Renewable Energy Absorption Oriented Many-Objective Probabilistic Optimal Power Flow

• Published in: IEEE transactions on network science and engineering Vol. 11; no. 6; pp. 5432 - 5448
• Main Authors: Li, Yuanzheng, He, Shangyang, Li, Yang, Ding, Qiang, Zeng, Zhigang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Absorption; Algorithms; Costs; Energy absorption; Ensemble learning; Feasibility studies; Greenhouse gases; Load flow; many-objective optimization; Multiple objective analysis; Optimization; optimization algorithm; Power flow; Power system stability; probability optimal power flow; Renewable energy; Renewable energy absorption; Renewable resources; Voltage control; Wind power generation
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2023.3290147

To achieve the net zero emission of greenhouse gases, renewable energy (RE) has been highly penetrated into the power system. However, the high absorption of RE may violate operational constraints of the power system and impact its secure and economic operation. In contrast, if some of the penetrated RE is curtailed, the above issue would be addressed. However, this causes energy waste. Therefore, a many-objective probabilistic optimal power flow (MOPOPF) model is proposed in this paper, orienting to the absorption of RE by minimizing its curtailments and supporting secure and economic objectives, simultaneously. To well resolve this model, an ensemble learning based group search optimizer with multiple producers (ELGSOMP) is developed, where the ensemble learning would enhance the convergence of the original group search optimizer with multiple producers by releasing its dilemma. Then, case studies in this study are conducted on a modified IEEE 30-bus benchmark and a real power system. Obtained results show the feasibility of our proposed MOPOPF, and the outperformance of ELGSOMP compared with other algorithms.