Multi-Source Distributed Energy Resources Management System Based on Pattern Search Optimal Solution Using Nonlinearized Power Flow Constraints

The continuous improvement of energy storage and distributed generation technologies, in conjunction with demand-side pricing policies set by governments worldwide, modify electricity customers' behavior, with potentially adverse effects on the quality of power delivered to the end-users. A major technical challenge is the optimization of power dispatch to minimize customer operating costs under constraints from the supply side. Therefore, this research proposes a multi-source distributed energy resources management system capable of delivering a compromise solution for power supply, storage, and demand-side in different electric pricing policies, using non-linear operating restrictions such as cable loading, transformer loading, power factor, maximum contract demand, and voltage level, calculated using a power flow algorithm. To test the proposed management system, the authors evaluated two different scenarios in an existing microgrid: a time-of-use electricity tariff, and a real-time tariff. The results show that the proposed system can lead to savings of up to 40% of the total operating costs for consumers when applied to time-of-use electricity tariff and up to 20% when applied to real-time electricity tariff, considerably reducing the violation probabilities of power quality indicators set by local utility company regulations.