Operation of Hydroelectric Power Plants, Dam Reservoirs, and Energy Trade Using Artificial Neural Networks

Abstract

Appropriate operation of the dam reservoir in a hydroelectric power plant (HEPP) is necessary for energy planning, reservoir management, and efficient operation. For good energy planning, the operator needs to make an accurate estimate of the energy production capacity for the next day and plan for production when the energy need is highest. The energy produced in HEPPs depends on the level of water stored in the reservoir, which is directly connected to the reservoir flow. As the water level in the reservoir varies throughout the year depending on climatic conditions, it is important to estimate energy production in order to operate the HEPP most effectively. In this study, the next-day energy production of the HEPP was estimated using a neural network with two hidden layers, each with 10 neurons. A neural network with a hidden layer of 20 neurons was used to estimate future electricity prices and the best hours for market clearing price (MCP). This study found that using short-term training provided the best hourly estimation of MCP, with an average accuracy of 90%; the daily estimation of MCP was ≥95%.