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arXiv · 2409.00367

Distributionally Robust Joint Chance-Constrained Optimization for Electricity Imbalance: Integrating Renewables and Storage

Amir Noori·Babak Tavassoli·Alireza Fereidunian
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Abstract

Integrating Distributed Energy Resources (DERs) with peer-to-peer (P2P) energy trading offers promising solutions for grid modernization by incentivizing prosumers to participate in mitigating peak demand. However, this integration also introduces operational uncertainties and computational challenges. This paper aims to address these challenges with a novel scalable and tractable distributionally robust joint chance-constrained (DRJCC) optimization framework that effectively facilitates P2P energy trading by enhancing flexibility provision from large-scale DER operations under uncertain supply and demand. Therefore, a practical framework is proposed to solve the core challenges of DRJCC by integrating three key components: (1) a Wasserstein ambiguity set that effectively quantifies uncertainty with sparse data, (2) a CVaR-based approximation of joint chance constraints to balance computational efficiency with risk control, and (3) a privacy-preserving ADMM algorithm that enables distributed implementation through decomposition. To discern patterns in the data that indicate collaboration potential and adjust ambiguity sets for improved efficiency, K-means clustering is applied to historical scenarios. Simulation results show that the proposed framework reduces peak demand by approximately 28% and total community costs by around 31%, underscoring its effectiveness in enhancing grid robustness, operational reliability, and economic optimization in renewable-based energy management.

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Amir Noori, Babak Tavassoli, Alireza Fereidunian. 2024-08-31. Distributionally Robust Joint Chance-Constrained Optimization for Electricity Imbalance: Integrating Renewables and Storage. https://doi.org/10.22034/tjee.2025.63090.4884

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