Author:
Cheng Min,Yan Wenlin,Zhang Dan,Liu Xufei,He Lei,Xu Mingyu,Yao Qiang
Abstract
AbstractA self-adaptive energy storage coordination control strategy based on virtual synchronous machine technology was studied and designed to address the oscillation problem caused by new energy units. By simulating the characteristics of synchronous generators, the inertia level of the new energy power system was enhanced, and frequency stability optimization was achieved. This strategy is integrated with the frequency response model of the new energy power system to improve the system's frequency regulation capability and achieve more stable and efficient operation. From the results, the damping of the system increased, the oscillation frequency decreased after a duration of about 15 s, and the system stability improved by 76.09%. The proposed strategy based on virtual synchronous generator adaptive energy storage coordination control strategy was improved by 83.25%. In addition, the proposed strategy has improved stability indicators and system completion efficiency by 40.57% and 22.21% respectively, both of which are better than the comparative strategies. As a result, this strategy significantly enhances the frequency regulation capability of the system, which has a positive effect on achieving efficient operation of the new energy power system and maintaining the stability of the power system.
Publisher
Springer Science and Business Media LLC
Reference21 articles.
1. Chen SY, Chang CH (2023) Optimal power flows control for home energy management with renewable energy and energy storage systems. IEEE Trans Energy Convers 38(1):218–229
2. Chen T, Wan J, Liu Y (2022) A tough, elastic ion gel with adaptive interface for high performance and safe lithium metal anodes. Chem Eng J 433(2):2–8
3. Chen J , Zeng Q , Li G , Xin Y, Li L, Wang P. Deviation-Free Frequency Control of MMC-MTDC Converter Based on Improved VSG//2019 4th IEEE Workshop on the Electronic Grid (eGRID).IEEE, 2019, 1–5
4. Ghavidel HF, Mousavi-G SM (2022) Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems. Int J Hydrogen Energy 47(33):14983–15000
5. Goud V, Rahul MR, Phanikumar G (2022) Prediction of growth velocity of undercooled multicomponent metallic alloys using a machine learning approach. Scripta Mater 207(1):2–6