AUTHOR=Zhang Zhihui , Yang Song , Ma Yunting , Sun Shumin , Yu Peng , Yang Fei 

TITLE=Constrained distributionally robust optimization for day-ahead dispatch of rural integrated energy systems with source and load uncertainties

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1411152

DOI=10.3389/fenrg.2024.1411152

ISSN=2296-598X

ABSTRACT=As a deep connection between agriculture and energy, the rural integrated energy system (RIES) is a micro-scale supply-distribution-storage-demand network, which provides an important means to realize the utilization of rural clean energy. This paper constructs a day-ahead scheduling model of the RIES to improve its economical effectiveness, where three energy carriers including biogas, electric power and heat are integrated. To address the source and load uncertainties composed of photovoltaic power, power load and heat load, this paper develops a constrained distributionally robust optimization (CDRO), which optimizes the cost expectation related to the extreme distribution to enhance the robustness, while limits the loss of the cost expectation in the historical distribution to ensure the economical effectiveness. Besides, an ambiguous set of the source and load uncertainties incorporating 1-norm and infinite-norm constraints is established, which realizes a flexible adjustment for the conservativeness of the CDRO. The distributionally robust dispatch is formulated as a deterministic programming in a two-stage solving framework, where the subproblem uploads its extreme probability distribution to the master problem, and these two problems are iteratively optimized until the convergence. Finally, the numerical simulations in a modern farm park prove the performance of the constructed dispatch model, and the flexibility of CDRO in balancing the economical effectiveness and robustness of the dispatch.The kth reference sample. t Index of dispatch periods. emp F Empirical cost expectation obtained by k Index of reference sample. SO. v Step index in two-stage optimization. emp F Empirical cost expectation obtained by Ambiguous set of source-demand DRO. uncertainty.Maximum gap in infinite-norm and 1- v Worst-case distribution set.Set of transferable periods.