TY - GEN
T1 - An Energy Cost Optimization Model for Electricity Trading in Community Microgrids
AU - Ghorbani-Renani, Nafiseh
AU - Odonkor, Philip
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Peer-to-peer energy market
KW - community microgrid
KW - energy trading model
KW - grid-interactive efficient building
KW - mixed-integer linear programming
UR - http://www.scopus.com/inward/record.url?scp=85142038062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142038062&partnerID=8YFLogxK
U2 - 10.1109/ISC255366.2022.9922504
DO - 10.1109/ISC255366.2022.9922504
M3 - Conference contribution
AN - SCOPUS:85142038062
T3 - ISC2 2022 - 8th IEEE International Smart Cities Conference
BT - ISC2 2022 - 8th IEEE International Smart Cities Conference
T2 - 8th IEEE International Smart Cities Conference, ISC2 2022
Y2 - 26 September 2022 through 29 September 2022
ER -