@Article{ee.2026.080177,
AUTHOR = {Cong Zhang, Peng Huang, Jun Li, Xin Ma, Yefan Shu, Dingkun Wang, Wan Chen, Zujun Ding, Jie Ji},
TITLE = {Optimizing Grid Integration of Photovoltaic-Storage Systems: A Multi-Objective Framework for Stability, Economic Efficiency, and Environmental Sustainability},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/27355},
ISSN = {1546-0118},
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.},
DOI = {10.32604/ee.2026.080177}
}