This paper presents a hybrid particle swarm optimization algorithm (HPSO) to solve the day-ahead selfscheduling for thermal power producer in competitive electricity market. The objective functions considered to model the selfscheduling problem are: 1) to maximize the profit from selling energy in day-ahead energy market subject to operational constraints and 2) at the same time, to minimize the risk due to uncertainty in price forecast. Therefore, it is a conflicting biobjective optimization problem which has both binary and continuous optimization variables considered as constrained mixed integer nonlinear programming. To demonstrate the effectiveness of the proposed method for self-scheduling in a dayahead energy market, the locational margin price (LMP) forecast uncertainty in PJM electricity market is considered. An adaptive wavelet neural network (AWNN) is used to forecast the dayahead LMPs. The effect of risk is explicitly modeled by taking into account the estimated variance of the day-ahead LMPs.
|Title of host publication||ISAP'09|
|Publication status||Published - 2009|
|Event||International Conference on Intelligent System Applications to Power Systems - Curitiba, Brazil|
Duration: 1 Jan 2009 → …
Conference number: 15
|Conference||International Conference on Intelligent System Applications to Power Systems|
|Period||01/01/2009 → …|
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- Hybrid particle swarm optimization,
- Day-ahead self-scheduling
- Electricity market.
- LMP forecast,