Simulation Data for Research Paper \u201cExchange MIQP ADMM: An Effective Heuristic for the Day-Ahead Scheduling of Distributed Energy Resources in Renewable Energy Communities\u201d

This dataset contains the raw simulation results associated with the research paper \u201cExchange MIQP ADMM: An Effective Heuristic for the Day-Ahead Scheduling of Distributed Energy Resources in Renewable Energy Communities.\u201d The simulations evaluate the proposed Exchange MIQP ADMM heuristic against centralized and standard consensus-based ADMM optimization approaches across varying Renewable Energy Community (REC) portfolio sizes, involving large-scale Mixed-Integer Quadratic Programming day-ahead scheduling scenarios. All experiments were conducted using a high-performance ARMv8 server computer using the open-source pycity_scheduling Python framework (https:\/\/git.rwth-aachen.de\/acs\/public\/simulation\/pycity_scheduling\/-\/tree\/v1.4.2) and the commercial optimization solver gurobi.