Affiliation:
1. School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
2. State Key Laboratory of Smart Grid Protection and Control, Nari Group Corporation, Nanjing 211106, China
Abstract
The mutual optimization of a multi-microgrid integrated energy system (MMIES) can effectively improve the overall economic and environmental benefits, contributing to sustainability. Targeting a scenario in which an MMIES is connected to the same node, an energy storage coordination control strategy and carbon emissions management strategy are proposed, and an adaptive step-size method is applied to improve the distributed optimization of MMIESs based on the alternating direction multiplier method (ADMM). Firstly, the basic framework of MMIESs is established, and a coordinated control strategy limiting the time of charge and the discharge of the battery storage system (BSS) is proposed. Then a multi-objective optimization model based on operating and environmental cost is formulated. Considering that different microgrids may be managed by different operators and a different convergence speed of multi-objective optimization iteration, an adaptive step-size distributed iterative optimization method based on ADMM is used, which can effectively reduce the cost and protect the privacy of each microgrid. Finally, a system composed of three microgrids is taken as an example for simulation analysis. The results of distributed optimization are accurate, and the proposed coordinated control strategy can effectively enhance the revenue of ESS, which verifies the effectiveness of the proposed method.
Funder
State Key Laboratory of Smart Grid Protection and Operation Control of China
National Natural Science Foundation of China
Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Reference25 articles.
1. A review on the integration and optimization of distributed energy systems;Ren;Renew. Sustain. Energy Rev.,2022
2. Naz, K., Zainab, F., Mehmood, K.K., Bukhari, S.B.A., Khalid, H.A., and Kim, C.H. (2021). An optimized framework for energy management of multi-microgrid systems. Energies, 14.
3. Multi-microgrid intelligent load shedding for optimal power management and coordinated control with energy storage systems;Sadoudi;Int. J. Energy Res.,2021
4. Fang, X., Wang, J., Song, G., Han, Y., Zhao, Q., and Cao, Z. (2019). Multi-agent reinforcement learning approach for residential microgrid energy scheduling. Energies, 13.
5. A decentralized solution to the DC-OPF of interconnected power systems;Bakirtzis;IEEE Trans. Power Syst.,2003
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献