Model predictive control of a microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV systems

This paper presents a new energy management system (EMS) based on model predictive control (MPC) for a microgrid with solar photovoltaic (PV) power plants and a quasi-Z-source cascaded H-bridge multilevel inverter that integrates an energy storage system (ES-qZS-CHBMLI). The system comprises three modules, each with a PV power plant, quasi-impedance network, battery energy storage system (BESS), and voltage source inverter (VSI). Traditional EMS methods focus on distributing the power among the BESSs to balance their state of charge (SOC), operating in charging or discharging mode. The proposed MPC-EMS carries out a multi-objective control for an ES-qZS-CHBMLI topology, which allows an optimized BESS power distribution while meeting the system operator requirements. It prioritizes the charge of the BESS with the lowest SOC and the discharge of the BESS with the highest SOC. Thus, both modes can coexist simultaneously, while ensuring decoupled power control. The MPC-EMS proposed herein is compared with a proportional sharing algorithm based on SOC (SOC-EMS) that pursues the same objectives. The simulation results show an improvement in the control of the power delivered to the grid. The Integral Time Absolute Error, ITAE, achieved with the MPC-EMS for the active and reactive power is 20 % and 4 %, respectively, lower than that obtained with the SOC-EMS. A 1,3 % higher charge for the BESS with the lowest SOC is also registered. Furthermore, an experimental setup based on an OPAL RT-4510 unit and a dSPACE MicroLabBox prototyping unit is implemented to validate the simulation result

Dynamic fuzzy logic energy management system for a multi-energy microgrid

While multi-energy microgrids (MEMGs) offer a promising approach to reduce energy consumption through coordinated integration of various energy vectors, research has primarily focused on static studies. These studies aim to optimize a particular cost function but neglect the dynamic aspects of the system operation. This paper presents a dynamic model of an MEMG comprising of electricity and thermal vectors. A novel dynamic fuzzy logic-based energy management system (EMS) is investigated, aiming to ensure energy balance (electric and thermal), optimize renewable energy utilization, and reduce the reliance on the local electricity grid and gas. Both the EMS and MEMG have been evaluated under different weather conditions and a 4-hour variable load profile. Furthermore, the EMS effectiveness has been verified through a real-time experiment using an OPAL-RT4512 unit and a dSPACE MicroLabBox prototype. The results show that the proposed fuzzy logic-based EMS outperforms a conventional EMS based on machine states (states-based EMS), achieving a notable reduction in electricity grid consumption of 80%, as well as a consumption reduction of 7.4% in the gas boiler and 5.4% in the electric boiler. Furthermore, the control performance results in a remarkable reduction in ITAE (42.57%), ITSE (89.10%), IAE (54.36%) and ISE (57.55%) for the hot water temperature control, and in ITAE (17.06%), ITSE (52.50%), IAE (31.19%) and ISE (29.99%) for the heating control

Fuzzy control for multi-energy microgrids

Multi-energy microgrids (MEMGs) have emerged as an effective solution for reducing greenhouse gas emissions. These systems leverage the coordination of multiple energy vectors to enhance efficiency and achieve greater independence from the main grid. This paper introduces a dynamic fuzzy-logic energy management system (EMS) designed for a MEMG that encompasses gas and electricity energy vectors. The thermal network of the MEMG comprises a gas boiler, an electric boiler, and a heat load. In parallel, the electrical network consists of a photovoltaic (PV) system, a battery energy storage system, an electric load, and a connection with the grid. The EMS plays a crucial role in evaluating the PV power generation and electric demand, and it adjusts the water temperature in the electric boiler to minimize reliance on the local grid. To evaluate the effectiveness of the MEMG and EMS, a simulation spanning 4.5 hours was conducted under various operating conditions for sun irradiance, heat, water, and electric demand. The results demonstrate the capability of the fuzzy-logic based EMS to reduce the dependence on the local grid, thereby showcasing the suitability of this approach in MEMGs