Bidding Strategy Of Hydropower Plants Based On System Marginal Price Forecast

The power industry worldwide is experiencing unprecedented establishing electricity markets. Under the electricity market environments, most important for the hydropower plants is to make profit maximized, which to a large extent, depends on bidding strategies employed. This dissertation focuses on the study of bidding strategy of hydropower generation companies in the electricity market environment, and puts forward a profit maximization model of hydropower plants bidding strategy based on forecasting the system marginal price(SMP).Firstly, the significance of optimal bidding strategy for hydropower plants under electricity market environment is introduced, and the approaches for making bidding strategy are particularly addressed.Secondly, the factors influencing the SMP in electricity market is analyzed, and SMP forecast is carried out based on similarity search and least square support vector machines(LS-SVM). Inputs of the LS-SVM are load obtained by similarity search and contiguous load, and grid search and cross validation are employed to search the optimal parameters for LS-SVM. Case study shows that this model has effectively increased the forecasting precision.Thirdly, a bidding strategy model of hydropower plants based on forecasted SMP is established. The model takes various constraints into accounts, and plans the water consumption and load in hydropower plants as a whole, in order to make its profit maximized.Finally, a modified genetic algorithm is employed to solve the bidding strategy optimization problem and gains the optimal bidding strategy. The modified genetic algorithm put forward by this dissertation adaptively adjusts the crossover and mutation probability, and has effectively overcome the premature of simple genetic algorithm and improved the ability to converge to the global optimum. The modified adaptive genetic algorithm is applied to solve the bidding strategy model for hydropower plants, and the results show the approach has effectively increased the economic benefit of hydropower plants.