An Energy Cost Optimization Model for Electricity Trading in Community Microgrids

In this study, we proposed a mixed-integer linear programming model to determine the optimal trading and operational strategies necessary to enable efficient peer-to-peer (P2P) energy trading and resource utilization within fully cooperative community microgrids. The proposed model considers tiered utility tariffs accounting for (i) the time-of-use (TOU) rate and (ii) the level of cumulative consumption. Given the heterogenous mix of prosumers and consumers common in community microgrids, the proposed model seeks to provide decision support for the optimal utilization of generated electricity by determining if it should be self-consumed, stored for future use, curtailed, or traded with peers. Likewise, the proposed approach determines operational strategies for non-prosumer peers with regards to sourcing electricity to satisfy their respective energy deficits. The model presents a scalable approach for energy cost savings for both prosumers and energy consumers regardless of their role in the peer market. To demonstrate this functionality, we leverage the proposed model to solve for the optimal trading strategy within a 5-building community microgrid. Real-world energy demand and generation data pertinent to 5 households in the New York region was sampled using the Pecan Street Inc. Dataport database. Results were compared to that of a traditional centralized grid model. The results highlight the benefits of P2P market design in comparison with the traditional unidirectional grid model. In addition, the outcomes underline that energy consumers satisfy most of their demand from the P2P market during peak hours to obtain greater cost savings.