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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Hua, Yukai Liao, Binrong Zhang, Hao Liu, Liming Li, Yuxuan Yang, Yongkuan Fujian Dongqiao Thermal Power Co Ltd Quanzhou 362000 Peoples R China Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China

ISBN: (纸本)9798350377859;9798350377842

To address the issues in the constrained multi-objective optimal scheduling problem of microgrids, such as encountering infeasible solutions and a low proportion of feasible solutions, we propose a constrained multi-objective optimization algorithm assisted by an additional objective function. Three swarms in a push-and-pull co-evolutionary framework are constructed: the first swarm optimizes the original constrained problem, the second swarm optimizes constraint violation as an additional objective to effectively increase the proportion of feasible solutions, and the third swarm uses push-pull search and improved constraint relaxation techniques to transform high-quality infeasible solutions into feasible ones during the search process. This helps the populations overcome infeasibility barriers and escape local optima. All feasible solutions found by the three populations during the search process are stored in a separate archive to ensure the feasibility and diversity distribution of the solutions. The proposed constrained multi-objective optimization algorithm is applied to optimize the scheduling of microgrid equipment by establishing a microgrid optimization model for combined cooling, heating, and power supply, with system operating cost and environmental management cost as the optimization objectives, and considering the constraints in actual operation. The results demonstrate that the proposed algorithm can reasonably adjust the output of each distributed power source and improve the flexibility and economy of microgrid optimization.

关键词： Microgrid optimal scheduling constrained multi-objective optimization constrained multi-objective optimization algorithm

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Battery energy storage system for enhancing the electrolyzer capacity factor in small-scale WindtH2 system with a smoothing control strategy: constrained multi-objective Pareto optimization and case study in Algeria

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JOURNAL OF ENERGY STORAGE 2022年 52卷

作者： Tebibel, H. Ctr Dev Energies Renouvelables Algiers 16340 Algeria

Stochastic nature of wind energy prevents the electrolyzer in wind-to-hydrogen (WindtH(2)) system to accomplish high capacity factor without the assistance of the battery energy storage system (BESS). Furthermore, design process focuses on the reliability of the system and its components to achieve low production cost. The goal of this investigation is to develop a decision making tool for solving constrained multi-objective optimization (CMO) problems to minimize the cost and power losses, and maximize the reliability of a small-scale WindtH(2) system subjected to various constraints. The proposed algorithm, which combines a CMO model with a smoothing control strategy (SCS), solves the CMO problem to find Pareto-optimal solutions. Dynamic SCS smoothes the power provided to the electrolyzer by the assistance of the BESS. It favors the electrolyzer full-capacity operation for enhancing its capacity factor. Minimum/maximum electrolyzer power input and safe thresholds of battery and H-2 tank state-of-charge are the main operational constraints taken into consideration by the CMO model. The four conflicting objectives considered in the optimization model are the Levelized Cost of H-2 (LCOH), Total H-2 Deficit (THD), Energy Dump Possibility (EDP) and a new indicator namely Electmlyzer Capacity Factor (ECF). Solution vector consists of seven decision variables including battery cycles spent and electrolyzer operation hours. The developed algorithm is applied to generate Pareto solutions of a case for industrial usage. Pareto results show: (1) The proposed algorithm with the associated indicators is useful for the design process;(2) The SCS significantly helps to improve the ECF;(3) Consistent relationships between the four objectives;(4) WindtH(2) system consisting of wind turbine farm of 836.5 kW, electrolyzer of 276 kW, battery of 765 kWh and H-2 Tank of 10.8 kg gives a LCOH of 27.01 $/kg, ECF of 20.5%, EDP of 8.19% and THD of 41.72%.

关键词： WindtH(2) system Smoothing control strategy constrained multi-objective optimization algorithm Electrolyzer capacity factor Total H-2 deficit

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