Optimal Capacity Configuration of Wind–Solar Hydrogen Storage Microgrid Based on IDW-PSO
Source abstract
Abstract
Because the new energy is intermittent and uncertain, it has an influence on the system’s output power stability. A hydrogen energy storage system is added to the system to create a wind, light, and hydrogen integrated energy system, which increases the utilization rate of renewable energy while encouraging the consumption of renewable energy and lowering the rate of abandoning wind and light. Considering the system’s comprehensive operation cost economy, power fluctuation, and power shortage as the goal, considering the relationship between power generation and load, assigning charging and discharging commands to storage batteries and hydrogen energy storage, and constructing a model for optimal capacity allocation of wind–hydrogen microgrid system. The optimal configuration model of the wind, solar, and hydrogen microgrid system capacity is constructed. A particle swarm optimization with dynamic adjustment of inertial weight (IDW-PSO) is proposed to solve the optimal allocation scheme of the model in order to achieve the optimal allocation of energy storage capacity in a wind–hydrogen storage microgrid. Finally, a microgrid system in Beijing is taken as an example for simulation and solution, and the results demonstrate that the proposed approach has the characteristics to optimize the economy and improve the capacity of renewable energy consumption, realize the inhibition of the fluctuations of power, reduce system power shortage, and accelerate the convergence speed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Microgrid Control and Optimization · Hybrid Renewable Energy Systems · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electricity, Shanghai Dianji University, Shanghai 201306, China
Department of Electricity, Shanghai University of Electric Power, Shanghai 201306, China
References (31)
Show source-provided references
1. Overview of new energy power system stability
2. Lian, Z. (2021). Research on Capacity Allocation Optimization of Hybrid Energy Storage System of Wind Power Heat Pump Based on Particle Swarm Optimization. [Master’s Thesis, Shenyang University of Technology].
3. Optimal configuration of hydrogen energy storage in low-carbon park integrated energy system considering electricity-heat-gas coupling characteristics
4. Lei, S., He, Y., Zhang, J., and Deng, K. (2023). Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition. Energies, 16.10.3390/en16114307 ↗
5. Li, W., Jin, R., Ma, X., and Zhang, G. (2023). Capacity Optimal Allocation Method and Frequency Division Energy Management for Hybrid Energy Storage System Considering Grid-Connected Requirements in Photovoltaic System. Energies, 16.10.3390/en16104154 ↗
6. Microgrid Development in China: A method for renewable energy and energy storage capacity configuration in a megawatt-level isolated microgrid10.1109/MELE.2017.2685818 ↗
7. Xiu, X. (2018). Research on Optimal Allocation of Energy Storage System Capacity and Life Cycle Economic Evaluation Method. [Master’s Thesis, China Agricultural University].
8. Optimization of double-layer energy storage capacity of wind, solar and electricity grid-connected based on improved slime mold algorithm
9. Sun, S. (2012). Study on Optimal Allocation and Economic Operation Model of Multi-Energy Microgrid. [Master’s Thesis, Hefei University of Technology].
10. Optimal configuration of wind-solar hybrid power supply system based on discrete probability model
11. Modeling and configuration optimization of the natural gas-wind-photovoltaic-hydrogen integrated energy system: A novel deviation satisfaction strategy10.1016/j.enconman.2021.114340 ↗
12. Application value, challenge and prospect of hydrogen energy storage in China’s new power system10.15302/J-SSCAE-2022.03.010 ↗
13. Power System Probabilistic Production Simulation Including Wind Farms
14. Yu, X. (2020). Study on Optimal Allocation and Operation of Comprehensive Energy System in the Park with Hydrogen Storage and Heat Storage. [Master’s Thesis, North China Electric Power University].
15. Optimal capacity allocation of photovoltaic hydrogen storage system considering load demand response
16. Overview of Hydrogen Energy Storage Technology in Large Scale Intermittent Renewable Energy Integration Application
17. Peng, S., Zhu, L., Dou, Z., Liu, D., Yang, R., and Pecht, M. (2023). Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources. Energies, 16.10.3390/en16093899 ↗
18. SARAP Algorithm of Multi-Objective Optimal Capacity Configuration for WT-PV-DE-BES Stand-Alone Microgrid10.1109/ACCESS.2020.3008553 ↗
19. Bi-Layer Optimization Model for Energy Storage Systems Under Wind and PV Access
20. Research review on optimal scheduling considering wind power uncertainty
21. Xie, X., Wang, H., Tian, S., and Liu, Y. (2015, January 26–29). Optimal capacity configuration of hybrid energy storage for an isolated microgrid based on QPSO algorithm. Proceedings of the International Conference on Electric Utility Deregulation & Restructuring & Power Technologies, Changsha, China.10.1109/DRPT.2015.7432592 ↗
22. Capacity Optimization of Hybrid Energy Storage Microgrid Considering Electricity-Hydrogen Coupling
23. Multi-source Coordinated Control Strategy Considering Battery’s SOC for Islanded DC Microgrid
24. Pan, X., Liu, K., Wang, J., Hu, Y., and Zhao, J. (2023). Capacity Allocation Method Based on Historical Data-Driven Search Algorithm for Integrated PV and Energy Storage Charging Station. Sustainability, 15.10.3390/su15065480 ↗
25. Improved optimal allocation method of wind-solar hybrid power generation system capacity
26. Photovoltaic maximum power point tracking based on improved particle swarm optimization
27. Shi, Y.H., and Eberhart, R.C. (1999, January 6–9). Empirical study of particle swarm optimization. Proceedings of the Congress on Evolutionary Computation, Washington, DC, USA.
28. A Particle Swarm Optimization Algorithm with Dynamic Adjustment of Inertia Weight
29. Particle Swarm Optimization Algorithm Based on Non-symmetric Learning Factor Adjusting
30. Optimal Capacity Configuration of Wind/PV/Storage Hybrid EnergyStorage System in Microgrid
31. Kennedy, J., and Eberhart, R.C. (1997, January 12–15). A discrete binary version of the particle swarm algorithm. Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, Orlando, FL, USA.