文章摘要
张晓花,朱陈松,路睿,许云帆,郑剑锋,强浩,周兴龙.基于多智能体系统并考虑需求响应的电力系统节能减排调度[J].电力需求侧管理,2019,21(5):35-40
基于多智能体系统并考虑需求响应的电力系统节能减排调度
Cost⁃emission dispatch based on multi⁃agent system considering demand response
  修订日期:2019-05-28
DOI:DOI:10.3969/j.issn.1009-1831.2019.05.008
中文关键词: 需求价格弹性  激励补偿  多智能体系统  拍卖算法  协同乘子
英文关键词: price elasticity of demand  incentive  MAS  auction algorithm  the cooperative multipliers
基金项目:国家自然科学基金项目(51207074);江苏省社会科学基金项目(18GLB016)
作者单位
张晓花 常州大学机械工程学院江苏常州213164江苏省绿色过程装备重点实验室江苏常州213164 
朱陈松 常州大学商学院江苏常州213164 
路睿 常州大学机械工程学院江苏常州213164 
许云帆 常州大学机械工程学院江苏常州213164 
郑剑锋 常州大学机械工程学院江苏常州213164 
强浩 常州大学机械工程学院江苏常州213164 
周兴龙 常州大学机械工程学院江苏常州213164 
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中文摘要:
      针对能源与环境日益突出的问题,将直接负荷控制和紧急需求响应结合,考虑自弹性和交叉弹性的动态需求价格弹性,形成多时间弹性的综合负荷经济模型,建立考虑需求响应的节能减排调度模型。通过多智能体系统(multi-agentsystem,MAS)将问题分解为一系列相互作用的代理,各代理的静态调度采用拍卖算法求解,代理间通过具有自适应协同乘子的协同进化代理进行协同,并优化各时段的激励补偿值,比较了激励补偿值为固定值和优化值情况下对结果的影响。结果表明本文的算法可提高计算效率;考虑需求价格弹性的调度可实现负荷曲线的削减与转移,弹性越大,削减负荷的能力越强,系统节能减排效果越好;综合考虑系统的节能与减排,增加了系统调度的全面性。
英文摘要:
      The energy and environment problems have become increasingly prominent, the direct load control and emergency response requirements are combined. Considering the self elasticity and cross-elasticity price, the multiperiod dynamic elastic loads are modelled, the cost-emission demand response dispatch model is established. The system is decomposed into the optimization of interactive agents by multi?agent system and agents’are solved by auction algorithm, the adaptive coevolution of agents is reached by cooperative co evolution agent with the adaptive cooperative multipliers.And the optimal incentive compensation value at each time interval is obtained. The influence of the incentive compensation value on the optimization resultsis compared under the fixed value and the optimal value. The results show that the combination of MAS and auction algorithm can improve the computation efficiency. Considering the demand elasticity, the dispatch can achieve the reduction and transfer of the load curve. The maximum level of reducing load is obtained with the maximum values of elasticity by the optimization of incentive value. Comprehensive energy saving and emission reduction into the system increase the comprehensive scheduling system.
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