Economic Operation Optimization Under Real-Time Pricing for an Energy Management System in a Redundancy-Based Microgrid

DC microgrids (MGs) represent self-contained systems for distributing and managing electricity locally, offering advantages in terms of efficiency and control. This article proposes the optimization of redundancy-based dc MG, emphasizing the integration of an intelligent algorithm to minimize operational costs under real-time pricing conditions and also increase the efficiency operation. The redundancy-based dc MG consists of a cascaded bidirectional Cuk converter (CBC) linked to a cascaded bidirectional boost converter, sharing two battery energy storage system (BESS) units as common inputs. Thereby, two boost converters interface the dc utility and fuel cell (FC) to the dc-link of the CBC. The redundancy-based dc MG operates with a coordinated and decentralized control among the dc utility, FC and two BESS units. The energy management system (EMS) employs a droop control for the dc utility and FC, while BESS units adhere to a state-of-charge (SoC)-sharing function. In addition, the particle swarm optimization dynamically adjusts EMS parameters for optimal real-time performance. Then, a stability analysis determines MG operational boundaries, and the effectiveness of the method is confirmed through experiments using Typhoon HIL and dSPACE setups. Finally, comparative evaluations against traditional SoC-based droop and genetic algorithms demonstrate the cost reduction of the proposed approach.