A real time peer-to-peer energy trading for prosumers utilizing time-varying building virtual energy storage

With the increasing penetration of distributed energy resources (DER) in the electric power system, Peer-to-Peer (P2P) energy trading has become a promising paradigm for future electric power systems. Building thermal load, which is an important demand side resource, should be considered carefully in the design of a P2P trading method. In this paper, we investigate the application of building thermal energy storage capability in P2P energy trading. We aggregate and model building thermal loads as a virtual energy storage and derive a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. Key parameters of TVESS, including charging/discharging rate, energy capacity, and state of charge (SOC), are analyzed so that TVESS can be embedded in the building prosumer model to participate effectively in electricity-oriented P2P energy trading. We propose a real time P2P energy trading method of prosumers based on model predictive control (MPC). This method consists of an energy supply and demand quantification stage and a transaction price optimization stage, which can effectively capture the time-varying nature of T-VESS. To preserve the trading preference for prosumers, we propose a distributed P2P energy trading actions implementation method based on the continuous double auction (CDA) considering multiple trading preference grades. Numerical results demonstrate that the proposed P2P energy trading method considering T-VESS can reduce the operational cost of prosumers by 3.7%, while also promoting the local integration of renewable energy by 3.1%. Furthermore, compared to existing P2P trading methods considering single preference grade, the proposed method greatly enhances the utilities of both individual prosumers and the overall community.

Automated summary

This paper investigates the application of building thermal energy storage in Peer-to-Peer (P2P) energy trading and proposes a time-varying virtual energy storage system (T-VESS) model to quantify the flexibility of a building. A real-time P2P energy trading method based on model predictive control is proposed, along with a distributed trading actions implementation method based on continuous double auction. Numerical results show that the proposed method can reduce the operational cost of prosumers by 3.7% and promote the local integration of renewable energy by 3.1%.