An adaptive relaxed cellular automata method for reliability-based hydropower operation of reservoirs

This paper presents a novel cellular automata Approach as the solution to reliability-based reservoir hydropower operation problems. The method is based on the observation that a low value of the penalty parameter would lead to partial enforcement of the constraints. Therefore, in this method, the constraints of the chance-constrained operation problem, namely operational and reliability constraints, are dealt with differently. A high enough value of the penalty parameter is used for the first set while a lower than enough value is used for the second set, leading to strong enforcement of the first set of the constraint and partial fulfillment of the second set. Since the proper value of the penalty parameter to be used for the reliability constraints is not known a priori, an adaptive method is proposed to find the proper value. The proposed model is used for operation of Dez reservoir in Iran and the results are presented and compared with those of a Genetic Algorithm. Hydropower operation is considered over short, medium, and long-term periods to demonstrate the efficiency and effectiveness of the proposed method for problems of different scales. The proposed model has proven to produce results superior to the results of GA with much reduced computational effort.