A stochastic approach to hydroelectric power generation planning in an electricity market

The paper discusses the planning of hydroelectric power generation. A stochastic optimization procedure is offered to solve the complex task of planning the operation of three hydroelectric power plants. The proposed stochastic optimization algorithm is based on time average revenue maximization, taking into account the random nature of the future energy prices and river water inflows. Random variables are predicted by using an algorithm based on artificial neural networks. For computing within the operational planning, the task is divided into three parts. First, middle-term planning is used to solve the water resources distribution task. The second and third parts are related to day-ahead operational and unit commitment planning; in those cases, nonlinear programming tools are used. A case study presents the results of electric power generation in the case of optimum water resource distribution in the storage basins of hydroelectric power plant's cascade. The paper proves the workability of the developed algorithm for maximizing the income value and is intended to enable and support improved planning and decision-making for electric power producers.