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The transition to renewable energy sources, particularly sources like wind and solar induces a dependency on weather in the supply side of electrical grids. At the same time, the move to electric mobility with uncontrolled charging induces extra peak loads on these grids. These developments can cause grid congestion or an imbalance between the renewable power supply and the demand. Locally balancing the power supply and demand in grid-connected microgrids can alleviate such issues on the main grid. This paper presents a model based control strategy to address the challenge of locally balancing the power supply and demand in a grid-connected microgrid to avoid reaching the threshold rated power output set for large buildings. The microgrid under consideration consists of photovoltaic power sources and a large fleet of electric vehicle chargers (> 150). A model predictive controller is developed that treats the daily vehicle charging as a batch process. Given vehicle charge objectives, the controller utilizes vehicle charger occupancy and photovoltaic power generation forecasting services to distribute power optimally over a fixed period of time. The optimization problem is formulated as a quadratic programming problem and is implemented utilizing open-source Python libraries. The controller was integrated into the control system of a microgrid situated at a corporate office in the Netherlands. The control system oversaw the operation of 174 vehicle chargers. The effectiveness of the model predictive control technology was demonstrated over a three-week period and led to an average daily grid peak power reduction of 59%. • A model-based control strategy is presented in a grid-connected microgrid. • The microgrid under consideration consists of PV and 174 vehicle chargers. • The controller's strategy minimizes power grid peaks and maximizes EV charge energy provided by the PV cells. • The controller was integrated and implemented in the microgrid system. • An average daily grid peak power reduction of 59% was achieved over a three week period. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for achieving optimal energy management (OEM) in renewable-based hybrid microgrids (HMGs). The HMGs exhibit enhanced efficiency, minimizing power loss while maximizing generated power, making them advantageous over traditional MGs. This new approach consists of a modified model predictive control based improved Firefly1to3 algorithm (MMPC-IFA1to3). Here, MMPC adeptly regulates converter switching phenomena, ensuring stability during uncertain conditions in HMG. On the other hand, FA1to3 states that for each member movement of a firefly population, occurs towards three members. Here, the improved nature of FA1to3 is achieved by using the chaotic function to fine-tune the regulation parameter. This unified method generates the best power references for both active and reactive powers, making switching operations in voltage source converters simpler. This paper mainly emphasizes active and reactive power management through objective function minimization. The proposed IFA1to3 approach effectively incorporates constraints to minimize costs, ensure power availability, and mitigate voltage deviations in renewable-based HMGs. Moreover, charging/discharging of energy storage devices and power exchange between the utility grid and DC MG are carried out through the MMPC-IFA1to3 algorithm by monitoring active and reactive loads simultaneously. The corresponding converters are modeled based on the bidirectional power flow to cope with active and reactive power management. Further, the robustness of the proposed approach is verified under different operating conditions, and outcomes are compared with benchmark techniques in a MATLAB/Simulink environment to evidence its superior EM in HMG. Finally, the proposed MMPC-IFA1to3 approach is validated in a real-time environment through the dSPACE DS 1104 embedded processor to evidence its industrial applications through its robustness and OEM during various case studies. • In the hybrid MG, MPC regulates converter of the connected sources, while MMPC controls the VSC converter. • A novel FA, combining FA1to3 with chaotic logistic function, optimizes power references generation in this study. • Proposed MMPC-IFA1to3 approach ensures voltage stability and effectively manages ARP during uncertainties in MG. • The proposed IFA1to3 is more effective for multi-objective functions optimization. • Real-time validation of the proposed approach in dSPACE DS1104 evidences its practical applicability. [ABSTRACT FROM AUTHOR]

• 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]

The integration of new elements and technologies to the power grid, driven by the expansion of electric microgrids, brings various challenges requiring a suitable control architecture for systems to operate ensuring economic and reliable access to electricity. This paper presents the development of a time-varying constraint real-time model predictive secondary voltage control (MPVC) strategy for microgrids built up in a multi-class Python environment with InfluxDB and SQLExpress databases storing the controller and microgrid data, and its experimental implementation with Modbus communication to physical devices at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The microgrid characteristics are described and the experimental setup is presented. The mathematical model of the resource used as case study is obtained through experimental identification, and it is integrated to the MPVC scheme. Polytopic invariant sets are used as terminal sets in the MPVC scheme to ensure stability of the solution, and time-varying constraints of reactive power availability are considered. Validation studies of the proposed control system in computational simulation are presented prior to real application in the UPB campus microgrid. Controller performance results are described for three different implementation scenarios of low, medium and high variability of external disturbances, presenting adequate voltage regulation around the reference value and considerable dependence on the meteorological forecast quality. The model predictive voltage controller is finally benchmarked against a commercial microgrid controller to evaluate its performance, standing out as a more comprehensive and tailor-made solution. • Five campus microgrids across Colombia with multilevel hierarchical EMS are presented. • A secondary voltage control architecture is proposed and tested in UPB microgrid.. • Implementation scenarios of low, medium and high irradiance variability are tested. • Controller performance is compared to the regulation functions of a commercial device. • The voltage controller architecture depends on the accuracy of disturbance forecast. [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]

This paper presents a hierarchical two-layer home energy management system to reduce daily household energy costs and maximize photovoltaic self-consumption. The upper layer comprises a model predictive controller which optimizes household energy usage using a mixed-integer linear programming optimization; the lower layer comprises a rule-based real-time controller, to determine the optimal power settings of the home battery storage system. The optimization process also includes load shifting and battery degradation costs. The upper layer determines the operating schedule for shiftable domestic appliances and the profile for energy storage for the next 24 h. This profile is then passed to the lower energy management layer, which compensates for the effects of forecast uncertainties and sample time resolution. The effectiveness of the proposed home energy management system is demonstrated by comparing its performance with a single-layer management system. For the same battery size, using the hierarchical two-layer home energy management system can achieve annual household energy payment reduction of 27.8% and photovoltaic self-consumption of 91.1% compared to using a single layer home energy management system. The results show the capability of the hierarchical home energy management system to reduce household utility bills and maximize photovoltaic self-consumption. Experimental studies on a laboratory-based house emulation rig demonstrate the feasibility of the proposed home energy management system. [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]