Improved chaotic bat algorithm and its application in multi-objective operation of cascade reservoirs considering different ecological flow requirements

With growing concerns on renewable energy and environment, the multi-objective operation (MOO), which considering the economic benefits and ecological benefits, becomes an important optimization problem. To handle this problem, a new multi-objective optimization approach named improved chaotic bat algorithm (ICBA) is proposed in this paper. In ICBA, chaos theory is used to generate initial population and update pulse emission rate to improve population diversity. The self-adaptive loudness update mechanism is designed to control the convergence speed according to the iterations process. Furthermore, the Montana Method with seasonal variation is proposed to calculate downstream ecological flow. The feasibility and effectiveness of the proposed ICBA method are demonstrated by the simulations of the Qingjiang cascade reservoirs in different hydrological years. Four scenarios are set up to compare the power generation results and the downstream ecological flow satisfaction rate under different ecological flow requirements. The results show that average annual operation schemes obtained by the ICBA can meet the minimum and suitable ecological flow requirements. Compared to the scenario 1 (optimization goal only consider the power generation requirement), the scenario 4 (optimization goal consider both power generation and ideal ecological flow requirement) proposed in this paper can improve the satisfaction rate of ideal ecological flow requirement, and has little influence on the average annual power generation. As compared with other several algorithms, the ICBA can obtain better operation results in different hydrological years and provide a new effective tool for designing reasonable operation schemes of cascade reservoirs.