求解电力资源集约化调度问题的分布估计算法
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摘要
针对电力资源调度问题的特点,提出一种有效的分布估计算法,算法采用置换排列编码,按照机器分配及其分布规则执行编码,并采用基于Pareto最优性的评价机制构建概率模型来描述空间中解的概率分布,同时利用Pareto优势解更新概率模型,并基于概率模型采样有潜力的所搜区域来生成新种群。该算法有助于电力资源在电网输送中集约化调度的操作。最后通过基于实例的仿真结果以及与已有算法的统计比较验证了所提算法的有效性。
This paper aimed at the characteristics of the electric power communication problem. we constructed of an effective distribution estimation algorithm which uses replacement arrangement code,performs encoding in accordance with machine allocation and distribution rules,and build a probability model based on probabilistic Pareto optimality evaluation mechanism to describe the spatial distribution of the solution. In addition,the Pareto- superior individuals are used to update the probability model,and the new individuals are generated by sampling the probability model among the promising search region. The algorithm helps solving dispatching intensification of electric power resources. Finally,the simulation results are based on examples and statistical comparison with existing algorithms to verify the effectiveness of the proposed algorithm.
This paper aimed at the characteristics of the electric power communication problem. we constructed of an effective distribution estimation algorithm which uses replacement arrangement code,performs encoding in accordance with machine allocation and distribution rules,and build a probability model based on probabilistic Pareto optimality evaluation mechanism to describe the spatial distribution of the solution. In addition,the Pareto- superior individuals are used to update the probability model,and the new individuals are generated by sampling the probability model among the promising search region. The algorithm helps solving dispatching intensification of electric power resources. Finally,the simulation results are based on examples and statistical comparison with existing algorithms to verify the effectiveness of the proposed algorithm.
引文
[1]刘绍银,李善风,唐从明.基于资源环境价值理念的火电厂电力调度策略研究[J].中国电力,2013,46(12):141-143.
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[3]王魁,张步涵,周杨,等.基于混沌量子粒子群算法的含风电场电力系统实时调度[J].电力系统自动化,2011,35(22):141-146.
[4]张景瑞,龙健,岳超源,等.水火电力系统短期优化调度的一种改进粒子群算法[J].控制与决策,2011,26(3):407-412.
[5]马超,练继建;基于聚类遗传算法的梯级水利枢纽短期电力调度优化[J].天津大学学报:自然科学与工程技术版,2010,43(1):1-8.
[6]张杰,刘晓明,孙东磊,等.基于混合遗传算法的电力系统源网协同调度[J].电网与清洁能源,2015,31(7):20-25.
[7]罗华永,张敏杰,杨宁,等.基于BPMN2.0标准的电力系统业务流程管理平台[J].电力系统自动化,2012,36(18):148-152.
[8]童晶,赵明旺.高效求解Pareto最优前沿的多目标进化算法[J].计算机仿真,2009,26(6):216-219.
[9]黄斌,陈德礼.多目标优化问题的有效Pareto最优集[J].计算机与数字工程,2009,37(2):28-30.
[10]杨娟.浅谈电力调度运行安全风险与防范策略[J].科技创新与应用,2015,35(12):159-159.
[11]李高望,鞠文云,段献忠,等.电力调度数据网传输特性分析[J].中国电机工程学报,2012(22):141-148.
[12]梁玉洁,许峰.自适应混合多目标分布估计进化算法[J].计算机工程与应用,2014,50(5):46-50.
[13]谢志强,辛宇,杨静.基于设备空闲事件驱动的综合调度算法[J].机械工程学报,2011,47(11):139-147.
[14]刘锋,王建军,杨德礼,等.面向多目标优化的一种混合进化算法[J].运筹与管理,2012,21(4):15-21.
[2]谢鲜.关于电力系统调度运行危险点分析及预防措施的探讨[J].中国新技术新产品,2015.21(11):189-189
[3]王魁,张步涵,周杨,等.基于混沌量子粒子群算法的含风电场电力系统实时调度[J].电力系统自动化,2011,35(22):141-146.
[4]张景瑞,龙健,岳超源,等.水火电力系统短期优化调度的一种改进粒子群算法[J].控制与决策,2011,26(3):407-412.
[5]马超,练继建;基于聚类遗传算法的梯级水利枢纽短期电力调度优化[J].天津大学学报:自然科学与工程技术版,2010,43(1):1-8.
[6]张杰,刘晓明,孙东磊,等.基于混合遗传算法的电力系统源网协同调度[J].电网与清洁能源,2015,31(7):20-25.
[7]罗华永,张敏杰,杨宁,等.基于BPMN2.0标准的电力系统业务流程管理平台[J].电力系统自动化,2012,36(18):148-152.
[8]童晶,赵明旺.高效求解Pareto最优前沿的多目标进化算法[J].计算机仿真,2009,26(6):216-219.
[9]黄斌,陈德礼.多目标优化问题的有效Pareto最优集[J].计算机与数字工程,2009,37(2):28-30.
[10]杨娟.浅谈电力调度运行安全风险与防范策略[J].科技创新与应用,2015,35(12):159-159.
[11]李高望,鞠文云,段献忠,等.电力调度数据网传输特性分析[J].中国电机工程学报,2012(22):141-148.
[12]梁玉洁,许峰.自适应混合多目标分布估计进化算法[J].计算机工程与应用,2014,50(5):46-50.
[13]谢志强,辛宇,杨静.基于设备空闲事件驱动的综合调度算法[J].机械工程学报,2011,47(11):139-147.
[14]刘锋,王建军,杨德礼,等.面向多目标优化的一种混合进化算法[J].运筹与管理,2012,21(4):15-21.