TY - GEN
T1 - PV Energy Storage Capacity Optimization for Receiving End Grid Based on Grey Wolf Algorithm
AU - Wang, Ziwei
AU - Gao, Congzhe
AU - Zhang, Dahui
AU - Chen, Junliang
AU - Li, Xiyan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Remote rural areas have high load randomness and grid fluctuations but are rich in solar energy resources, and the receiving end grid is vigorously developing renewable energy. However, due to the weak power supply capacity in remote rural areas and the instability and discontinuity of renewable energy generation, the quality of power supply cannot be guaranteed. Therefore the capacity optimization of renewable energy is of great significance. In this paper, for the capacity optimization problem of photovoltaic energy storage, it is proposed to establish a capacity optimization model by taking the economic optimization and the minimization of the grid variation coefficient as the objective function. Considering the load and time-of-use electricity pricing, it is proposed to solve the PV energy storage optimization problem under the constraints of remote rural areas by using the gray wolf algorithm. Finally, the effectiveness of the optimization results is illustrated by an example analysis, and the grid volatility decreases up to 40.94%, which helps to improve the power supply quality in remote rural areas.
AB - Remote rural areas have high load randomness and grid fluctuations but are rich in solar energy resources, and the receiving end grid is vigorously developing renewable energy. However, due to the weak power supply capacity in remote rural areas and the instability and discontinuity of renewable energy generation, the quality of power supply cannot be guaranteed. Therefore the capacity optimization of renewable energy is of great significance. In this paper, for the capacity optimization problem of photovoltaic energy storage, it is proposed to establish a capacity optimization model by taking the economic optimization and the minimization of the grid variation coefficient as the objective function. Considering the load and time-of-use electricity pricing, it is proposed to solve the PV energy storage optimization problem under the constraints of remote rural areas by using the gray wolf algorithm. Finally, the effectiveness of the optimization results is illustrated by an example analysis, and the grid volatility decreases up to 40.94%, which helps to improve the power supply quality in remote rural areas.
KW - PV energy storage
KW - capacity optimization
KW - economic
KW - grey wolf algorithm
KW - grid variation coefficient
UR - http://www.scopus.com/inward/record.url?scp=85182338093&partnerID=8YFLogxK
U2 - 10.1109/ICEMS59686.2023.10345105
DO - 10.1109/ICEMS59686.2023.10345105
M3 - Conference contribution
AN - SCOPUS:85182338093
T3 - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
SP - 2523
EP - 2528
BT - 2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Conference on Electrical Machines and Systems, ICEMS 2023
Y2 - 5 November 2023 through 8 November 2023
ER -
