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In this paper, DR programs are integrated with the unit commitment economic dispatch model for a single day to lower total operating costs for an insular microgrid. The proposed model takes into account the forecasting errors associated with wind, solar, and load demands. A new combined DR program is presented to enhance microgrid operation and financial effectiveness, benefiting microgrid consumers. The price elasticity and consumer profit are the foundation for DR modeling. The optimization problem is developed as mixed‐integer nonlinear programming (MINLP) and solved using GAMS software. For the case study, an insular microgrid consisting of two microturbines, a wind turbine, solar photovoltaic, and battery storage is considered. Optimization is carried out under both with and without the DR program. The outcomes show that by implementing TOU and DLC DR programs, the operating cost is reduced by 13.55% and 9.68%, respectively. While consumers experience a financial loss in TOU‐DR, they earn profit in DLC‐DR. Therefore, a combination of the two, i.e., TOU + DLC DR, is proposed, reducing operating costs by 10.73% while increasing profit for users. The suggested approach benefits the microgrid operator as well as its users, encouraging the construction and operation of insular microgrids in rural or isolated areas. [ABSTRACT FROM AUTHOR]

In recent years, a sharp increase in integration of renewable energy sources (RESs) in power system network has been observed. High penetration of RES interfaced with power electronics converters-inverters with reduced or no inherent inertia compromises modern power system's overall stability. Due to low inertia, voltage and frequency deviations far off the allowable threshold occur. To overcome this challenge, an adaptive inertia control strategy based on optimization technique is proposed. The improved particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques-based PID controller has been used to generate the appropriate virtual inertia coefficient for effective emulation of inertia in the presence of energy storage system. The conventional PSO suffers local optima stagnation, resulting in premature convergence during searching process in order to achieve global and local position. To address this issue, the velocity update equation was modified on inertia weight (w) using an additional exponential term with linear decreasing inertia weight PSO (LDIW-PSO). In this paper, exponential power is taken strategically instead of squaring it in order to reduce the number of iterations for faster convergence. Finally, a microgrid based on wind and solar energy is simulated using MATLAB/Simulink where three cases, 2% disturbance, 3% disturbance, and 4% disturbance, have been considered. Here, the evaluation of proposed system is carried out based on four main performance indices (ITAE, IAE, ISE, and ITSE). Furthermore, validation was done through hardware prototype to get experimental results in real time. The results from MATLAB simulation and experimental setup are in sync. [ABSTRACT FROM AUTHOR]

The efficiency of on-site consumption of new energy and the economy of dispatching strategy for that in modern microgrids are increasingly concerning, which are closely related to the microgrid control model with source-load uncertainty. To this end, this paper proposes the multiagent hierarchical IQ (λ)-HDQC regulation strategy to realize the source-load-storage-charging collaborative control of the microgrid model with high-permeable new energy. The first layer adopts the IQ (λ) strategy, which avoids the overestimation and underestimation problems of traditional reinforcement learning by the coupled estimation method. The second layer adopts the HDQC allocation strategy, which solves the problem of low utilization of new energy in the proportional allocation method and improves the adaptability of the regulation strategy in the complex stochastic environment. The interaction of the two-layer strategies realizes the source-network-load-storage-charge global dynamic interactive regulation of microgrids. Indicators of energy efficiency are constructed in this paper to measure the simulation results. And the superiority of the proposed strategy is verified through the simulation results of the microgrid system. [ABSTRACT FROM AUTHOR]

In this paper, the optimal operation of microgrids (MGs) with thermal blocks, distributed generations (DGs), storage systems, and responsive loads is presented to achieve optimal scheduling of active, reactive, and thermal power of the mentioned elements in the day-ahead (DA) reactive power and energy market environment. The thermal block has a combined heat and power (CHP) system, a boiler, and thermally responsive loads. This scheme minimizes the difference between the total operating costs of the MG and power sources and the total revenue gained from the sale of energy and reactive power of the mentioned elements in the markets located in the MG. It is constrained by the AC power flow equations, network operation constraints, and the operating model of these elements. Furthermore, this scheme is subject to the uncertainties of energy price, load, and renewable power. In this paper, to access the optimal resistant solution against the maximum prediction error associated with the mentioned uncertainties, a robust model based on information gap decision theory (IGDT) is used. Finally, by implementing the proposed scheme on a 119-bus radial MG, the obtained numerical results confirm the ability of the scheme to simultaneously improve the economic and operational situation of the MG. The proposed scheme succeeded in improving energy cost, energy loss, voltage drop, and power factor of the distribution substation by roughly 101%, 44%, 41%, and 16% compared to power flow studies, even in the worst-case scenario caused by uncertainties. [ABSTRACT FROM AUTHOR]

Driven by the carbon peaking and carbon neutrality target, the large-scale grid-connected of renewable energy such as wind and solar has increased, and the volatility and randomness have posed new challenges to the stability of the power grid frequency. In this case, battery energy storage is a grid auxiliary resource with fast response and adjustable parameters, which can provide frequency support for the grid system in a short period. This paper studies the frequency regulation strategy of large-scale battery energy storage in the power grid system from the perspectives of battery energy storage, battery energy storage station, and battery energy storage system, respectively. First of all, the droop control based on logistic function and the virtual inertia control based on piecewise function are proposed for battery energy storage frequency regulation, which improves the performance of battery energy storage power output effectively. Second, the weighting factor is set according to the current battery charge to achieve the most optimal distribution of frequency regulation power for each battery pack in the battery energy storage station. In the end, a control framework for large-scale battery energy storage systems jointly with thermal power units to participate in system frequency regulation is constructed, and the proposed frequency regulation strategy is studied and analyzed in the EPRI-36 node model. The results of the study show that the proposed battery frequency regulation control strategies can quickly respond to system frequency changes at the beginning of grid system frequency fluctuations, which improves the stability of the new power system frequency including battery energy storage. In addition, this paper also provides a certain reference for the construction of the new power system dispatching that integrates "Generation-Grid-Load-Storage" in the future. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy sources into modern electric grids has led to a rise in uncertain factors that must be managed to maintain voltage security during reactive power optimization (RPO). Traditional deterministic RPO methods fail to account for these uncertainties, which can result in power grid security issues such as voltage violations. To address these challenges, this paper proposes a data-driven interval-based reactive power optimization method (IRPOM). The IRPOM represents the uncertainties associated with renewable power generation and load demands as intervals within the RPO problem formulation. The proposed method uses an improved particle swarm optimization algorithm to solve the RPO problem. In each iteration, the uncertain power flow is solved using the optimizing-scenarios method- (OSM-) based interval power flow (IPF) algorithm. This approach calculates the real power losses and checks whether state quantities, including voltage, power flow, and generator output, exceed their limits. Furthermore, a data-driven modeling approach is introduced to reduce the conservativeness of the IRPOM solutions. The effectiveness of the proposed method is demonstrated through detailed computational analysis on a modified IEEE 30-bus system. The results show that the proposed approach ensures economic efficiency while maintaining a low bus voltage threshold crossing probability close to zero. [ABSTRACT FROM AUTHOR]

The global demand of electrical power has increased enormously due to various reasons such as fast-changing and challenging technologies, climatic change, economic growth, and lifestyle of mankind. Due to global warming issues and the depletion of conventional sources of power generation, the utilization of renewable energy sources has drastically increased. So, many challenges exist in integrating the microgrid with AC grid and load. The alternative arm converter (AAC) is among the most innovative converter topologies used in high voltage direct current (HVDC) applications. This research work presents a new control strategy to generate pulses to trigger the switches in the AAC in a proper sequence to obtain a smooth output waveform. The AAC output is controlled by implementing various controllers such as the proportional-integral-derivative (PID) controller, fractional order PID (FOPID) controller, and FOPID controller tuned by metaheuristic algorithm-bacterial foraging optimization technique (BFOT). Also, a comparative analysis is performed based on the spectral analysis of the output voltage obtained. In comparison to other controllers, the FOPID controller optimized by the bacterial foraging optimization technique (BFOT) produced the least total harmonic distortion (THD) of the AAC output voltage. In addition, this paper also discusses about the performance and analysis on the design of an energy management system (EMS) to optimally utilize the energy sources such as PV system, wind system, and battery based on their availability which feed the AAC. The energy management system controls the entire integrated system in association with an integrated CUK-SEPIC converter to fulfil the load demand at the point of common coupling, either from the microgrid or the AC grid. A nine-level AAC model is designed integrating the microgrid, grid, and industrial loads with an energy management system using MATLAB/Simulink. The performance parameters of the entire model are analysed at every stage to provide stabilized output to meet the load demand. [ABSTRACT FROM AUTHOR]

Considering the increasing penetration of renewable energy sources (RESs) into power grids, adopting efficient energy management strategies is vital to mitigate the uncertainty issues resulting from the intermittent nature of their output power. This paper aims to resolve the energy management problem by presenting an adaptive robust optimization (ARO) model in which uncertainties associated with solar and wind powers, consumer demand, and electricity prices are considered. The proposed model comprises three stages, one master and two subproblems, using both linearized and convexified power flow equations. Meanwhile, a new optimization-based bound tightening (OBBT) method is also presented to strengthen the relaxation. The proposed solution method solves the microgrid (MG) operation management problem with high accuracy due to considering the convexification method in a reasonable computational time and reducing operational costs compared to conventional models. The numerical results indicate the benefits of the proposed ARO and solution approach over traditional methods. [ABSTRACT FROM AUTHOR]

DC microgrids have been quite popular in recent times. The operational challenges like control and energy management of the renewable-driven standalone DC microgrids have been an interest of research. This paper presents a bidirectional quasi Z-source DC-DC converter (BQZSDC). This converter topology has been developed based on a conventional buck-boost type bidirectional converter, and it interfaces the storage system and the common DC bus. The challenge, however, lies in effectively managing the uncertain renewable energy sources and the storage system and catering for the loads simultaneously. An effective control strategy is needed for that energy management and to achieve various microgrid objectives. This paper deals with one such effective control strategy implemented for BQZSDC. That is, the fixed-frequency double-integral sliding mode control (FF-DISMC) controls the converter to regulate the DC bus voltage and battery current. A detailed analysis of the controller is conducted, and its performance is evaluated for both charging (buck) and discharging (boost) modes. Simulations have been performed in MATLAB, showing that the controller performs satisfactorily in achieving the objectives of voltage regulation and battery current regulation. Finally, the performance of the proposed controller is validated with the hardware setup. [ABSTRACT FROM AUTHOR]

Transformation of the distribution network, integrated with Distributed Energy Resources (DER), into a MicroGrid (MG) is an attractive approach in improving the reliability and resilience of the network. In this paper, by introducing the important and necessary aspects of the MG formation problem, an effective methodology for transformation of a distribution network into the MG is proposed. In the presented methodology, optimal allocation of DERs within MG is obtained by optimization of power losses and voltage stabilization along feeders. Moreover, the short-circuit constraints are also taken into account simultaneously while allocating DERs. The reason for the latter is that the integration of DERs alters the amplitude and direction of the short circuit current, which can cause the overall fault current to exceed the designed fault-level capability of the circuit breakers. This issue may put CBs at risk of failure and degrading MG resilience. In this regard, resilience-oriented evaluation of the MG formation, from the perspective of considering short circuit level and coordination of OCRs, is also performed. Another advantage of short circuit studies is devising complete and economic overcurrent-based protection plan for MG in both operating modes (grid-connected and islanded). It should be pointed out that adopting an appropriate control routine is a key element in the MG formation. Hence, a control strategy, based on Energy Storage System (ESS) utilization, is suggested in this paper. This feature, which is another contribution of the paper, will guarantee seamless transition to island mode and stable operation of the MG. The proposed formulation of the MG formation is solved using a modified multi-objective PSO and the Pareto-optimal set is obtained. However, since the optimal solutions are nondominated to each other, a Fuzzy Satisfaction Method (FSM) is utilized to find the optimal solution among the Pareto-set. Utilization of the proposed methodology leads to the successful transformation of the distribution network into a MG, which not only considers the load flow constraints but also preserves the optimal coordination of the protective relays and resilience of the MG. In addition, seamless transition and stable operation of the MG in island mode is achieved. [ABSTRACT FROM AUTHOR]

In recent years, low-voltage direct current (LVDC) microgrids are becoming more attractive because they represent a solution to integrate renewable sources, storage, and electronic loads bringing some advantages in comparison with traditional AC grids. However, the protection of such a network involves many challenges, especially in the case of LVDC microgrids with more than one feeder and multiple energy sources. Indeed, the traditional protection breakers used for an AC grid cannot isolate the faults and protect the components of a DC grid, while the use of solid-state circuit breakers increases energy losses. This paper deals with the analysis and design of the protection schemes for LVDC microgrids through the combination of mechanical circuit breakers and hybrid circuit breakers. This solution has the advantage of energy loss reduction but introduces further issues due to the slow transition times of the mechanical circuit breakers. Thus, a completely decentralized control system capable of overcoming the fast fault-clearing time, cost-effectiveness, and selectivity issues is designed to protect from pole-to-pole faults. The proposed control strategy is compared with a centralized protection scheme available in the literature through numerical simulation. The two algorithms show similar performances, with a mean voltage dip duration of less than 30 ms and a maximum voltage dip duration of about 100 ms in the most severe fault condition, but the proposed solution is more reliable and flexible since it does not depend on the communication system. [ABSTRACT FROM AUTHOR]

The operation of power systems needs uninterrupted and precise wide-area real-time monitoring for secure and automated control of power networks, therefore, there is a requirement of robust state estimation (SE) by the smart control centre. An iterative free static state estimation (IFSSE) scheme is developed using the linear measurement model (LMM) in this paper for microgrids (MGs) integrating distributed generation energy resources (DGERs). The developed scheme of IFSSE utilizes M-estimator (ME) concept, which is combined with the linear static state estimation (SSE) approach in order to provide robust estimation. The developed IFSSE scheme is iteration-free due to the implementation LMM that is achieved from placing the microphasor measurement units (μPMUs) and separate bad data processing (BDP) is not required for this proposed scheme. The proposed scheme's performance has been verified by employing it on modified IEEE-14 bus test systems (BTS) integrating DGERs and modified IEEE 30 BTS integrating DGERs then comparison has been done by taking two cases, i.e., linear SSE without BDP and linear SSE with BDP. [ABSTRACT FROM AUTHOR]

To improve the voltage and frequency stability and suppress the voltage harmonics of a microgrid, a virtual synchronous generator (VSG) control strategy based on the harmonic current bypass control is proposed in this paper. First, a robust voltage control link is added to the virtual excitation component of the traditional VSG control strategy to reduce the amplitude error of the inverter's driving voltage caused by the reactive power-voltage droop coefficient. A harmonic current bypass control link is then used to adjust the inverter's voltage signal to reduce the total harmonic distortion of the microgrid. Finally, a simulation model of the microgrid is established using the MATLAB/Simulink software, and its stability is analyzed. In addition, the proposed control strategy is also compared with the traditional droop control and traditional VSG control strategies, focusing on the frequency instability and harmonic suppression performance of the microgrid. The numerical simulation results verified the effectiveness and superiority of the improved VSG control strategy proposed in this paper. [ABSTRACT FROM AUTHOR]

In recent years, renewable resources have widely been used to provide necessary energy due to the increasing fossil fuel prices, environmental pollution concerns, and the necessity to meet the growth in energy demands. The output of renewable resources especially solar and wind energies is associated with meteorological parameters, so their reliability creates many challenges for the energy sector. The consumption peak of the gas network is taken into account to adjust the frequency of the microgrid (MG). Both gas network load and electric load distributions are adjusted at the same time. In a multicarrier network, the frequency is regulated in a nonlinear manner. Meanwhile, new necessary loads for production and electric vehicles have imposed new loads on the power network; if proper management is not performed to respond to these new loads, the increase of network frequency deviations may lead the network to fail and even break down. In this paper, a network of various sources including the wind turbine, solar panel, storage (battery and flywheel), electric vehicle (EV), diesel generator (DG) electric power generation, and multicarrier energy hub (MCEH) with combined heat and power (CHP) was designed to examine vehicle-to-grid (V2G) electric vehicles. The ANFIS adaptive fuzzy control method was used to provide a fine-tuning frequency of the network. A comparison between the suggested approach and a fuzzy controller system was carried out to examine the superiority of the introduced approach to the frequency control. The simulations were obtained using MATLAB/SIMULINK software. The simulation outcomes indicated that the SMART controller can achieve good efficiency in frequency regulation and reliable output power in the examined microgrid. Further comparison in terms of effective (RMS) values and maximum frequency deviation indicates the superior performance of the proposed method over the fuzzy method. [ABSTRACT FROM AUTHOR]

Due to the strong uncertainties of renewable energy and load demands, the new type of power systems is facing severe challenges in terms of generation control and load dispatch. Considering the uncertainties of the penetrated renewable energy and diversified load demands, this paper proposes a random fuzzy power flow analysis method (RFPF) to exactly depict the impact on the various flows of power systems. In the RFPF, the random fuzzy models of wind turbine generators, photovoltaic generators, and load demands are established utilizing the stochastic probability functions and fuzzy interval to reveal the uncertainties with high precision. Afterwards, the random fuzzy mean value is developed as an index of accuracy, and the fuzzy number of the output variables is extracted under the 95% confidence level. Furthermore, the proposed RFPF model is executed by applying a three-point estimate method (3PE) to figure out the corresponding power flow of the power system, costing less computation burden compared with the Monte Carlo simulation. Simulation studies conducted on the IEEE-33 system verifies the accuracy of the RFPF and the efficiency of the 3PE. [ABSTRACT FROM AUTHOR]

In a conventional AC distribution system, the conservation voltage reduction (CVR) strategy is widely employed to lower down the voltage of specific load to lower the power consumption. The wide applicability of demand side management (DSM) using CVR in a stand-alone microgrid through VSI-based energy sources is a thrust area, that is, not examined yet and needs to be explored. The fast dynamics and flexible control are the characteristics of the voltage-current droop method which further increases the inertia of the voltage source inverter. For utilizing these advantages of this droop method, it is required to determine the accurate droop gain to properly coordinate the distribution of power among DGs. In this paper, the voltage-current droop method is utilized to carry out the function of DSM, and a modified droop computation method for voltage-current droop is formulated to determine the impedance from initial of the DG point to the downstream end for multifeeder network. As most of the droop control techniques emulate the conventional power grid such as Q-V droop control which reduces voltage with the increase of reactive power, the research prospect is very high in devising the new droop computation method for voltage-current droop for accurate control of power. In addition to it, the work is extended to apply the benefits of voltage-current droop to execute DSM strategy in standalone MG. Moreover, the capability of the proposed estimation of droop parameter is implemented on a standalone 5-bus single-feeder multi-DGs network, and furthermore, the scheme is applied to IEEE-9 bus multifeeder multi-DGs network to show the applicability of the proposed scheme. The simulation results produced from MATLAB/Simulink are compared with the decentralized power-based droop method and conventional voltage-current droop technique to analyse the performance of the devised scheme. [ABSTRACT FROM AUTHOR]

In order to optimize the economy and environmental protection of microgrid, this paper establishes a demand response model based on comprehensive satisfaction, combines the advantages of the classical multiobjective particle swarm algorithm and multiobjective firefly algorithm, and proposes a hybrid particle swarm optimization and firefly algorithm (HPSOFA) to solve the joint economic and environmental dispatch problem of microgrid and improve the wind and light consumption capacity. The proposed improved algorithm introduces a random perturbation term and adaptive learning coefficients, and the algorithm is selected by the dominance relationship of individuals, which improves the diversity of populations and increases the possibility of the algorithm to solve the global optimum. The proposed algorithm is used to solve the test functions of different dimensions, and the results show that the Pareto front of HPSOFA has better distribution and accuracy, which verifies the effectiveness of the proposed algorithm. Simulation analysis is carried out using power data from a region of Liaoning Province, China, and experiments conclude that the operating cost and environmental cost of HPSOFA are significantly lower than other algorithms. [ABSTRACT FROM AUTHOR]

This paper develops a distributed fixed-time quadratic coordinated control strategy for isolated DC microgrids with time delays. The proposed control scheme based on the droop control strategy is intended to realize bus voltage recovery and current balance distribution under time delays. We first model the isolated DC microgrids as a single integrator system with input delays by the Artstein's transform. The current balance and voltage recovery are achieved by using fixed time control, which does not depend on the initial state of the system. Based on the Lyapunov functional method, the stability analysis criterion of the system is derived. In order to verify the effectiveness of the proposed control method, the DC microgrid system is simulated in Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Isolated microgrids (MGs) face challenges in performance stability and active/reactive power sharing as a result of frequency/voltage deviations and mismatched line impedance issues. In this paper, a consensus-based multiagent system (MAS) is proposed as a solution to restore voltage/frequency deviations and enable true power sharing. The invention of an Intelligent Distributed Secondary Control Scheme (IDSCS) can efficiently achieve hoped-for outcomes. The proposed IDSCS features estimation and compensation sublayers. For the estimation sublayer, discrete dynamic consensus algorithm-based state estimators are presented to collect average information of frequency, voltage, and reactive power. Each DG is viewed as an agent sharing information with its immediate neighbors through a sparse cyber communication network. In the compensation sublayer, online tuned proportional integral (PI) controllers using artificial neural networks (ANNs) are proposed as an intelligent voltage and frequency compensators. This combination uses the simplicity of the PI controller mathematical formula and ANN's ability to deal with parameter variations and nonlinearity. Due to the global nature of the frequency parameter, the active power-sharing compensator is unnecessary. For compensating reactive power deviations, ANNs-based reactive power controllers are proposed. Furthermore, at the primary control level, the proposed strategy employs discrete-time proportional resonant (PR) controllers in a stationary reference frame, eliminating the need for any α β / d q or d q / α β transformations. Distributed implementation of the proposed method guarantees system scalability without MG topology or demand pattern expertise. The control scheme was validated using hypothetical MAS in MATLAB Simulink platform. The simulation findings indicate the proposed MG system can effectively distribute power among the DGs while maintaining voltage and frequency stable. [ABSTRACT FROM AUTHOR]

Microgrids are considered to be smart power grids that can integrate Distributed Energy Resources (DERs) in the main grid cleanly and reliably. Due to the random and unpredictable nature of Renewable Energy Sources (RESs) and electricity demand, designing a control system for microgrid energy management is a complex task. In addition, the policies of microgrid agents are changing over time to improve their expected profits. Therefore, the problem is stochastic and the policies of the agents are not stationary and deterministic. This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. The proposed method solves a distributed optimization problem for each self-interested decision maker. Interactions between the decision makers and the environment during the learning phase lead the system to converge to the optimal equilibrium point in which the benefits of all the agents are maximized. Simulation studies using a real dataset demonstrate the effectiveness of the proposed method for the hourly energy management of microgrids. [ABSTRACT FROM AUTHOR]

Summary: The high penetration of distributed energy resources (DERs) introduces considerable challenges to the operation of distribution grids. To facilitate the management of DERs, distribution grids are divided into multiple cells. However, for cell coordination, existing methods require extensive time to solve the operation constraints. To accelerate the speed of solving problems, this paper proposes a decentralized optimization method for cells based on the steady‐state security region (SSSR) in power injection spaces. Based on the SSSR, this paper decomposes the entire optimal problem model into several subproblems. Meanwhile, the node voltage constraints and line current constraints of the optimization model both become a set of inequalities of linear combinations of variables and the variables in the objective and constraints are identical for each cell. Furthermore, a decentralized optimization model is established for the minimum power loss, and coordination among cells is realized by Karush‐Kuhn‐Tucker (KKT) conditions. Therefore, the entire optimization process requires less time and few iterations. To verify the effectiveness of the proposed method, simulations for a modified PG&E 69‐bus distribution grid are performed in the MATLAB environment. The optimization results reveal that the average calculation time is improved to 2.84 seconds compared to 27.32 seconds for the centralized method and 88.9 seconds for the alternating direction method of multipliers (ADMM), and the number of iterations is reduced to 3 compared to 35 with ADMM. [ABSTRACT FROM AUTHOR]

Summary: This paper is concerned with the two‐stage hierarchical control of microgrids (MG) that can operate in both grid‐connected and off‐grid modes. The procedure consists in the segmentation of the control scheme in the primary and secondary levels, thus enabling the application of faster and slower dynamics according to the process. A decentralized droop control‐based method is used in the primary level, while a distributed multi‐agent coordination scheme is applied in the secondary one. The multi‐agent system is run on Python Agent DEvelopment (PADE) environment, a FIPA‐compliant agent framework. The key contributions this paper provides are the control strategies of reactive power output of non‐dispatchable sources, dynamic sharing of energy storage systems, and the management of loads of microgrids, all of them based on multi‐agent system. Co‐simulations using PSCADTM and PADE are performed considering several operating scenarios of the MG resources under different load conditions. Simulation results validate the accurate performance of the proposed control strategies. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a control scheme to reduce the circulating current in parallel inverters of autonomous microgrid. The proposed scheme is based on adaptive modulation index, which is generated by a controller operating along with conventional voltage‐current‐droop controller. Three‐phase inverters are connected in parallel for increased power capability and reliability. These paralleled inverters share common load in a desired ratio with the help of droop controller. One of the operational challenges in paralleled inverter system is that of circulating current which causes power loss, decreased efficiency, current distortion and reduced life. In order to reduce the circulating current, the reference modulation index is adjusted adaptively through an independent controller. The original modulation index is revised by making proportional adjustment toward voltage and current drop errors. This way, the proposed controller works independently and does not affect the performance of conventional droop controller. The design procedure for the proposed controller based on system modeling is also presented in the paper. The effectiveness of proposed controller is verified through simulation and experimental validation. A comparison of performance with a recently published PID‐based coordinated controller is also presented. The results establish the superiority of proposed scheme for balanced, unbalanced and nonlinear load conditions. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an advanced controller for multi‐input bidirectional DC–DC power converter (MIPC) for hybrid energy storage system (HESS). When batteries are used for energy storage, their rates of charge and discharge are low, and this sets up current stress on the battery, decreasing its life. Supercapacitors (SC), with their higher power density, can react immediately to sudden fluctuations and can take care of this issue. However, SC alone cannot be used for storage, as they cannot supply power for longer durations. In HESS, batteries and supercapacitors are used together, as their contrasting characteristic makes them a perfect combination for energy storage. The HESS is interfaced with DC microgrid using MIPC. MIPC provides decoupled control of battery and SC power and also facilitates energy exchange between storage devices within the system. A controller is designed for DC microgrid application, with its operation modified to control both HESS charging and discharging operation, making it a unified controller. Conventional control schemes neglect uncompensated power from the battery system, and power sharing depends entirely on a low‐pass filter (LPF). In the control scheme proposed in this paper, uncompensated power from the battery system is utilized to improve the SC system. This approach reduces the current stresses, increases the life cycle of the battery, improves the overall system performance to the step change in PV generation and load demand, and provides faster DC grid voltage regulation. Simulation and experimental results are developed for the proposed controller by varying photovoltaic (PV) generation and load demand, providing faster DC link voltage regulation. [ABSTRACT FROM AUTHOR]

Summary: In DC microgrids, power converters are essential for interconnecting and controlling power flow from the renewable sources such as solar PV, wind, fuel cell, and so on with the DC grid link. A lot of DC‐DC power converters available in the literatures are used to boost the low voltage from the renewable sources to the high voltage and also to track the maximum power from these sources. To boost the output voltage, high gain converters are required. To increase the static gain of the converters, many techniques such as using voltage lift, switched capacitor, coupled inductor, and so on are used. This paper presents a novel high gain DC‐DC converter that does not use the above techniques for voltage boosting. An inductive reactive element is added in series with a switch to boost the voltage gain. This inductor is connected in parallel with the source voltage when the switches are in ON position and in series with the source when the switches are in OFF position. The proposed converter is designed to produce the output voltage of 380 V for the input of 48 V. The converter is designed to handle the power of 1 kW. With the use of the voltage boosting element, a maximum voltage gain of 28 is achieved and the efficiency of converter is about 91.9%. The performance of the proposed converter is simulated and analyzed for various conditions such as variation in input voltage, reference voltage, and load power and also for motor loads. The experimental prototype model is also built and the hardware results are shown in this paper to validate the simulation results. [ABSTRACT FROM AUTHOR]

Rural and remote area electrification is of grave concern around the globe. Therefore, well-planned and cost-effective microgrids integrating renewable energy sources are emerging as effective solutions. However, the microgrid's stable operation and its future deployment is affected by the perturbations caused due to uncertainity in renewable sources, dependency on the battery state of charge, and load variation. So, considering the possible concerns affecting the planning and development of a microgrid for any given region, this paper proposes a comprehensive performance assessment of the hybrid residential microgrid based on a load model of Kibber village in Himachal Pradesh, India. The proposed approach is divided into three parts for the best planning of microgrids. Firstly, the MATLAB–Simulink software technically analyzes the system performance under perturbations considering the available renewable sources. Secondly, an economic analysis using HOMER Pro software is done to examine the cost-effectiveness of the proposed microgrid model through the simulation of electrical loads for Kibber village, considering the available renewable sources. Lastly, a real-time analysis of the proposed prototype of programmable logic controller-based hardware test bench has been developed, aiming for future regional microgrid deployment. System voltage, frequency, power shared, percentage of load met, energy cost, available renewable energy resource, etc. have been considered for validating the proposed controller. The proposed comprehensive assessment of the microgrid model is reproducible with necessary modifications for any geographical location. It will be helpful for its future deployment aiming at rural and remote electrification. [ABSTRACT FROM AUTHOR]

Summary: Microgrids are the localized network to support electricity consumers either in an islanded manner or in integration with macrogrid. Microgrids are generally vulnerable to major issues like improper voltage regulation, circulating current, and unequal power sharing. In this paper, conventional droop controller for current sharing is upgraded with a Boomerang Trajectory based Sliding Mode Controller (BT‐SMC). In the proposed BT‐SMC technique, the error trajectories are traced and controlled toward the equilibrium point accomplishing the system to reach the stable state. Also, the dynamic changes in the load are governed and controlled by adjusting the droop resistance in the parallel converters. The BT‐SMC generates an equivalent control signal based on the control law and maintains the PWM pulses on par with the droop value and duty ratio. Additionally, this paper concerns the precondition for power sharing and power management among the different RES at different availability conditions by adopting a Source Shifting Algorithm (SSA). The hand‐in‐hand process of BT‐SMC with SSA provides a proper load sharing with a wide stability region in the phase plane. The proposed self‐adapting and self‐tracking system is evaluated in Matlab/Simulink and the results were validated in real time by using dSPACE MicroLabBox—DS‐1202. [ABSTRACT FROM AUTHOR]

This paper presents a flexible-reliable operation strategy for clean microgrids (MGs) consisting of power sources, energy storage systems (ESSs), and responsive loads. The proposed strategy attempts to provide the minimum expected operating cost for the MG, power sources, predicted pollutant emission, and expected energy not-supplied (EENS) because of an N − 1 event considering different applications. The objective function of the problem is subject to several constraints including AC optimal power flow equations, flexibility and reliability boundaries, and model of power sources and active loads. The Pareto optimization based on the weighted sum method is then incorporated to obtain a single-objective model. Probabilistic programming is also used to model the uncertainties of load, renewable energy, electrical energy price, and MG equipment accessibility. The ant-lion and crow search algorithms are merged to solve the problem and find a reliable optimal solution. A standard MG is employed to test the proposed strategy and indicate its capability in enhancing technical and economic indicators of the MG while ensuring that the MG is clean. [ABSTRACT FROM AUTHOR]

Summary.Aiming at the problems of insufficient scalability and slow response speed of the traditional three-layer control structure based on the time scale, this study proposes a distributed two-layer control structure. The primary control uses traditional power-frequency droop control, and the second-level control adopts a consensus protocol to simultaneously achieve the goals of frequency synchronization, frequency non-difference, and power optimization in a distributed manner, which can effectively improve the performance of microgrid frequency adjustment and power optimization. The cyber layer of the AC microgrid cyber-physical system (CPS) is extremely vulnerable to denial-of-service (DoS) attacks, resulting in the inability to achieve control objectives. For this reason, this paper designs a consensus algorithm based on event-triggered and a predictive compensation control link that combines empirical mode decomposition (EMD) and extreme learning machine (ELM) on the basis of the two-layer control structure. Finally, a 4-node islanded microgrid simulation example is used to verify the effectiveness of the proposed strategy. The simulation results show that the two-layer control strategy can achieve microgrid frequency recovery and power optimization while effectively responding to different degrees of DoS attacks. [ABSTRACT FROM AUTHOR]

In this paper, virtual inertia control (VIC) is suggested to increase the frequency stability in islanded microgrid (MG) clusters. The aim of the suggested control method is to improve damping characteristic of MG clusters including different distributed generations (DGs). The optimal/robust values of the VIC parameters are tuned by a μ-synthesis robust control method. The proposed robust/optimal VIC-based control method is confirmed by various scenarios. Computer simulation and hardware-in-the-loop (HIL) tests are used to show the effectiveness of the suggested method in increasing the damping of the power system. Clearly, different characteristics of the dynamic responses and the results show the practicality of the suggested robust VIC. [ABSTRACT FROM AUTHOR]

Time delays are enforced into the LFC system of microgrid (MG) while transmitting the control signals from the central controller and also aggregating the controllable loads of Demand Side Response (DSR). Due to these delays, the system dynamic performance gets affected leading to system instability. The time delays may be fixed or time varying. In this paper, a cyber physical microgrid is modeled with Dynamic Demand Control (DDC) loop including time delays, and the stability of the proposed model is analyzed both for fixed and time varying delays. The impact of fixed time delays on the stability of the proposed microgrid is theoretically evaluated using Frequency Sweeping Technique (FST). To examine the delay dependent stability of the proposed system with time varying delays, including external load perturbations, a matrix stability criterion is formulated and analyzed. The novelty of the method lies in the modelling of LFC with structured load perturbations and also investigating its impact on the stability of LFC using frequency domain approach. Since the effect of load perturbations is considered for the analysis, a more realistic operating condition can be portrayed in a microgrid system. The inclusion of DDC load in LFC of the microgrid provides better transient response even with larger time delays. The accuracy of the proposed method is verified through simulation results. [ABSTRACT FROM AUTHOR]

In recent times, with the increase in the penetration of various renewable energy sources (RESs) into power systems, the complications related to the stability issues have increased. The main contribution of this paper is an in‐depth analysis of research in microgrid based on small‐signal, transient, and voltage stability. The small‐signal stability has been discussed based on uncertain load, limitation in power generation capacity, and nature of sluggish feedback observed in few microgrid systems. Instability caused by a transient phenomenon in microgrid has been thoroughly analyzed for losses in distributed energy resources (DERs), islanding and transition modes of operation, load shedding, and faults causing instability. The voltage fluctuations are duly considered in the perspective of load shedding, type of loads, power unbalancing, and different types of faults in microgrid systems. The existing controllers have been compared based on steady‐state error, response time, and robustness etc. The voltage, frequency, and active/reactive power control are analyzed based on centralized, decentralized, hierarchal/distributed control schemes aiming stabilization of microgrid systems. Finally, stability improvement features have been explored in detail, finding the implementation of microgrid stabilizers, energy storage devices, FACTS devices, load balancing, resource forecasting including the adaptive controller. Hence, the uniqueness of this paper can be considered as a state‐of‐the‐art review of the abovementioned stability components in the microgrid system which is not available in the published literature with a similar amount of vastness. [ABSTRACT FROM AUTHOR]

Summary: In the stochastic programming involving multiple renewable energy sources such as solar and wind, uncertainties and correlations exist simultaneously in various random variables. To accurately describe them, a large number of scenarios are necessary to be generated, which may render the original programming problem intractable and require critical scenario reduction operations. This paper addresses two crucial issues in scenario reduction: (a) how to preserve the correlation properties of the generated scenarios after reduction and (b) how to guarantee the quality of decision results after reduction. For the first issue, the concepts of correlation sensitivity and partial correlation loss are proposed to specifically retain some key correlations that have an important impact on the solution stability. For the second issue, this paper proposes a novel scenario reduction method that aims to minimize the partial correlation loss and maximize the probabilistic similarity degree before and after reduction. Comparative studies are performed to evaluate the validity and universality of the proposed method. Numerical results of the microgrid economic operation optimization problem show that the reduced scenario set obtained by eliminating 97.8% scenarios can provide approximate target value to the problem with less than 5% accuracy errors and CPU time reduction of around 93.4%. [ABSTRACT FROM AUTHOR]

This paper presents a brief review of state‐of‐the‐art operation and control strategies of distributed energy resources, energy storage systems, and electric vehicles in the microgrid. Control of microgrid with a considerable number of distributed energy resources, small energy storage units, and electric vehicles require flexible and scalable control strategies. The objective of this paper is to review the latest centralized, decentralized, multi‐agent, model predictive, cooperative, and competitive control strategies to control and coordinate the distributed energy resources, energy storage systems, and electrical vehicles in microgrid. This paper also addresses the internet of things‐based control strategies in microgrid. Finally, a conceptual framework is presented with intelligent agent and internet of things‐based control to address the issues related to the future clustered microgrids. [ABSTRACT FROM AUTHOR]

Background: The smart grid systems with advanced cyber technologies are vulnerable to the false data injection attacks. The attacks will bypass the online state estimation monitoring, steal energy by a false dispatch in the electricity markets, and gain financial benefits. In this paper, the FDI attack case in the electricity markets of a smart grid power system is considered with micro‐grids connected. The estimated transmitted power is modified, leading to the change of electricity prices which will bring the benefit to the attacker. Further, influence on the optimization output of energy management for a Micro‐grid has been discussed. By trading online electricity with the main grid, the micro‐grids are able to maintain power balance and reduce generation cost of the whole smart grid system power system. Then different optimal generation outputs of Micro‐grid and total generation cost of the power system are also shown and discussed. Aims: The focus of this paper is to investigate the FDI attack with limited information of the system topology and dynamics, and to discuss the economic impact of such limited attacks, where the estimated transmitted power is modified and the electricity prices are correspondingly changed for benefits. Different optimal generation outputs of Micro‐grid and total generation cost of the power system are also shown and discussed. Materials & Methods: MG‐based electricity markets model of a SG power system is considered for FDI attacks. Strategy with partial information is proposed from the attack's view.In real‐time market, optimization output changes of energy management for a MG when considering the electricity trading are discussed.The quick time detection method is developed with the feature extraction technique for an accurate real‐time estimation of the fault detection, when the operator is with partial information of the system topology.Digital signal processing approaches of the discrete wavelet transform (DWT) are adopted to better extract the time‐frequency characteristics of the input transient signals and improve the detection. Results: By considering of the distributed characteristics of MGs and the optimal operation requirements of system, we transformed the problem into a standard stochastic convex program, then solved the problem by the scenario generation method. We used a modified‐IEEE standard 14‐bus system and an IEEE standard 118‐bus system to show the effect of false data injection attacks in electricity prices and the influence of the optimization output of energy management for a MG. We employed modified‐online CUSUM algorithm to establish the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. Discussion Modified‐online CUSUM algorithm is proposed with wavelet transform and feature extraction method for the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. The proposed detectors are also proved to have quick and reliable responses to small attack magnitudes, which are difficult to be detected by traditional CUSUM algorithm. Further, the detection method is employed on IEEE standard 118‐bus system to be compared. The simulation results have shown that the proposed model is also adapted to complex system. Conclusion: In this paper, the impact of false data injection attacks on real‐time electricity markets of the SG power system with MGs connected is investigated. Attack strategies are designed of worst‐case robustness about grid dynamics when the attacker has partial information about the grid, where the attacker can still manipulate nodal prices of the real‐time markets and maximize the profit under different degrees of uncertainties. The optimal generation outputs of MGs are discussed for the ability of maintaining power balance of the whole SG power system by trading online electricity. Besides, the detection problem with partial information of the grid topology is also investigated from grid operator's view, and a new modified detection method is proposed to defend FDI attack when considering the existence of the unknown. Modified‐online CUSUM algorithm is proposed with wavelet transform and feature extraction method for the quickest detection of the attacks. Numerical simulation results have shown that the proposed scheme is robust to the time‐varying dynamic system, and is efficient in terms of detection accuracy for the random and structured attacks. [ABSTRACT FROM AUTHOR]

Summary: Energy management of the grid connected microgrid (MG) systems with low cost using a novel hybrid algorithm is implemented in this paper. The novel proposed hybrid technique is the combined performance of both the Random Forest (RF) and Coral Reefs Optimization (CRO) algorithm and in this way it is named as RFCRO. Optimum operation of micro‐sources for decreasing the electricity production cost by hourly day‐ahead and real‐time scheduling is the main objective of the paper. The proposed hybrid technique is to manage the power flows between the energy sources and the grid. To achieve this point, demand response and minimum cost of energy are determined. This technique is involved with the mathematical optimization problems that necessitate more than one fitness function to be optimized simultaneously. By using the inputs of MG‐like wind turbine, photovoltaic array, battery, and fuel cell with corresponding cost functions, the RF is employed to predict the load demand. CRO clarifies the squirrel in optimizing the configuration of MG based on the load demand. The proposed hybrid technique is implemented in MATLAB/Simulink working platform and compared with other solution techniques like BFOANN, ALO, GOAPSNN, and RBFNN‐SSA technique. The comparison result reveals that the superiority of the proposed technique confirms its ability to solve the problem. [ABSTRACT FROM AUTHOR]

Objective: The main goal of this paper is to comprehensively model demand dispatch (DD) operation concept on prosumer plug in electric vehicles (PEVs) in the framework of a microgrid in a smart distribution grid. Method: Here, PEVs role as demand side assets that make commitments as dispatchable time‐flexible prosumers with microgrid operator (MGO). Therefore, they directly help MGO in efficient operation of the microgrid. Modeling and formulation of the proposed one level MG‐PEVs‐DD optimization problem, considering convenience of PEV drivers, MG operation reliability and loss of load, are investigated in this paper. In order to investigate the effect of various levels of DD commitment on MG operation cost and quantify worthiness brought by DD, a DD participation index (DDP) is defined. Moreover, extensive simulations are carried out on different days of the year to verify weather conditions, which influence MG generation and operation cost. The LP optimization operation problem for 24 hours of the day is solved by classic optimization in MATLAB. Results: The effectiveness of the proposed model is demonstrated on a real‐test MG system under different scenarios of conventional operation and other scenarios of applying DD. Simulation results reveal that the proposed MG‐PEVs‐DD model yields much more decrement in microgrid operation cost in comparison with conventional operation methods. Results show that increasing DDP index of PEVs, significantly decreases the cost of microgrid operation. Moreover, PEV driver's convenience has been suitably considered by the MGO through the suggested method. MG total operation cost for the cloudy coldest day of the year, is approximately twice higher than the sunny hottest day due to considerable decrement of output solar power generation in cloudy days. Discussion: Consumer‐side asset optimization by utilizing dispatchable assets, like PEVs or dispatchable loads would considerably help in handling the uncertainty in MG operation through demand dispatch operation concept with lower operation cost. [ABSTRACT FROM AUTHOR]

Linking AC and DC microgrids via a bidirectional AC-DC interfacing converter has emerged the hybrid AC-DC microgrid. The DC bus is connected to the grid by a series converter to mitigate the grid voltage's power quality problems. To enhance the reliability and to increase transferring active and nonactive powers between DC and AC main buses, the AC-DC bidirectional interfacing converters are connected in parallel. In this paper, the optimal number (n) of parallel-connected bidirectional interfacing converters is obtained to minimize the annual cost of investment and the reliability cost. A two-step deciding algorithm is proposed to find n. First, active and nonactive powers between DC and AC buses are obtained with power quality and power flow considerations. Based on two types of powers, two reliability indices are calculated, including the expected energy shortage (EES) based on active power and the expected Volt-Amps shortage (EVAS) based on nonactive power. The sum of two reliability index costs is considered as the reliability cost. Next, a decision-making strategy is executed to determine the optimal n by making a trade-off between minimization of two objectives: the annual cost of investment and the reliability cost. The simulations of a grid-tied hybrid AC-DC microgrid are done with the experimental measurement data of DC and AC loads and distributed generations. Simulation results verify the performance of the represented approach. [ABSTRACT FROM AUTHOR]

Deployment of DC Electric Spring (DCES) technology is an innovative demand side management (DSM) program that helps to mitigate voltage interruptions. Conventional control strategy uses proportional-integral controllers to regulate DC spring parameters. As PI controllers have a trade-off between settling time and peak overshoot, they may not be able to achieve the desired dynamic performance under varying DC microgrid (DCMG) conditions. In this regard, this paper proposes an inverted zero compensator for the control of shunt DCES (ShDCES) in DCMG. This controller regulates the ShDCES current and terminal voltage under all operating conditions with reduced settling time and peak overshoot. A power management algorithm is also proposed to optimize the operation of ShDCES under the presence of varying PV generation and critical and noncritical loads. A detailed mathematical analysis of ShDCES and inverted zero compensator design are furthermore incorporated to validate the strategy. The simulation of the proposed method is performed in MATLAB/Simulink environment. The comparison analysis with the conventional controller proves the effectiveness of the new controller. The results are also validated using real-time simulator OP4510RTS. [ABSTRACT FROM AUTHOR]

This paper endeavours a control scheme for optimal control of voltage and frequency of power inverter which relied upon distributed generation in cognitive microgrid using soft computational methods. It gives sound direction on how to control the active power flow, voltage‐frequency regulation, and sharing of power among various components of MG. The classical voltage–frequency control strategy in parallel with the modern control method is well excavated in the present paper. Moreover, modulation techniques, variants of reference frames, power quality indices are covered blatantly. Thus, the paper gives fast insight and understanding to the engineers working in this niche area. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new control strategy for enforcing a AC‐DC hybrid microgrid to attain stable responses in both steady state and dynamic operating conditions. The proposed controller is designed based on combining direct Lyapunov method and small signal linearization (SSL). The dynamic parts of converter control functions are achieved through providing the global asymptotical stability of the hybrid microgrid by using its total saved energy function. Then, the steady‐state components of the controller, unlike other existing Lyapunov‐based methods, are obtained according to SSL‐based mathematical terms as the main novelty of this paper. This novelty simultaneously provides several contributions within the proposed control technique structure as (a) the direct control of the PCC voltage frequency and DC‐link voltage errors, (b) the enhanced decoupling feature for DC/AC converter current, and (c) the direct control of the input voltage error for bidirectional DC/DC converter. Finally, comparative simulation results in MATLAB/Simulink software are presented to verify the superiority of proposed controller combined with SSL under AC and DC load variations, discharging/charging of storage unit, different grid power factors and AC loads shared among grid side and DC sources. [ABSTRACT FROM AUTHOR]

Summary: Protection is the main challenge for the operation of microgrids. This paper presents a new data mining method for identification of faulted line section for the protection of microgrids. This method uses wavelet packet transform (WPT) to extract a set of features from the fault voltage and current waveforms. The features are then pre‐processed and used for identifying the faulted line section through classification. Three classifiers are examined in this paper. In order to improve the classifiers performance, two pre‐processing steps are applied on the features. First, using three different feature selection methods, the irrelevant and redundant features are removed from the feature set. Secondly, using a supervised discretisation technique, the continuous features are converted into finite interval features. The performance of the proposed method is investigated and compared with previous methods using extensive simulation study on a complex microgrid. The results indicate that with only six discretised features, the proposed method has a fast and accurate performance by using the three classifiers examined, whereas one is more effective. Moreover, the proposed method only uses the measurement at the main substation, obviating the need for communication links to exchange data as used by most previous methods. [ABSTRACT FROM AUTHOR]

Summary: Integration of renewable energy sources provides energy security, substantial cost savings, and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management (MGEM) is a challenging task for microgrid operator (MGO) for optimal energy utilization with penetration of renewable energy sources, energy storage devices, and demand response. In this paper, optimal energy dispatch strategy is established for grid‐connected and stand‐alone microgrids integrated with photovoltaic, wind turbine, fuel cell, microturbine, diesel generator, and battery storage system. Techno‐economic benefits are demonstrated for the hybrid power system considering uncertainty of load and generation. So far, MGEM problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and emission‐related objectives need to be addressed for effective MGEM. In this paper, MGEM is formulated as mixed‐integer linear programming, and a new multi‐objective solution is proposed for MGEM. Demand response is also included in the optimization problem to demonstrate its impact on optimal energy dispatch and techno‐commercial benefits. Simulation results are obtained for the optimal capacity of each component, charging/discharging scheduling, state of charge, power import from main grid, net present cost, cost of energy, initial cost, operational cost, fuel cost, and cost of emissions taking care of seasonal load variation throughout the year. The results show that CO2 emission in stand‐alone microgrid is reduced by 41.74% compared with traditional system with diesel generator. Simulation results obtained with the proposed method has been compared with various evolutionary algorithms to verify its effectiveness. [ABSTRACT FROM AUTHOR]

Small-scale islanded microgrid technology has the potential to utilize renewable sources for electrification in extreme areas where conventional transmission of power is difficult. But due to the absence of a utility grid in the islanded operation, voltage and frequency control becomes the major concern. Instead of using a centralized large battery storage system, electric vehicle- (EV-) based distributed energy storage may provide a dynamic and much cheaper energy storage solution for small-scale systems in the long run. The only issue is to effectively manage the dynamics of EV penetration for achieving the overall control in the islanded system. To address the same issue, this paper proposes a novel segregation-based inverter control structure for an islanded microgrid while employing EVs as an energy storage system, thus eliminating the need for centralized storage. The designed integrated control structure simultaneously attends to the voltage and frequency regulation need of the system, overcoming the issue of control lag, along with energy storage and management aspects in the microgrid operation for controlling the power flow under various practical scenarios. Extensive experimental studies are performed in MATLAB/SIMULINK environment, which indicates that the proposed integrated control structure gives a satisfactory performance under various scenarios encompassing load variation, renewable energy uncertainties, and EV dynamics in comparison with conventional control schemes employed in the islanded microgrid. [ABSTRACT FROM AUTHOR]

Multiple DC subgrids in close proximity can be connected to form a DC microgrid. The interconnected DC subgrids enhance reliability and efficient utilization of resources. In this paper, a novel coordinated power management strategy is proposed for interconnected subgrids of a DC microgrid. Each DC subgrid has PV and battery units. The proposed strategy is implemented on interlinking bidirectional converters (IBCs). The strategy considers the state-of-charge (SoC) and charging/discharging current of the batteries for SoC balancing-based power sharing through IBCs. A fuzzy logic controller is implemented to decide the amount of power flow through the IBCs from SoC and the charging/discharging current of battery units. Moreover, with the coordinated strategy, a high SoC battery takes more loads compared to a lower SoC battery. The proposed strategy supports fast charging and fast discharging of batteries depending on the SoC level. In addition, the coordinated strategy can seamlessly transfer the control of IBC from the power regulating mode to the voltage regulating mode without any additional control scheme during an outage of the battery unit. The feasibility of the proposed strategy is validated through simulation in MATLAB/Simulink and OPAL-RT real-time digital simulator. [ABSTRACT FROM AUTHOR]

Recently, increasing the number and severity of the natural hazards requires the resilience assessment and enhancement of the power system, especially the microgrid system. The emergency distribution generations have great potential to enhance the resilience of microgrid against blackouts under the emergency environment. This paper investigates the resilience assessment of the microgrid system under natural hazard, where emergency distribution generations are firstly pre-positioned on system nodes and then reconfigured after system damage occurs. A new resilience metric index and an efficient approximation computation method are provided for the resilience assessment of the focused problem. A new pre-position strategy and a new reconfiguration strategy on emergency distribution generations are proposed for the microgrid system emergency restoration under natural hazard. Also, a framework of resilience assessment is provided for problem-solving. The results of extensive experiments on the modified IEEE 30-Bus system and modified IEEE 118-Bus system confirm the effectiveness of the resilience assessment methodology and the superiority of proposed restoration strategies. [ABSTRACT FROM AUTHOR]

In this paper, two (series and parallel versions) quasi Z source inverter (qZSI)-based multioutput series-parallel topologies capable of supplying multiple three-phase AC and single boost DC outputs simultaneously are presented. The proposed parallel version topology can supply n number of AC outputs along with one boost DC with constant voltage and variable current. Similarly, the series version topology can give n number of AC including one boost DC output with constant current and variable voltage. The outputs of the proposed topologies can feed directly to DC/AC microgrids and multiple residential loads simultaneously without using any extra adapter/regulator and thereby avoiding local power conversion to meet the load demand. A hybrid pulse width modulation technique with constant frequency is used to operate the proposed topologies. The performance of the proposed topologies is verified by developing the prototypes of 2.18 kW (for parallel version) and 2.02 kW (for series version) for two three-phase AC outputs and one boost DC output. The developed prototypes show measured efficiency of 90.01% for the parallel and 89.95% for the series version topology. [ABSTRACT FROM AUTHOR]

This paper proposes an iterative approach to adjust the control parameters of multiple power converters within a microgrid, which operates in grid-connected and grid-islanded modes. The adequate control parameters are determined using an optimisation-based strategy, local measurements, and results obtained in previous algorithm iterations. The proposed objective function is based on the integral time-weighted absolute error (ITAE), modified to improve the microgrid control performance. The proposed approach addresses the control tuning complexity by considering an incremental strategy, starting from adjusting basic microgrids, continuing to the setting of intermediate microgrids, and finalising when the target microgrid control is fine adjusted. The results obtained at the CIGRE LV benchmark microgrid validate the proposal, obtaining during faults, the maximum deviations from rated frequency around 2.2% and 0.3% in grid-islanded or grid-connected cases, respectively. Also, voltage and power references are adequately followed during steady-state regardless of the microgrid operation mode, where the steady voltage profile is below 10% variation. The obtained results demonstrate the proposed approach advantages, which straightforwardly adjust multiple converters integrated into a microgrid. The main contributions are as follows: (a) the microgrid model is not required, and (b) only measurements at the point of common coupling of the distributed energy resource are used; consequently, the proposed tuning approach is especially applicable to complex microgrids. Finally, finely adjusted microgrids are required for further operation or protection studies where complex system configurations are commonly required. [ABSTRACT FROM AUTHOR]

In this paper, two (series and parallel versions) quasi Z source inverter (qZSI)-based multioutput series-parallel topologies capable of supplying multiple three-phase AC and single boost DC outputs simultaneously are presented. The proposed parallel version topology can supply n number of AC outputs along with one boost DC with constant voltage and variable current. Similarly, the series version topology can give n number of AC including one boost DC output with constant current and variable voltage. The outputs of the proposed topologies can feed directly to DC/AC microgrids and multiple residential loads simultaneously without using any extra adapter/regulator and thereby avoiding local power conversion to meet the load demand. A hybrid pulse width modulation technique with constant frequency is used to operate the proposed topologies. The performance of the proposed topologies is verified by developing the prototypes of 2.18 kW (for parallel version) and 2.02 kW (for series version) for two three-phase AC outputs and one boost DC output. The developed prototypes show measured efficiency of 90.01% for the parallel and 89.95% for the series version topology. [ABSTRACT FROM AUTHOR]

The usage rate of electric vehicles (EVs) is gradually increasing. Recharging of EVs should be carried out repeatedly over time, and the energy needed for this is high and increasing. With the present infrastructure, we cannot supply the required energy, and therefore, we need to implement a model that expands the power grid to satisfy our energy requirements. This paper proposes a convolutional neural network-based dynamic capacity expansion (CNN-DCE) for EV charging. Flower pollination optimization algorithm (FPOA) was used to improve the hyperparameters of CNN during training. The main aim is to reduce the cost of installing additional capacity resources and to reduce the operational cost. To cope with the load growth, different capacity resources are installed at different years of the planning boundary. Five statistical indices, such as mean squared error, mean absolute error, correlation coefficient, and scatter index, are used to evaluate the performance of CNN. The capacity expansion plan in the microgrid is achieved by expanding the energy of battery energy storage systems, microturbines, and solar and wind energy systems. The queuing delay for the EVs waiting in a queue for recharging has been considered. The performance of the proposed CNN-DCE is studied and compared with three other state-of-the-art methods. The results show that the resources reduce the planning cost to 26% for the short-term planning horizon, the long-term plan has 150% of the expansion, and the wind energy system covers 48% of the expansion cost. [ABSTRACT FROM AUTHOR]