Virtual Power Plant Optimization Service-Benchmark of Solvers

This work provides a comprehensive analysis of the optimization of a Virtual Power Plant (VPP), that consider the presence of energy storage systems and controllable loads, through the benchmarking of various solvers. It delves into the development of a Mixed Integer Linear Programming (MILP) algorithm aiming at optimizing energy management and exchange within a VPP, that takes into account the operation of shift electric appliances and battery storage systems among different houses. The proposed model aims to minimize the overall electricity cost while ensuring that the energy demand of the system is met, the battery state of charge is maintained within safe operating limits, and the shift electrical appliance is scheduled. Furthermore, the experimental comparisons, the study evaluates the performance of commercial and open-source solvers in handling the complex dynamics of energy demand and supply. The findings highlight the importance of solver selection in enhancing the management, scalability, and reliability of VPP optimization strategies, offering insights into the optimal combination of programming interfaces and solvers for efficient VPP operation.