Research On Cascaded Hydropower Plants Bidding Strategies In Electricity Market

With the acceleration process of power marketablization, and the implementation of operation mechanism of"Separation of power plant from power network, competitive price of listing power network". Power generating enterprises have become the main body of market competition. Under the power market environment, how cascaded hydropower plants participate in market competition, optimizes production process of power generation and adopts the tactic price in order to achieve the maximum profit of power generation have become the foremost problem. This thesis studies the following aspects with the prediction of marginal electricity prices in power system, allocation of generation capacity and the competitive price tactics as the main contents. The BP neural network trained by PSO method is adapted to predict the marginal electricity price in the system, and analyzes the competitive price tactics of the clearing price of power generating plant based on the market prediction. Accordingly, a hydropower plant with maximum profits as the objective is analyzed and calculated so as to obtain the optimal structure tactics of electricity quantities in the case of the competitive price of listing power network.