SolarTrader

Abstract

Distributed solar energy resources (DSERs) in smart grid systems are rapidly increasing due to the steep decline in solar module prices. This DSER penetration has prompted utilities to balance the real-time supply and demand of electricity proactively. A direct consequence of this is virtual power plants (VPPs) that enable solar generated energy trading to mitigate the impact of the intermittent DSERs while also benefiting from distributed generation for more reliable and profitable grid management. However, existing energy trading approaches in residential VPPs do not actually allow DSER users to trade their surplus solar energy independently and concurrently to maximize benefit potential; they typically require a trusted third-party to play the role of an online middleman. Furthermore, due to a lack of trading algorithms, these approaches do not necessarily result in fair solar energy saving among all the VPP users in the long term. We propose Sola/Trader, a new solar energy trading system that enables unsupervised, distributed, and long term solar energy trading in residential VPPs. In essence, SolarTrader leverages a new multi-agent deep reinforcement learning approach that enables peer-to-peer solar energy trading among different DSERs to ensure that both the DSER users and the VPPs maximize benefit. We implement SolarTrader and evaluate it using both synthetic and real smart meter data from 4 U.S. residential VPP communities that are comprised of ~229 residential DSERs in total. Our results show that SolarTrader can reduce the aggregated VPP energy consumption by 83.8% when compared against a non-trading approach. Furthermore, SolarTrader achieves a ~105% average saving in VPP residents' monthly electricity cost. We also find that SolarTrader-enabled VPPs can achieve a fairness of 0.05, as measured by the Gini Coefficient, a level equivalent to that achieved by the fairness-maximizing Round-Robin approach.