Loss-aware Pricing Strategies for Peer-to-Peer Energy Trading

TL;DR

This work proposes a novel loss-aware pricing strategy for P2P energy markets that addresses challenges while incentivizing participation in the cooperative energy trading market, and proposes an ADMM-based hyper-gradient descent method for solving this problem.

Abstract

Peer-to-peer(P2P) energy trading may increase efficiency and reduce costs, but introduces significant challenges for network operators such as maintaining grid reliability, accounting for network losses, and redistributing costs equitably. We propose a novel loss-aware pricing strategy for P2P energy markets that addresses these challenges while incentivizing participation in the cooperative energy trading market. The problem is formulated as a hierarchical Stackelberg game, where a grid operator determines network tariffs while prosumers optimize their trades based on these tariffs while guaranteeing that network constraints are satisfied. The algorithm is designed to minimize and recover their cost from the trading parties, while also minimizing the total cost of the hubs. The mechanism dynamically adjusts tariffs based on location and network topology, discouraging loss-intensive trades. Finally, the complete framework includes the computation of fair trading prices, ensuring all market participants benefit equitably. An ADMM-based hyper-gradient descent method is proposed for solving this problem. Extensive numerical simulations using the benchmark IEEE 33-bus system demonstrate significant cost reductions and improved network efficiency through reduction in network losses compared to constant tariff schemes. Results highlight the adaptability and scalability of the proposed mechanism to varying network configurations and size, demand profiles, and seasonal conditions.

Figures (7)

• Figure 1: (a) Illustration of the IEEE 33 bus benchmark test system with 5 energy hubs connected at different busses. (b) An example energy hub.
• Figure 2: Results of the proposed algorithm: (a)Trading tariff computed for every trade in the network each day and (b) the resulting energy traded between the hubs in the network.
• Figure 3: Net energy traded by each hub in the network.
• Figure 4: Overall cost reduction, reduction in network losses, total trades and the total trading tariff paid to the network using optimal $\boldsymbol{\gamma}^{\star}$ computed using the proposed algorithm and constant $\boldsymbol{\gamma}$ values of 0, 0.005, 0.01, and 0.05.
• Figure 5: Cost reduction for different trading prices and the optimized prices. The social welfare cost reduction of the network is also depicted and is independent of the bilateral trading price.