A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading; however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multi-stage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage.