Frontiers in Energy Research (Jul 2024)
Constrained distributionally robust optimization for day-ahead dispatch of rural integrated energy systems with source and load uncertainties
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 proposes a day-ahead scheduling model of the RIES to improve its economical effectiveness, where three energy carriers, namely, 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 limiting the loss of cost expectation in the historical distribution to ensure economical effectiveness. In addition, an ambiguous set of the source and load uncertainties incorporating 1-norm and infinity-norm constraints is established, which realizes a flexible adjustment for the conservativeness of 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.
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