Arbitrage Strategy of Renewable-Based Microgrids via Peer-to-Peer Energy-Trading

In this paper, an arbitrage strategy is proposed for renewable-based microgrids (MGs) to overcome the volatile behavior of renewable energy sources (RESs) such as photovoltaic and wind in a newly emerged business space in which peer-to-peer (P2P) energy-trading in transactive energy markets (TEMs) set up between a day-ahead market (DAM) and real-time markets (RTMs). To identify arbitrage opportunities created from the price difference between the P2P and real-time trades, a bi-level risk-constrained stochastic programming with interval coefficients (BRSPIC) is presented. In the first stage of the decision-making, scenarios are employed to deal with the DAM prices uncertainties. In the second stage, P2P energy-trading competition is modelled by a bi-level programming based on non-cooperative leader-follower games. While the social welfare of peers is maximized at the lower level, the MG maximizes its profit at the upper level. By getting closer to real-time, interval coefficients are considered in the third stage to cope with the uncertainties of RESs and loads, as well as RTM prices. The conditional value-at-risk (CVaR) is enforced the model to control the risk of profit variability. By using Karush-Kuhn-Tucker (KKT), the BRSPIC is transformed into a single level optimization. Then, it is linearized and solved by a mixed-integer linear programming (MILP) solver. By evaluating the proposed model on a test system, it is evident that the MG increases more than 3.1% of its profit by the arbitrage strategy. By considering CVaR, a fully risk-averse decision decreases the profit of MG by 27%, although it would be so conservative decision.