Aggregator-Network Coordinated Peer-to-Peer Multi-Energy Trading via Adaptive Robust Stochastic Optimization

This paper proposes a new peer-to-peer (P2P) trading framework for a coupled multi-energy system comprising a three-phase unbalanced distribution network (DN) and a district heating network (DHN). The aggregators and the network operators coordinate to minimize the total cost and satisfy the operational constraints under the uncertainties from renewable energy sources. In the lower layer, the aggregator for each region optimizes the intra-regional energy scheduling and the inter-regional P2P trading, which are formulated as an energy management model and a dynamic double-auction market clearing model. An adaptive robust stochastic optimization (RSO) approach is developed to address the renewable power uncertainty modeled as a scenario-based ambiguity set. In the upper layer, based on the scheduling and trading results, the network operators optimize the DN and DHN operation decisions including network reconfiguration and Volt/Var regulation, and request necessary lower-layer adjustments if the operating violations cannot be fully mitigated. Simulation results show that the proposed framework can effectively minimize the aggregators' daily costs and the energy loss of the networks, while removing potential network operation violations.