I developed a resilience-cost-oriented energy trading model for socially vulnerable community microgrids, designed to decide both trading values and payment values per kW, unlike existing models that consider only total payment. The model combines distributed optimization of individual microgrid problems with a central market clearance mechanism, enabling cooperative trading that ensures social equity across participants, in contrast to traditional game-based models focused solely on individual benefit. This work was conducted as part of a U.S. Department of Energy, Solar Energy Technologies Office funded project. 

The problem is solved using a Lagrangian-based approach and a modified ADMM algorithm with binary constraint treatment. The method demonstrated reliable convergence to market requirements, achieving up to 17% improvement in resilience and a 36% reduction in trading costs. The work has been published and presented at IEEE INTELEC 2025.

Paper DOI:  10.1109/INTELEC63987.2025.11214754
Github Repository: https://github.com/FarzaneEzzati/MGTrade-LpBoxADMM