Data-Driven Distributionally Robust Co-Optimization of P2P Energy Trading and Network Operation for Interconnected Microgrids

This paper proposes a data-driven distributionally robust co-optimization model for the peer-to-peer (P2P) energy trading and network operation of interconnected microgrids (MGs). In particular, three-phase unbalanced MG networks are considered to account for the implementation practices, and the emerging soft open point (SOP) technology is used for the flexible connection of the multi-MGs. First, the energy management in individual MGs is modeled as a distributionally robust optimization (DRO) problem considering the P2P energy trading options and various operational constraints. Later, a novel decentralized and incentive-compatible pricing strategy is developed for P2P energy trading using the alternating direction method of multipliers (ADMM). Furthermore, the uncertainties in load consumption and renewable generation (RG) are taken into account and the Wasserstein metric (WM) is used to construct the ambiguity set of the uncertainty probability distributions (PDs). Consequently, only historical data is needed rather than prior knowledge about the actual PDs. Finally, the equivalent linear programming reformulations are derived for the DRO model to achieve computational tractability. Numerical tests on four interconnected MGs corroborate the advantages of the proposed P2P energy trading scheme and also demonstrate that the proposed DRO model is more effective in handling the uncertainties compared to the robust optimization (RO) and the stochastic programming (SP) models.

Automated summary

This paper presents a data-driven distributionally robust co-optimization model for P2P energy trading and network operation of MGs. The model considers various operational constraints and uncertainties from load consumption and RG, utilizing emerging technologies to address them effectively.