This paper defines an agile power system as a system in which the physical structure can change due to the connection/disconnection of subsystems, such as microgrids. These systems pose serious difficulties in their operations and control. Some of these challenges are reviewed. One operational issue is the energy balancing or the load frequency control problem when microgrids with a low inertia are connected to the main grid. The paper identifies the need for new rules of operation. Furthermore, in this paper, decentralized and data-driven voltage control designs are proposed, compared and illustrated on a microgrid. They take into consideration the structure and the usage of the large amount of data provided by the proliferation of sensors.

Metal oxide surge arresters (MOSAs) are a popular solution for dealing with overvoltages due to lightning and switching in power distribution networks. As a result, a MOSA’s performance and longevity have a significant impact on the quality of energy and the frequency of outages. A MOSA performance is determined by several elements such as leakage current, partial discharge, and thermal image measured in various ways. In this study, different techniques for diagnostic and condition monitoring of MOSAs are discussed, and each method’s advantages and disadvantages are investigated. Additionally, the results of practical tests on two 20 kV healthy and degraded MOSAs are investigated and compared.

We present a data prepossessing method for parameter identification based on clustering and hypothesis testing in a power distribution network to successfully achieve a more accurate result. This method considers the similarities of data in both spatial relationship and statistical theory, then builds a sophisticated data processing method to improve the performance of dynamic model-based parameter identification models, i.e., Markov chain Monte Carlo and sequential model-based global optimization. We applied this data processing method to the actual feeder data with no adjustment of the other condition. The experiment shows that our method achieves a 4.8% improvement in accuracy at most.

Step-up converters are widely used in many applications, such as renewable energy generation with photovoltaic panels and fuel cell stacks. In many cases, the required voltage gain is larger for those applications than a traditional boost converter can achieve. Several large-voltage gain converters have been recently studied. This paper introduces a converter topology in which the voltage gain is larger than a traditional boost converter. The main advantages of the proposed topology are: (i) it provides a large voltage gain without the use of an extreme duty cycle; (ii) its capacitors require a smaller voltage to be sustained compared with other, similar state-of-the-art converters; (iii) the voltage among the ground input and output is not pulsating; and (iv) it can be synthesized with commercial, off-the-shelf half-bridge packed transistors. The proposed converter can be employed in different applications, such as distributed generation and microgrids. This paper presents the steady-... [more]

In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation of the electricity industry has produced a demand for wholesale organized marketplaces. Price predictions, which are primarily meant to establish the market clearing price, have become a significant factor to an energy company’s decision making and strategic development. Recently, the fast development of deep learning algorithms, as well as the deployment of front-end metaheuristic optimization approaches, have resulted in the efficient development of enhanced prediction models that are used for electricity price forecasting. In this paper, the development of six highly accurate, robust and optimized data-driven forecasting models in conjunction with an optimized Variational Mode Decomposition method and the K-Means clustering algorithm for... [more]

The current article analyzes the impact of charging electric vehicles and battery energy storage systems on the photovoltaic hosting capacity of low-voltage distribution networks. A Monte Carlo-based simulation is used to analyze predominantly rural, intermediate and predominantly urban residential regions facing different penetrations of electric vehicles utilizing uncontrolled and controlled charging, and evaluate their impact on photovoltaic hosting capacity. Subsequently, electric vehicles are replaced or supplemented by residential battery energy storage systems, and their combined impact on the hosting capacity is studied. The results revealed that electric vehicles solely do not improve the hosting capacity unless they are connected to the network during sunshine hours. However, controlled storage provides a remarkable increase to the hosting capacity and exceptional contribution in combination with electric vehicles and customers with high loads. Finally, a feasibility analysis... [more]

This paper proposes a buffered microgrid with a modular grid interface consisting of a modular back-to-back converter. The proposed method provides a flexible strategy that enables both the load and generation expansion of the microgrid, with no sizing constraints on the initial stage. The method maintains the physical separation of the buffered microgrid from the grid by using back-to-back converters, which ensures a safe, secure and seamless operation in both islanded and grid-connected operation modes. The proposed modular structure allows an energy exchange prioritization either between the energy storage systems and the grid or between the energy storage units themselves, depending on the recommended/desired operational strategy. The prioritizations are achieved by using sets of dead zones in the control of the interfacing converters. In order to control the voltage and frequency, an inverse-droop-based dq-frame current control method was implemented in PSCAD/EMTDC to substantiate... [more]

Microgrids are becoming popular nowadays because they provide clean, efficient, and lowcost energy. Microgrids require bulk storage capacity to use the stored energy in times of emergency or peak loads. Since microgrids are the future of renewable energy, the energy storage technology employed should be optimized to provide power balancing. Batteries play a variety of essential roles in daily life. They are used at peak hours and during a time of emergency. There are different types of batteries i.e., lithium-ion batteries, lead-acid batteries, etc. Optimal battery sizing of microgrids is a challenging problem that limits modern technologies such as electric vehicles, etc. Therefore, it is imperative to assess the optimal size of a battery for a particular system or microgrid according to its requirements. The optimal size of a battery can be assessed based on the different battery features such as battery life, battery throughput, battery autonomy, etc. In this work, the mixed-integer... [more]

The advent of renewable energy sources (RESs) in the power industry has revolutionized the management of these systems due to the necessity of controlling their stochastic nature. Deploying RESs in the microgrid (MG) as a subset of the utility grid is a beneficial way to achieve their countless merits in addition to controlling their random nature. Since a MG contains elements with different characteristics, its management requires multiple applications, such as demand response (DR), outage management, energy management, etc. The MG management can be optimized using machine learning (ML) techniques applied to the applications. This objective first calls for the microgrid management system (MGMS)’s required application recognition and then the optimization of interactions among the applications. Hence, this paper highlights significant research on applying ML techniques in the MGMS according to optimization function requirements. The relevant studies have been classified based on their... [more]

The European Union’s energy and climate policies are geared on reducing carbon dioxide emissions and advancing sustainable energy, focusing on a faster propagation of renewable energy sources to decarbonize the energy sector. The management of locally produced energy, which can be implemented by a microgrid capable of either being linked to the main grid or operating independently, is equally crucial. Additionally, it seems that electricity storage is the only practical way to manage energy effectively within a microgrid. Energy storage is hence one of the main technological parameters upon which future energy management has to be based. Especially during crisis periods (such as the COVID-19 pandemic or the ongoing energy crisis), storage is a valuable tool to optimize energy management, particularly from renewables, in order to successfully cover demand fluctuation, hence achieving resilience, while at the same time reducing overall energy costs. The purpose of the paper is to analyze... [more]

Superconducting magnetic energy storage (SMES) systems are characterized by their high-power density; they are integrated into high-energy density storage systems, such as batteries, to produce hybrid energy storage systems (HESSs), resulting in the increased performance of renewable energy sources (RESs). Incorporating RESs and HESS into a DC bus to obtain a DC microgrid concept is considered a more attractive option for solving system reliability and stability issues. In this paper, we focus on employing SMES systems based on their characteristics of a fast response and high efficiency, which contribute to increased battery lifespan, and reducing the mismatch between the energy supplied by the renewable energy source and the load demand by compensating for the effects of partial shading on the PV system on the DC bus side and the imbalance in loads on the other side of the AC bus. The incremental conductance algorithm (INC) is used for maximum power point tracking (MPPT) in the PV sy... [more]

Microgrids (MG) are small-scale electric grids with local voltage control and power management systems to facilitate the high penetration and grid integration of renewable energy resources (RES). The distributed generation units (DGs), including RESs, are connected to (micro) grids through power electronics-based inverters. Therefore, new paradigms are required for voltage and frequency regulation by inverter-interfaced DGs (IIDGs). Notably, employing effective voltage and frequency regulation methods for establishing power-sharing among parallel inverters in MGs is the most critical issue. This paper provides a comprehensive study, comparison, and classification of control methods including communication-based, decentralized, and construction and compensation control techniques. The development of inverter-dominated MGs has caused limitations in employing classical control techniques due to their defective performance in handling non-linear models of IIDGs. To this end, this article r... [more]

Different control strategies for microgrid applications have been developed in the last decade. In order to enhance flexibility, scalability and reliability, special attention has been given to control organisations based on distributed communication infrastructures. Among these strategies, the implementation of consensus protocol stands out to cooperatively steer multi-agent systems (i.e., distributed generators), which is justified by its benefits, such as plug and play capability and enhanced resilience against communication failures. However, as the consensus protocol has a long trajectory of development in different areas of knowledge including multidisciplinary subjects, it may be a challenge to collect all the relevant information for its application in an emerging field. Therefore, the main goal of this paper is to provide the fundamentals of multi-agent systems and consensus protocol to the electrical engineering community, and an overview of its application to control systems... [more]

This paper proposes a Data-Driven (DD) framework for the real-time monitoring, detection, and mitigation of False Data Injection (FDI) attacks in DC Microgrids (DCMGs). A supervised algorithm is adopted in this framework to continuously estimate the output voltage and current for all Distributed Generators (DGs) with acceptable accuracy. Accordingly, among the various evaluated supervised DD algorithms, Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are utilized because of their low computational burden, efficiency in operation, and simplicity in design and implementation in a distributed control system. The proposed framework is based on the residual analysis of the generated error signal between the estimated and actual sensed signals. The proposed framework detects and mitigates the cyber-attack depending on trends in generated error signals. Moreover, by applying Online Change Point Detection (OCPD), the need for a static user-defined threshold for the residual analysis of the gen... [more]

A record amount of renewable energy has been added to global electricity generation in recent years. Among the renewable energy sources, solar photovoltaic (PV) is the most popular energy source integrated into low voltage distribution networks. However, the voltage limits and current-carrying capacity of the conductors become a barrier to maximizing the PV-hosting capacity in low voltage distribution networks. This paper presents an optimization approach to maximize the PV-hosting capacity in order to fully utilize the existing low voltage distribution network assets. To achieve the maximum PV-hosting capacity of the network, a novel method based on the dynamic line rating of the low voltage distribution network, the coordinated operation of voltage control methods and the PV re-phasing technique was introduced and validated using a case study. The results show that the proposed methodology can enhance the PV-hosting capacity by 53.5% when compared to existing practices.

The rising demand for electricity, economic benefits, and environmental pressures related to the use of fossil fuels are driving electricity generation mostly from renewable energy sources. One of the main challenges in renewable energy generation is uncertainty involved in forecasting because of the intermittent nature of renewable sources. The demand also varies according to the time of day, the season, the location, the climate, and the availability of resources. Microgrids offer a potential solution for the integration of small-scale renewable energy sources and loads along with energy storage systems and other non-renewable sources. However, intermittent generation and varying demand need to be matched to provide stable power to consumers. Therefore, it is crucial to design an energy management system to effectively manage the energy sources and supply loads for reliable and efficient operation. This paper reviews different techniques proposed in the literature to achieve the obje... [more]

Microgrids are defined as a cluster of loads, distributed energy resources, and storage devices, and they are receiving worldwide attention due to the increasing rate of consumption of nuclear and fossil fuels and the community demand for reducing pollutant emission in electricity generation fields [...]

Accurate electricity price forecasting (EPF) can provide a necessary basis for market decision making by power market participants to reduce the operating cost of the power system and ensure the system’s stable operation. To address the characteristics of high frequency, strong nonlinearity, and high volatility of electricity prices, this paper proposes a short-term electricity price forecasting model based on a two-layer variational modal decomposition (VMD) technique, using the sparrow search algorithm (SSA) to optimize the long and short-term memory network (LSTM). The original electricity price sequence is decomposed into multiple modal components using VMD. Then, each piece is predicted separately using an SSA-optimized LSTM. For the element with the worst prediction accuracy, IMF-worst is decomposed for a second time using VMD to explore the price characteristics further. Finally, the prediction results of each modal component are reconstructed to obtain the final prediction resu... [more]

Supplied with electricity and natural gas, local energy systems (LESs) with gas-fired generations increase the operational flexibility of urban energy supply. However, the increasing usage of these LESs may lead to adverse impacts on the urban energy system supply via power and/or gas distribution networks. Dynamic interactions between the LESs, electricity, and gas networks subject to different disturbances need to be investigated due to the complexity of the problem. To address this issue, this paper first presents the topology and operating mode of the LESs as well as the relationship with power and gas networks. Second, an extended microturbine model is developed to reflect the nonlinear dynamic propagation of disturbances between the two networks. A general model of the interconnected LESs is developed to analyze the mutual impacts between gas and electricity networks under different modes. Finally, an iterative method is proposed to simulate the mechanism of disturbance propagati... [more]

High penetration of renewable energy sources into isolated microgrids (µGs) is considered a critical challenge, as µGs’ operation at low inertia results in frequency stability problems. To solve this challenge, virtual inertia control based on an energy storage system is applied to enhance the inertia and damping properties of the µG. On the other hand, utilization of a phase-locked loop (PLL) is indispensable for measuring system frequency; however, its dynamics, such as measurement delay and noise generation, cause extra deterioration of frequency stability. In this paper, to improve µG frequency stability and minimize the impact of PLL dynamics, a new optimal frequency control technique is proposed. A whale optimization algorithm is used to enhance the virtual inertia control loop by optimizing the parameters of the virtual inertia controller with consideration of PLL dynamics and the uncertainties of system inertia. The proposed controller has been validated through comparisons wit... [more]

The conventional volt-VAR control (VVC) in distribution systems has limitations in solving the overvoltage problem caused by massive solar photovoltaic (PV) deployment. As an alternative method, VVC using solar PV smart inverters (PVSIs) has come into the limelight, which can respond quickly and effectively to solve the overvoltage problem by absorbing reactive power. However, the network power loss, that is, the sum of line losses in the distribution network, increases with reactive power. Dynamic distribution network reconfiguration (DNR), which hourly controls the network topology by controlling sectionalizing and tie switches, can also solve the overvoltage problem and reduce network loss by changing the power flow in the network. In this study, to improve the voltage profile and minimize the network power loss, we propose a control scheme that integrates the dynamic DNR with volt-VAR control of PVSIs. The proposed control scheme is practically usable for three reasons: Primarily,... [more]

With the large-scale integration of distributed energy resources (DER) into passive distribution networks with voltages of up to 1 kV, these networks are being converted into microgrids. When the topology and operating conditions change, several challenging issues arise related to the functioning of the protection devices (PD) that are in operation. Most DERs, including renewable generators, are integrated into microgrids by means of inverters. In the event of short circuits (SC) in microgrids, these DERs provide a fault current contribution of no more than 1.2−2.0 Irated at the fault location. This makes it difficult to identify the fault location and to carry out the selective disconnection of the faulty element by means of conventional PDs. This article provides an overview of engineering solutions for improving conventional protection schemes that have been historically used in passive distribution networks, as well as for creating modern protection schemes based on innovative prin... [more]

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. Fo... [more]

This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming... [more]

The grid-connected operation of charging/discharging stations changes the original load, power supply, and network structures of the distribution network. It also affects the power flow level and direction and leads to a reduction in the sensitivity and reliability of the relay protection system. The grid connections may even lead to improper operation or failure of the relay protection protocols. In order to solve the above problems, this paper proposes a method that constructs the protection criteria by using the characteristics of the positive sequence comprehensive impedance after internal and external faults. Specifically, based on the difference in amplitude of the differential positive sequence impedance in the feeder after the charging/discharging station is connected to the grid, positive sequence comprehensive impedance is proposed for the purpose of establishing longitudinal differential protection. The results show that the vertical protection principle based on the positiv... [more]