An improved MOPSO algorithm for multi-objective optimization of reservoir operation under climate change

Applying the new multi-objective algorithms for the operation of a multi-reservoir system in hydropower plants

The optimal operation of the multi-purpose reservoir system is a difficult, and, sometimes, non-linear problem in multi-objective optimization. By simulating biological behavior, meta-heuristic algorithms scan the decision space and can offer a set of points as a group of solutions to a problem. Because it is essential to simultaneously optimize several competing objectives and consider relevant constraints as the main problem in many optimization problems, researchers have improved their ability to solve multi-objective problems by developing complementary multi-objective algorithms. Because the AHA algorithm is new, its multi-objective version, MOAHA (multi-objective artificial hummingbird algorithm), was used in this study and compared with two novel multi-objective algorithms, MOMSA and MOMGA. Schaffer and MMF1 were used as two standard multi-objective benchmark functions to gauge the effectiveness of the proposed method. Then, for 180 months, the best way to operate the reservoir system of the Karun River basin, which includes Karun 4, Karun 3, Karun 1, Masjed-e-Soleyman, and Gotvand Olia dams to generate hydropower energy, supply downstream demands (drinking, agriculture, industry, environmental), and control flooding was examined from September 2000 to August 2015. Four performance appraisal criteria (GD, S, Δ, and MS) and four evaluation indices (reliability, resiliency, vulnerability, and sustainability) were used in Karun's multi-objective multi-reservoir problem to evaluate the performance of the multi-objective algorithm. All three algorithms demonstrated strong capability in criterion problems by using multi-objective algorithms' criteria and performance indicators. The large-scale (1800 dimensions) of the multi-objective operation of the Karun Basin reservoir system was another problem. With a minimum of 1441.71 objectives and an average annual hydropower energy manufacturing of 17,166.47 GW, the MOAHA algorithm demonstrated considerable ability compared to the other two. The final results demonstrated the MOAHA algorithm's excellent performance, particularly in difficult and significant problems such as multi-reservoir systems' optimal operation under various objectives.

Research on optimal operation of cascade pumping stations based on an improved sparrow search algorithm

For the low efficiency and large loss of cascade pumping stations, aiming to maximize system efficiency, an optimized scheduling model of cascade pumping stations is established with consideration of multiple constraints, and the optimal scheduling method based on the improved sparrow search algorithm (BSSA) is proposed. The BSSA is initialized by the Bernoulli chaotic map to solve the insufficient initial diversity of the sparrow search algorithm (SSA). The random boundary strategy is introduced to avoid local optimum when dealing with the scheduling problem of pumping stations. The performance and improvement strategy of BSSA are verified by eight benchmark functions. Results show that BSSA has better convergence accuracy and faster speed. BSSA is applied to a three-stage pumping station considering three flow conditions, and compared with the current scheme, particle swarm optimization and genetic algorithm optimization schemes, the operation efficiency of SSA can be increased by 0.72-0.96%, and operation cost can be reduced by ¥263,000/a-¥363,300/a. On this basis, the improvement of 0.04-0.30% and ¥14,800/a-¥109,900/a can be further achieved by the BSSA, which confirms the feasibility and effectiveness of BSSA to solve the pumping station optimal scheduling problem. The findings present useful reference for the optimized scheduling of pumping station system.