Microgrid Research Articles

IoT Systems Based Microgrid: A Review

Smart microgrids, as the foundations of the future smart grid, combine distinct Internet of Things (IoT) designs and technologies for applications that are designed to create, regulate, monitor, and protect the microgrid (MG), particularly as the IoT develops and evolves on a daily basis. A smart MG is a small grid that may operate individually or in tandem with the electric grid, and it is ideal for institutional, commercial, and industrial consumers, as well as urban and rural societies. A MG can operate in two methods (stand-alone and grid-connected), with the ability to transition between modes due to local grid faults, planned maintenance, expansions, deficits and failures in the host system, and other factors. Energy storage is the process of storing and converting energy that can be used for a variety of purposes, including voltage and frequency management, power backup, and cost optimization. IoT is designed to deliver solutions for optimal energy management, security protocols, control methods, and applications in the MG, with numerous distributed energy resources (DER) and interconnected loads. The use of IoT architecture for MG operations and controls is discussed in this research. With the use of power grid equipment and IoT-enabled technology

Resilience enhancement of power distribution system using fixed and mobile emergency generators based on deep reinforcement learning

Growing extreme weather-related events have increased the need for a resilient emergency response in the Power Distribution Systems (PDSs). This paper proposes a novel restoration scheme that involves forming Micro-Grids (MGs) and dispatching Mobile Emergency Generators (MEGs) between them. Due to the PDS variability and extensive decision space, conventional model-based approaches aren't suitable for implementing the proposed scheme. Therefore, a model-free framework is designed to control the restoration process. The proposed problem is formulated as a Markov Decision Process (MDP) considering uncertainties associated with load profile, and renewable resources. Deep Q-Network (DQN), a fundamental algorithm in Deep Reinforcement Learning (DRL), is employed to solve the proposed MDP model. DQN algorithm can learn optimal control strategy by interacting with the simulated environment at the training phase and then deploy the learned policy in a real-world application. The main objective of the designed DQN model is to maximize the restored loads and ensure a high level of post-restoration reliability for Critical Loads (CLs). MATLAB software is considered as the simulated environment to perform power flow calculation and MEGs routing. The efficiency and applicability of the proposed method are evaluated by conducting experiments on the modified IEEE 13-bus, 37-bus, and 123-bus networks.

A Wasserstein distributionally robust model for transmission expansion planning with renewable‐based microgrid penetration

AbstractThis article introduces a Wasserstein distance‐based distributionally robust optimization model to address the transmission expansion planning considering renewable‐based microgrids (MGs) under the impact of uncertainties. The primary objective of the presented methodology is to devise a robust expansion strategy that accounts for both long‐term uncertainty and short‐term variability over the planning horizon from the perspective of a central planner. In this framework, the central planner fosters the construction of appropriate transmission lines and the deployment of optimal MG‐based generating units among profit‐driven private investors. The Wasserstein distance uncertainty set is used to characterize the long‐term uncertainty associated with future load demand. Short‐term uncertainties, stemming from variations in load demands and production levels of stochastic units, are modeled through operating conditions. To ensure the tractability of the proposed planning model, the authors introduce a decomposition framework embedded with a modified application of Bender's method. To validate the efficiency and highlight the potential benefits of the proposed expansion planning methodology, two case studies based on simplified IEEE 6‐bus and IEEE 118‐bus systems are included. These case studies assess the effectiveness of the presented approach, its ability to navigate uncertainties, and its capacity to effectively optimize expansion decisions.

Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid

AbstractThis study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

An Adaptive Secondary Control Based on Recursive Neural Networks for Isolated Microgrids

This work proposes a model reference adaptive control based on recursive neural networks. This secondary-level controller corrects the deviations on the voltage and frequency setpoints of a simple primary control in an isolated microgrid (MG) with multiple distributed generators (DGs). The controller has two nonlinear autoregressive with external input neural networks to emulate the microgrid and guide the whole system according to an appropriate reference model. The networks are trained with synthetic data from a simulation MG and the order of the networks are obtained with a deterministic method. The method is tested with a three-units MG in a Matlab simulation under different working conditions. Results show the proper performance of the proposed controllers in comparison to a PI strategy.

PD distributed control and stability analysis for DC microgrids with multiple time delays

Distributed control is widely used for coordination of multiple distributed generations (DGs) in a microgrid (MG), but the information transmission in practice is inevitably suffered from time delays, which will deteriorate system performance and even cause system instability. To solve this problem, a proportional-differential (PD) distributed control method for DC MGs is proposed in this paper. Firstly, a small-signal system model considering multiple time delays including measurement delay and communication delay is established. The introduction of time-delayed differential terms results in the system being transformed into a neutral time-delayed system (NTDS). Then, the time delay margin (TDM) under different delayed scenarios is accurately determined by the frequency-domain stability analysis, and the relationship between the TDM and the differential gain is quantified. Compared with the conventional proportional (P-type) distributed control method, the TDM is enhanced effectively. In addition, an indicator is proposed to quantify the enhancement effect of introducing differential terms on TDM. Finally, the effectiveness of the proposed method and the accuracy of the stability analysis are verified by simulation and hardware-in-loop (HIL) tests.

Coordination in islanded microgrids: Integration of distributed generation, energy storage system, and load shedding using a new decentralized control architecture

For an islanded microgrid (MG) to work reliably, it is essential to manage the control of distributed energy resources, including generation and storage units, as well as loads, in a coordinated manner. In islanded microgrids, the safe energy storage limits must be accounted for coordination to avoid rapid damage or degradation to the storage units. In this paper, a novel control method is introduced to coordinate distributed generation (DG) and energy storage systems (ESS) in an islanded MG to enhance penetration and complete exploitation of DGs and ESSs and maintain balanced state of charge (SoC). A decentralized modified droop function (MDF) is suggested to share power and to control voltage and frequency without communication link. This controller uses virtual impedance to separate active and reactive power in the primary level control loop. Moreover, based on the ESS's charge state and its control as a frequency control support system, the proposed controller enables DGs to operate in both voltage and frequency control mode (VFCM) and active power control mode (APCM) to avoid overcharging. Likewise, to prevent from over discharging and maintain the frequency within permissible range, a new load shedding strategy is implemented to minimize the cycles of load connection and disconnection. The validation and effectiveness of the proposed control system are demonstrated by simulation results.

Resiliency planning of distribution network using of active distribution network partitioning

Today, micro-grids (MGs) include all kinds of energy storage systems (ESSs), wind turbines (WTs), photovoltaic (PV), combined heat and power (CHP), etc., also demand response are active on the demand side. In this paper, single-level robust methods for partitioning and planning the active distribution network (ADN) into several MGs are presented. According to the desired purpose, the objective function of the model is investment costs minimization for installing the capacity of distributed generations (DGs) and switches, the activity of responsive loads based on the forecast of the generation of non- DG, losses and the risk of the load points of the costumers. On the other hand, maximizing the income from the MGs energy sales to the upstream grid and the technical constraints include optimal power flow (OPF) equations. The mentioned problem is a complex nonlinear model, and therefore, the improved genetic algorithm (GA) is used. In order to validate the efficiency, the improved method has been used on a 25-bus ADN including five switches. The simulation results obtained from the case studies prove the fact that the use of the retrofitted model increases the investment costs of the MG, especially in the case of an island operation, in contrast to the active presence of responsive loads that significantly reduce costs.

Comparative harmonic elimination techniques for supraharmonic reduction in microgrid

In order to reduce voltage distortion and supraharmonic (SH) emission in microgrid (MG) systems with electric vehicle (EV) charging stations, this research compares several harmonic elimination approaches. The increasing deployment of EVs has led to the integration of EV charging stations within MG systems, presents challenges in maintaining a high power quality (PQ). Voltage distortions and SH emissions are caused due to non-linear loads and the intermittent nature of EV charging, which have an effect on the performance and dependability of the MG. In order to solve these problems, multilevel converters (MLCs) are used to produce high-quality waveforms. MLCs use harmonic elimination methods to cut down on SH emissions, which improves the PQ overall. Sinusoidal pulse width modulation (PWM), selective harmonic elimination (SHE), space vector modulation (SVM), and random-PWM (RPWM) techniques are among the harmonic elimination methods compared and analyzed. The results will enable the selection of the most appropriate strategy for minimizing voltage distortion and SH emission in MG systems, while providing valuable insights into the effectiveness of each method.

Review of energy management systems and optimization methods for hydrogen‐based hybrid building microgrids

AbstractRenewable energy‐based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long‐term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen‐based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting‐edge methodologies, such as machine learning‐based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy (EMS) methodologies for hydrogen‐based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

Optimal planning and partitioning of multiple distribution Micro-Grids based on reliability evaluation.

Power system researcher have turned to Micro-Grids (MG) for higher reliability, greater flexibility, lower operating costs and losses, and lower CO2 emissions at the distribution system. This paper presents a single-level stochastic optimization framework for planning and partitioning of a distribution system including Multiple Micro-Grids (MMGs). The main objective is to minimize the total cost of the system including investment, operation, total losses and reliability costs of the distribution network. The proposed model takes into account the viewpoints of MG owners and distribution system operators, simultaneously. The voltage stability index is introduced to identify the optimal site of MG investment. To deal with uncertainties caused by renewable generations, the Firefly Algorithm (FA) and probability-tree method is used to create various operation scenarios of Photo-Voltaic (PVs) and Wind Turbines (WTs). This model is solved through the genetic algorithm in MATLAB, and to evaluate its effectiveness, numerical studies have been carried out on the experimental IEEE distribution network with four specified locations for investing MGs and seven Tie Switches (TS) for network partitioning. Simulation results reveal that optimal locations for MG investment are determined in such a way all MGs connect to the buses near the beginning of the feeder and as a result, load point reliability is improved, total active power losses are reduced, and the energy program becomes more optimized.

Distributed predefined-time secondary control under directed networks for DC microgrids

DC microgrids (MGs) have gained significant attention due to their ability to complement renewable energy generation. In order to improve the operational stability of DC MGs, a predefined-time-based secondary controller is proposed to restore voltage drops caused by droop control and achieve current allocation among agents according to expected ratios. To conserve communication resources, the communication topology is established as a directed network. Each distributed generator (DG) receives voltage and current information from neighboring devices and uses its own current and voltage regulators to achieve control objectives. Compared to existing controllers, the convergence time upper bound of the proposed approach is independent of the initial value and can be directly adjusted as a parameter. The stability of the proposed control strategy is then verified using Lyapunov theory. Finally, hardware-in-loop test results are conducted to validate the performance of the control strategy, and its superiority is demonstrated through comparison with existing control methods.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.

A Comprehensive Review Based on the Game Theory with Energy Management and Trading

This paper reviews the use of game theory tools to study the operation and design of modern power grids. The contribution of this work is to summarize the literature to highlight the versatile solution capability of game theory by focusing on the interconnected objectives of energy trading and energy management. This review was conducted with a focus on various applications in energy systems, including general energy markets, micro grids (MGs), virtual power plants (VPP), electric vehicles (EVs), and smart homes, and explores how game theory can summarize the solutions for pricing, bidding, demand side management, and resource optimization. A key finding is the suitability of game theory for modeling decentralized energy systems where strategic incentives can lead to outcomes that benefit both individuals and society. It also discusses the limitations, challenges, and potential benefits of game theory in complex power systems. This study provides researchers and policy makers with a comprehensive overview of current research and insights into the potential of game theory to shape the future of energy systems.

Ethereum blockchain-based hierarchical cooperative power sharing in a group of microgrids

ABSTRACT Cooperative power-sharing (CPS) is one of the cutting-edge power management strategies the power system planners contemplate transforming from its traditional structure to a more decentralized framework as the globe prepares for a future with low carbon emissions. Cooperative microgrids are considered one of the future power system configurations where individual microgrids (MGs) can support each other through CPS. This paper introduces a decentralized energy market (DEM) to operate CPS hierarchically between MGs, groups of MGs, and the utility grid. The security of power-sharing transactions and the privacy of MGs pose challenges in terms of confidentiality and data integrity. To deal with these issues, a DEM framework based on distributed ledger technology (DLT) is proposed to ensure power transactions between cooperative MGs with a guarantee. A scheme is built on the Ethereum testnet to estimate the performance and validation of the proposed smart contracts. A case study with actual data for a group of four MGs is presented to validate the effectiveness of the proposed framework. The proposed blockchain approach results in money transactions resulting in MG1 receiving $485; while MG2 gets $525, MG3 and MG4 generates a revenue of $435 and $362.5 respectively. The numerical results demonstrate that MGs can actively share power with peers and achieve economic benefits.

Honey Badger Algorithm Based DVR Controllers for Improved Power Quality‏ in a Microgrid Combined of (Wind System\ Grid\Nonlinear Load)

Power quality (PQ) is crucial in today's energy supply networks, where even little voltage fluctuations can have a big impact on how well household appliances and technologies operate. The suggested dynamic voltage regulator (DVR) approach helps to create a new generation power grid that is more dependable and effective. In this study, the honey badger optimizer (HBO) is used to optimize the controllers of the DVR for improving PQ via voltage control. The efficacy of the optimized DVR is further increased by its integration with a microgrid (MG) wind supply. The suggested technique makes use of a low-complexity control approach for voltage regulation to adjust for harmonic distortion, swells, and voltage dips in the addressed system. The technique accomplishes voltage improvement, bus stabilization, energy-efficient utilization, and harmonic distortion reduction by using the DVR in conjunction with an MG wind supply. Various voltage disturbances, such as balanced and unbalanced swell and sag, voltage imbalance, notching, various fault states, and power system harmonic distortion, are taken into consideration to show the approach's usefulness. The findings indicate PQ enhancement, demonstrating that the load voltage roughly matches the nominal value.

Elimination of power and frequency oscillations for AC microgrid with parallel virtual synchronous generator and synchronous generator

The integration of conventional synchronous generators (SGs) and virtual synchronous generators (VSGs) in microgrids (MGs) is increasingly common due to the growth of renewable energies. However, demand fluctuations and grid faults can pose significant challenges to the stability of the MG, causing system frequency and active power oscillations. This study proposes control approaches for an isolated/islanded AC MG that consists of an VSG and an SG to mitigate power and frequency oscillations. The proposed methods utilize the VSG’s adjustable damping coefficient, which is determined by intelligent controls. The proposed strategies significantly reduce power fluctuations by 53% and frequency deviations by 75%, thereby improving system stability and reliability. The suggested controllers use short-term large damping to effectively amplify the system’s AC frequency dynamic response and enhance power delivery across different power-sharing modes. The controllers do not require additional communication infrastructure and rely solely on the local AC frequency for feedback. Furthermore, a novel synchronization mechanism with positive effects on system stability is presented.

Coordination of medium-voltage distribution networks and microgrids based on an aggregate flexibility region approach

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Frequency control using fuzzy active disturbance rejection control and machine learning in a two‐area microgrid under cyberattacks

AbstractThere is a change in the traditional power system structure as a result of the increased incorporation of microgrids (MGs) into the grid. Multi‐area MGs will emerge as a result, and issues related to them will need to be addressed. Load frequency control (LFC) is a challenge in such structures, which are more complicated due to variations in demand and the stochastic characteristics of renewable energy sources. This paper presents a cascade fuzzy active disturbance rejection control technique to deal with the LFC problem. In order to tune different parameters of controllers, a newly developed heuristic algorithm called the Gazelle optimization algorithm (GOA) is also employed. Moreover, due to the fact that multi‐area MGs are regarded as cyber‐physical systems (CPSs), a relatively new concern for LFC problems is their resilience to cyberattacks such as false data injection (FDI) and denial of service (DoS) attacks. Therefore, this research also presents a novel machine learning approach called parallel attack resilience detection system (PARDS) to deal with the LFC problem in the presence of cyberattacks. The efficiency of the proposed strategy is investigated under different scenarios, such as non‐linearities in the power system or server cyberattacks.

Frequency stabilization in microgrid with PV system based on maximum power extraction: A sliding mode approach

The article addresses the critical issue of frequency regulation in microgrids (MG) integrated with solar photovoltaic (PV) systems, which rely on maximum power point tracking (MPPT) using sliding mode control (SMC). The frequency stability of MGs is a significant concern due to the inherent variability of renewable energy sources, making frequency regulation challenging. This research proposes a dual-objective control strategy. Firstly, it ensures the solar PV system maximizes power extraction via a buck-boost converter controlled by SMC. Simultaneously, it maintains frequency regulation in the MG under load disturbances using SMC. The SMC technique guarantees precise convergence to the maximum power operating point and minimizes frequency deviations in the MG. The proposed solution demonstrates effective maximum power extraction under varying irradiance levels and reliable frequency regulation despite system parameter uncertainties and nonlinearities such as generation rate constraints (GRC) and governor deadband (GDB). Additionally, the SMC-based design achieves faster convergence compared to traditional proportional-integral (PI) controllers optimized using intelligent techniques. Stability analysis, performed using Bode and Nyquist plots, further confirms the robustness of the proposed system. The model was developed and tested in the MATLAB Simulink environment, showcasing its efficacy and reliability.