This method demonstrates improved computational efficiency for generating capacities in power systems, suggesting viable solutions through parallel processing.
A parallel method for solving high-dimensional mixed integer linear programming (MILP) problems, which arise from the optimization of the structure of generating capacities of electric power systems, has been developed. Each such MILP problem is divided into the main task of finding the structure of generating capacities and into a set of independent MILP subtasks of much smaller dimensionality that determine the optimal load modes of power equipment under different operating conditions of the power system. The main problem is solved using metaheuristic algorithms, and the set of independent subproblems is solved in parallel by the SCIP solver, each of which radically speeds up the solution of the problem as a whole. Numerical experiments with the evolutionary center algorithm demonstrated 99 % of global optima and speedups of up to 13.38 on 64 threads. At the same time, both computational speed and memory consumption increase almost linearly with the number of threads. The proposed method scales well on HPC platforms and is an effective tool for strategic planning of power systems.
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Saukh et al. (2025) studied this question.
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