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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]

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]

A microgrid cluster is composed of multiple interconnected microgrids and operates in the form of cluster, which can realize energy complementation between microgrids and significantly improve their renewable energy consumption capacity and system operation reliability. A microgrid optimal dispatch based on a distributed economic model predictive control algorithm is proposed in this paper. Firstly, the control task of the microgrid power generation system is defined, which is required to meet the load demand while reducing the economic loss of the system and realize dynamic economic optimization. The global objective function is designed based on the control task, and the detailed design method of the distributed economic model predictive controller is given. The control law is obtained by an iterative calculation using the Nash optimal method, which can effectively reduce the amount of data in the communication network. Finally, a microgrid group composed of four microgrids is used as an example for simulation verification. The simulation results show that the distributed economic model predictive control algorithm proposed in this paper has good economic benefits for microgrid dispatching. [ABSTRACT FROM AUTHOR]

Microgrids have become an alternative for integrating distributed generation to supply energy to isolated communities, so their control and optimal management are important. This research designs and simulates the three levels of control of a DC microgrid operating in isolated mode and proposes an Energy Management System (EMS) based on Model Predictive Control (MPC), with real-time measurement feedback for optimal energy dispatch, which ensures power flow distribution and operation at minimum cost while extending the lifespan of the BESS. The EMS can react to disturbances produced in the lower control levels. The microgrid's performance is analyzed and compared in two scenarios without EMS, and with EMS against changes in irradiation and changes in electricity demand. The fulfillment of the power balance is evaluated by analyzing the power delivered by each generation unit, the operating cost, and the state of charge of the battery (SOC). [ABSTRACT FROM AUTHOR]

In order to give full play to the frequency regulation ability of multiple types of resources such as wind power, energy storage, and controllable load in a microgrid, this paper proposes a hierarchical cooperative frequency regulation control strategy of wind-storage-load in a microgrid based on model prediction. Firstly, according to the operation characteristics of each resource in the microgrid, a hierarchical cooperative frequency regulation architecture of wind-storage-load is constructed. On this basis, the frequency regulation control models of wind power, energy storage, and controllable load are established, respectively, and the calculation method of the characteristic index of the system frequency response is proposed. Then, taking the maximum frequency deviation as the stratification index, a hierarchical cooperative frequency regulation control strategy of wind-storage-load based on model prediction is proposed, and a power compensation strategy for connecting the wind turbine frequency support is proposed for the wind turbine speed recovery stage. Finally, a microgrid model including wind power, energy storage, and controllable load is built on Matlab/Simulink for simulation analysis. The simulation results show that the proposed control strategy can control wind power, energy storage, and controllable load to participate in frequency modulation in advance, and improve the frequency stability of the system. [ABSTRACT FROM AUTHOR]

This research proposes an improved finite control set direct power model predictive control method (FCS-DPMPC) for grid-tie distributed generation (DG). FCS-DPMPC predicts the system outcomes using the system model. During the next sampling time, a voltage vector is defined using the cost function to minimize the power ripple, consequently allowing flexibility for power regulation. Furthermore, the impact of implementing a one-step delay is studied and compensated through a model forecast pattern. In addition, a new two-step horizon technique has been developed to minimize switching frequency and computation burden. Simulation results for single DG and parallel operated DGs in a grid-tie manner confirm the effectiveness of the suggested control strategy, which signifies that this is an appropriate approach for distributed generation in microgrids. [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]

This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including preventing the dependency of the converter control system on the operating point conditions, plug-and-play capability, and robustness against the MG uncertainties and unknown load dynamics. [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]

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]

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]

In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of observer and reducing the control complexity. In addition, the Zeno behavior is excluded through detailed analysis. Furthermore, the stability of the algorithm is verified by theoretical derivation of Lyapunov. Finally, the effectiveness of the algorithm is proved by simulation. [ABSTRACT FROM AUTHOR]

Optimization of economic aspects of microgrid operation in both grid-connected and islanded mode leads to contradictive definitions of optimality for both modes. There is no general agreement on how to cope with this duality. To address this issue, as well as modern energy market requirements and a better renewable energy utilization necessity in the case of large facilities, a comprehensive control solution utilizing the appropriate model is needed. In response, the authors propose a hybrid microgrid model covering fundamental features and designed to work in conjunction with two switched receding horizon control laws. A relevant controller is chosen according to the current microgrid operation mode and its cost function tailored to specific demands of the islanded or grid-connected operation. Performed research led to a new switched hybrid model predictive control approach focused on microgrid economic optimization. This approach utilizes an appropriate hybrid microgrid model also contributed by the authors. The introduced solution turned out to be effective in overall energy cost reduction in the case of large commercial facilities, regardless of grid-connection and renewable generation scenarios. Furthermore, it also provides satisfactory renewable energy and storage capabilities utilization in changing grid connection conditions. [ABSTRACT FROM AUTHOR]

Microgrid (MG) is a novel concept for a future distribution power system that enables renewable energy sources (RES). The intermittent RES, such as wind turbines and photovoltaic generators, can be connected to the MG via a power electronics inverter. However, the inverter interfaced RESs reduce the total inertia and damping properties of the traditional MG. Consequently, the system exhibits steeper frequency nadir and the rate of change of frequency (RoCoF), which may degrade the dynamic performance and cause the severe frequency fluctuation of the system. Smart loads such as inverter air conditioners (IACs) tend to be used for ancillary services in power systems. The power consumption of IACs can be regulated to suppress frequency fluctuation. Nevertheless, these IACs, regulating power, can cause the deviation of indoor temperature from the temperature setting. The variation in indoor temperature should be controlled to fulfill residential comfort. This paper proposes a multi-objective decentralized model predictive control (DMPC) for controlling the power consumption of IACs to reduce MG frequency fluctuation and control the variation in indoor temperature. Simulation results on the studied microgrid with the high penetration of wind and photovoltaic generator demonstrate that the proposed DMPC is able to regulate frequency deviation and control indoor temperature deviation as a user preference. In addition, the DMPC has a superior performance effect to the proportional-integral (PI) controller in terms of reducing frequency deviation, satisfying indoor temperature preferences, and being robust to the varying numbers of IACs. [ABSTRACT FROM AUTHOR]

The droop control scheme based on Q − ω and P − V characteristics is conventionally employed to share the load power among sources in an islanded low-voltage microgrid with resistive line impedances. However, it suffers from poor active power sharing, and is vulnerable to sustained deviations in frequency and voltage. Therefore, accurate power sharing and maintaining the frequency and voltage in the desired ranges are challenging. This paper proposes a novel microgrid control strategy to address these issues. The proposed strategy consists of a virtual flux droop and a model predictive control, in which the virtual flux is the time integral of the voltage. Firstly, the novel virtual flux droop control is proposed to accurately control the power sharing among DGs. Then, the model predictive flux control is employed to generate the appropriate switching signals. The proposed strategy is simple without needing multiple feedback control loops. In addition, pulse width modulation is not required and tuning challenges for PI regulators are avoided. In order to evaluate the effectiveness of the proposed microgrid control strategy, simulation analysis is carried out in Matlab/Simulink software environment. The results show that accurate power sharing is achieved while a good dynamic response is provided. Furthermore, the voltage and frequency deviations are significantly improved. [ABSTRACT FROM AUTHOR]

The high penetration of photovoltaic (PV) systems and fast communications networks increase the potential for PV inverters to support the stability and performance of microgrids. PV inverters in the distribution network can work cooperatively and follow centralized and decentralized control commands to optimize energy production while meeting grid code requirements. However, there are older autonomous inverters that have already been installed and will operate in the same network as smart controllable ones. This paper proposes a decentralized optimal control (DOC) that performs multi-objective optimization for a group of PV inverters in a network of existing residential loads and autonomous inverters. The interaction of independent DOC groups in the same network is considered. The limit of PV inverter power factor is included in the control. The DOC is done by the power flow calculation and an autoregression prediction model for estimating maximum power point and loads. Overvoltage caused by prediction errors resulting in non-optimal commands from the DOC is avoided by switching to autonomous droop control (ADC). The DOC and ADC operate at different time scales to take account of communication delays between PV inverters and decentralized controller. The simulation of different scenarios of network control has proved the effectiveness of the control strategies. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24 h, only the prediction accuracy of the first 75 min was relevant for the presented setting. [ABSTRACT FROM AUTHOR]

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and its applications in microgrids are described and summarized. [ABSTRACT FROM AUTHOR]

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weather conditions and load requests, demonstrates the ability of the NMPC to supervise the multi-node microgrid resulting to optimal energy management and reduction of the auxiliary power devices operation. [ABSTRACT FROM AUTHOR]

Stability and reliability are of the most important concern for isolated microgrid systems that have no support from the utility grid. Interval predictions are often applied to ensure the system stability of isolated microgrids as they cover more uncertainties and robust control can be achieved based on more sufficient information. In this paper, we propose a probabilistic microgrid energy exchange method based on the Model Predictive Control (MPC) approach to make better use of the prediction intervals so that the system stability and cost efficiency of isolated microgrids are improved simultaneously. Appropriate scenarios are selected from the predictions according to the evaluation of future trends and system capacity. In the meantime, a two-stage adaptive reserve strategy is adopted to further utilize the potential of interval predictions and maintain the system security adaptively. Reserves are determined at the optimization stage to prepare some extra capacity for the fluctuations in the renewable generation and load demand at the operation stage based on the aggressive and conservative level of the system, which is automatically updated at each step. The optimal dispatch problem is finally formulated using the mixed-integer linear programming model and the MPC is formulated as an optimization problem with a discount factor introduced to adjust the weights. Case studies show that the proposed method could effectively guarantee the stability of the system and improve economic performance. [ABSTRACT FROM AUTHOR]

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-objective model predictive control (MPC) designed for the power management of a multi-stack fuel cell (FC) system integrated into a renewable sources-based microgrid. The main advantage of MPC is the fact that it allows the current timeslot to be optimized while taking future timeslots into account. The multi-objective function solves the problem related to the power dispatch at time that includes criteria to reduce the multi-stack FC degradation, operating and maintenance costs, as well as hydrogen consumption. Regarding the scientific literature, the novelty of this paper lies in the proposal of a generalized MPC controller for a multi-stack FC that can be used independently of the number of stacks that make it up. Although all the stacks that make up the modular FC system are identical, their levels of degradation, in general, will not be. Thus, over time, each stack can present a different behavior. Therefore, the power control strategy cannot be based on an equal distribution according to the nominal power of each stack. On the contrary, the control algorithm should take advantage of the characteristics of the multi-stack FC concept, distributing operation across all the stacks regarding their capacity to produce power/energy, and optimizing the overall performance. [ABSTRACT FROM AUTHOR]

Power distribution systems nowadays are highly penetrated by renewable energy sources, and this explains the dominant role of power electronic converters in their operation. However, the presence of multiple power electronic conversion units gives rise to the so-called phenomenon of Constant Power Loads (CPLs), which poses a serious stability challenge in the overall operation of a DC micro-grid. This article addresses the problem of enhancing the stability margin of boost and buck-boost DC-DC converters employed in DC micro-grids under uncertain mixed load conditions. This is done with a recently proposed methodology that relies on a two-degree-of-freedom (2-DOF) controller, comprised by a voltage-mode Proportional Integral Derivative (PID) (Type-III) primary controller and a reference governor (RG) secondary controller. This complementary scheme adjusts the imposed voltage reference dynamically and is designed in an optimal fashion via the Model Predictive Control (MPC) methodology based on a specialized composite (current and power) estimator. The outcome is a robust linear MPC controller in an explicit form that is shown to possess interesting robustness properties in a wide operating range and under various disturbances and mixed load conditions. The robustness and performance of the proposed controller/observer pair under steady-state, line, and mixed load variations is validated through extensive Matlab/Simulink simulations. [ABSTRACT FROM AUTHOR]

This article presents the analysis of the influence of household appliances on the quality of the energy consumed by the end-user. The results of the research, then, concern the final consumer (the lowest level of the power grid). The research was conducted on 120 combinations of electrical appliances connected into a grid. Each combination consisted of three devices working simultaneously in a micro-grid. The obtained and statistically analyzed data proved that there are several types of appliances that have a great influence on the power quality (PQ) parameters changes. The results of the conducted experiments indicate the devices which influenced significantly the total harmonic distortion of voltage (THDV), the voltage frequency (FREQ) and the voltage fluctuation (V). Specific features of particular devices were examined in terms of their significance for the power quality deviation. This showed the most important features which should be considered while working out the prediction model. The future of smart grids resides in data analysis, predictive models and real-time optimization. One of the key characteristics is the reducing energy consumption generated by renewable energy sources. This phenomenon, namely looking for problems connected with sustainable power quality and their appropriate solution, is described in this article. We performed an extended analysis of the smart home appliances influence of individual quantities on a real model. Furthermore, we explored devices with a high impact on chosen power quality indicators. In the end, we discuss their specific behavior and relevance to the above-described phenomenon to improve the predictive model utility. [ABSTRACT FROM AUTHOR]

In the last few years, one of the most important challenges of power technologies has been the integration of traditional energy production systems and distributed energy resources. Large-scale photovoltaic systems and wind farms may decrease the quality of the electrical grid service, mainly due to voltage and frequency peaks and fluctuations. Besides, new functionalities, such as the operation in islanded mode of some portions of the medium-voltage grid, are more and more required. In this respect, a model predictive control for voltage and frequency regulation in interconnected local distribution systems is presented. In the proposed model, each local system represents a collection of intelligent buildings and microgrids with a large capacity in active and reactive power regulation. The related model formalization includes a linear approximation of the power flow equations, based on stochastic variables related to the electrical load and to the production from renewable sources. A model predictive control problem is formalized, and a closed-loop linear control law has been obtained. In the results section, the proposed approach has been tested on the Institute of Electrical and Electronics Engineers(IEEE) 5 bus system, considering multiple loads and renewable sources variations on each local system. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) interest is growing very fast due to the environment urgency and their capability to integrate renewable energy in a flexible way. In particular, islanded MGs in which distributed energy resources (DERs) are connected to the infrastructure with power electronic converters have attracted the interest of many researchers of both academia and industry because management, control and protection of such systems is quite different from the case of traditional networks. According to their operation mode, MGs that power electronic converters can be divided into grid-forming, grid-feeding and grid-supporting inverters. In particular, grid forming inverters are asked to impose voltage and frequency in the MG. This paper aims to propose a model predictive control (MPC) based approach for grid-forming inverters in an islanded MG. The MPC strategy is implemented because of its capability to define the optimal control actions that contemporarily track the desired reference signals and accounts for equality and inequality constraints. The overall problem formulation (objective function and relevant constraints) is described step by step and considers the specificity of the considered DC source. The proposed approach allows for the obtaining of very good results in terms of readiness against disturbances, even if it requires being fed only by local measurements. In order to validate the developed method, this paper proposes an experimental validation of the designed MPC controller in order to show its correct operation on a real system in a power hardware in the loop set-up using a rapid control prototyping approach. [ABSTRACT FROM AUTHOR]

This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a new application of different direct matrix converter topologies used as power interfaces in AC, DC, and hybrid microgrids, with model predictive current control. Such a combination of a converter and control strategy leads to a high power quality microgrid voltage, even with a low power quality main grid voltage and even during the connection and disconnection of a variety of loads and generation sources to the microgrids. These robust systems are suitable for applications in which sensitive loads are to be supplied and these loads are connected close to distributed-generation sources with inherent intermittent behavior. The authors also propose the use of new direct matrix converter configurations with a reduced number of switches in order to achieve reduced cost, reduced failure rate, and higher reliability, which are very desirable in microgrids. Finally, the authors also introduce new hybrid direct matrix converter topologies that provide interesting options for the islanded operation of the microgrids with the use of a battery system. In other words, the proposed hybrid direct matrix converters result in flexible hybrid microgrid configurations integrating DC and AC devices with high power quality and high power supply reliability. [ABSTRACT FROM AUTHOR]