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]

With a proliferation of diverse types of renewable distributed generation (DG) into the distribution network, an equivalent model of an active distribution network (ADN) is extremely important, since the detailed modeling of the whole ADN is much more complex and time consuming. However, different studies developed different model structures of ADNs, which are difficult to be applied in a power system simulation. At the same time, the DG’s low voltage ride through the (LVRT) control was not considered in the existing ADN model, which may lead to a large modelling error. In this paper, a general equivalent model is developed for the ADN with a significant amount of DGs, based on a two-step modelling method. Step one, motivated by the dynamic similarities between the doubly-fed induction generator (DFIG)-based wind turbines, direct drive permanent magnet synchronous generator (DDPMSG)-based wind turbines, and photovoltaic (PV) generation, a general model structure of a renewable DG is in... [more]

In an electrical microgrid, distributed renewable generation is one of the main tools used to achieve energy sustainability, cost efficiency and autonomy from the grid. However, reliance on intermittent power sources will lead to a mismatch between generation and demand, causing problems for microgrid management. Flexibility is key to reducing the mismatch and providing a stable operation. In such a context, demand response and energy storage systems are the main factors that contribute to flexibility in a microgrid. This paper provides an assessment of the technical and economic impacts of a microgrid at the building level, considering photovoltaic generation, battery energy storage and the use of electric vehicles in a vehicle-to-building system. The main novel contributions of this work are the quantification of system efficiencies and the provision of insights into the design and implementation of microgrids using real on-site data. Several tests were conducted using real on-site d... [more]

This paper presents a power distribution system that prioritizes the reliability of power to critical loads within a community. The proposed system utilizes reinforcement learning methods (Q-learning) to train multi-port power electronic interface (MPEI) systems within a community of microgrids. The primary contributions of this article are to present a system where Q-learning is successfully integrated with MPEI to reduce the impact of power contingencies on critical loads and to explore the effectiveness of the subsequent system. The feasibility of the proposed method has been proven through simulation and experiments. It has been demonstrated that the proposed method can effectively improve the reliability of the local power system—for a case study where 20% of the total loads are classified as critical loads, the system average interruption duration index (SAIDI) has been improved by 75% compared to traditional microgrids with no load schedule.

In autumn 2021, the world faced the first round of energy crisis [...]

With the growing need for decarbonization, the future gas demand will decrease and the necessity of a gas distribution network is at stake. A remaining industrial gas demand on the distribution network level could lead to industry becoming the main gas consumer supplied by the gas distribution network, leading to the question: can industry keep the gas distribution network alive? To answer this research question, a three-stage analysis was conducted, starting from a rough estimate of average gas demand per production site and then increasing the level of detail. This paper shows that about one third of the German industry sites investigated are currently supplied by the gas distribution network. While the steel industry offers new opportunities, the food and tobacco industry alone cannot sustain the gas distribution network by itself.

The reliability of energy supply is an important factor for end-users of electricity. Although many advances and efforts have been made by distribution companies to guarantee energy quality, weak feeders and grids are still usually found. As an alternative to minimize such problems, Battery Energy Storage Systems (BESSs) can be used to supply energy to users in the case of power outages or major energy quality problems. This paper presents test results on a real application scenario in a microgrid with different load configurations in the moment of interruption. The tests were compared to each other to analyze the impact found in each scenario. In addition to those, real unpremeditated cases of power quality problems were also discussed, and the performance of the utilized BESS was evaluated.

Nowadays, the high penetration of automation on smart grids challenges electricity companies in providing an efficient distribution networks operation. In this sense, distribution system reconfiguration (DSR) plays an important role since it may help solve real-time problems. This paper proposes a methodology to solve the DSR problem using artificial immune systems (AIS) based on a new, efficient, and robust approach. This new methodology, called Enhanced Artificial Immune Systems (EAIS), uses the values of the currents in wires for intelligent mutations. The problem is accomplished by a multi-objective optimization with fuzzy variables, minimizing power losses, voltage deviation, and feeders load balancing. A comparison with other DSR solution methods is presented. The method is compared with two other previously proposed methods with the help of the 33-bus, 84-bus, and 136-bus distribution systems. Different scenarios are analyzed, including the optimal location of the Distributed Ge... [more]

The wide use of communication layers in DC microgrids to transmit voltage and current measurements of each distributed generator unit (DGU) increases the possibility of exposure to cyber-attacks. Cyber-attackers can manipulate the measured data to distort the control system of microgrids, which may lead to a shutdown. This paper proposes distributed mitigation layers for the false data injection attacks (FDIA) on voltages and currents of DGUs in meshed DC microgrids. The proposed control strategy is based on integrating two layers for cyber-attack detection and mitigation to immune the primary and the secondary control loops of each DGU. The first layer is assigned to mitigate FDIAs on the voltage measurements needed for the voltage regulation task of the primary control loop. The second layer is devoted to the mitigation of FDIAs on the DGU current measurements, which are crucial for the secondary control level to guarantee the proper current sharing of each DGU. Artificial neural net... [more]

This paper presents an algorithm for solving the power flow (PF) problem of droop-regulated AC microgrids (DRACMs) operating in isolated mode. These systems are based on radial distribution networks without having a slack bus to facilitate conventional computations. Moreover, distributed generation units have to distribute the power and voltage regulation among themselves as a function of operating frequency and voltage droop rather than having a slack bus that regulates voltage and power demands. Based on the conventional backward/forward sweep algorithm (BFS), the proposed method is a derivative-free PF algorithm. To manage the absence of a slack bus in the system, the BFS algorithm introduces new loops, equations, and self-adaptation procedures to its computation procedures. A comparison is presented between the proposed BFS algorithm and other state-of-the-art PF algorithms, as well as PSCAD/EMTDC. In comparison to existing algorithms, the proposed approach is fast, simple, accurat... [more]