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As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge-discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional-integral-derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy. [ABSTRACT FROM AUTHOR]

Optimal energy management of microgrids enables efficient integration of renewable energies by considering all system flexibilities. For systems with significant seasonal imbalance between energy production and demand, it may be necessary to integrate seasonal storage in order to achieve fully decarbonized operation. This paper develops a novel model predictive control strategy for a renewable microgrid with seasonal hydrogen storage. The strategy relies on data-based prediction of the energy production and consumption of an industrial power plant and finds optimized energy flows using a digital twin optimizer. To enable seasonal operation, incentives for long-term energy shifts are provided by assigning a cost value to the storage charge and adding it to the optimization target function. A hybrid control scheme based on rule-based heuristics compensates for imperfect predictions. With only 6% oversizing compared to the optimal system layout, the strategy manages to deliver enough energy to meet all demand while achieving balanced hydrogen production and consumption throughout the year. • PV with seasonal H 2 storage enables fully sustainable operation of industrial microgrids. • Model predictive control overcomes perfect foresight limitation in energy management. • H 2 production for long-term storage is incentivized by adding an extra cost term. • Hybrid predictive control enables a low cost system with nearly perfect yearly operation. [Display omitted] [ABSTRACT FROM AUTHOR]

To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOCUC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOCB was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified. [ABSTRACT FROM AUTHOR]

In the present study, the economic model predictive control (EMPC) scheme is presented for the time of use tariff application of photovoltaic and diesel generator backup system, which is connected to the grid‐connected microgrid. Through the use of the closed‐loop control system, the prior open‐loop optimal control is enhanced. Minimizing the grid energy and fuel costs by evaluating the fuel level restrictions in the diesel tank is the main goal of this study. To accommodate the restrictions among controllable variables, this control method fulfils the load requirements. To gain the benefits of feedback and predictions, optimal power scheduling is modelled as a control problem. Furthermore, Blockchain technology is applied to secure interchanged information in the grid to avoid manipulating data by attackers. Specifically, the analysis is divided into two scenarios. The first one takes place in the alternative style when a blackout happens between [7 am and 6 pm] h and the other occurs in a grid energy mode when the network is available over 24 h. Energy performance, cost savings, and daily revenue have all been improved by EMPC. As a result, the daily energy savings are up to 52%, whereas diesel energy does not go over 85%. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the bus voltage in a low-inertia DC microgrid is prone to be affected by internal power fluctuations, an adaptive virtual inertia control strategy for a grid-connected converter of a DC microgrid based on an improved model prediction is proposed. Firstly, the adaptive analog virtual synchronous generator (AVSG) is introduced into the voltage outer loop by combining the inertial parameters with the voltage change rate, and the flexible adjustment of the inertial parameters is realized. Secondly, the improved model predictive control is introduced into the current inner loop to realize the fast-tracking of the given current value and improve the dynamic characteristics of the control system. Finally, a system model is established based on Matlab/Simulink for simulation. The results show that compared with the traditional virtual inertia control strategy, the proposed control strategy has smaller bus voltage fluctuation amplitude and better dynamic performance; when a 10 kW load mutation occurs, the magnitude of bus voltage drop is reduced by 60%, and the voltage recovery time is shortened by 30%. The proposed control strategy can effectively improve the stability of DC bus voltage and the operation ability of the system under asymmetric conditions. [ABSTRACT FROM AUTHOR]

Although there have already been many works on the model predictive control (MPC) for load frequency control (LFC) of modern power systems, limited works can be found in the literature related to the output feedback MPC. This article studies an N‐step synthesis approach of output feedback MPC for LFC systems considering the problems of communication efficiency and network security. First, to improve the communication efficiency, an adaptive event‐triggering (AET) scheme involving two adaptive laws is designed to reduce the number of transmitted data packages which offers more flexible compared with existing event‐triggering schemes; Second, to handle the network security, a new model of LFC power system combining the AET scheme and random deception attack under an unified framework is established; Moreover, a synthesis approach of output feedback MPC with N‐step strategy is addressed for LFC of power system by parameterizing the infinite control moves into a series of output feedback laws. Compared with the existing predictive load frequency control methods, the present technique is shown as an useful way to improve the control performance since more degrees freedom is introduced by the N‐step strategy. Finally, the simulation experiment is carried out to verify our technique. [ABSTRACT FROM AUTHOR]

As microgrids are the main carriers of renewable energy sources (RESs), research on them has been receiving more attention. When considering the increase in the penetration of renewable energy sources/distributed generators (DGs) in microgrids, their low inertia and high stochastic power disturbance pose more challenges for frequency control. To address these challenges, this paper proposes a model predictive control (MPC) secondary control that incorporates an unknown input observer and where RESs/DGs use a deloading virtual synchronous generator (VSG) control to improve the system's inertia. An unknown input observer is employed to estimate the system states and random power disturbance from the RESs/DGs and load to improve the effect of the predictive control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. The RESs/DGs are given priority to participate in secondary frequency control due to the proper weighting factors being set. An islanded microgrid model consisting of multiple photovoltaic and wind power sources was built. The simulation results demonstrate that the proposed method improves the system frequency, restoration speed, and reduces frequency deviations compared with the traditional secondary control method. [ABSTRACT FROM AUTHOR]

This paper proposes a battery state of charge (SOC)-based energy management strategy using hierarchical distributed model predictive control (HDMPC) for a standalone microgrid on solar-powered long-endurance aircraft. The microgrid was innovatively designed as a two-layer structure in which the first layer consists of a photovoltaic generation and battery storage system named the PV battery module (PBM). The second layer, named the microgrid subsystem (MGSS), consists of several PBMs, each of which supplies power to a specific DC load on the aircraft. The control system is divided into two levels: the grid-level model predictive control (MPC) and the converter-level MPC. The grid-level MPC adopts a distributed model predictive control strategy to obtain the reference power of each module. The converter-level MPC calculates the control variables of converters using a supervisory model predictive control (SMPC) strategy. The new microgrid structure and the proposed control strategy have improved the reliability of the energy system and increased its energy utilization rate. [ABSTRACT FROM AUTHOR]

In recent years, microgrid (MG) deployment has significantly increased, utilizing various technologies. MGs are essential for integrating distributed generation into electric power systems. These systems' economic dispatch (ED) aims to minimize generation costs within a specific time interval while meeting power generation constraints. By employing ED in electric MGs, the utilization of distributed energy resources becomes more flexible, enhancing energy system efficiency. Additionally, it enables the anticipation and proper utilization of operational limitations and encourages the active involvement of prosumers in the electricity market. However, implementing controllers and algorithms for optimizing ED requires the independent handling of constraints. Numerous algorithms and solutions have been proposed for the ED of MGs. These contributions suggest utilizing techniques such as particle swarm optimization (PSO), mixed-integer linear programming (MILP), CPLEX, and MATLAB. This paper presents an investigation of the use of model predictive control (MPC) as an optimal management tool for MGs. MPC has proven effective in ED by allowing the prediction of environmental or dynamic models within the system. This study aims to review MGs' management strategies, specifically focusing on MPC techniques. It analyzes how MPC has been applied to optimize ED while considering MGs' unique characteristics and requirements. This review aims to enhance the understanding of MPC's role in efficient MG management, guiding future research and applications in this field. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven nonparametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small gridconnected microgrid with PV generation, a battery storage system, and a noncontrollable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels. [ABSTRACT FROM AUTHOR]

Microgrids face significant challenges due to the unpredictability of distributed generation (DG) technologies and fluctuating load demands. These challenges result in complex power management systems characterised by voltage/frequency variations and intricate interactions with the utility grid. Model predictive control (MPC) has emerged as a powerful technique to effectively address these challenges. By applying a receding horizon control strategy, MPC offers promising solutions for optimising constraints and enhancing microgrid operations. The purpose of this review paper is to comprehensively analyse the application of MPC in microgrids, covering various levels of the hierarchical control structure. Furthermore, this paper explores the emerging trend of employing MPC across microgrid applications, ranging from converter control levels for power quality to overarching energy management systems. It also investigates the future research perspectives by considering the challenges associated with establishing MPC-based microgrid control. The key conclusion derived from this review paper is that the implementation of MPC techniques in microgrid operations can greatly improve their overall performance, efficiency, and resilience. This paper thoroughly examines the various challenges faced in MPC-based microgrid operations, underscoring the significance of conducting research in advanced artificial intelligence (AI)-based MPC methods. It highlights how these cutting-edge AI techniques can bring about economic benefits in microgrid operations, addressing the complex demands of efficient energy management in a rapidly evolving landscape. The presented insights strive to enhance the comprehension and adoption of MPC techniques in microgrid settings, actively contributing to the ongoing improvement of their operational processes. By shedding light on key aspects and offering valuable guidance, this work aims to propel the advancement and effective utilisation of MPC methodologies in microgrids, ultimately leading to optimised performance and enhanced overall operations. [ABSTRACT FROM AUTHOR]

Smartload with series-connected dc electric spring (ES) and noncritical load (NCL) structure can compensate for load voltage and improve the power quality. In this article, a distributed virtual inertial control framework based on model predictive control is proposed for low inertia in the dc microgrid. First, the current prediction model of the ES bidirectional full-bridge dc/dc converter is presented. Based on the virtual inertia control, model predictive control is used to optimize virtual capacitance during the operation. When the system suffers from disturbance, virtual capacitance increases to slow down the change of the dc bus voltage. When disturbance happens, the system can be stabilized quickly by reducing the virtual capacitance. Then, the consensus algorithm based on distributed control is established after defining the NCL voltage deviation rate, which can achieve the balance of NCL voltage deviations, and effectively avoid the more significant voltage deviation in one of NCLs. Furthermore, the small-signal model of the proposed control method is developed, and the influence of the proposed controller on the small-signal stability is described by employing the eigenvalue analysis method. Finally, based on the RT-Lab hardware-in-the-loop simulation system, comparisons and analysis are made to verify the effectiveness of the proposed control method under power step conditions. [ABSTRACT FROM AUTHOR]

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 study presents a scenario-based model predictive control (MPC) approach to minimize the cotton farm microgrid operational cost under uncertainties. Uncertainties in cotton farms may come from renewable energy generation, water demand, precipitation, and evaporation, so the cotton field pumping system operation can be formulated as a stochastic MPC problem to accommodate uncertain climate conditions and real-time changes in irrigation demand. Scenario generation and reduction techniques can obtain typical scenarios and their probabilities. The typical scenarios can be used in the MPC iterative step to facilitate modelling the proposed stochastic optimization problem. This study discusses static and dynamic uncertainty modelling techniques used for MPC, and each technique is analysed separately in grid-connected and islanded microgrids through case studies. In the grid-connected dynamic scenario-based MPC, the operational cost is AU$ 18,797 over the entire irrigation period, which is AU$ 8759 lower than that of the standard MPC. Furthermore, for the islanded dynamic scenario-based MPC, the operational cost is AU$ 24,443 over the entire irrigation period, which is AU$ 6721 lower than the standard MPC. • A model predictive control under uncertainties is proposed for a microgrid. • The static and dynamic scenario-based approach is used to model the uncertainties. • Case studies using a cotton farm microgrid are analysed to validate the method. [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]

Distributed energy resources (DER) are integrated into a microgrid through dc-dc power electronic converters. The bidirectional dc-dc converter regulates charging and discharging operations of ESS. Model predictive control (MPC), is a high-performance control technique for these converters, but it is limited in robustness to parameter mismatch, model uncertainties and sensor measurement noise. Therefore, in this paper, an improved hybrid cascade-parallel extended state observer (CP-ESO) is proposed, for model-free predictive control, to guarantee both robustness to parameter/model uncertainties and measurement noise suppression. A novel structurally-adaptive ESO scheme is also proposed to improve the disturbance rejection of CP-ESO during transient response. These results are supported by analysis and design guidelines for the selection of optimal sub-frequencies. Experimental results, for a bidirectional dc-dc boost converter, validate the effectiveness of the proposed methods against model uncertainties, external disturbances from variable input voltage and load, as well as measurement noise. • Analysis and design guidelines for multi-frequency extended state observers (ESO). • Improved cascade-parallel extended state observer with better noise suppression. • Novel structurally-adaptive ESO that gives better transient disturbance rejection. • Experimental validation with a bidirectional dc-dc boost power converter. [ABSTRACT FROM AUTHOR]

This research paper presents a novel long-horizon move-blocking based direct power model predictive control (DPMPC) strategy, uniquely designed to enhance the dynamic performance of boost converters in the grid-connected mode within DC microgrids. A precise dynamic model is developed considering a boost converter connected to a DC-link supplying constant power and resistive loads. To predict the system's dynamic behavior over an extended interval and enhance its performance in the presence of constant power loads, the long-horizon based finite control set model predictive control (FCS-MPC) method is introduced. To address the computational complexity associated with the conventional long-horizon DPMPC approach, a move-blocking (MB) strategy is incorporated into FCS-MPC, reducing the computational burden while improving the dynamic performance of the boost converter. Unlike recent studies that utilize DPMPC with short prediction interval, this paper presents evidence that a longer prediction interval is crucial for maintaining system stable and enhancing the dynamic response and also utilizing MB to make the control implementable in the real-time. The simulation results conducted using MATLAB/Simulink demonstrate the effective performance of the proposed strategy across various operating conditions of the boost converter. Experimental results further validate the strategy's capability to enhance the dynamic performance of the boost converter. Importantly, the experimental findings confirm the feasibility of implementing the proposed long-horizon move-blocking DPMPC strategy in real-time applications. • Developing an accurate dynamic model for a boost converter. • Proposing a long-horizon direct power model predictive control strategy. • Limiting the number of calculations and the execution time of the proposed strategy using move-blocking approach. [ABSTRACT FROM AUTHOR]

In this paper, model predictive control (MPC) is proposed for single-phase electric springs (ESs) with the help of the existing δ control, which is realized by controlling the instantaneous phase angle of the predefined sinusoidal reference of a certain controller. System modeling is analyzed first to get differential forms of state variables. The discrete-time state space model is obtained through first-order approximation. Critical load (CL) voltage can be predicted by the prediction of ES voltage and line current. The operating modes of ESs can be determined and the reference signal for CL voltage can be provided by δ control. As a result, cost function is obtained as the absolute value of the error between predicted CL voltage and its predefined reference. Two typical operating functions such as pure reactive power compensation mode and power factor correction (PFC) mode are selected and simulated to validate the proposed control and analysis. It is revealed that both control objectives can be achieved with the proposed MPC and δ control. Additionally, the total harmonic distortion on the critical load is limited to about 0.5%, which is better than other existing methods. [ABSTRACT FROM AUTHOR]

In recent years, in response to increasing environmental concerns, advances in renewable energy technology and reduced costs have caused a significant increase in the penetration of distributed generation resources in distribution networks. Nonetheless, the connection of distributed generation resources to distribution networks has created new challenges in the control, operation, and management of network reliability. This article is a review on the model predictive control (MPC) for distributed energy resources (DER) in microgrids. The solutions of MPC for energy conversion of solar photovoltaic, wind, and energy storage systems are covered in detail. MPC's applications for increasing reliability of grid-connected converters under (a)symmetrical grid faults are also discussed. The promising potentials of the applications of MPC to the stable multi-variable control performance of DERs are highlighted. This work reflects strong symmetry on MPC control strategies and provides guidance map for readers to facilitate future research works in these exciting fields. [ABSTRACT FROM AUTHOR]

Transportation electrification and charging infrastructure development has to gain momentum in order to go hand-in-hand with the fast advances in the electric vehicle technology. Setting up dc fast charging stations connected to bipolar DC microgrid is a great viable option to utilize the distributed energy resources for transportation electrification. It also helps to eliminate power quality issues in ac grid that may arise due to the unpredictable charging/discharging behaviour of EVs. This paper focuses on model predictive control of a three-level bidirectional dc–dc converter suitable for interconnecting bipolar DC microgrid with dc fast charging stations or battery energy storage. State space analysis is done, and discrete model is developed. Simulation of the proposed system with model predictive control is done in Simulink MATLAB. Real-time hardware in loop performance is tested and verified using Typhoon HIL 402. The proposed converter is able to mitigate the voltage unbalance issues arising in the bipolar DC microgrid and is capable of controlling bidirectional power flow, hence suitable for V2G/G2Voperation. [ABSTRACT FROM AUTHOR]

In microgrid hierarchical control, primary control to stabilize system and secondary control to eliminate frequency/voltage deviations are both necessary for islanded microgrids. In this paper, a switching event-triggered (SET) resilient control scheme for microgrid primary and secondary levels has been proposed. First, droop control and model predictive control (MPC) are used for power sharing and driving signal generation respectively on primary level. An SET control method to trigger different MPC cost functions is proposed to balance output voltage quality and switching frequency. Then, distributed consensus and pinning control based method is used on secondary level. An SET mechanism to dynamically adjust secondary ratios to coordinate the transient deviation and response speed is proposed. Next, to deal with severe accidents, a resilient control scheme integrating primary and secondary levels is designed to maintain the system sound operation and improve the system immunity. Finally, the effectiveness of the proposed control scheme has been demonstrated by simulation with comprehensive scenarios. • Microgrids are typical complex cyber–physical systems that need to be regulated hierarchically. • A switching event-triggered mechanism for triggering different cost function coefficients has been proposed for the model predictive controller in primary control. • In secondary control, a switching event-triggered control method is also proposed for the dynamic adjustment of distributed secondary control ratios. • To deal with severe accidents, a resilient control scheme that cooperates with primary and secondary controls is enabled. [ABSTRACT FROM AUTHOR]

Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper. [ABSTRACT FROM AUTHOR]

A frequency and voltage control strategy based on a decentralized and communication-less approach is proposed in this work and applied to Photovoltaic-Storage-Microturbine islanded Microgrids (MGs). The approach is based on the Model Predictive Control (MPC) technique. Thanks to the use of local measurements, each source can nullify the steady-state voltage and frequency errors by means of a dedicated MPC controller. Consequently, the proposed approach unifies the advantages of classic droop and master/slave controllers due to the absence of communication links among devices and due to the absence of a secondary centralized control loop. [ABSTRACT FROM AUTHOR]

Summary: The renewable energy (e.g., solar photovoltaic)‐based grid‐connected microgrid (MG) with composite energy storage system (CESS) is feasible to ensure sustainable and quality power to the commercial and domestic load demands. Effective control systems provide the dynamic performance of such deployed MGs. This paper investigates the application of the finite control‐set model predictive controller (FCS‐MPC) for solar photovoltaic‐based grid‐connected MGs with composite energy storage. FCS‐MPC considers the discrete nature of interfacing (DC–AC and DC–DC) converters into the control algorithm to predict the optimal control command for reference current tracking. Compared to classical control approaches, FCS‐MPC provides a fast‐dynamic response, discards the modulation stage, and effectively handles the effects of dynamic source and load variations and nonlinear loads. The control system encapsulates a proportional–integral (PI) controller, low‐pass filter (LPF), power management algorithm (PMA) unit, and followed by the FCS‐MPC approach. The CESS unit consists of a battery and supercapacitor storage system. The efficacy of the FCS‐MPC approach is verified through the MATLAB/Simulink results. Solar photovoltaic variation due to irradiance, utility grid availability, electricity pricing, and dynamic load variations are considered in the implementation. Furthermore, real‐time simulation of the proposed method is validated using OPAL‐RT OP4510 real‐time simulator. [ABSTRACT FROM AUTHOR]

The focus of this study is on the grid-forming operation of the Energy Router (ER) based on Model Predictive Control (MPC). ER is regarded as a key component of microgrids. It is a converter that interfaces the microgrid (s) with the utility grid. The ER has a multiport structure and bidirectional energy flow control. The ER concept can be implemented in Nearly Zero-Energy Buildings (NZEB) to provide flexible energy control. A concept is proposed where the ER works as a single grid-forming converter. The challenge is to keep the predefined reference voltage and frequency inside the NZEB in all possible modes, including the idle operation mode, current sources, and nonlinear load control. To gain stability and output voltage quality, the MPC is proposed. The design of the modified MPC algorithm with improved dynamics performance is explained. PLECS software is utilized to verify the proposed algorithm. The results demonstrate the suitable performance of the proposed control method in terms of total harmonic distortion of the output voltage. The influence of weighting coefficiencies is evaluated, showing the higher impact of the capacitor filter voltage on lowering the total harmonics distortion of the output voltage. Finally, the capability of the control system toward step change in the reference value is evaluated. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of Dynamic Adaptability (DA) feature, added to a multi-objective Model Predictive Control (MPC) of a basic grid-tied inverter with a secondary function for switching frequency reduction. The classical MPC is enhanced with dynamically varying secondary function weightage in accordance with the demand of the operating condition for self-reliant change-over across grid-tied mode and grid-forming modes in an Inverter Dominated Microgrids (IDM) environment. A customized DA database has been developed through prior investigation on the IDM, considering possible operating conditions with diverse combinations of the sampling frequency and the secondary function weight. Investigations unveiled that there is a unique value of secondary weight for every operating condition, beyond which the reference tracking is lost. This necessitated the dynamic assignment of the weight to achieve a desirable inverter performance. The dynamic weightage assignment is accomplished by accessing the DA database through the priority signals for harmonic standards, inverter losses and reference tracking, respectively. The DA imparted MPC is tested for its mode transfer capability with desired performance under all operating modes of IDM in simulations with a 15 kVA grid-tied/forming inverter and the results are presented. Further, the experimental investigations are conducted with a 10 kVA inverter on a 5-bus laboratory scale IDM emulator along with hardware-in-loop. Both inter-mode and intra-mode transitions were initiated in the IDM and the inverter performance are presented here. Both the simulation and the hardware results yielded competent values of reference tracking accuracy and mode transition times. [ABSTRACT FROM AUTHOR]

Despite its advantages over its AC counterparts, DC microgrids present a lot of challenges. One of these challenges is the instability issues caused by constant power loads (CPLs). CPLs deteriorate the system's performance due to their incremental negative impedance characteristics. In this paper, a DC microgrid composed of a PV/battery system feeding a pure CPL was considered. A continuous-time model predictive control combined with a disturbance observer was applied to the DC–DC bidirectional converter. The purpose of the composite controller is to address the nonlinearity of the CPL and to maintain the stability of the system in a large operating region under load and PV generation variations. To show the performance of the system, several tests were performed under PV power and CPL power variations. Simulation results show good performance in terms of transient response, optimal tracking, and stability in a large operating region. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) distributed generators (DGs) are inherently stochastic and have low inertia owing to their weather dependence and connection to an inverter. Frequency regulation presents a significant challenge for the high penetration of PV-DGs to microgrids. Recently, a virtual synchronous generator (VSG) has been proposed to enhance the inertia of microgrids because it mimics the behavior of synchronous generators. However, few studies have utilized VSG-based DGs for secondary frequency regulation owing to their stochastic power output. In this study, a model predictive control scheme integrated with two-layer moving-horizon estimation (TL-MHE) observer is proposed and applied to the secondary frequency control of a PV high-penetration microgrid. The local observer estimates the power disturbances in each PV-DGs, whereas the centralized observer estimates the system states and load disturbances. The two-layer observer design decouples multiple observations and reduce the order of the observation equations. Compared with conventional centralized observer-based controllers, the proposed scheme can obtain better observation results. The model prediction controller optimizes the restorative power from each DG in real time to minimize frequency fluctuations, thus affording better control performance. Simulation results show that the proposed TL-MHE method can accelerate the system frequency recovery, reduce frequency- fluctuations peaks, and reduce frequency deviation by more than 80% compared with the conventional method. • A model predictive control scheme integrated with two-layer moving-horizon estimation observer is proposed. • The two-layer observer design decouples multiple observations and reduce the order of the observation equations. • We use deloading virtual synchronous generator control to make the renewable energy participating in the secondary frequency regulation. • The proposed method overcomes the stochasticity of each generating unit and load. [ABSTRACT FROM AUTHOR]

• A model predictive control-based energy management for an electro-thermal microgrid. • The strategy minimizes operating costs and pollution avoiding battery degradation. • Analyze the state of health of the battery system to reduce degradation. • A comparison with a Unit Commitment approach under different generation scenarios. Access to electricity is a fundamental key to developing countries' growth. Microgrids (MG) have become potential solutions to provide energy to isolated rural areas in a safe and environmentally friendly way. Therefore, proposals for alternative solutions to bridge the electrification gap in the Ecuadorian Amazon – which has the most significant percentage of its population without access to electricity – are of significant interest. Consequently, this paper presents the design of an Energy Management System (EMS) based on Model Predictive Control (MPC) for an isolated electro-thermal microgrid comprising a photovoltaic generator, a diesel generator, a lithium-ion battery Energy Storage System (ESS), electrical loads, and a domestic hot water system. The EMS aims to supply energy reliably and safely, minimize the MG's operation costs, and extend the ESS's useful life while satisfying the end users' comfort. This study includes an estimated degradation model for the ESS's State of Health (SOH), an essential parameter contributing to reducing the microgrid's operating costs in the long term. Simulation results using one-year data present the influence of the prediction horizon on the MG's scheduling. In addition, the benefits of the proposed EMS are highlighted by comparing it with the results achieved by a Unit Commitment approach, where it is demonstrated that the proposed EMS presents a reduction in MG operating costs and greenhouse gas emissions while maximizing the utilization of renewable energy and extending the lifespan of the ESS. Finally, an experimental validation, using a Typhoon Hardware-in-the-loop HIL-402 device in real-time operation, stands out the effectiveness and feasibility of the proposed MPC-based EMS. [ABSTRACT FROM AUTHOR]

Recently, the integration of optimal battery dispatch and demand response has received much attention in improving DC microgrid operation under uncertainties in the grid-connect condition and distributed generations. However, the majority of prior studies on demand response considered the characteristics of global frequency variable instead of the local voltage for adjusting loads, which has led to obstacles in operating DC microgrids in the context of increasingly rising power electronic loads. Moreover, the consideration of voltage-dependent demand response and optimal battery dispatch has posed challenges for the traditional planning methods, such as stochastic programming, because of nonlinear constraints. Considering these facts, this paper proposes a model predictive control-based integrated voltage-based demand response and batteries' optimal dispatch operation for minimizing the entire DC microgrid's operating cost. In the proposed model predictive control approach, the binary decisions about voltage-dependent demand response and charging or discharging status of storage batteries are determined using a deep-Q network-based reinforcement learning method to handle uncertainties in various operating conditions (e.g., AC grid-connect faults and DC sources variations). It also helps to improve the DC microgrid operation efficiency in the two aspects: continuously avoiding load shedding or shifting and reducing the batteries' charge and discharge cycles to prolong their service life. Finally, the proposed method is validated by comparing to the stochastic programming-based model predictive control method. Simulation results show that the proposed method obtains convergence with approximately 41.95% smaller operating cost than the stochastic optimization-based model predictive control method. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive control strategy for 15 level Packed-U-Cell inverter that satisfies multiple-objectives of low current total harmonic distortion (THD), capacitor voltage balances, supply of desired active and reactive power, as well as lower switching and lower voltage stresses on the switching devices. The proposed device performs well under dynamic conditions and can successfully track the current command during step changes in the power demand. A detailed modeling is presented and discussed. MATLAB/Simulink is used for obtaining the simulation results, and the results are validated in the real time by using a hardware-in-the-loop (HIL) Typhoon 402 real-time emulator. [ABSTRACT FROM AUTHOR]

Multi-port solid-state transformer (SST) characterized by high scalability is expected to be widely used in AC/DC hybrid microgrid. However, the DC bus voltage deviation and dynamic response speed are two key issues in its application. Therefore, a model predictive direct power control (MPDPC) of the three-port SST (TPSST) is proposed. Compared with the traditional PI-based method, the oscillation of the DC bus voltage is inhibited, and the steady-state performance is improved with the MPDPC method. Moreover, a direct power path is incorporated into the control system in order to integrate the rectifier and dual active bridge (DAB) stage. This power path delivers the power required by the load to the DAB stage and the rectifier stage for power control, thereby improving the dynamic response of the system in the process. Besides, since the MPDPC strategy is a control scheme for the whole system, the design of the control system is simplified. Finally, a low power prototype has been built and tested, and the experimental comparison with the traditional voltage control (TVC) and model predictive voltage control (MPVC) method verifies the efficiency improvement of the proposed control strategy. [ABSTRACT FROM AUTHOR]

The harmful outcome of fossil fuel emission and growing concern about the deficiency of power resources has started a new requirement of cleaner, reliable, and sustainable energy resources. Thus, wind power and solar energy based systems are the best emerging energy sources amongst the various renewable energy resources. The proposed system involves a boost converter with an inverter circuit, where first solar and wind energy is regulated, and the inverter is employed for grid integration. In the proposed work, model predictive control is applied to a 3-phase inverter and it uses a model to estimate the predictions of upcoming values of the system voltage for a provided sequence of voltages. Further, the cosine firing angle arrangement is also used for adjusting the output voltage of the boost converter. The three-phase inverter is also employed as a harmonic reduction device, this allows the utility grid to continuously supply or absorb the good quality of power. The proposed approach escapes the use of nonlinear and linear controllers and it does not include the modulator, hence this is a simple method and the control algorithm is easy to implement. The outcomes show that the planned scheme has excellent performance. [ABSTRACT FROM AUTHOR]

Modular building energy management strategy based on a three-level hierarchical model predictive control is proposed in the paper. Building zones, central medium conditioning and microgrid subsystems are controlled independently by individual linear and nonlinear model predictive controllers, and further integrated together as levels of hierarchical coordination control structure based on price-consumption information exchange. The three-level coordination provides a holistic energy management strategy and enables significant demand response ancillary services for buildings as prosumers, while retaining the independence of required expertise in very different building subsystems. The approach is applied for daily operation scheduling of a full-scale building consisting of 248 offices. Models of building subsystems are obtained by identification procedures on measurement data. Compared to rule-based control, detailed realistic simulations show that the overall building operation cost for typical days in summer is reduced by 9-12% for level-by-level energy-optimal and by 15-24% for price-optimal, coordinated operation. The application of predictive control in the proposed way also improves the indoor comfort substantially. [ABSTRACT FROM AUTHOR]

Collective self-consumption is driving the transformation of distribution networks towards independently controllable community microgrids. The objective of this work is to utilize limited heterogeneous flexibilities in the system to perform grid-support functions to evade additional investment on dedicated storage facilities. We propose a cooperative receding horizon control that allows optimal use of the collective resources over the prediction horizon. The conventional linear state-space model employed in the predictive control is replaced by a novel rate-based model that propagates state derivatives rather than the states across the prediction horizon, thus eliminating offset errors. The proposed cooperative predictive control strategy with rate-based model has been applied to the microgrid frequency control problem. The proposed control strategy has been illustrated on a power hardware-in-the loop platform by modeling a microgrid system with grid-supporting ders. The simulations reveal the effectiveness of the proposed strategy by comparing the control solution with a dedicated storage facility and its replacement distributed flexible resources. [ABSTRACT FROM AUTHOR]

The number of river floods has increased worldwide, as well as in Nakorn Ratchasima Province in Thailand. To prevent disasters, the Royal Irrigation Department (RID) constructed thirteen regulating structures to control discharges. Currently, the local controllers spatially control these structures to minimize the subsystem's damage, regardless of the effects on the overall system performance. In this study, the concept of combining real-time flood management tools and the cascade Model Predictive Control (MPC) as well as application of GIS has been proposed and verified with the 2013 flood event. The distributed control of the existing hydraulic structures on the large scale of the Lamtakong River made optimal use of the retention basin storage capacity with the considerations of both local performance and global system interactions. The model proposes an optimal gate opening of each cascade at the specified time, from the beginning until the end of flood hydrographs. The results of the controlled water level indicate the efficiency of the CMPC, which is more satisfied satisfactory than the PID and practice technique of RID, as evidenced by the water level of 0.5 m that is lower than the level of the riverbank at Nakhon Ratchasima City. A comparison of flood areas between the historical flood in year 2013 and Management by CASCADE MPC shows by GIS flood map that in the case of Management by CASCADE MPC, it can reduce the flood area in Nakhorn Ratchasima city by almost 60%. [ABSTRACT FROM AUTHOR]

Increasing integration of photovoltaic (PV) system in electric grids cause congestion during peak power feed-in. Battery storage in PV systems increases self-consumption, for consumer's benefit. However with conventional maximising self consumption (MSC) control for battery scheduling, the issue of grid congestion is not addressed. The batteries tend to be fully charged early in the day and peak power is still fed-in to grid. This also increases battery degradation due to increased dwell time at high state of charge (SOC) levels. To address this issue, this work uses a model predictive control (MPC) for scheduling in PV system with battery storage to achieve multiple objectives of minimising battery degradation, grid congestion, while maximising self consumption. In order to demonstrate the improvement, this work compares the performances of MPC and MSC schemes when used in battery scheduling. The improvement is quantified through performance indices like self consumption ratio, peak power reduction and battery capacity fade for one-year operation. An analysis on computation burden and maximum deterioration in MPC performance under prediction error is also carried out. It is concluded that, compared to MSC, MPC achieves similar self consumption in PV systems while also reducing grid congestion and battery degradation. [ABSTRACT FROM AUTHOR]

In this study, virtual inertia control-based vehicle-to-grid (V2G) concept for the secondary load frequency control of AC islanded microgrids (MGs) is presented. Since autonomous modern power grids typically have much smaller inertia in comparison to conventional grids, a very fast and high-performance control plant is necessitated for controlling reserved power plants (e.g., batteries) to overcome any voltage and frequency instability. This issue can be solved by the short-term battery energy storage system, inverter, and proper inertia control technique. Because of the high cost of the battery energy storage system, utilization of controllable loads like electric vehicle (EV) to low-inertia modern power grids is presented for the decreasing of the needed capacity of the energy storage system. Hence, an optimal non-integer model predictive control is applied for the secondary frequency regulation in low-inertia stand-alone MGs with the V2G technology. Since the performance and accuracy of the applied non-integer controller are depended on its parameters’ value, a modified heuristic optimization algorithm is proposed for tuning the controller's parameters. In the end, the validity and feasibility of the proposed method are investigated by the model-in-the-loop experimental results. [ABSTRACT FROM AUTHOR]

In this article, we propose and experimentally validate a scheduling and control framework for distributed energy resources (DERs) that achieves to track a day-ahead dispatch plan of a distribution network hosting controllable and stochastic heterogeneous resources while respecting the local grid constraints on nodal voltages and lines ampacities. The framework consists of two algorithmic layers. In the first one (day-ahead scheduling), we determine an aggregated dispatch plan. In the second layer (real-time control), a distributed model predictive control (MPC) determines the active and reactive power set-points of the DERs so that their aggregated contribution tracks the dispatch plan while obeying to DERs operational constraints as well as the grid's ones. The proposed framework is experimentally validated on a real-scale microgrid that reproduces the network specifications of the CIGRE microgrid benchmark system. [ABSTRACT FROM AUTHOR]

Summary: With the new edge of smart grids technology, the concept of efficient energy management among cooperative microgrids (MGs) distribution networks for the next generation of energy trading applications has become more and more challenging. This article presents smart energy dispatch for multi‐MGs (MMGs) systems based on the cooperative distributed economic model predictive control (DEMPC) within a hierarchical structure. The optimization framework consists of two levels. First, a DEMPC coordinator computes the optimal energy dispatch solution, taking into account the energy surplus with a power‐sharing policy, the retail electricity price based on the time of use (TOU), and the distributed generators units. For the second level, each local controller executes the energy scheduling reference from the first level. Thus, the MMGs system achieves a global supply‐demand balance. Moreover, the proposed approach improves the utilization of renewable power and demonstrates optimal energy dispatch while reducing operating costs. Finally, to validate the efficiency and resiliency of our proposed approach, two cases of study are presented based on three interconnected MGs system under a cooperative and non‐cooperative optimization. Simulation results show the advantage of the cooperative strategy with 28% for MG 1, and 20% for MG 2 in terms of daily operating costs savings. [ABSTRACT FROM AUTHOR]

This study presents a model predictive control (MPC)-based centralised control approach for maintaining the voltages of the nodes within permissible limits in the presence of high photovoltaic (PV) penetration. The proposed control scheme coordinates the actions of on-load tap changer (OLTC) and PV inverters optimally to fulfil the desired objectives. The objectives are minimisation of change in control variables, slack variables, energy loss, and voltage error. These objectives are weighted to form the overall objective function. Three rules are formulated based on the severity of voltage magnitudes. The weights of the objectives are adjusted according to these pre-defined rules. Simulations are performed in an active distribution network (ADN) integrated with and without microgrids, where both demands and generations vary hourly over the day. As power is injected by the microgrids during most of the time of the day, the excursions of node voltages are slightly higher in the microgrids integrated ADN. Moreover, the incorporation of the proposed rule-based MPC drastically reduces the energy loss due to active power loss in distribution networks, as is evident from the simulation results obtained by comparing the proposed approach with an existing MPC-based approach. [ABSTRACT FROM AUTHOR]

Negative sequence voltage affects the consequences of the presence of zero-sequence voltage in different ways under various grid fault conditions. The prime objective of the work presented in this article is to confine the point of common coupling (PCC) phase voltages within the stipulated limits during faults considering all the sequence voltages of a microgrid. Depending on the phase and amplitude of sequence voltages, the proposed scheme defines a control parameter to classify all possible faults into two categories. In the first category, the defined control parameter decides the positive and negative sequence grid currents to accommodate the zero-sequence voltage at the PCC within the stipulated limits. In the second category, the zero-sequence current is supplied along with the positive and negative sequence currents to achieve the objective. Accordingly, a four-leg inverter is employed to accomplish the voltage limit goals under various fault conditions. Resistive-inductive grid impedance of the microgrid is considered for the implementation of the proposed voltage support scheme. The LC-filter-based model predictive control is employed to control the four-leg three-level inverter for achieving three objectives, such as grid current control, active damping, and dc-link capacitor voltage balancing simultaneously. Analysis and experimental results are reported to support the concept. [ABSTRACT FROM AUTHOR]

Wave energy is a renewable energy with a high density. There are different types of wave power generation systems (WPGSs), including Archimedes wave swing (AWS) coupled to a linear permanent magnet synchronous generator. This study proposes a model predictive control (MPC) for AWS‐based WPGS. The predictive current controller is implemented in a generator‐side converter as a substation for proportional integral (PI) controllers with pulse‐width modulation (PWM). Also, a supercapacitor energy storage system with bidirectional DC/DC converter is proposed to maintain DC link voltage stability. A DC/DC converter is used to keep DC microgrid bus voltage at the desired value. The effectiveness of the proposed MPC method is tested at various operating conditions and compared with conventional PI controllers with PWM in the MATLAB/SIMULINK environment. [ABSTRACT FROM AUTHOR]

The unbalanced and distorted grid phenomena are expected to be a critical factor for future microgrids due to extensive use of unbalanced and nonlinear loads. These microgrid conditions keep changing depending on the load profile. The active power oscillation, which happens due to polluted grid conditions, results in dc-link voltage fluctuation. Therefore, not only lifetime of the batteries and dc-link capacitors are compromised but also the stability of the system is affected. Active power oscillation can only be reduced by compromising with the harmonic distortion of the grid currents. Therefore, to cope with various expected abnormal microgrid conditions, a tradeoff between active power oscillation and total harmonic distortion of grid current is required. This article proposes a novel flexible reference current generation technique by using a tuning parameter to reduce the active power oscillation flexibly. The generated reference current comprises not only the positive and negative sequence currents but also lower order harmonic components. The peak values of all the components present in the reference current are decided in a stationary reference frame. Since model predictive controller tracks multifrequency reference currents without any additional requirement and with good stability margin, it has been used to control the neutral point clamped grid-tied three-level inverter. The concept is validated through simulation and experimental results. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with the application of model predictive control (MPC) to optimize power flows in a network of interconnected microgrids (MGs). More specifically, a distributed MPC (DMPC) approach is used to compute for each MG how much active power should be exchanged with other MGs and with the outer power grid. Due to the presence of coupled variables, the DMPC approach must be used in a suitable way to guarantee the feasibility of the consensus procedure among the MGs. For this purpose, we adopt a tailored dual decomposition method that allows us to reach a feasible solution while guaranteeing the privacy of single MGs (ie, without having to share private information like the amount of generated energy or locally consumed energy). Simulation results demonstrate the features of the proposed cooperative control strategy and the obtained benefits with respect to other classical centralized control methods. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) and their advanced control systems have the ability to improve energy efficiency and mobility of the transportation system, but hundred percent market penetration of CAVs will not happen in the near future. CAVs are thus expected to share the road with human-driven vehicles (HDVs), which can act in suboptimal manners. Since vehicles on road constitute an interactive system, where actions of one vehicle influence the actions of its surrounding vehicles, suboptimal behavior of HDVs affect all their surrounding vehicles including the CAVs. This paper thus focuses on the development of a suggestion-based control framework for CAVs in interactive mixed traffic environments. Our mixed traffic environment includes both CAVs and connected HDVs that have vehicle-to-vehicle (V2V) communication capabilities. In this suggestion-based control framework, a CAV is considered to provide velocity and lane-change suggestions to multiple surrounding HDVs to follow. These suggestions are the velocities and lane-change actions the CAV wants the HDVs to follow to improve its own and the group's energy efficiency and mobility. To take into account the interactive nature of the environment, the CAV is considered to jointly plan its own trajectory, evaluate the suggestions to the surrounding HDVs, and predict the actions of its surrounding vehicles. The joint planning requires solving the problem in joint state- and action-space, and in this paper, we develop a Monte Carlo Tree Search (MCTS)-based trajectory planning approach for the CAV. Since the joint action- and state-space grows exponentially with the number of agents and can be computationally expensive, we propose an adaptive action-space by pruning the actions resulting in unsafe trajectories for each agent. The trajectory planning approach is followed by a low-level model predictive control (MPC)-based motion controller, which aims at tracking the reference trajectory in an optimal fashion. Simulation studies show the proposed control strategy's efficacy compared with existing baseline methods. Simulation studies show improvements in fuel efficiency ranging from 4% to 50% for HDVs and from 1% to 30% for the CAV compared to the baseline. • A suggestion-based hierarchical control framework is proposed for vehicular systems. • CAV provides velocity and lane-change suggestions to multiple HDVs. • Decisions are made in a joint state- and action-space. • MCTS-based trajectory planner developed with action-space pruning. [ABSTRACT FROM AUTHOR]

This paper presents a bidirectional single-inductor multiple-port (BSIMP) converter for integrating hybrid energy storage system (HESS) into DC microgrids, where the HESS is the combination of different types of energy storages (ESs). A control method based on model predictive control (MPC) is proposed to regulate the BSIMP converter for the HESS. With the MPC-based method, the ESs are controlled following their characteristics. Simultaneously, the DC microgrid bus voltage can be maintained against the fluctuations of the load consumption and renewable generation. Compared to conventional bidirectional single-input single-output converters, the proposed BSIMP converter significantly reduces the component count, system size and cost to integrate the HESS into DC microgrid. To verify the performance of the proposed BSIMP converter with MPC-based method, a series of hardware-in-loop (HIL) experimental cases are studied. Moreover, the comparison with previous works is conducted. The HIL experimental and comparison results demonstrate the successful regulation of HESS using the BSIMP converter in DC microgrid. [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 dual model predictive control (DMPC) method based on virtual trajectory is proposed in this paper, in order to achieve autonomous cooperative formation control of underactuated unmanned surface vehicles (USVs) in complex ocean environment. Firstly, the formation tracking error model of the USVs is designed, and considering the large initial state tracking error of USVs, a virtual transition trajectory is put forward to guide the design of tracking controller. To solve the mutation problem caused by misalignment of the follower USV's desired trajectory in the early stage, an improved differential tracker is introduced into virtual leader USV to smooth the desired trajectory of the follower USV. In addition, a dual mode switching strategy is designed to decide when and how to quit the transition of the virtual USV. A nonlinear disturbance observer is introduced to compensate the complex marine environment interference. Finally, by introducing terminal penalty function and linear state feedback controller, the Lyapunov function is constructed to prove the control stability of the proposed model predictive control method in finite time domain, and the effectiveness and reliability of the proposed method are verified by simulation experiments. • A dual MPC method based on virtual USV trajectory is proposed for the cooperative formation control of USVs. • An improved differential tracker is introduced into virtual leader USV to smooth the desired trajectory of the follower. • A dual-mode switching strategy is designed to select which trajectory as the USVs' tracking trajectory. [ABSTRACT FROM AUTHOR]