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

This paper proposes a bi-level peer-to-peer (P2P) multi-energy trading framework for a coupled distribution network (DN) and district heating network (DHN). At the lower level, each nodal agent represents its intra-nodal prosumers to optimize the local energy scheduling and P2P trading strategies based on the modified Nash bargaining theory, and a distributed algorithm is then adopted to enable individual agents to make their strategies autonomously only with the sharing of trading information. Once the lower-level P2P bargaining is settled, each agent is required to submit its nodal net loads and trading adjustment tolerances to the network operators. At the upper level, the network operators minimize the line power losses while satisfying network operation constraints by reconfiguring the DN and DHN as well as enforcing necessary trading adjustments from the lower-level agents when the network violations incurred by the P2P trading cannot be fully solved by network reconfiguration. Mathematically, the DN operation is modelled based on the linearized DistFlow with a set of new radiality constraints to sufficiently ensure the radial structure of the DN. The DHN operation is formulated as a quasi-linear thermal flow model independent of mass flow rate and water temperature, by which the computation complexity and limitations associated with traditional DHN formulations are addressed. Finally, a multi-energy network consisting of an IEEE 33-bus DN and a 23-node DHN is used to demonstrate the effectiveness of the proposed P2P trading framework and the efficiency of the solution algorithms.