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• Study of microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV system. • Development of energy management system based on model predictive control (MPC-EMS). • Results compared with proportional sharing algorithm based on SOC level (SOC-EMS). • Best results obtained from MPC-EMS. 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 results. [ABSTRACT FROM AUTHOR]

• A novel tri-stage online dispatch framework for a microgrid is proposed. • The proposed framework coordinates the EV charging behavior at different timescales. • Multiple uncertainties introduced by EV, PV, and load are taken into account. • The real-world data of eight EVs in Beijing is collected to model EV charging behavior. This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced. [ABSTRACT FROM AUTHOR]

Hydrogen-based hybrid storage system has a high energy density, which can operate as the long-term storage system, and play an important role in future smart cities. In the hydrogen storage system, fuel cell, hydrogen tanks, and electrolyzer are often combined together and operating with complex electrochemical reactions. How to efficiently operate the hydrogen storage system and considering the convoluted electrochemical reactions is a problem. In addition, multiple hydrogen storage systems are often grouped together to supply the demands. Thus, cooperating the dispatching of these storage systems is another complicated problem. In this paper, we first present a two-dimension model considering temperature influences for hydrogen-based microgrid, where a regression method is adopted. Moreover, a combined allocating-and-dispatching methodology involving two layers is proposed to cooperate the multiple storage systems. Specifically, both TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and fuzzy logic are adopted as the first-layer allocating algorithm. Then, the model predictive control (MPC) is utilized as the second-layer dispatching algorithm. Based on the combined method, power is firstly allocated to hybrid storage system considering each hybrid storage system health conditions, and secondly scheduled to battery storage and hydrogen storage based on MPC method. The simulation results showed that with the combined Dematel-TOPSIS and MPC algorithm, the degradation index and operation cost were the smallest among three algorithms, and can further extend the lifetime of hybrid hydrogen storage systems in microgrids. • Temperature has a positive influence on the operation of the hybrid storage system. • A combined TOPSIS and model predictive control methodology. • Two-layer operation algorithm can healthily dispatch power to storage system. • The performance is improved based on the multi-criteria decision. • Dematel-TOPSIS algorithm can extend the lifetime of storage systems. [ABSTRACT FROM AUTHOR]

• Developed microgrid control algorithm that includes energy market participation. • Model predictive control showed up to 13.73% improvement over logic-based control. • Shorter time steps for control actions and longer prediction horizons reduce costs. • Participation in ancillary service market reduces operating expenses up to 23.47%. • Battery maintained 38.78% greater SOC to bid into ancillary service markets. This study develops two model predictive control approaches to optimize microgrid dispatch, one with participation in real-time ancillary service markets and the other without participation. Results are compared to a baseline logic-based control with case study data taken from a grid-tied 326 kW solar photovoltaic, 634 kW/634 kWh battery, and 350 kW diesel generator microgrid portfolio designed to serve an office building. Annual performance evaluations show that model predictive control algorithms can reduce operating expenses by up to 13.73% when compared to logic-based controls, and through participation in ancillary service markets, model predictive control can reduce net operating expenses by up to 23.47%. Revenue from ancillary service equated to 12.03% of operating costs, with approximately two-thirds of revenue from spinning reserve and one-third from non-spinning reserve. Model predictive control with ancillary services maintained battery state of charge an average of 38.78% higher than batteries dispatched by model predictive control without market participation. This reduced battery cycling losses, minimized battery operation and maintenance expenses, and improved battery lifetime. Sensitivity analyses indicate that model predictive control with more granular time steps and longer prediction horizons changes the dispatch schedule to further reduce operating costs. Intraday simulations indicate that both model predictive control algorithms can adapt to differences in environmental conditions and pricing signals to minimize operational costs. This generalizable finding suggests the inherent modularity, scalability, and robustness of the proposed algorithms can benefit a variety of microgrid configurations and use cases. [ABSTRACT FROM AUTHOR]