@Article{ee.2026.079356,
AUTHOR = {Xiaole Luo, Haitao Lan, Yang Song, Yunliang Zhao, Yingnan Bai, Yinan Yang, Haichao Peng, Xiaojing Wang},
TITLE = {Distributionally Robust Co-Optimization of Rural PV-Storage Distribution Networks Incorporating Adjustable Agricultural Load Characteristics},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26913},
ISSN = {1546-0118},
ABSTRACT = {Rural distribution networks are experiencing increasing integration of distributed photovoltaic (PV) generation, yet the inherent intermittency of PV output and the pronounced peak-valley disparities of agricultural loads pose significant challenges to grid economy and security. To address photovoltaic (PV) fluctuations and agricultural load peak-valley disparities in rural distribution networks, this paper proposes a distributionally robust cooperative planning method integrating load elasticity. Firstly, a refined agricultural load model is established: irrigation loads couple pump head-flow characteristics with load shifting mechanisms; greenhouse loads incorporate curtailable heating and temperature-light constraints; production loads combine time-shiftable machinery charging with rigid sensor demands. A two-stage distributionally robust optimization model is then formulated to minimize total investment-operation costs under PV uncertainty, innovatively embedding the CMA-ES algorithm for coordinated electricity consumption strategy optimization. Simulations on a modified IEEE 33-node system demonstrate: compared to conventional planning, the proposed method reduces total costs by 15.6% and mitigates peak-valley differences by 28.5% while maintaining voltage deviations within 1.04–1.05 p.u., verifying the synergy of distributed PV-storage systems and load elasticity.},
DOI = {10.32604/ee.2026.079356}
}