An adaptive bi-level optimization model for market integration of community energy storage in local trading and upstream energy and regulation services

The emergence of community energy storage (CES) in smart energy systems presents a critical opportunity to enhance flexibility within local energy markets while also enabling participation in both local energy trading and upstream energy and regulation markets. Advanced CES systems, comprising integrated battery and hydrogen storage units alongside fuel cells and electrolyzers, play a pivotal role in enabling energy communities to optimize resource utilization, offer storage services to prosumers, and participate in upstream markets. This study models the competitive interaction between the CES operator and prosumers using a Stackelberg game-theoretic framework, where the CES operator acts as the leader and the prosumers as followers. The CES maximizes its profit through local energy trading and participation in upstream energy and regulation markets, while prosumers minimize their billing costs by trading energy with the CES and participating in demand response programs. The proposed structure is modeled using a mixed integer linear programming (MILP) approach and solved with the CPLEX solver, allowing for precise optimization of CES operations. Various scenarios are analyzed to assess system performance under diverse market and operational conditions. The results demonstrate that the adaptive bi-level optimization model effectively integrates CES into energy trading and regulation markets, providing reliable reserve services with minimal impact on profitability. This approach highlights the potential of CES in advancing sustainable and economically viable energy communities.

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Published in: Journal of Energy Storage
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.est.2025.116715