Internal Solar Energy Credits Exchange in a Microgrid, Case Study of a Townhouse Complex

This paper addresses the critical challenge of optimising solar energy utilisation in residential microgrids through an internal energy credits exchange system. The research aims to develop and validate a comprehensive energy management approach that maximises economic benefits for both solar and non-solar users within solar-powered residential microgrids; the energy management approach must be easy to implement with minimal interventions and additional costs. The study presents results from a three-year field implementation in an Australian townhouse complex, comprising two microgrids with 19 and 24 units respectively, and seven solar installations. The proposed system introduces several novel elements: (1) an internal energy credit exchange mechanism based on real consumption patterns, (2) a balanced tariff model set at 65 % of the base internal tariff, determined through collective decision-making, and (3) a flexible credit accumulation system with overpayment refund capabilities. Field data collected at quarterly intervals demonstrates that in this architecture over up to 35 % of total energy consumed by non-solar users is produced by their solar neighbours. If solar energy export is accounted for with the mathematical model presented in this paper, it allows for either radical reduction of energy costs, or continuous credit accumulation for solar users depending on their overall energy consumption. It also provided consistent 5–10 % cost reduction (community solar discounts) to non-solar users. The developed and implemented approach offers simple methodology to account for solar energy export into small residential microgrids offering significant advantages to solar users over traditional direct grid connection. Significant economic benefits are achieved to both solar and non-solar users vie fairer feed-in tariffs for solar users and community solar discounts for non-solar users. The methodology and findings can be applied to similar residential microgrids globally, especially in contexts where embedded network architectures are prevalent. Mathematical model presented in this work and validated via real-life field testing can be applied to automated energy metering data flow with short sampling time of several minutes allowing for real-time pricing implementation. It is readily applicable to the use of intelligent energy management systems and automatic and user-induced flexible demand response.