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This paper presents a reliable microgrid for residential society with control methods to design grid forming inverter (GFI) to emulate the seamless-synchronization features to deal with the dynamic concerns of the grid. In this microgrid a photovoltaic (PV)-based GFI with a control scheme in parallel with a PV-battery supported inverter with an enhanced current control is considered to operate under non-ideal grid voltage conditions. The PV-GFI is adaptable to operate either in islanded mode (IAM) or grid integrated mode (GIM) depending under the grid conditions. An adapted power control technique is implemented such that power demand of local loads and harmonic currents are compensated by respective distributed generation. Moreover, the active power demand of all local loads is shared between the main grid and respective local DG. The adapted power controller achieves transient free operation even under the load disturbances. The proposed control methods of microgrid are validated in simulation under various operating conditions.

With increased penetration of renewable power and the nonlinear loads in the distributed generation (DG) systems, increased power quality concerns are exhibited in the active distribution networks, especially the challenges associated with the current and voltage harmonics in the system. Various conventional harmonic compensation techniques are developed for voltage-controlled DG inverters in past, majority involve either multiple proportional-integral (PI) or proportional-resonant (PR) controllers in eliminating grid current harmonics. The current-controlled inverters, on the other hand, are not preferred in industrial applications, accounting to their wide variations in the switching frequency. A novel and adaptive harmonic voltage control is developed here, for voltage-controlled DG inverters, which neither uses any PI regulators nor imposes stability issues associated with nonideal implementation of infinite gains of PR controllers. Interestingly, the developed control logic can be used for DG inverters, both in grid-connected and off-grid operational modes. Furthermore, this strategy allows a network operator to use this as an additional supplement that can be enabled/disabled as per the network requirement. The control logic exploits the property of an optimal-switching-vector controller, i.e., accurate output voltage tracking. Simulations results demonstrate the effectiveness of the controller, to suppress grid current harmonics and load voltage harmonics in grid-interfaced (GI) and off-grid modes, respectively, ultimately satisfying the mandatory IEEE standard-1547. Experimental results verify the viability of the controller for practical applications.

Differential current protection has been a reliable approach for line protection in DC systems. However, due to its dependence on communication for terminal current information, resiliency of such a protection approach is poor against external cyber intrusions (ECI) of different types, i.e., false data injection (FDI) and time synchronization attacks (TSA). As a result of which, the system is prone to collapse due to line tripping in the presence of coordinated ECIs. The scope of paper is to propose a resilient protection approach for medium voltage DC (MVDC) microgrids differentiating between the real faults and the ECIs in a system. The approach also detects ECIs on an adjacent line occurring simultaneous to a real fault where the system deploys current limiting reactors (CLR). Phase-modal transformation is carried out to obtain the mode voltages (line and zero) across these CLRs. The mode voltages across the faulty mode networks are utilised along with differential current protection to propose a resilient protection approach against ECIs, which is sensitive to high impedance faults (HIFs). Further, a sensitivity function is formulated which gives the variation of decisive variables i.e., mode voltages with respect to change in decisive parameters i.e., fault resistance and fault location.

This work aims to develop a solar- battery energy storage (BES) based system, which ensures an uninterruptable supply to loads irrespective of the availability of the grid. This system comprises of a solar photovoltaic (PV) array, a BES, the grid and local residential loads. A new control is implemented such that the active power demand of residential loads, is fed from the PV array, a BES unit and the utility grid. In this system, the power control operates in different power modes, which delivers the benefits to the end-users with the integration of BES and an excess of PV array power, which is sold back to the grid. For this, effective control logic is developed for the grid-tied voltage source converter (VSC). Moreover, this system deals with the issue of an integrating power quality enhancement along with the power generation from the solar PV source. The cascaded delayed signal cancellation (CDSC) based phase-locked loop (PLL) is implemented for grid synchronization during the grid voltage distortion. The developed control is easily implemented in a real-time controller (dSPACE-1202).

An air conditioner load has a major role in power consumption from utility grid in tropical countries. Most of the air conditioning systems are migrated to variable speed drive technology that optimizes the power utilization. Renewable energy sources like photovoltaics (PV) and energy storage system (ESS) like batteries produce DC power. In this paper, considering the advantages of variable speed drive technology, the power is directly injected to the DC side of the variable speed drive from PV array and the battery. The compressor at the outdoor unit consumes major portion of power from the grid and therefore the additional power is integrated to this unit to support the grid. The voltage level of the battery is boosted to match with the DC bus voltage level of the grid using a bidirectional isolated DC-DC converter. A low power DC-AC conversion stage is present to run the indoor unit and to supply the auxiliary power. In this methodology, air conditioner is operated uninterruptedly and high power DC to AC (50 Hz) conversion stages are eliminated, thus it reduces the blukiness of the system, cost and conversion losses. A seamless bidirectional power is exchanged between the battery and utility grid without interrupting the main operation of the air conditioner.

In low-voltage dc distribution networks (DDNs), the load terminals typically employ voltage-source inverters to provide ac supply to the consumers. These load terminals can potentially experience poor voltage regulation especially in the presence of intermittent photovoltaic sources and uncoordinated charging patterns of plugged-in electric vehicles. Variations in power absorption and generation at different points can potentially create significant voltage drop/rise at the load terminals and may reduce the power quality at the ac side of the loads. In order to mitigate this issue, this article suggests a real-time coordinated supervisory control strategy that estimates the present state of the network and generates a set of references for the controllable sources such that the load voltage deviations are minimized. This article also proposes a power-electronic-converter-based soft open point (SOP) that provides a seamless connection of the remote end and gives an additional degree of freedom to control the circulating current among the feeders. Detailed description and control strategy of the SOP is presented. Steady-state analysis carried out on a three-feeder DDN is presented with and without the proposed SOP. Real-time simulation with an Ethernet-based transmission control protocol/Internet protocol communication system along with experimental results is presented to validate the implementation and effectiveness of the control strategy.

The intermittent nature of renewable energy sources results in an interruptible and inadequate power. The integration of the battery energy storage to renewable energy sources provides reliable and continuous power to the loads. Further, the high penetration of renewable energy sources raises the power quality problems in the grid. This work deals with performance improvement of PV‐battery energy storage based energy conversion system achieved through implementing a multi‐structural adaptive circular noise estimation control for its grid connected mode. The control is developed for the grid side voltage source converter, which provides the power quality improvement and automated transition from grid connected mode to standalone mode or vice‐versa. It provides multi‐functional features such as harmonics extraction, DC offset elimination, and power quality improvement in PV‐battery energy storage based energy conversion system even at nonlinear loading condition in grid connected mode. In standalone mode, the amplitude and waveform of load voltage are controlled sinusoidal by the voltage control. Performance of PV‐battery energy storage based energy conversion system is studied to validate the acceptability of these controls according to the IEEE‐519 standard.

The proposal focuses on the role of data centres (DCs) and electric vehicle (EV) energy storage systems (ESSs) for frequency regulation and it provides a new opportunity for different resources to participate in the power sector. Frequency fluctuations are introduced with the emergence of inverter-dominated renewable energy sources (RESs) as it does not provide rotational inertia to the grid, such as synchronous generators. In this study, the virtual synchronous generator (VSG) controlled inverter compensates for the lack of inertia. Particularly, this research work analyses the involvement of DC and EV batteries as ESSs in designing an integrated technical virtual power plant (VPP) to support frequency variations using the VSG concept. Microgrid (MG) simulations are performed in MATLAB/ Simulink platform. Case studies are carried out to validate the grid export and grid import performance of the model by using variations in the load and the solar irradiance of the MG. It is verified that bidirectional power flow takes place between the proposed VPP components and the grid that enables the grid with high penetration of RESs. The work provides a conceptual framework for future contributions towards the smarter usage of assets such as ESS and a greener future.

This article proposes an economic and power quality based control of distributed generation system in autonomous mode. The control strategy proposed here, feeds nonlinear load from photovoltaic and battery energy storage, with a diesel generator (DG) operated in on / off mode instead of continuous mode. The coordinated controls of voltage source converter and buck/boost dc/dc converter achieve the multiple objectives of harnessing maximum renewable power, maintaining battery state of charge, and continuous power supply to the critical loads, while maintaining the power quality within permissible limits as per the IEEE 519 standard. Furthermore, minimum fuel consumption and maximum fuel efficiency, are obtained while operating DG, along with maintaining its currents free from harmonics even in presence of nonlinear loads, using novel improved robust mixed norm based adaptive control technique. Simulation results and experimental validation of proposed strategy, prove its satisfactory performance under different scenarios.

This chapter focuses on two main topics ‐ Renewable energy and Smart Grid. It covers operation and control aspects of different sources, namely reactive power control in the scope of wind power integration. The chapter discusses wind power, photovoltaic generation control, and forecasting. On the demand side, demand response (DR) is discussed as a tool to optimally integrate distributed generation (DG), namely the one based on renewables. The chapter addresses storage means and electric vehicles (EVs) as a consumption/generation resource with interesting characteristics for the integration of renewables. It presents the energy resource scheduling mathematical formulation considering DG units, storage systems, EVs, direct load control DR programs, and the acquisition of energy to external suppliers. The problem can be solved as a mixed‐integer nonlinear programming problem. It presents an ERS problem of large‐scale nature solved with different kinds of algorithms from deterministic, hybrid, and meta‐heuristics.'.

This paper presents a disturbance observer approach for frequency regulation for a low-inertia microgrid system consisting of various distributed energy resources (DERs). The mismatch in load and generation is estimated using the disturbance observer and is used as a supplement to the classical integral controller to vary the set points of various controlled DERs. Since the disturbance-observer-based approach is an algorithm based on local frequency measurement, it reduces the capital expenditure in implementing additional sensors. Further, in case of microgrids, the communication infrastructure is mostly built with public networks, which can introduce random delays. Therefore, the performance of the proposed scheme is also tested in the presence of random communication delays. A sensitivity analysis is also presented to show that the performance of the proposed control scheme is much better than the integral controller even when there are variations in the system parameters. Finally, the proposed approach is tested in a laboratory-scale microgrid setup in which the frequency of a synchronous generator is controlled using the disturbance-observer-based approach. The results obtained in the experimental setup show that the proposed approach can be easily implemented in a real system in order to achieve adequate frequency control.

Damping of low-frequency inter-area oscillations in a power system has been the main concern for ascertaining the small-signal stability. Much of these efforts had been paid on designing damping controller with controller parameters obtained by offline analysis using a linearized model of the power system around a most probable operating point. A modern power system is however subjected to frequent changes in operating points due to load, renewable penetration and topology variations. An adaptive controller has the advantage of flexibility in getting the controller parameters auto-tunned online thus can be used for different operating conditions. This paper presents a WADC controller using a self tunning control (STC) strategy. The evaluation of the designed controller under various conditions has been carried out with renewable integrated IEEE 11 bus and IEEE 39 bus test systems. From the results, it is found that the proposed WADC can provide improved damping on inter-area oscillations under variable operating conditions.

Inverter based AC microgrid & Boost converter based DC Microgrid have been used to simulate a hybrid Microgrid. AC microgrid is implemented considering one inverter & the combined load. Boost conerter & a DC load is used for the representation of DC microgrid. A current controlled interlinking converter is used to connect AC microgrid to DC microgrid. A PLL has been used to phase lock interlinking converter with the inverter for the power flow between the two. The complete design process of compensator used for PLL for the interlinking converter has been explained. Bi-directional flow of power through interlinking converter has been shown by simulation.

This paper proposes a multilevel hybrid control scheme, including grid control and node control, for an islanded 48-V solar photovoltaic-based low voltage dc microgrid that aims to overcome the drawbacks of centralized and decentralized control schemes. The analyzed microgrid includes a 35-kW rooftop solar system as the main power source at bus-1 with battery storage, 5-kW hybrid energy storage system (Vanadium Redox flow battery with super capacitor) at bus-2, and variable loads such as electrical vehicles at bus-3. In the case of a central failure, the proposed hybrid control scheme is capable of seamlessly switching between high bandwidth communication and low bandwidth communication channels of communications to implement a distributed control scheme. The central supervisory controller is responsible for updation of grid characteristics and sending/receiving information to/from local node controllers, which are responsible for bus voltage regulation and energy management. The control hierarchy features optimized and safe operation (charge and discharge) of storage devices in dc microgrids. The paper also demonstrates the application of battery-supercapacitor systems to absorb system transients during load changes. The simulation showcases the continuous flow of information and decision processes via each level of control while simultaneously taking into consideration the constraints of each subsystem. The scheme has been simulated in MATLAB/Simulink environment for various case studies to evaluate system robustness. Further, the proposed scheme has been tested experimentally with its prototype and its results are explored.

Electric vehicles (EVs) if become a major presence on the worlds’ road can have an adverse impact, i.e., line overloading, fuse blowouts, transformer failures, and at worst case blackouts in active distribution system. To handle such impact, the existing literature suggested a day-ahead scheduling of EVs in active distribution system providing a tentative prediction of energy requirements for EVs. However, this paper proposes a novel web-based registration system that gathers the actual upcoming energy requirements from EV owners well in advance. Thereby, bridging the gap between the grid and end users resulting in more reliable EV's information, such as initial and expected state of charge (SoC) and arrival and departure time. These input data are further processed using a proposed social welfare maximization algorithm that determines the price responsive schedule for EVs and calculates the distribution locational marginal price (DLMP) signal. This DLMP signal is high in the period of overloading and low during the time with less load. DLMP signal is finally shared with the end users via a web application that facilitates them to choose most economic charging slots. The proposed scheme is validated on a referenced active distribution system. The simulation and results show the effectiveness of the proposed scheme in preventing overloading of lines, satisfying EVs energy requirements, and evaluating their cost incurred during charging hours. Additionally, the results validate the resiliency of the proposed algorithm against potential damages, such as change of line flow limits, change of expected SoC, stay hours of EV, and the change in total number of EVs.

This article proposes the idea of using Solar Energy (SE) as a source of power for designing and developing a standalone air-cooling system. This type of application is particularly suited for rural areas that have a considerable amount of solar radiation and have no access to grid systems. The proposed system is comprised of a photovoltaic (PV) array, DC-DC boost converter and DC-DC buck converter. Two Permanent Magnet Brushless Direct Current (PMBLDC) motors are employed to drive a centrifugal water pump and an air blower coupled to their individual shafts. The air blower is connected with a DC-DC boost converter that ensures a maximum power point (MPP) operation. A centrifugal water pump is connected with a DC-DC buck converter. The challenging task in an air cooling system is to maintain the constant speed of the blower under variable irradiance conditions. The power provided by the solar PV array can be shared between the two DC-DC converters in such a way that the BLDC motor connected with the blower will maintain a constant speed by maintaining the constant voltage of the DC-DC boost converter. The rest of the available PV power will be handled by the pump. The suitability of the proposed system for various perturbations is evaluated by simulating the proposed system using a MATLAB/Simulink and is validated by conducting experiments. Case studies have been performed with a variable PV irradiance and the results are explored.

This paper presents a supervisory control scheme for the mitigation of voltage regulation issues in a ring dc microgrid with energy hub (EH). The EH is installed with battery banks, proton exchange membrane based fuel cell and electrolyzer stacks representing an energy source-storage unit. In a ring dc system, power injection and absorption at different buses can cause significant voltage deviations throughout the system. This situation is further aggravated due to the intermittent nature of PV power generation. To resolve this issue, a supervisory control scheme is proposed which utilizes a centralized communication system to estimate the network voltage profile and generates desired voltage and current references for the controllable sources. The proposed system consists of dispatchable and non-dispatcable energy sources. Conventionally, in a grid connected system, a bidirectional voltage source converter (VSC) maintains the network voltage while the energy storage system remains at standby/fully charged. This paper suggests to utilize both VSC and EH in a coordinated manner, so as to maintain the voltage at the load terminals within a tolerance band. The proposed ring dc microgrid model is simulated in MATLAB/Simulink environment and the simulation results validate the effectiveness of the supervisory control under wide variations in system operating conditions.

This paper proposes a distributed finite-time secondary controller to achieve average voltage regulation and proportionate current sharing within a finite settling time for autonomous network of dc microgrids. It is employed by using a distributed finite-time control approach which maintains average voltage regulation of the system facilitating proportionate current sharing simultaneously by virtue of dynamic consensus between its neighbors. The proposed scheme ensures an improved performance over the conventional distributed secondary methods by reducing overshoots and chattering which is significant for critical operation of the loads. The proposed control strategy is simulated in MATLAB/Simulink environment to test link-failure resiliency, plug and play capability, and controller performance under communication delays within a tolerable upper bound on delay determined using time-delay analysis. Moreover the effect of variable time delays in different transmission medium is also simulated to test the practicality of the approach. This strategy is further tested on a 500-W FPGA-based experimental prototype to validate the control approach under different scenarios.

This paper presents a two layer energy trading framework for energy exchange among electric vehicles (EVs) and charging stations (CSs). The first layer includes peer to peer energy trading among EVs which is achieved by using assignment theoretic game model. The second layer is energy exchange among CSs which is achieved via buyer driven modified Gale Shapley model. A two layer cooperative energy trading model aims to reduce the grid dependency, power losses and ensures the optimal performance of the charging station.

To increase the uses of electric vehicle (EV) at remote locations and minimize the grid burdening in urban areas, an off-grid charging station (OGCS) plays a significant role. The OGCS seeks energy from renewable energy sources (RES). Amongst all RES, the photovoltaic (PV) is the best suitable clean energy source due to abundance and simple installation. However, PV energy fluctuates due to change in irradiance and it can not generate the constant energy. Therefore, an energy storage device is required to meet the energy demand and improve the sustainability of the charging station. Thereby, a system has been proposed which consists of an energy storage system (ESS) along with the PV source and EV charger. The proposed system includes a PV array with a boost converter, two bi-directional converters (BDC) and ESS. The BDC has been used for charging/discharging of the EV and ESS. The energy generated from PV is not sufficient to meet the demand during the absence of or reduced sunlight, thus, the ESS can meet the required demand. On the other hand, while the generation is more than the demand, the ESS utilizes the excess clean energy to make the proposed system stable. This results, a reliable off-grid, efficient and pollution-free EV charging station. Furthermore, the proposed system has been implemented in MATLAB/Simulink environment to verify the system performance.

This paper presents a comprehensive review on renewable energy based electric vehicle charging techniques, energy storage system with grid support functionality. The classification of electric vehicles charging based on standards, and charging techniques have been reviewed. Based on efficiency, feasibility and reliability, renewable energy like solar PV for electric vehicle charging are addressed. The energy storage system must be properly managed and safely maintained to improve the overall efficiency of electric vehicles for service longevity. A qualitative analysis of power electronics topologies along with its advantages and disadvantages are also discussed. Furthermore, a comprehensive analysis of Vehicle-Grid-Integration (VGI) infrastructure its capability, benefits/potential, challenges such as technological, environmental, economic etc. have also been highlighted. A comparative overview of power electronics topologies suitable for electric vehicle charging with VGI infrastructure is also exhibited.

State of charge (SOC) estimation has been an essential requirement for battery management system as seen in recent years especially for Electric Vehicles (EVs). The SOC stipulates the battery's amount of charge left so as to indicate how long more it can be used to drive an EV. Hence, accurate and uninterrupted monitoring of lithium-ion batteries (LiBs) is of utmost priority in order to maintain EVs efficient energy usage and battery life cycle. Although there has been a long research on estimating SOC, there are no accurate and easy electronic equipments available that can measure the SOC. Deep learning algorithms have shown high accuracy and reliable SOC estimation due to their ability to process large amount of data with ease under noisy conditions. This article proposes an efficient approach to estimate SOC by incorporating residual prediction error based wavelet-convolutional long-short term memory (ConvLSTM) deep learning model. Publicly available lithium ion battery dataset was used in this article for experimentation and validation of the results. Due to stochastic nature of data, multilevel wavelet decomposition has been analysed for improving accuracy of estimation. The proposed method results well in terms of accuracy and generalization capability.

In this work multiple parallel photovoltaic (PV) inverter based microgrid developed to enhance the reliability and accessibility of electricity in remote areas. The employed microgrid structure designed to operate in the grid connected mode (GCM) as well as isolated mode (IAM) and vice-versa. In this microgrid, (PV)-battery interfaced main inverter with a smooth mode transition control in parallel with a PV supported inverter with an enhanced current control is considered to operate under the grid connected conditions and in the off-grid mode of operation. In IAM, the main PV-inverter works as grid forming inverter and maintained constant voltage and frequency across the load to meet the power quality for the continuous operation of local nonlinear load. The battery storage units support the PV units during peak load periods and maintain the power balance in the microgrid. Controlled charging and discharging profile of battery is achieved by the DC-DC bidirectional converter (DBC). The control strategy of microgrid has been verified with simulations are offered in this paper.

To enhance the accessibility and reliability for a distributed generation system (DGS), a grid-tied photovoltaic (PV) generation system based on multiple parallel connected PV-inverters is developed for microgrid application in this work. This microgrid is designed to operate in the grid tied mode (GTM) as well as a standalone mode (SAM) and vice-versa. Here, a grid-forming inverter (GFI) technology is utilized for system with high penetration of renewables. The single stage PV-battery interfaced GFI inverter in parallel with a PV array supported inverters is considered to operate in the GTM as well as in (SAM) of operation. In SAM, the grid forming inverter maintains constant voltage and frequency across the load The control strategies of each unit are independent and using multi-loop controllers with switching logic. In the GTM, each PV panel in DGS is interfaced with an inverter with independent maximum power point tracking (MPPT) to harvest maximum power. The battery supports the PV units during peak load periods and maintains the power balance in the microgrid. Controlled charging and discharging profile of the battery is achieved by a DC-DC bidirectional converter (DBC). The control strategy of microgrid is verified with simulated results in detail.

Solar photovoltaics (SPV) are widely favoured energy generation system that has seen its rapidly growing installed capacity in the power system structure from past few decades. This grows concern of operation and control due to its high stochastic nature and dependence on weather variables such as temperature, irradiance, humidity etc,. This makes SPV power forecasting necessary in order to manage and plan to get useful insights. This article proposes a hybrid wavelet and genetic algorithm (GA)-based long short term memory (LSTM) deep neural network model to forecast SPV output power of a 58 MW utility scale SPV plant installed in the Florida state. The data are obtained from publicly available NREL database with 5-min resolution. Temperature and relative humidity along with historical SPV output power has been used as input features to the neural network model. Discrete wavelet transform is applied in order to denoise the data and due to its inconstancy, which increases the data dimension and helps in improving forecasting accuracy. GA has been combined with LSTM to find the optimized window size and LSTM units. The proposed method is then compared with different benchmark methods such as persistent/naive, state vector regression (SVR) and long short term memory-deep neural network (LSTM-DNN) model structure. The results shows an improvement of accuracy in terms of performance metrics most commonly used in machine learning such as mean squared error, root mean squared error, mean absolute error and r-squared values.

In this paper, a new control is utilized for the grid integrated-solar PV (Photovoltaic)-wind-battery energy storage system (GISWBS) with an automated transition capability between the grid tied mode (GTM) and standalone mode (SAM) of operation. The main aim behind the hybrid wind-solar PV and battery-based distributed generation (DG) system is the fact that both solar and wind power have exhibit complementary power profiles, which help in smoothing of intermittency in power and higher reliability. A single voltage source converter (VSC) is utilized for feeding AC local loads and interaction with the grid. This microgrid presents a focus on enhancing the power quality along with the generation of renewable energy sources (RESs). For the improvement in power quality, a generalized integrator (GI) type observer based current control scheme is used here, which controls VSC under GTM of operation. In SAM, the voltage control is implemented to regulate the frequency and voltage at desirable level according to the IEEE standard 1547–2018 standard. The battery is used to compensate for the intermittent nature of the power generated from RESs and connected varying loads. Moreover, during the unavailability of the grid, the loads have a continuous power supply due to the automated transition.

The parallel operation of various voltage source inverter (VSIs) based distributed generation (DG) units in autonomous mode is ensured by sharing the load power in a decentralized manner using $(P-f)$ and $(Q-V)$ droop control framework. It is well established that as the $X/R$ ratio of the feeder impedance interfacing DGs to the point of common coupling (PCC) decreases, there exist a Strong static coupling between active and reactive powers. However, when these DGs operate in droop control scheme there may exist a dynamic coupling between their active and reactive powers. In this work, dynamic relative gain array method is used to analyse the existence of dynamic coupling between the active and reactive powers of DGs in desired frequency range by formulating a reduced order multivariable transfer function model relating active power and reactive power of each DG with respect to their voltage angles and voltage magnitude.

The dual active bridge (DAB) is one of the key elements in different DC microgrid clusters. DAB also has isolation features and bi-directional power transfer capabilities between two power buses of similar or different levels, i.e. low voltage and high voltage buses. Due to the galvanic isolation and modularity nature of DAB, the output can be easily interconnected for efficient and reliable power sharing. In this paper, this advantage of modularity is applied in microgrid applications. A robust bi-directional power sharing control strategy is developed between DABs from different high voltage buses to low voltage buses in order to improve the voltage stability of the microgrid. The designed controller considering the non-linearity and uncertainty ensures stable DC bus voltage and reliable power sharing among DABs under different operating conditions.

This paper demonstrates a three-port topology, which is implemented to connect the solar photovoltaic (PV) array and electrical vehicle (EV) to the grid. This topology is derived by the three converters, which are sharing common DC link voltage (V DC ). This system operates in different modes, which are defined as, 1) V2G mode where, EV battery feeds to the grid; 2) R2 (V+G) mode, where PV array feeds to the grid and EV; and 3) R2 (V+H) mode, where PV array feeds to the EV battery and the load. In this paper, a multi-functional VSC control is implemented to achieve reactive power compensation, harmonics mitigation, unity power factor (UPF) in each defined mode of EV interfaced solar PV energy conversion system (EVPVES). The adaptive vectorial filer (AVE) is implemented for mitigating the harmonics generated by nonlinear loads connected at PCC and to maintain the sinusoidal currents. When the power outage occurs in both the grid and the PV array, EV works as an off-line uninterruptible power supply (UPS) to feed the power to the local loads. Test results validate the performance of EVPVES in different operating modes in steady-state and dynamic conditions.

Need for comprehensive development of photovoltaic (PV) based micro-inverter interface for various rural applications is observed. This paper demonstrates the design, development, and real-time field experimentation of an energy-efficient, PV based single-phase micro-inverter for rural/remote applications. The proposed dual-stage single-phase inverter takes the advantage of interleaved DC/DC boost converter for cancellation of 180 ο phase-shifted harmonics and application of voltage doubler circuit results in a reduction of the transformer turns ratio with low flux leakage and thus endeavoring for better conversion efficiency at a reduced size of magnetics. Further, high-frequency switching at the first leg and low switching frequency at another leg of the full-bridge converter ensures lower switching losses at light load conditions. Experimental results are carried out at field location (latitude: 26.431°, longitude: 74.707°) in Rajasthan, India. The interleaved boost DC/DC converter efficiency is found approximately 91.73% at full load and 77.74% at 10% load. Micro-inverter overall efficiency is observed in between 57.34% to 91.6%.

This paper presents a solar PV (Photovoltaic)-BES (Battery Energy Storage) based Energy Conversion System (PVBCS), comprising high gain DC-DC boost converter (HGBC), BES with a bidirectional converter and nonlinear loads. The HGBC is utilized to achieve high step-up voltage gain with simple structure. Two inductors of HGBC having the same level of inductance are discharged in series during off period of switch and are charged in parallel during on period of switch. Moreover, a voltage source inverter (VSI) is utilized for DC to AC power conversion and supplies the solar PV power to loads and the utility. Under the off-grid condition, VSI operates in voltage control mode. An enhanced power quality performance of the PVBCS is achieved by mSOGI-L (Modified Second Order Generalized integrator Loop) based D-Q transformation control. It eliminates the load current harmonics and generates the fundamental active component of AC current and voltage and reduces the complexity of VSI's control. Here, HGBC is realized the maximum power point in the solar PV array by via the MPPT algorithm. The bidirectional converter (BDC) coupled BES supports critical loads under the low PV array output and off-grid condition. Simulated results have presented under different steady state and dynamic conditions. to validate the acceptability of the system with control algorithm.

Among various sources of renewable energy, solar photovoltaic (PV) and wind are two high flying and promising substitutes to meet the future electricity demand in smart microgrid (µ-grid). However, the high penetration of renewable energy sources into the grid, rises the power quality problem such as harmonics and distortion in grid current and grid voltage. Therefore, this paper deals with implementation of an adaptive quadrature-complex variable filter (AQ-CVF) based control technique which provides harmonics filtering and mitigation of power quality issues associated with solar PV and battery energy storage (BES) coupled grid interfaced system, connected with different nonlinear loads at the PCI (Point of Common Interconnection). The bidirectional converter (DBC) controls the BES to support critical loads under deficient power generation by RES. The adverse operating conditions due to variable nature of input PV power and output load power of hybrid solar PV-wind-BES coupled grid interfaced system (HSWGIS) are also highlighted in this work. Under different steady state and dynamic conditions various operating modes are also presented-to validate the acceptability of the control technique for PV-BES coupled grid interfaced system. A perturb & observe (P&O) algorithm with a boost converter (BC) is implemented for the maximum power extraction of the renewable energy. Performance of HSWGIS is analyzed to validate the acceptability of control techniques according under various operation condition.

Electric load demand increases tremendously especially for a Metropolitan city like Delhi due to climatic conditions, population growth, local area development, industries expansion, air pollution, thermal devices usage, etc. Hence, the accuracy of electric load demand forecasting (ELDF) is a deciding factor for the power distribution network to retain as an efficient and consumer friendly network. The proposed day-ahead ELDF models are created on a new architecture with an addition of new input features such as electricity tariff rate and consumer type as compared to existing input features considered already for load demand forecasting prevailing literatures. The models are developed in one of the latest deep learning techniques called standard LSTM (Long Short-Term Memory) networks.

It is well-known that distributed control can improve the resiliency of dc microgrids against multiple link failures as compared to centralized control. However, the control layer is still vulnerable to cyber attacks. Unlike widely studied false data injection attacks, which involve adding false signals on top of the existing ones in the controller or communication links, hijacking attacks completely replace the existing signals. As a result, the compromised agent(s) diverge from steady state owing to imbalance in the iterative rule of consensus algorithm. To detect hijacking attacks, a novel distributed screening (DS) methodology is proposed. In addition to that, a fault detection (FD) metric is provided to assist the proposed attack detection strategy in differentiating between hijacking attacks and sensor faults. This reduces the complexity of decision making in the attack mitigation approach. Furthermore, interoperability of the proposed detection metrics allows simultaneous detection of sensor faults and hijacking attacks. The performance of the proposed detection metrics is evaluated under simulation and experimental conditions to conclude that it successfully detects the attacked agent(s) as well as sensor fault(s).

The intermittent nature of renewable energy sources (RES) disturbs the reliable operation of grid interfaced system. The grid integrated solar PV (photovoltaic) with battery energy storage (BES) support provides the reliable and continuous power to nonlinear loads. The high penetration of RES into the utility grid (UG) enhances the power quality (PQ) problems such as disturbances, fluctuations and harmonics in grid current. In this paper variable digital filter (VDF) control provides high harmonics elimination in the hybrid solar PV battery energy storage (BES) coupled with the grid interfaced system (HPVBGS) for nonlinear loads at the PCC (Point of Common Coupling). The variable digital filter (VDF) architecture is used to remove the harmonics and achieve distortion free fundamental signal of load current with high-speed and low implementation complexity. Test results validate the performance of the controller under different operation modes of HPVBGS in steady state as well as for dynamic conditions.

Under the conventional schemes, the battery management systems are usually designed from the system operator’s perspective aligning them with common modes of operation to either ensure the longevity of each battery or an economically viable option. However, in DC microgrids with the batteries reinforced by local investments of each customer, their operation is primarily governed by the consumers’ preferences. To support this feature, this paper introduced a local voting scheme using a binary distributed protocol based power management strategy for an autonomous network of PV-battery based cooperative DC community microgrid, allowing each customer to vote to undergo either energy balancing or load sharing between the batteries in a distributed manner based on their respective future usage. Moreover, it manifests these objectives using a voting index based on majority & emergency events which is used to resolve the system operation. The proposed control strategy is simulated to demonstrate its effectiveness for various voting scenarios under physical disturbances such as communication delay, link failure, converter failure & data packet loss and experimentally validated on a 600 W FPGA based experimental prototype.

DC distribution networks and micro grids are gaining importance enabling the integration of hybrid distributed generation, energy storage units, modern loads including electric vehicles and different micro grid topologies, thus permitting the consequent development of DC Distribution Systems (DCDS). For maximum utilization of distribution network without compromising the security and reliability of the supply system, determination of critical loading is crucial. With this objective, this paper proposes a modified DC continuation power flow based approach for determining the loadability limit of the active DCDS. DC-DC converters play an important role in bus voltage regulation and system loadability. Impact of these converters must be accounted in any analysis and planning of active DCDS. In this paper, two different mathematical models of lossless DC-DC converters are developed to account them in power flow model of the system. The proposed approach can be applied to both radial and meshed DCDS. Impacts of integration of converters in the system are illustrated by results of two realistic DCDS and results are compared with the corresponding AC Distribution Systems (ACDS).

This paper proposes an adaptive event-triggered communication-assisted distributed secondary cooperative control strategy using a parameter projection law-based estimate of states to reduce communication burden. It overcomes the drawbacks of operating in an open loop manner between two triggering time instants in traditional zero-order hold-based event triggering schemes using a full state feedback control strategy to update the control input simultaneously. Moreover, real-time precision of information and model uncertainties is well dealt owing to the adaptive mechanism via an event-triggering condition designed using the Lyapunov technique to ensure the stability of the system. This strategy is used in tandem to achieve global average voltage regulation and proportionate load sharing in dc microgrids for various disturbances without sacrificing system performance. The proposed control strategy is simulated in MATLAB/Simulink environment and tested on a 500-W FPGA-based experimental prototype to validate the control approach under different scenarios.

This paper presents a step in microlevel photovoltaic (PV) summation technology for combining unsymmetrical PV modules with differences in specification and age, relocation on nonfertile land, or multilocation mounting by designing an add-on modular hardware interface to a multi-input single-output (MISO) dc bus, using an MISO controller employing a suitable maximum power point (MPP) tracker under partial shading/long distance/mismatching PV module conditions along with a sensor-less PV vector control inverter (PV–VCI) driving a three-phase asynchronous squirrel cage induction motor dedicated to irrigation, with the facility of autoconfiguration/autotuning at the beginning. A PV source gets automatically added/removed in a ramp profile in proportion to the load in steps, thus keeping on only when required, enabling high efficiency, minimum heating, and prolonged life as that of a PV module. The contribution includes a reduced PV energy cost per watt by 24.10%, improved return on investment by 27.48%, improved water discharge yield by 11.06%, and improved MPP tracker impact by 14.59% with 90.88% MISO system efficiency compared to a normal scheme.

Electric vehicles (EVs) which are assumed to revolutionize the automobile industry are being adopted widely across the globe at a pace higher than estimated. However, the uncontrolled charging pattern of these loads can raise the peak demand and can potentially damage the transformers that are supplying power to a residential area. In addition, EV’s battery charging in peak hours can increase an average household electricity bill significantly. Thus, this paper proposes a heuristic method which suggests most suitable charging/discharging instances for an EV battery in a time-of-day regime. The key feature of this method is its simplified structure that requires basic input parameters, such as real-time price signal, household demand, and EV’s parameters. In addition, a heuristic method can quickly find the solution such that it can be implemented in simple computing devices. The scheme has been implemented on hardware and has proven its effectiveness. Results show that the algorithm works in two folds in reducing cost and energy dependence on the grid.

In this paper, power quality features such as harmonics mitigation, power factor correction with active power filtering are addressed in a single-stage, single-phase solar photovoltaic (PV) grid tied system. The Power Balance Theory (PBT) with perturb and observe based maximum power point tracking algorithm is proposed for the mitigation of power quality problems in a solar PV grid tied system. The solar PV array is interfaced to a single phase AC grid through a Voltage Source Converter (VSC), which provides active power flow from a solar PV array to the grid as well as to the load and it performs harmonics mitigation using PBT based control. The solar PV array power varies with sunlight and due to this, the solar PV grid tied VSC works only 8–10 h per day. At night, when PV power is zero, the VSC works as an active power filter for power quality improvement, and the load active power is delivered by the grid to the load connected at the point of common coupling. This increases the effective utilization of a VSC. The system is modelled and simulated using MATLAB and simulated responses of the system at nonlinear loads and varying environmental conditions are also validated experimentally on a prototype developed in the laboratory.

In this paper, a dynamic power management scheme (PMS) is proposed for a standalone hybrid ac/dc microgrid, which constitutes a photovoltaic (PV)-based renewable energy source, a proton exchange membrane fuel cell (FC) as a secondary power source, and a battery and a supercapacitor as hybrid energy storage. The power management algorithm accounts for seamless operation of the microgrid under various modes and state-of-charge limit conditions of hybrid energy storage when all the sources, storages, and loads are connected directly at the dc link. The PMS generates current references for dc converter current controllers of the FC, the battery, and the supercapacitor. The average and fluctuating power components are separated using a moving average filter. The dc-link voltage regulation under dynamic changes in load and source power variation is proposed. Also, PV power curtailment through control is formulated. The proposed power management is modified and extended to multiple PV generation systems and batteries, with all the sources and storages geographically distributed and operating under multitime-scale adaptive-droop-based control with supervisory control for mode transition. The proposed PMS is validated using simulation results. Also, field programmable gate array/Labview-based laboratory-scale experimental results are presented to validate the PMS under various critical conditions.

This paper proposes a repetitive learning-based phase locked loop (RLPLL) to improve power quality of the grid-connected dc microgrids under distorted grid voltage in a weak grid. The harmonic component present in grid current in a high impedance network amplifies the distortion in voltage, which often leads to instability. Since the behavior of the conventional synchronous reference frame PLL (SRF-PLL) varies, owing to the proportional-integral gains constrained to harmonic rejection bandwidth ultimately leading to a sluggish response. However, RLPLL accommodates this limitation with a comparable dynamic performance and enhanced harmonic attenuation properties. This has been achieved by using a Lyapunov-based approach for harmonic estimation, which facilitates the periodicity and boundedness of the harmonic component to obtain an adaptive learning-based update. To deal with the computational burden, this paper also provides a low-computing alternative model of the proposed strategy. The dynamic response of RLPLL along with a comparative analysis with SRF-PLL is governed by many events directly affecting the dc voltage, which is critical for the operation of dc microgrids. Its performance is validated under different scenarios in a 1-kVA field programmable gate array-based experimental setup.

A robust controller design for the voltage control of an autonomous three-phase voltage source converter (VSC) is proposed. As compared with the conventional proportional plus integral (PI) controllers, fractional order controllers make the VSC system robust due to their fractional characteristics. The fractional PI $\lambda$ controller has an additional degree of freedom $\lambda$ along with $k_p$ and $k_i$ gains of the conventional PI controller. Detailed modeling of a VSC is used in the controller design process so as it include inner current control and filter dynamics. The outer fractional voltage controller is designed such that the VSC system satisfies a required phase margin, with improved robustness in the system and capability to attenuate the noise. The overall system stability is analyzed using both bode plot and step response, and these responses are compared with a conventional PI controller. Further, the dynamic performance of the fractional controller is evaluated by simulating the nonlinear system. A hardware prototype is also developed to demonstrate the practical realization of the controller.

This paper proposes the development of silicon carbide junction field effect transistor-based high-frequency link (HFL) modular multilevel converter (MMC). HFL has been used in conjunction with MMC to achieve higher current density with less foot print, which is the main requirement for exhaust gas treatment. The proposed system has been developed for various scalable ranges such as 6, 8 and 10 modules per arm to generate square wave unipolar/bipolar pulse and exponential decay pulses. These pulses have been generated for 1 kV/cm with 100 µs and 1000 µs and tested. The proposed system has been developed mathematically and simulated in Matlab/Simulink. Thus, obtained results have been verified by conducting an experiment on 3 kV prototype and results are explored.

This study proposes a novel wind turbine generator (WTG) imbalance fault classifier using gene expression programming (GEP). Proposed GEP fault classifier is able to achieve very high classification accuracy with relatively small number of samples. Ours is a first attempt at designing a WTG imbalance fault identifier using GEP for fault segregation. The identifier does not assume prior knowledge of WTG model. Raw current data of permanent magnet synchronous generator stator side are processed through empirical mode decomposition to generate 16 intrinsic mode functions or IMFs. Classifier employs the J48 algorithm to further prune these 16 IMFs to eight most relevant input variables which serve as inputs to the GEP imbalance fault classifier. The authors compare performance of the proposed GEP classifier with other contemporary artificial intelligence (AI) based classifiers such as neural networks and support vector machines. Simulation results and performance comparison against other AI approaches elucidate that the proposed GEP-based identifier could serve as an important tool for WTG fault diagnosis.

Droop control has been extensively used by academia and industry for managing frequency fluctuations at microgrid (MG). This controller generates series of reference signals to different distributed energy sources (DES) such as diesel generator, PV, and wind system based on their respective maximum capacities. However, the combination of inertial and noninertial DES leads to frequency fluctuations at MG. Therefore, this paper proposes a novel frequency support strategy by controlling the operation of DES and controllable loads, i.e., electric vehicles (EVs). Moreover, fleet of EVs batteries maximum capacity depends upon constraints, such as state of charge (SoC), random arrival, and departure patterns. Hence, to accomodate EVs’ batteries with droop controller, it has been enhanced with communication links and feedback mechanism and referred as modified droop controller (MDC). This MDC would be now able to dispatch reference signals to both DES and EVs. However, for meeting reference signal given to fleet of EVs, an aggregator (AG) is proposed. AG can alter the charging and discharging rates of EVs’ thereby satisfying EVs’ needs in terms of battery's SoC. Extensive simulations have been done using the proposed scheme on data available from Santa Rita Jails’ MG.

A modified control strategy for enhanced operation for a single phase rooftop PV connected to a single phase utility grid is presented in this paper. The current control is modified such that the quadrature references produced are independent of the grid side harmonics, which aids in the optimal control performance, even in polluted grid conditions. The transformations for the quadrature components are achieved using an inverse park phase locked loop (PLL), which enhances the PV performance, especially in the single phase. To assist the controller actions in the most efficient way, a TS fuzzy control for the DC link voltage regulation is used. Thus an improved performance for the rooftop PV With the proposed control topology is observed over the conventional control. The efficacy of the control scheme is tested in the MATLAB/SIMULINK ® environment.

This paper proposes a vehicle-to-grid (V2G) charging framework has been proposed in this paper owing to the optimal charging/discharging characteristics of the electric vehicles’(EVs) batteries with respect to the change in frequency in the grid. Since battery overcharging deteriorates its lifetime, the proposed charging framework ensures virtual tripping of the vehicles from the plug-in charging terminal once it is fully charged. Additionally, a faster DC bus voltage droop controller has been used for the voltage source converter (VSC) instead of the conventional low bandwidth PI controller to overcome the sluggish response. A negative feedforward signal alongwith the droop controller has been used to achieve DC voltage regulation. On basis of extensive simulation carried out in MATLAB/SIMULINK, the results demonstrate the effective performance of the V2G charging strategy as well as the faster performance of droop controller over PI controller.