| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 300515 | Renewable Energy | 2013 | 7 Pages |
The optimization of wind power capacity for an electric power system is studied with the system operation, economy and reliability emphasized. The economic aspect is evaluated in view of the system-wide social cost, which includes the social cost of conventional and wind electricity generation, the reserve cost, the value of lost load and the opportunity cost of wind curtailments. The probabilistic methods are adopted to assess the system reliability in terms of the loss-of-load probability (LOLP) and to estimate the spinning reserve depending on the uncertainty of wind power generation and load demand forecast. Within the probabilistic framework, the wind power capacity planning problem is addressed by the chance constrained programming (CCP) approach, which is capable to handle with such an optimization problem containing random variables and probabilistic constraints. The CCP-based wind power capacity planning problem aims at minimizing the system social cost while satisfying the reliability criteria and operational constraints. A case study is done to determine the optimal wind power installation for a typical isolated power system. This paper provides system planners and policy makers with an approach to initiating wind power investment and incentives in order to facilitate the development of a cost-effective wind market.
► We develop a chance constrained programming (CCP) based wind capacity planning model. ► We consider the variability and uncertainty of wind power generation and load demand. ► We emphasize the impact of wind power on system operation, economy and reliability. ► We assess system reliability and quantify reserve capacity in a probabilistic way. ► We employ a hybrid intelligent algorithm to solve the CCP problem.
