The rising integration of renewable energy installations and large-scale unscheduled consumer loads has heightened the need for demand-side flexibility in grid-connected assets. With the adoption of the pan-European PICASSO market, the secondary reserve mechanism (aFRR) has been divided into two stages: the day-ahead capacity market and the real-time energy activation, unlocking new revenue streams for deferrable loads and flexible resources. However, as the energy activation of the committed power is purely determined by grid imbalances, the energy balance of the asset could deviate from the predicted day-ahead trajectory, leading to unmet demand or unexploited profits. This paper proposes an optimization algorithm for a boiler in Denmark to enable optimal trading in the PICASSO real-time energy market. The framework incorporates the heat demand baseline, thus allowing minor deviations to foster market profitability. Results demonstrate the viability of concurrent participation in ancillary services while fulfilling the boiler’s regular operational requirements, and assess the impact of aFRR participation on achieving the objective of minimizing operational costs.

Semiconductor devices, especially MOSFETs (Metal–oxide–semiconductor field-effect transistor), are crucial in power electronics, but their reliability is affected by ageing processes influenced by cycling and temperature. The primary ageing mechanism in discrete semiconductors and power modules is the bond wire lift-off, caused by crack growth due to thermal fatigue . The process is empirically characterized by exponential growth and an abrupt end of life, making long-term ageing forecasts challenging. This research presents a comprehensive comparative assessment of different forecasting methods for MOSFET failure forecasting applications. Classical tracking, statistical forecasting and Neural Network (NN) based forecasting models are implemented along with novel Temporal Fusion Transformers (TFTs). A comprehensive comparison is performed assessing their MOSFET ageing forecasting ability for different forecasting horizons. For short-term predictions, all algorithms result in acceptable results, with the best results produced by classical NN forecasting models at the expense of higher computations. For long-term forecasting, only the TFT is able to produce valid outcomes owing to the ability to integrate covariates from the expected future conditions. Additionally, TFT attention points identify key ageing turning points, which indicate new failure modes or accelerated ageing phases.

As the world transitions towards variable renewable energy sources such as wind and solar, the demand for flexibility in the power system is increasing. Additionally, the electrification of transportation is driving up electricity demand. To address these challenges, electricity storage has emerged as a crucial solution. This study focuses on integrating battery electricity storage systems (BESS) in fast electric vehicle charging stations (EVCS) to provide ancillary services to the grid. The objective is to develop day-ahead bidding and scheduling algorithms tailored for infrastructure owners considering market and technical requirements specific to frequency regulation services in East-Denmark (DK2). A seasonal auto-regressive integrated moving average (SARIMA) model is also implemented to forecast ancillary service prices. Through a month-long test period, the developed method is compared to the optimal solution using perfect price information. Our findings offer insights into the benefit of multi-market strategies and ancillary service provision for fast EVCS and BESS owners, contributing to the understanding of sustainable energy integration and grid stability.

This article proposes an innovative control strategy for the Dual Active Bridge (DAB) power converter, enhancing the Moving Discretized Control Set - Model Predictive Control (MDCS-MPC) algorithm for voltage and current control. The proposed approach enables fast dynamic response, precise control, and no steady-state error in both voltage and current regulation. By utilizing a predictive model of the system variables, the algorithm anticipates their behavior for various actuation scenarios, while effectively managing output current perturbations. To ensure safe operation within specified limits, two current limiting methods are introduced: a dynamic current limiter and a dynamic reference algorithm. Experimental results are provided to assess the performance of the control algorithm for two scenarios, a resistive load and a programmable voltage source in series with a small resistor, to emulate a battery. These algorithms are particularly interesting for applications that demand control of both voltage and current within a safe operational range, such as battery systems.

With Europe's ambitious goals of reducing greenhouse gas emissions, the gradual replacement of traditional fossil-based generators by grid-connected converters is becoming prominent. Synchronization stability plays a crucial role in the operation of grid-forming converters, where power grid characteristics, particularly grid impedances, significantly impact synchronization stability. This paper presents a novel approach to grid impedance identification using the extended Kalman filter (EKF) concept. The identified parameters are utilized to fine-tune the model-predictive controller (MPC) of the converter's current control loop, ensuring strict constraints on input and output variable amplitudes. The performance of the proposed algorithm heavily relies on the weighting factors employed in both the MPC and EKF algorithms, posing a challenge in the existing literature. To address this issue, an artificial neural network (ANN) is employed to optimize the weighting factors in the proposed algorithm. Experimental tests conducted in PowerLabDK are presented to evaluate the effectiveness and performance of the proposed approach. The results demonstrate an accuracy of over ninety-six percent in the identified parameters during the experimental tests.

This paper addresses the challenging issues of harmonics and unbalanced behavior in future power grids with a high penetration of renewable energy sources, particularly in weak grid environments. It focuses on the harmonic compensation capabilities of power electronic converter interfaced distributed generators (DGs). While voltage-controlled DG converters have been extensively studied for voltage harmonic compensation, limited attention has been given to current-controlled mode (CCM) DGs, which are commonly used in renewable energy generators and energy storage systems. To address this gap, the paper proposes a novel continuous control set model predictive current controller (MPC) for harmonic and unbalanced compensation. The proposed control strategies offer several advantages, including fast control response, absence of steady-state error, the ability to enforce hard constraints on input and output variables, robustness, and adaptability. Experimental tests were conducted at the Smart Converter Lab, PowerLabDK, Technical University of Denmark to validate the proposed approach.

One of the challenges associated with the green transition involves advances in digitization and the integration of intelligent demand-response services to improve energy effi-ciency and unlock on-the-fly adaptability of electrical consumers. This paper presents a cloud-based optimization framework that relies on synergies between the Internet of Things (loT) and Digital Twin (DT) technology to provide energy-saving services in the wastewater pump station located in Bornholm, Denmark. The real-time collected data from the pump station establish the backbone of the DT, enabling high-fidelity simulations of system behaviors under different conditions and assessing the effectiveness of various energy -saving strategies. Moreover, the proposed methodology incorporates a Model Predictive Control (MPC) combined with the inflow forecasts to unlock energy optimization potential. Finally, the implementation of a pruned Neural Network Imitator (NNI) executed on an IoT gateway shows significant improvement in operating the pumps in the most efficient zone by emulating the functionality of MPC, tailoring control strategies for real-time efficiency improvement.

With the increased penetration of renewable energy sources, the power grid moves towards more distributed and uncertain power generation. In order to cost-effectively counter-balance the renewables' variability, demand side response has emerged as an attractive candidate technology over the recent years. Moreover, with Internet of Things (IoT) infrastructure implemented, demand side response can be controlled over the internet. Demand side response then offers an opportunity to use these connected assets to improve the stability of the power grid in return for payments from the grid operator. As a specific example of the demand response, this paper demonstrates how wastewater pumping stations with storage tanks can be used to provide ancillary services. The technical requirements that assets need to fulfill in order to participate in the frequency containment reserve (FCR-D) services are first described. The ancillary service is then explained and evaluated which technical aspects are the most relevant and challenging for wastewater pump stations. Moreover, an approach to calculate the flexible energy and power available at the pumping station is proposed for a real-world station in Bornholm Island, Denmark.

This article proposes a novel attack identification mechanism for Internet of Things-based (IoT-based) converter-composed dc grids, where each agent collects its own and neighbors’ measurement data for output regulation to meet a preceding power-sharing consensus. Independent from model-free or average-model-based attack detection theories, this mechanism is mainly inspired by converter stitching behavior analysis. Correspondingly, when facing latent signal substitution or agent instigation attacks, through comparing estimated signals with received ones for signal source authentication, both self-sensors and neighbors will be inspected. Eventually, not only can such attacks be detected but also will respective attack sources be identified. A simulation case of 4-agent 800-V IoT-based dc grid on Simulink and a hardware case of 3-agent 90-V IoT-based dc grid on the dSpace testing platform were investigated. Experimental results revealed that the estimation ratio error kept lower than 3.9% and all attacks were successfully identified, verifying the effectiveness of the proposed mechanism.

Dual active bridge (DAB) converters are becoming one of the most reliable interfaces due to their high voltage range, easy realization of zero voltage switching (ZVS), galvanic isolation, etc. To meet specific consumer requirements in DC microgrids, DAB converters operate with different topologies. In this paper, a finite control set model predictive control (FCS-MPC)-based power sharing control is proposed when DAB converters are in different structures which are input series-output series, input parallel-output parallel, input parallel-output series, and input parallel-output parallel connecting structures. By analyzing the power balance relationship of the input port and output port, it determines the variable (input voltage/current or output voltage/current) which should be controlled to realize the power sharing. And then the cost function is designed based on the control strategy. Besides, this paper proposes a Kalman filter based parameter estimation method for the DAB converter. In this case, it guarantees the robustness of the MPC algorithm. Finally, the simulation results prove the effectiveness of the proposed method.

Aiming at the instability problem of traditional power synchronization control (PSC) in a stiff grid, an advanced PSC based on modular multilevel converter (MMC) is proposed in this paper, which can effectively improve converter stability in high short circuit ratio (SCR) scenarios. The key point of the proposed PSC is to eliminate the impact of the arm inductor voltage and capacitor voltage ripple on the converter output voltage, which means a generalized decoupling process in the PSC controller. Based on the decoupling correction, MMC can be treated as a controlled voltage source regardless of the internal impedance circuit and the power oscillation between MMC and the stiff grid can be suppressed effectively. The control strategy is verified and compared with traditional PSC in time simulations.

Electric motors in applications such as pumps, fans and compressors are responsible for over half of the electricity consumption in developed countries. Some of these motors have a certain flexibility that can be aggregated and used to stabilise the electrical network. Flexibility is the duration that a motor can be altered without impacting functional requirements. The pump stations can unlock their flexibility through adaptive control of power-electronic-based variable speed drives. However, the application’s availability must be correctly predicted so that the critical applications controlled by power electronic drives are not compromised when operated outside standard conditions (i.e., to provide flexibility). This work identifies short- and long-term flexibility prediction methods for wastewater pumps. First, a method is developed to clean up and improve the real-world data, which is then used to develop two different models. The first model predicts how long a pump can be turned off without endangering its application. The second model performs a 24-hour-ahead prediction for the expected load on the pump. We discuss strengths and weaknesses and compare both models, identifying the best way to arrange both models to achieve an improved flexibility prediction.

Utilizing energy storage systems (ESSs) to perform multiple grid supporting services is an effective way to rationalize the investment of ESS. It is crucial to choose the matching energy storage technologies (ESTs) for achieving the specific stackable services as well as reducing investment costs and increasing revenue. Therefore, the EST suitable for providing stackable services, which includes energy arbitrage and frequency regulation, is proposed in this paper based on a framework. The framework considers the influence of technical, economical, and lifetime parameters. It mainly consists of technical preselection and economic analysis. At first, the requirements of provided services to ESS are adopted as the hard constraints to select the technically feasible ESTs. For economic analysis, a cost-benefit evaluation model for stackable services is proposed. It can quantitatively describe the influence of ESS parameters on cost and revenue. The analysis is based on the estimated parameters of ESTs in 2022. The load data and frequency regulation data are from the IEEE 33-bus distribution system and the PJM market.

The papers in this special section focus on applications of artificial intelligence in industrial power electronics and systems. The grid infrastructures and modernization, as well as the integration of renewable energies and using smart meters, can generate a large amount of data. These can lead to high complexity in the power system/electronics operation and control. Moreover, grid contingencies due to the natural disasters and cyber/physical attacks are highly unpredictable and costly preventable, which require fast and reliable data processing to preserve grid reliability and resiliency. Furthermore, the reliability of the power electronics devices and interfaces are very important, and can be improved by using the large data of measurements during long term operation. To this end, artificial intelligence (AI) techniques can potentially make it possible to provide new solutions to power electronics and power system operations and analysis. This special issue aims to investigate applications of AI in power system operation, analysis, planning, cybersecurity, as well as power electronics control, modulation techniques, reliability of the power electronics switches, and efficiency improvement in power electronics applications.

With the development of the high-power wind turbine systems, permanent-magnet synchronous generator with direct-drive configuration is attractive for high-power wind energy conversion systems. As one of the key technologies of wind energy conversion, control technology plays an important role in the performance of wind power systems. Due to its excellent dynamic performance and multi-objective control, model predictive control has become an alternative and promising method. However, the cumbersome process of tuning weighting factors is its main drawback. In this work, a new control structure, as socalled dynamic sequential model predictive control is proposed. It avoids using weighting factors and overcomes the drawbacks of sequential model predictive control. The proposed control strategies are validated and compared with the sequential model predictive control in Plecs simulation.

This paper proposes a novel robust controller design for a class of nonlinear systems including hard nonlinearity functions. The proposed approach is based on Takagi-Sugeno (TS) fuzzy modeling, nonquadratic Lyapunov function, and nonparallel distributed compensation scheme. In this paper, a novel TS modeling of the nonlinear dynamics with signum functions is proposed. This model can exactly represent the original nonlinear system with hard nonlinearity while the discontinuous signum functions are not approximated. Based on the bounded-input-bounded-output stability scheme and L1performance criterion, new robust controller design conditions in terms of linear matrix inequalities are derived. Three practical case studies, electric power steering system, a helicopter model and servo-mechanical system, are presented to demonstrate the importance of such class of nonlinear systems comprising signum function. Furthermore, to show the superiorities of the proposed approach, it is applied to these systems; and, the experimental real-time hardware-in-the-loop results are compared with the published literature with the same topic.

The modelling and adaptive sliding-mode control of a high-order buck-boost converter is presented. The converter gives a high voltage gain and presents low current stress on its circuit components. The full-order model of the converter is derived by using a combination of the averaged state-space modeling approach and a duty ratio constraint deriving method. An adaptive sliding-mode controller for this converter is then proposed in which the adaptive law calculates the estimated load conductance to generate the required sliding surface. The stability analysis of the adaptive sliding-mode controlled system is performed. In addition, experimental results comparing the performance of the proposed adaptive sliding-mode controller with that of an existing robust sliding-mode controller are provided to present the advantages.

With the recent development of power electronic equipment in marine industry, the deployment of pulse power loads in the Shipboard power systems (SPSs) is constantly expanding. During the usage of a pulse power load (PPL), a huge amount of energy is consumed within a short period of time which brings new threats to the reliability and stability of the SPSs. Technically, the negative effects of PPLs to SPSs can be alleviated by powering specialized energy storage systems (ESSs). On the other hand, the challenges of the PPL accommodation on SPSs become more intensified when the systems are coupled with the communication networks. In this paper, an intelligent controller is developed for counteracting the effect of PPLs problem on a Cyber-Physical Shipboard Microgrid (CPSMG). This study presents an optimal general type-2 fractional order fuzzy P + fuzzy I + fuzzy D (GT2FOFP+FI+FD) controller for the secondary load frequency control (LFC) of the CPSMG. In order to boost the output performance of the LFC, an enhanced JAYA algorithm (EJAYA) is utilized for the online setting of the GT2FO-FP+FI+FD controller coefficients. Finally, a real-time CPSMG hardware-in-the-loop (HIL) is adopted to investigate the applicability of the suggested scheme in dealing with the impacts of PPLs and communication degradations from a systemic perspective.

This paper provides a small-signal model and stability analysis of interconnected AC microgrids (MGs) connected through back-to-back converters (BTBCs). The proposed modeling method of the networked microgrids (NMGs) is derived and it is generalized for any number of NMGs through BTBCs. Different BTBC control parts are analyzed to study their impact on the NMG stability. The eigenvalue analysis and participation matrix are employed to identify dynamic modes of BTBC DC voltage controller. For two NMGs, main participating state variables and corresponding parameters in the dominant low-frequency modes (LFMs) are recognized, then acceptable ranges of the parameters are calculated using the sensitivity analysis (SA). The contribution of the control BTBC parameters including PLL and DC voltage controller parameters in the small-signal stability margin are shown. In addition, to show the BTBC control impact on the NMGs stability in the time domain, simulation results are provided for the two NMGs in SimPowerSystems/Matlab environment.

This paper proposes a novel fault-tolerant control method for modular multilevel converter (MMC) with an improved level-shifted carrier PWM, based on the concept of virtual voltage. First, an improved phase disposition level-shifted (PD-LS) PWM with only one carrier in each arm is adopted. Then, based on the virtual voltage idea, a simplified control method is proposed to realize fault-tolerant operation without carriers reconfiguration. Compared with traditional fault-tolerant control methods, due to the reduced carriers generation and decreased comparisons between carriers and modulation waves, the modulation process of the proposed scheme can be dramatically simplified during both the pre-fault and the post-fault process. The effectiveness of the proposed method is verified by simulation and experiment results.

The three level neutral-point clamped (3L-NPC) topology is one of the most widely used multilevel topologies in the low and medium voltage applications and also one of the most commercialized topologies. Although it offers many benefits compared to the conventional two level topology, it suffers from a considerable unequal loss distribution among the inner and outer switches and the clamping diodes. To solve this problem, we are proposing a control algorithm based on the finite control set model predictive control (FCS-MPC) that can provide a more balanced stress distribution. For implementing the proposed control algorithm no additional measurements are required nor thermal models of the semiconductor devices. The algorithm benefits are even more noticeable during low voltage ride through (LVRT) scenarios when the output voltage level of the converter is low and the current amplitude is high. Obtained simulation results confirm the positive effects on the thermal redistribution and also the junction temperatures of the most stressed devices are reduced. Effects of the algorithm are also verified on an experimental set-up.

The stability of DC microgrids (DC MG) that are connected to constant power loads (CPL) is of prime importance for the operation of shipboard power systems (SPS). To control the DC MG of SPS effectively, the instantaneous power value of the time-varying uncertain CPLs is necessary. Since the integration of current sensors is costly and the resistance of these sensors degrades ripple filtering, instantaneous power estimation of CPLs is proposed. This paper investigates the development of an extended Kalman filter (EKF) to estimate the power of time-varying uncertain CPLs alongside estimation of CPLs' and sources' currents in a DC MG. By augmenting the CPLs powers into the DC MG states, a joint estimation problem is presented to effectively estimate the currents of the DC MG as well as the CPLs powers values. The proposed approach is applied to a DC MG that feeds one CPL. Experimental results show the effectiveness of the proposed EKF in the estimation of instantaneous CPLs powers and the currents of the CPLs and the source.

Increasing power electronics controlled distributed generation resources and active loads provide a future distributed system with more controllability as well as challenges to synthesize heterogeneous devices. From system engineering's perspective, control and operation of the future distribution system need more insight into the system architecture of the grid. In this paper, in light of the start-of-the-art control strategies for microgrids which rely on power electronics systems, a grid architecture model for future distribution system is proposed based on microgrid clusters. Both the physical and cyber structures for this architecture are described. Two illustrative examples are presented to explain different control methods that can be adopted in this model to harmonize different devices. This architecture can be used to guide the system design for the smart distribution system.

An energy management system of a microgrid (MG) has several basic objectives; e.g. to maximize the utilization of renewable energy resources (RES), to protect the internal components from overloading, and to ensure that the MG operates reliably under any operating conditions. Although many control techniques are available in the literature to monitor and control the energy flows among distributed RES in MGs, formal verification of those techniques was not proposed yet. The emphasis of this paper is to design and validate energy management system for a MG which consists of a solar photovoltaic (PV) array, a pair of battery energy storage systems (BESes), a diesel generator (DG) and a load (LD). The physics and dynamics of the MG are defined as energy flow invariants and the designed behaviours are abstracted, modelled and validated in this work. Therefore, we have considered an invariant based flow technique to manage the energy flow in an MG. The results are validated and verified with UPPAAL, a powerful industrial tool which is commonly used to verify the correctness of real-time systems like supervisory controllers, communication protocols and others.

Next generation power management at all scales is highly relying on the efficient scheduling and operation of different energy sources to maximize efficiency and utility. The ability to schedule and modulate the energy storage options within energy systems can also lead to more efficient use of the generating units. This optimal planning and operation management strategy becomes increasingly important for off-grid systems that operate independently of the main utility, such as microgrids or power systems on marine vessels. This work extends the principles of optimal planning and economic dispatch problems to shipboard systems where some means of generation and storage are also schedulable. First, the question of whether or how much energy storage to include into the system is addressed. Both the storage power rating in MW and the capacity in MWh are optimized. Then, optimal operating strategy for the proposed plan is derived based on the solution from a mixed-integer nonlinear programming (MINLP) problem. Simulation results showed that including well-sized energy storage options together with optimal operation management of generating units can improve the economic operation of the test system while meeting the system's constraints.

In this paper we propose a new decentralized control strategy applied to a DC Microgrid in order to manage the power delivery of storage devices into a common DC-link, avoiding high-bandwidth communication (HBC) between the storage devices (SD) and alternative sources. Batteries and Ultracapacitors (UC) are used as SD and the common DC-link is fed by alternative sources such as photovoltaic panels, wind turbines and fuel cells as well. The batteries are used to supply/absorb extra power in steady-state regime while the UC absorbs the power transients caused by variations on the power production or load connections. The proposed strategy uses as input for the batteries control only the DC-link voltage and state of charge (SOC), while for the UC only the DC-link voltage and UC terminal voltage are used to achieve the power sharing among the storage devices, equalization of the batteries and voltage restoration of the UC without HBC. The DC-link voltage is not restored in order to work as the sharing signal between storage devices. Additionally, the DC Microgrid is connected to the AC grid in order to deliver the extra power to the distributed system; however, the voltage variation on the DC-link does not affect the power quality of the produced energy. Simulated and experimental results are presented to demonstrate the feasibility of the proposed approach.

With penetration of plug-in electrical vehicles (PEV), fast charging stations (FCS) are expected to constitute be a considerable portion of total energy consumptions. This paper proposes a hybrid power coordinating control strategy implemented in a fast charging station equipped with flywheel energy storage system, in order to provide a comfortable charging circumstance for PEV as well as some ancillary services (active power and reactive power support) for grid by four-quadrate bidirectional operation. The distributed bus signaling (DBS) method is employed for coordinating the control between grid converter and flywheel converter to avoid the highly dependent on communication. Finally, the experimental results based on dSPACE1006 are presented to show the effectiveness of the proposed control structure.

With the increasing penetration of renewable energy, microgrids (MGs) have gained a great attention over the past decade. However, a sudden cut out of the MGs due to grid fault may lead to adverse effects to the grid. As a consequence, reactive power injection provided by MGs is preferred since it can make the MG a contributor in smooth ride through the faults. In this paper, a reactive power support strategy using droop controlled converters is proposed to aid MG riding through three phase symmetrical voltage sags. In such a case, the MGs should inject reactive power to the grid to boost the voltage in all phases at AC common bus. However, since the line admittances from each converter to point of common coupling (PCC) are not identical, the injected reactive power may not be equally shared. In order to achieve low voltage ride through (LVRT) capability along with a good power sharing accuracy, a hierarchical control strategy is proposed in this paper. Droop control and virtual impedance is applied in primary control loop while secondary control loop is based on dynamic consensus algorithm (DCA). Experiments are conducted to verify the effectiveness of the proposed control strategy.

In this paper, a distributed multiagent based algorithm is proposed to achieve SoC balance for DES in the DC microgrid by means of voltage scheduling. Reference voltage given is adjusted instead of droop gain. Dynamic average consensus algorithm is explored in each agent to get the required information for scheduling voltage autonomously. State-space analysis on a single energy storage unit and simulation verification shows that the proposed method has two advantages. Firstly, modifying the reference voltage given has less impact on system stability compared to gain scheduling. Secondly, by adopting multiagent methodology, the proposed distributed control has less communication dependence and more reliable during communication topology changes.

This paper presents consensus-based distributed control strategies for voltage regulation and power flow control of dc microgrid (MG) clusters. In the proposed strategy, primary level of control is used to regulate the common bus voltage inside each MG locally. An SOC-based adaptive droop method is introduced for this level which determines droop coefficient automatically, thus equalizing SOC of batteries inside each MG. In the secondary level, a distributed consensus based voltage control strategy is proposed to eliminate the average voltage deviation while guaranteeing proper regulation of power flow among the MGs. Using the consensus protocol, the global information can be accurately shared in a distributed way. This allows the power flow control to be achieved at the same time as it can be accomplished only at the cost of having the voltage differences inside the system. Similarly, a consensus-based cooperative algorithm is employed at this stage to define appropriate reference for power flow between MGs according to their local SOCs. The effectiveness of proposed control scheme is verified through detailed hardware-in-the-loop (HIL) simulations.

Paralleled dc converter systems are widely used in distribution systems and uninterruptable power supplies. This paper implements a hierarchical control in a droop-controlled dc-dc conversion system with special focus on improving system efficiency which is dealt within the tertiary regulation. As the efficiency of each converter changes with output power, virtual resistances (VRs) are set as decision variables for adjusting power sharing proportion among converters. It is noteworthy that apart from restoring the voltage deviation, secondary control plays an important role to stabilize dc bus voltage when implementing tertiary regulation. Moreover, system dynamic is affected when shifting VRs. Therefore, the stability is considered in optimization by constraining the eigenvalues arising from dynamic state space model of the system. Genetic algorithm is used in searching for global efficiency optimum while keeping stable operation. Simulation results are shown to demonstrate the effectiveness of the method.

In this paper, a distributed coordinated control scheme based on frequency-bus-signaling (FBS) method for a low-voltage AC three phase microgrid is proposed. The control scheme is composed by two levels. Firstly a primary local control which is different for the DGs and the ESS is proposed. The ESS adopts FBS control which is based on changing slightly the bus frequency in the microgrid when the state-of-charge is near to the limit. This way, the DG controller when detecting that the frequency is increasing, will reduce the injected power by using a virtual inertia control loop. Then secondary control is implemented to restore the frequency deviation produced by the primary ESS controller while preserving the coordinated control performance. Real-time simulation results show the feasibility of the proposed approach by showing the operation of the microgrid in different scenarios.