Economic optimization configuration of energy storage in distribution network considering the attenuation of aging photovoltaic output
|更新时间:2025-10-16
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Economic optimization configuration of energy storage in distribution network considering the attenuation of aging photovoltaic output
Vol. 44, Issue 1, Pages: 175-182(2025)
作者机构:
三峡大学电气与新能源学院,湖北,宜昌,443002
作者简介:
基金信息:
DOI:
CLC:TM715
Published:2025
稿件说明:
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HU Haipeng. Economic optimization configuration of energy storage in distribution network considering the attenuation of aging photovoltaic output[J]. 2025, 44(1): 175-182.
DOI:
HU Haipeng. Economic optimization configuration of energy storage in distribution network considering the attenuation of aging photovoltaic output[J]. 2025, 44(1): 175-182.DOI:
Economic optimization configuration of energy storage in distribution network considering the attenuation of aging photovoltaic output
The escalating prevalence of aging distributed photovoltaic (PV) systems has given rise to challenges such as voltage fluctuations and curtailed energy production in large-scale grid-connected systems lacking energy storage. These issues can be effectively mitigated through the strategic deployment of energy storage solutions. In order to enhance the cost-effectiveness of energy storage configuration
an economic optimization method for distribution network energy storage that takes into account the attenuation of aging photovoltaic output is proposed. The impact of aging PV generation decay rate and operational lifespan on actual PV output is thoroughly analyzed
leading to the development of a mathematical relationship between PV output and rated energy storage power and capacity. Building upon this analysis
an optimization model for energy storage configuration is established with a focus on minimizing life cycle costs and reducing voltage deviations at distribution network nodes. This comprehensive approach considers factors such as construction operating costs
network losses
and peak-valley arbitrage income. Finally
particle swarm optimization is employed to solve this complex problem using the IEEE 33-node system. The results demonstrate that the proposed method can achieve significant reductions in both operating cost (33.91%) and life cycle cost (6.01%)