Application of an improved multi-objective particle swarm algorithm in power purchase risk assessment
This paper proposes an improved multi-objective particle swarm optimization algorithm. The algorithm improves the local search ability by introducing the idea of local disturbance and variation operation, and maintains the external file with the idea of non-dominated sorting genetic algorithm. We ha...
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| Published in: | Dianli Xitong Baohu yu Kongzhi Vol. 40; no. 8; pp. 116 - 120 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | Chinese |
| Published: |
16.04.2012
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| Subjects: | |
| ISSN: | 1674-3415 |
| Online Access: | Get full text |
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| Summary: | This paper proposes an improved multi-objective particle swarm optimization algorithm. The algorithm improves the local search ability by introducing the idea of local disturbance and variation operation, and maintains the external file with the idea of non-dominated sorting genetic algorithm. We have a detailed analysis of the risk of purchasing electricity of grid company, and establish a model of the risk to purchase electricity taking the minimum of Conditional Value-at-Risk (CVaR) and the maximum of the expected revenue as goals. This model makes up the flaw of Value at Risk (VaR) that it is not able to reflect the loss rear part information, guards against the small probability extreme risk, reduces the possibility of grid company to have the disastrous risk, and does not need the priori knowledge. We use the improved particle swarm algorithm to solve the model, each time a set of optimal solutions can be calculated, and the optimal solution evenly distributed in the optimal front end, which provides a |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
| ISSN: | 1674-3415 |