置中考虑了风电、光伏、微型燃气轮机3种DG的选址和定容;并针对输出功率不确定的风力发电、光伏发电和电动汽车建立了概率模型,利用蒙特卡洛模拟法将随机性问题转化为确定性问题,实现含不确定因素的配电网随机潮流计算;最后由带自适应变异机制的IQPSO算法全局寻优得到最优配置方案。以IEEE 33节点测试配电系统为例,验证了所提模型和方法的有效性和实用性。展开更多
Many concrete real life problems ranging from economic and business to industrial and engineering may be cast into a multi-objective optimisation framework. The redundancy of existing methods for solving this kind of ...Many concrete real life problems ranging from economic and business to industrial and engineering may be cast into a multi-objective optimisation framework. The redundancy of existing methods for solving this kind of problems susceptible to inconsistencies, coupled with the necessity for checking inherent assumptions before using a given method, make it hard for a nonspecialist to choose a method that fits well the situation at hand. Moreover, using blindly a method as proponents of the hammer principle (when you only have a hammer, you want everything in your hand to be a nail) is an awkward approach at best and a caricatural one at worst. This brings challenges to the design of a tool able to help a Decision Maker faced with these kinds of problems. The help should be at two levels. First the tool should be able to choose an appropriate multi-objective programming technique and second it should single out a satisfying solution using the chosen technique. The choice of a method should be made according to the structure of the problem and to the Decision Maker’s judgment value. This paper is an attempt to satisfy that need. We present a Decision Aid Approach that embeds a sample of good multi-objective programming techniques. The system is able to assist the Decision Maker in the above mentioned two tasks.展开更多
文摘Many concrete real life problems ranging from economic and business to industrial and engineering may be cast into a multi-objective optimisation framework. The redundancy of existing methods for solving this kind of problems susceptible to inconsistencies, coupled with the necessity for checking inherent assumptions before using a given method, make it hard for a nonspecialist to choose a method that fits well the situation at hand. Moreover, using blindly a method as proponents of the hammer principle (when you only have a hammer, you want everything in your hand to be a nail) is an awkward approach at best and a caricatural one at worst. This brings challenges to the design of a tool able to help a Decision Maker faced with these kinds of problems. The help should be at two levels. First the tool should be able to choose an appropriate multi-objective programming technique and second it should single out a satisfying solution using the chosen technique. The choice of a method should be made according to the structure of the problem and to the Decision Maker’s judgment value. This paper is an attempt to satisfy that need. We present a Decision Aid Approach that embeds a sample of good multi-objective programming techniques. The system is able to assist the Decision Maker in the above mentioned two tasks.
