Bian D.,Hohai University |
Wei Z.-N.,Hohai University |
Huang X.-Q.,Anqing Power Supply Company |
Sun G.-Q.,Hohai University |
Sun Y.-H.,Hohai University
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | Year: 2013
In the electricity market system, distributed generations (DGs) are usually owned by users, which can eliminate the peak load and support emergency power. In the background of electricity market, in order to optimize the distribution network at the greatest degree, DGs in this paper will be considered as dispatched equipments during the distribution network reconfiguration optimization process, and the injected power is regarded as optimization variables. DG injected power, network loss, and benefit distribution are changed into comprehensive costs according to tariff, and the minimum of costs is taken as the objective function, and improved quantum evolutionary algorithm (IQEA) is proposed to solve the reconfiguration problem. The improved algorithm uses modified strategy to enhance the efficiency of search, combining with loop quantum collapse strategy to avoid the infeasible solutions to improve the configuration efficiency. The effectiveness and correctness of the method is verified through the simulation results of IEEE 33-node system.
Wei Z.,Hohai University |
Xiang Y.,Hohai University |
Sun G.,Hohai University |
Huang X.,Anqing Power Supply Company
Dianwang Jishu/Power System Technology | Year: 2012
To meet the demand of energy-saving and emission reduction, under the background of power system containing carbon-capture plant a multi-objective dynamic optimal power flow model, in which both CO2 emission and active network loss are considered, is built. Based on fussy set theory, the multi-objective optimization problem is turned into the single objective problem, by which the maximum satisfaction degree is attained, and solved by interior point method. To verify the effectiveness of the proposed model, simulation of IEEE 30-bus 6-machine system is performed, and simulation results show that using the proposed multi-objective dynamic optimal power flow model the CO2 emission can be reduced while the active network loss is taken into account.
Jiang D.-L.,Zhengzhou University |
Jiang D.-L.,No 71781 Unit |
Wang K.-W.,Zhengzhou University |
Yang P.,Anqing Power Supply Company |
Cui W.,Zhengzhou University
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | Year: 2012
To solve the problems of anthropic factors and complex evaluation model, the idea of clustering is applied to power quality comprehensive evaluation. Power quality is estimated synthetically by the fuzzy cluster analysis. In clustering process, the indexes of power quality comprehensive evaluation are regarded as the characteristic values, and the data points with known power quality level are included in sample data set. According to the principle of 'like attracts like' and choosing the optimal threshold, the power quality levels of the sample data points are found, and then the comprehensive evaluation of power quality is realized. Practical effect in two examples proves that the proposed method is effective. In this evaluation, the model is simple and has better expansibility without any personal factor interference. Besides that, it is better to be used for the power quality comprehensive evaluation of regional grid together, which has a good application prospect.
Yu T.,South China University of Technology |
Liu J.,South China University of Technology |
Hu X.,Anqing Power Supply Corporation
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | Year: 2012
As for the problem that usual optimal power flow algorithm can not meet the timely demand of the complex power grid., this paper presents a novel distributed Q(λ) learning algorithm based on complex districted power grid, which deals no auxiliary process with the optimal power flow (OPF) mathematical model and whose internal agent independently undertakes each district's learning duty with the standard multi-step Q(λ) learning algorithm, and then coordinately cooperate to reach the optimization of the whole system. The result of the application in IEEE118 bus bar demonstrates that the distributed Q(λ) learning algorithm provides a new feasible and effective method to the complex grid OPF problem.