Open Access

ARTICLE

Optimizing Grid Integration of Photovoltaic-Storage Systems: A Multi-Objective Framework for Stability, Economic Efficiency, and Environmental Sustainability

Cong Zhang^1,2, Peng Huang², Jun Li², Xin Ma², Yefan Shu², Dingkun Wang², Wan Chen^1,2, Zujun Ding¹, Jie Ji^1,2,*
1 Engineering Department, Huai’an Hongneng Group Crop, Huai’an, China
2 Faculty of Automation, Huai’an University, Huai’an, China
* Corresponding Author: Jie Ji. Email:
(This article belongs to the Special Issue: Advances and Emerging Trends in Photovoltaic Technologies, Energy Storage, and Green Hydrogen)

Energy Engineering https://doi.org/10.32604/ee.2026.080177

Received 04 February 2026; Accepted 05 May 2026; Published online 29 June 2026

Abstract

To address the challenges of intermittent regulation and multi-objective coordination in high-penetration photovoltaic (PV) grid integration systems, this study constructs a PV-storage-charge synergistic optimization framework. Through the deep integration of multi-timescale forecasting and dynamic scheduling mechanisms, the dispatchability, economic efficiency, and environmental compatibility of the power grid are significantly enhanced. Taking a distributed photovoltaic (PV) system with a capacity of 3191.1 MW in the Huaian region as an empirical case, a complete methodological system is established, encompassing multi-source data fusion, geographically adaptive prediction, improved multi-objective particle swarm optimization (MOPSO), and holistic validation. The study proposes a geographically adaptive PV prediction algorithm, which reduces the root mean square error (RMSE) and mean absolute error (MAE) by 40% and 50%, respectively. An MOPSO algorithm based on non-dominated sorting and crowding distance calculation synergistically optimizes energy storage scheduling, achieving a 42.3% reduction in the net load peak-valley difference and a 41.9% decrease in grid peak power demand. Empirical results demonstrate: a 20% increase in the monthly average grid-connected capacity, annual clean power generation exceeding 3 GWh, a 15% reduction in ancillary service costs, and superior performance compared to benchmark models across metrics including PV penetration rate and life-cycle cost. Sensitivity analysis identifies PV efficiency, energy storage capacity, and peak-valley electricity prices as core sensitive parameters, verifying the system’s robustness. This research provides a theoretical foundation and an engineering solution for the low-carbon and stable operation of power grids with a high share of renewable energy.

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Keywords

Photovoltaic grid integration; multi-objective optimization; energy storage scheduling; MOPSO algorithm; geographically adaptive prediction; carbon emission reduction

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