Joint chance-constrained energy-reserve co-optimization for distribution networks with flexible resource aggregators

Jie Zhu, Yinliang Xu, Nengling Tai and Hongbin Sun

Applied Energy, 2025, vol. 388, issue C, No S0306261925004155

Abstract: As distributed renewable energy sources become increasingly integrated into distribution networks, it becomes crucial to harness the flexibility of demand-side distributed energy resources (DDER) to mitigate the uncertainties associated with renewable energy generation. This paper proposes an affine transformation-based DDER aggregated operational model (AOM) and develops a day-ahead energy-reserve co-optimization model for distribution networks with aggregators (AGGs). Distinct from prior research, the proposed model incorporates joint chance constraints (JCCs) to account for the impact of balancing reserves on the AOM and the propagation of uncertainty within the distribution network due to reserve activation. This ensures the reliability of the balancing reserves provided by AGGs and maintains the system's operational state within safety constraints when reserves are deployed to manage uncertainty. Additionally, an iterative risk adjustment method is introduced to solve the joint chance-constrained programming, using a sample-based posteriori approach to evaluate the joint violation probability of JCCs, overcoming the conservatism of Boole's Inequality. In the IEEE-141 bus network case, the proposed aggregation method increases AGG profits by 33.9 % and 13.1 %, while reducing operating costs by 9.8 % and 4.9 %, compared to the two existing aggregation methods. Furthermore, the iterative risk adjustment method reduces operating costs by 9.6 % relative to the Boole's Inequality approach.

Keywords: Distributed energy resource; Aggregation; Joint chance-constrained programming; Uncertainty; Iterative risk adjustment method (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125685

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