Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

Abstract

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

Keywords

power intelligence center (PIC) / coordination optimal model / power network planning / hybrid algorithm

References

[1] BaiX. Q., WeiH., FujisawaK., WangY.. Semidefinite programming for optimal power flow problems [J]. International Journal of Electrical Power and Energy Systems, 2008, 30(6/7): 383-392

[2] PaiP. F., HongW. C.. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms [J]. Electric Power Systems Research, 2005, 74(3): 417-425

[3] BakirtzisA. G., BiskasP. N., ZoumasC. E., PetridisV.. Optimal power flow by enhanced genetic algorithm [J]. IEEE Trans on Power Systems, 2002, 17(2): 229-236

[4] KandilN., WamkeueR., SaadM., GeorgesS.. An efficient approach for short term load forecasting using artificial neural networks [J]. Electrical Power and Energy Systems, 2006, 28(8): 525-530

[5] LiuL., YanD.-j., GongD.-c., ZhangH.-m., LiD.-peng.. New method for short term load forecasting based on particle swarm optimization and fuzzy neural network [J]. Proceedings of the CSU-EPSA, 2006, 18(3): 47-50

[6] LinY. L., ChangW. D., HsiehJ. G.. A particle swarm optimization approach to nonlinear rational filter modeling [J]. Expert Systems with Applications, 2008, 34(2): 1194-1199

[7] MukherjeeV., GhoshalS. P.. Intelligent particle swarm optimized fuzzy PID controller for AVR system [J]. Electric Power Systems Research, 2007, 77(12): 1689-1698

[8] YogR. S.. Evolutionary programming based optimal power flow and its validation for deregulated power system analysis[J]. International Journal of Electrical Power and Energy Systems, 2007, 29(1): 65-75

[9] BasuM.. Optimal power flow with FACTS devices using differential evolution [J]. International Journal of Electrical Power and Energy Systems, 2008, 30: 150-156

[10] TaoX.-h., HuangM.-xiang.. A load forecasting method based on fuzzy regulation and neural network [J]. Proceedings of the CSU-EPSA, 2000, 12(5): 37-41

[11] PaiT. Y., TsaiY. P., LoH. M., TsaiC. H., LinC. Y.. Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent [J]. Computers and Chemical Engineering, 2007, 31(10): 1272-1281

[12] XiongH.-g., ChengH.-z., LiH.-yu.. Optimal reactive power flow incorporating static voltage stability based on multi-objective adaptive immune algorithm [J]. Energy Conversion and Management, 2008, 49(5): 1175-1181

[13] HongW. C.. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model [J]. Energy Conversion and Management, 2009, 50(1): 105-117

[14] LiaoG. C.. Short-term thermal generation scheduling using improved immune algorithm [J]. Electric Power Systems Research, 2006, 76(5): 360-373

[15] LiuJ.-c., YanS.-l., QiJ.-xun.. Research on building business intelligence center in dynamic alliance [J]. China Market, 2008, 1(2): 66-67

[16] LiuJ.-c., NiuD.-xiao.. A novel recurrent neural network forecasting model for power intelligence center [J]. Journal of Central South University of Technology, 2008, 15(5): 726-732

[17] LiC.-b., WangK.-cheng.. A new grey forecasting model based on BP neural network and Markov chain [J]. Journal of Central South University of Technology, 2007, 14(5): 713-718