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The high penetration of distributed renewable energy resources (DERs) on the user side is essential for developing the low-carbon power system. Peer-to-peer (P2P) energy trading has emerged as an effective method to consume the DERs' surplus energy locally. However, the uncertainty of massive DERs poses challenges to the system operation. We thus propose a coupling market framework, where the P2P energy trading market can participate in both energy and ancillary service markets. Since the operation problems are mainly caused by uncertainty sources (i.e., PV generations or wind turbines), these independent uncertainty sources are first charged with uncertainty marginal prices derived from the distributionally robust economic dispatch models. These payments can be allocated to reserve providers. Then, the P2P energy trading is modeled as an equivalent federated power plant (FPP) to provide ancillary services and energy for the other market entities. The FPPs' reserve capacities are generated considering intra- and inter-FPP uncertainty. Finally, an extreme point scanning algorithm is developed to efficiently identify whether the FPPs can ensure the market operation and allocate the payments according to their reserve contributions. Case studies verify the theoretical properties and show the practicability of the proposed algorithms.
Xia et al. (Sat,) studied this question.