multi-objective optimization of hydropwoer multi-objective optimization of hydropower reservoirs operation based on the pattern of PAB markets

In recent years, the structure of the electricity industry has undergone a change and since November 2003, when the electricity market of the country was launched, its monopoly structure has become a competitive structure. In this market, the forecast of electricity prices is not only necessary in pricing but also plays an important role in finding the optimal operation strategy by the power plant's users. In this paper, an artificial neural network approach is used to predict daily energy prices in peak hours and its results have been used to optimize the multi-purpose operation of the reservoir of Karun 5 dam. The intended goals include two goals of maximizing annual income and maximizing the minimum daily energy production. The multi-objective optimization method NSGA II was selected to solve the multi-objective optimization problem. In order to apply the proposed model, Karun 5 dam and hydroelectric power station was selected as case study. Since the output of the multi-objective optimization model is a wide range of non-dominated responses, In order to select the optimal solution among obtained solutions, Nash bargaining, non-symmetric Nash, Kalai-Smoronvski and Area Monotonic was implemented.