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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bo Wang You Li Watada, J. |
| Copyright Year | 2011 |
| Description | Author affiliation: The Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan (Bo Wang; You Li; Watada, J.) |
| Abstract | 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. |
| Starting Page | 1090 |
| Ending Page | 1095 |
| File Size | 170992 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007313 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Convergence Equations Mathematical model Power demand Spinning Reactive power Fuzzy set theory Test systems Power supply reliability Unit commitment problem Fuzzy Value-at-Risk Local convergence averse binary particle swarm optimization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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