P2P Trade With Prosumers' Actual Approximate Utility Functions Within Near-Potential Games Framework

There is a potential conflict between P2P trade and distribution network operator (DSO) decisions, which slows down the expansion of P2P trade. Expanding the P2P trade requires reducing this conflict and increasing DSO awareness of the actual behavior of prosumers. In other words, mitigating this conflict requires that DSO approximates prosumers' utility functions (PUFs) based on their actual behavior. On the other hand, PUFs approximated based on the actual behavior of prosumers have various parameters such as freedom in decision-making, collective influence, privacy, and marginal cost/utility. This is a mathematical challenge for DSO because this class of PUFs may not be convex or continuously differentiable. Hence, in this paper, a near-potential game (NPG) framework is proposed to support the design of P2P trade with PUFs belonging to this class. Also, to develop a realistic model of P2P trade, we classify prosumers into residential and non-residential classes and assume that prosumers have limited information about each other's decisions. Then within an NPG framework, we introduce a learning model, whereby each prosumer obtains an estimate of the prosumers' decisions in P2P trade.