The conventional prediction of future power demands are always made based on the historical data. However, the real power demands are affected by many other factors as weather, temperature and unexpected emergencies. The use of historical information alone cannot well predict real future demands. In this study, the experts' opinions from related fields are taken into consideration. To deal the uncertainty of historical data and imprecise experts' opinions, we employ fuzzy variables to better characterize the forecasted future power loads. The conventional unit commitment problem (UCP) is updated here by considering the spinning reserve costs in a fuzzy environment. As the solution, we proposed a heuristic algorithm called local convergence averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. The comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.
|ホスト出版物のタイトル||IEEE International Conference on Fuzzy Systems|
|出版ステータス||Published - 2011|
|イベント||2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei|
継続期間: 2011 6 27 → 2011 6 30
|Other||2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011|
|Period||11/6/27 → 11/6/30|
ASJC Scopus subject areas