中国电机工程学报
中國電機工程學報
중국전궤공정학보
ZHONGGUO DIANJI GONGCHENG XUEBAO
2014年
25期
4199-4206
,共8页
盛万兴%张波%邸宏宇%邹锐%王孙安
盛萬興%張波%邸宏宇%鄒銳%王孫安
성만흥%장파%저굉우%추예%왕손안
需求响应%免疫优化算法%动态抗体记忆库%疫苗接种%智能开关
需求響應%免疫優化算法%動態抗體記憶庫%疫苗接種%智能開關
수구향응%면역우화산법%동태항체기억고%역묘접충%지능개관
demand response (DR)%immune optimization algorithm%flexible antibody memory%vaccine inoculation%intelligent switch
自动需求响应是智能电网与用户实现信息和能量互动的重要实现手段。为解决基于实时电价的自动需求响应技术应用中包含多类负荷用户的电能综合规划问题,建立优化问题的数学模型。针对该模型提出一种基于动态抗体记忆库的免疫优化算法。设计依据二重亲和度阈值检测的抗体记忆库更新机制,在优化结束后能够为用户提供多个备选可行解。采用先验知识疫苗接种的方法,提高算法的求解精度。通过抗体种群的优值马尔可夫链的转移概率分析,证明了算法的收敛性;利用实际算例验证了所提算法的有效性。对比分析的结果表明,所提算法比其他算法具有更好的全局优化能力和搜索效率。
自動需求響應是智能電網與用戶實現信息和能量互動的重要實現手段。為解決基于實時電價的自動需求響應技術應用中包含多類負荷用戶的電能綜閤規劃問題,建立優化問題的數學模型。針對該模型提齣一種基于動態抗體記憶庫的免疫優化算法。設計依據二重親和度閾值檢測的抗體記憶庫更新機製,在優化結束後能夠為用戶提供多箇備選可行解。採用先驗知識疫苗接種的方法,提高算法的求解精度。通過抗體種群的優值馬爾可伕鏈的轉移概率分析,證明瞭算法的收斂性;利用實際算例驗證瞭所提算法的有效性。對比分析的結果錶明,所提算法比其他算法具有更好的全跼優化能力和搜索效率。
자동수구향응시지능전망여용호실현신식화능량호동적중요실현수단。위해결기우실시전개적자동수구향응기술응용중포함다류부하용호적전능종합규화문제,건립우화문제적수학모형。침대해모형제출일충기우동태항체기억고적면역우화산법。설계의거이중친화도역치검측적항체기억고경신궤제,재우화결속후능구위용호제공다개비선가행해。채용선험지식역묘접충적방법,제고산법적구해정도。통과항체충군적우치마이가부련적전이개솔분석,증명료산법적수렴성;이용실제산례험증료소제산법적유효성。대비분석적결과표명,소제산법비기타산법구유경호적전국우화능력화수색효솔。
Automatic demand response is an important realization means of achieving information and energy interaction between smart grid and users. In application of spot price based automatic demand response, to solve the problem of integrated power planning for a user with various loads, a mathematical model of optimization was established. According to this model, a flexible antibody memory based immune optimization algorithm was proposed. To provide the user several alternative feasible solutions after the optimization process, a refresh mechanism according to double affinity threshold detection was designed. The accuracy of this algorithm was improved by use of prior knowledge vaccines inoculation. The convergence of this algorithm was proved by the transition probability analysis of antibody population best values’ Markov chain. Then the feasibility of proposed algorithm was verified by the optimization result of a practical example. Comparison result shows that the proposed algorithm has a better performance of global optimization and searching efficiency than other algorithms.