Operation Of Microgrid Research Articles

Power Sharing at Rooftop Solar PV System Based Community Microgrid Using Helioscope Software

Due to increase in use of rooftop solar PV system and increase of electricity price, the power sharing among the prosumers of community microgrid become an interesting research area. This study proposes a power sharing mechanism that captures the interaction within a community microgrid. The efficient management of energy sharing is crucial for the efficient operation of community microgrids. To design rooftop solar PV systems, Helioscope software is employed. One of the key features of Helioscope is its ability to efficiently arrange arrays and blocks of solar panels based on the designated location within the software. The power sharing is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a load demand and PV generation mechanism is designed to deal with the uncertainty of PV energy and load consumption.

A Decentralized Energy Management System Inculcating Supply–Demand Ratio (SDR) and Miners’ Stake Through Blockchain Smart Contract for Community Microgrid Operation

A Decentralized Energy Management System Inculcating Supply–Demand Ratio (SDR) and Miners’ Stake Through Blockchain Smart Contract for Community Microgrid Operation

Ant-Lion Optimization Algorithm Based Optimal Performance of Micro Grids

Ant-Lion Optimization Algorithm Based Optimal Performance of Micro Grids

A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids

DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However, the reliable operation of DC microgrids relies on the early detection and location of faults to ensure an uninterrupted power supply. This paper aims to develop fast and reliable fault detection and location mechanisms for DC microgrids, thereby enhancing operational efficiency, minimizing environmental impact, and contributing to resource conservation and sustainability goals. The fault detection method is based on compressed sensing (CS) and Regression Tree (RT) techniques. Besides, an accurate fault location method using the feature matrix and long short-term memory (LSTM) model combination has been provided. To implement the proposed fault detection and location method, a DC microgrid equipped with photovoltaic (PV) panels, the vehicle-to-grid (V2G) charging station, and a hybrid energy storage system (ESS) are used. The simulation results represent the proposed methods’ superiority over the recent studies. The fault occurrence in the studied DC microgrid is detected in 1 ms, and the proposed fault location method locates the fault with an accuracy of more than 93%. The presented techniques enhance DC microgrid reliability while conserving renewable resources, vital to promoting a greener and more sustainable power grid.

A ground fault location algorithm in double‐circuit transmission lines with T‐off connection to an industrial microgrid by using current and voltage phasors information of a single terminal

AbstractThe paper presents a ground fault location algorithm (GFLA) in double‐circuit transmission lines (DCTL) with a T‐off connection to an industrial microgrid (IMG) containing electric arc furnaces (EAF), renewable distributed generations (RDG), and static VAR compensator (SVC). The design presented utilizes the information on the current and voltage phasors of one side of the transmission lines (TL) during the fault period, and there is no need for the availability of information on the type of faulty phase(s) and even the faulty section. To find the exact location of the fault in this design, the voltage phasor equations governing the fault point in positive, negative, and zero‐sequence (PNZS) domains, the zero‐sequence current (ZSC) equation of the fault point, and the boundary condition equation of the fault point have been used. The simulation system is implemented in Simulink/MATLAB using accurate modelling of all network components. The algorithm has been tested and evaluated in different fault conditions, including various locations, phase types, phase‐to‐ground connection resistance, occurrence time, and microgrid (MG) operation modes. The successful results of various tests of the proposed design as well as the analysis of various sensitivities confirm the satisfying performance of the suggested algorithm.

Reinforcement learning for energy management of an islanded microgrid: Analysis of state, state space and reinforcement function

The energy management (EM) of a microgrid is a challenging problem because of the stochasticity of renewable sources and loads, which can be formulated as a multi-stage decision-making problem. Reinforcement learning (RL), is found to be suitable for solving multi-stage decision-making problems under uncertainty. With the recent advancements in deferrable types of loads like that of electric vehicles, there is flexibility to manage peak demand, thereby saving the microgrid's operation cost. In this paper, we systematically explain for the first time how RL can be used to solve the scheduling problem of an isolated MG. A novel Q-learning algorithm employing a state-dependent action set is proposed to solve an isolated microgrid's EM problem in the presence of critical and deferrable loads. Various scenarios of wind turbine output power are generated to test the algorithm under a stochastic environment. In this work, the action set is divided into two: one with all the actions included and the second with a reduced number of actions. The entire state space is also divided into two: one set in which the deferrable load needs to be allotted and other where it need not be allotted. The advantage of such a method is that it substantially reduces the computational burden even if the state space's dimension is higher. Also, the judicious selection of reinforcement function to meet all the objectives and constraints is illustrated with case studies. The proposed method yields optimized scheduling with significant cost reductions compared to existing work.

Distributed Generation and Demand Response Coordination for Optimal Operation of Microgrid Using Modified Elephant Herd Optimization

The optimal operation and coordination of distributed generation (DG) sources and demand response (DR) programs help to minimize the operating cost of microgrids along with other mutual benefits. The model is developed, and the multiobjective problem subjected to the set of constraints is solved by using the modified elephant herd optimization (MEHO) technique in this work. The expected output of the model is the optimal generation scheduling plan of both the dispatchable and non-dispatchable DG and optimized cost for the isolated microgrid operation. The other objectives related to the environmental aspects and reliability are also considered for optimization. DG operation is coordinated with the day ahead DR programs to minimize the costs while satisfying the operation limits of the DG sources and consumer demand. The novelty of the work lies in finding the optimal solution considering the multiobjective for optimal DG operation and analyzing the impact of different types of DR programs on microgrid operation by the MEHO technique. The effectiveness of the proposed model and validity of the algorithm is further tested on a 14 bus microgrid benchmark system. Through simulation and optimization results it is observed that the proposed algorithm has less computational time and better accuracy.

Development and implementation of unmanned vehicles through artificial intelligence involving communication system with sensors and control parameters

Physical situations that are duplicated in virtual settings are what the term "digital twin" refers to. Utilizing the Internet of Things and sensors to improve microgrid operation, the Microgrid Digital Twin (MGDT) exhibits bidirectional data sharing and sophisticated communication characteristics. Smart grid functionality is enhanced, limits are set in real-time, and data is securely stored by MGDT. It directs the planning and execution phases of microgrid operations. Concurrently, industry and transportation innovation is propelled by the Industrial Revolution. AI-driven ground-dependent controls, communication systems, and sensors are essential for breakthroughs in unmanned vehicle safety. This abstract clarifies how important digital twins more specifically, MGDTs are to system operations optimization. In addition to highlighting technological advancement and the use of AI to ensure the safety of unmanned vehicles, it provides insights into the potentially revolutionary applications of digital twins across a range of industries.

Performance evaluation of communication networks for networked microgrids

The main goal of cyber-physical power systems (CPPSs) is to provide seamless collaboration between the physical and cyber systems, bridging disciplines, such as communication networks and control systems. Controlling the operation of Microgrids (MGs) as a cyber-physical power system must include both the physical and cyber layers. Generally, the microgrid control center MGCC is employed for reliable operation management between the different local controllers within the networked microgrids (NMGs). Therefore, an excessive number of communication channels between the MGCC and the local controllers will exist, affecting the control performance of the NMGs. This paper focuses on developing an innovative communication network emulation model for standalone DC networked microgrids using a Network Simulator (NS3), miming the communication infrastructure to control the networked microgrid system. Through the real-time simulation, the performance of the proposed communication network model was evaluated by employing two transmission protocols, User Datagram Protocol (UDP) and Transmission Control Protocol (TCP), to facilitate the data exchange between the MGCC and the local controllers in each MG while operating under different traffic loads. The comparative results of using these protocols in terms of the key performance indicators (KPIs) of the communication performance metrics, such as the mean delay, percentage of packet loss, and transmitted and received bitrate, reveal notable insights into their suitability in data transmission for controlling and managing the operation of the NMGs.

A Fast Repetitive Control Strategy for a Power Conversion System

With the expansion of renewable energy sources, the stable and high-quality operation of microgrids composed of new energy sources has attracted widespread attention. Among them, the power conversion system (PCS), as an important part of microgrids, plays a crucial role in their operation and management. The PCS operation modes are classified into grid-connected and off-grid modes. However, in off-grid mode, due to the access of nonlinear and unbalanced loads, the output voltage quality of a PCS is worse, and the voltage waveform distortion is serious. To solve these problems, a fast repetitive control (FRC) strategy is proposed for a power conversion system with an Active Neutral Point Clamped (ANPC) architecture of three levels. The voltage loop control strategy can be applied to the voltage/frequency (V/f) mode and the grid-forming mode. The control strategy can effectively realize the suppression of the harmonics of the output voltage and has a 100% capability to carry unbalanced loads. Finally, a 1725 kVA PCS prototype is developed, and the proposed control strategy is verified using the MT3200 HIL semiphysical simulator of ModelingTech in the V/f mode as an example. This practically verifies the feasibility and validity of the proposed control strategy, which has a certain degree of engineering practicability and reference due to the simplicity of the design and the ease of realization.

Specifying angular reference for robust three-phase state estimation

While the phase angle of any of the bus voltages can be chosen as the angular reference in the state estimation formulation of positive sequence networks, the same approach does not readily extend to three-phase network state estimation problem. It is commonly assumed that there is at least one bus where the bus voltages are perfectly balanced with phase angles displaced ±120° and these balanced three phase voltages are used as the three-phase reference in solving the three-phase state estimation problem. This assumption may be quite realistic in transmission networks, and for distribution networks with a strong transmission system connection. However, it might not be realistic to assume existence of a perfectly balanced reference bus in today’s distribution systems with ever increasing penetration of renewable sources or for isolated operation of microgrids. In this paper, a novel state estimation formulation will be presented which facilitates correct solution irrespective of the existence of buses with perfectly balanced voltages. The new formulation is general, and lends itself to bad data processing. It yields accurate results in any three-phase power system irrespective of its operating conditions (balanced or highly unbalanced), configuration (isolated microgrid, connected to transmission system, etc.) and whether or not it contains any synchronous generators. The performance of the method is validated using the IEEE 123 bus three-phase system.

A decentralized framework for economic‐resilient operation of networked microgrids, considering demand response program

AbstractThe occurrence of natural disasters can disrupt the normal functioning of distribution networks. In this situation, the activeness of distribution networks in the sense of benefiting from energy sources and related structures such as microgrids (MGs) makes them benefit from the capability of structures such as MGs, in case of High‐Impact Low‐Probability (HILP) events, so that consumers are able to disconnect from main grid and trade with other MGs. Thus, the energy management of networked MGs needs a new operation structure to encourage them providing each other with the required energy by creating the necessary incentives. This paper presents a novel cooperative framework aimed at enhancing the resilience of MGs through the establishment of peer‐to‐peer (P2P) energy trading among their constituents. In the present model, MGs engage in energy transactions with one another, aiming to minimize costs through the cooperative operation of networked MGs. The utilization of the suggested model enables MGs to enhance network resilience within the framework of peer‐to‐peer energy transactions, while also achieving cost reductions in comparison to islanding and non‐cooperative operation. The numerical results obtained demonstrate the efficacy of the proposed methodology in enhancing the resilience of MGs and mitigating their operational expenses.

Chance-Constrained optimal scheduling of power and hydrogen systems in power distribution network with significant photovoltaic and carbon offsetting strategies

The urgent need to address climate issues increases the significance of sustainable strategies in power networks. This study focuses on the optimal operation of a hydrogen-based microgrid incorporated with photovoltaics (PVs), time-of-use demand response (TOU-DR) program, distributed generation (DG) units, hydrogen fuel stations (HFSs), and battery storage units. Employing a multi-objective approach, the study aims to minimize emissions and system operation costs, being validated by implementing it on the IEEE 33-bus test network. To address the carbon emissions issue, the Power-to-gas (P-to-G) facility is utilized to capture and recycle CO2 pollutions. The implementation of a carbon trading market mechanism is also adopted to further enhance the generation capacity of the PV units and facilitate the achievement of sustainable development goals. Besides, the presence of uncertainties in renewable generation, demand, operational characteristics of hydrogen vehicles, and market prices pose challenges for the system operator, which is addressed by employing the chance constraint programming (CCP) method. Results show that using the P-to-G system has led to a 10 % emissions reduction. However, this is accompanied by a corresponding 25 % increase in operational costs. Additionally, when operating under the CCP constraints, the system's storage level has been increased by 10 % to ensure optimal performance.

Smart grid optimization considering decentralized power distribution and demand side management

AbstractThe increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%.

A comprehensive overview of DC‐DC converters control methods and topologies in DC microgrids

AbstractMicrogrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed.

Distributed optimal operation of PV-storage-load micro-grid considering renewable and load uncertainties

Micro-grid is one of the important carriers for renewable energy integration. Optimizing the operation mode of micro-grid can improve economic benefits and reduce risks brought by uncertainties. To maximize the economic benefits of photovoltaic-storage-load micro-grid, a chance-constrained optimal operation model considering renewable and load uncertainties is proposed in this paper. Firstly, the photovoltaic (PV) power and load forecast error distribution function based on fitting method is obtained. Then, a probabilistic power balance constraint based on chance constrained theory is established to reflect risks brought by uncertainties. To protect the information privacy of different entities, the alternating direction multiplier method (ADMM) with self-adaptive parameters is used to the proposed optimal model in a distributed way. Case study demonstrates that the proposed model can achieve a profit and risk balance of micro-grid scheduling by setting different confidence levels, and the information privacy of micro-grid operator and users is well protected.

Optimizing Distribution System Resilience in Extreme Weather Using Prosumer-Centric Microgrids with Integrated Distributed Energy Resources and Battery Electric Vehicles

Electric power networks face vulnerabilities from various hazards, including extreme weather and natural disasters, resulting in prolonged outages and service disruptions. This paper proposes prosumer-centric networked electrical microgrids as a solution. EMGs integrate DERs, like SPV panels, WTs, BESSs, and BEVs, to form autonomous microgrids capable of operating independently during grid disruptions. The SMA was used to identify the appropriate allocation of DERs and BEVs to improve the resilience of the system. Prosumers, acting as both producers and consumers, play a crucial role by generating and sharing electricity within the microgrid. BEVs act as mobile energy storage units during emergencies. Load management and demand response strategies prioritize the energy needs for essential facilities, ensuring uninterrupted operation during adverse weather. Robust communication and control systems improve the emergency coordination and response. The resilience analysis focused on two case studies: moderate and severe damage, both under varying weather conditions. Simulations and experiments assessed the microgrid performance with different levels of DERs and demand. By testing on the IEEE 69-bus RDS, evaluated the EMGs’ strengths and limitations, demonstrating their potential to enhance distribution grid resilience against natural disasters.

Consensus-based secondary frequency control for parallel virtual synchronous generators

In off-grid operating modes, microgrids with Virtual Synchronous Generators (VSG) controlled inverters are limited to primary frequency control capabilities. When operating under the primary frequency control, if VSG's output frequency exceeds the safe operational range, it can adversely affect stable operation. Consequently, there is a need to implement a secondary frequency control strategy. This paper presents an analysis of the conventional secondary frequency regulation in VSGs. It reveals that when multiple VSGs in parallel to the Point of Common Coupling (PCC) with varied line impedances, the proportionate distribution of each VSG's active power output, post frequency control, does not align with predetermined ratios. In response to this challenge, the paper introduces a quasi-distributed secondary frequency control approach, tailored for systems with multiple feed-in points. This approach modifies the traditional method of compensating for frequency deviation through average ratio integral compensation, replacing it with a compensation based on a consensus algorithm. This negates the need for global communication and additional impedance measuring equipment. By facilitating information exchange between neighboring converters, it enables global control, effectively resolving the issue of line impedance parameters impacting the distribution of VSG's active power output. This decouples the previously strong correlation between line impedance and VSG's active power. The proposed method is characterized by its strong adaptability, scalability, and swift dynamic adjustments, significantly enhancing the fault tolerance of information exchange and the stability of microgrid operations. Finally, the correctness and effectiveness of the proposed improved control strategy are verified by simulation and HIL-based platform.

Development of PLC-Based Hardware Test-Bench Prototype for Solar-Wind-Battery-Based Microgrid System’s Control Algorithm Validation

Programmable Logic Controllers (PLCs) are gaining traction in microgrid operation due to their real-time deployment, reliable and robust operation, and fast response, even in harsh environments. Recognizing the critical need for robust and reconfigurable microgrid control and operation, this research proposes a hardware test-bench prototype using the Reconfigurable Allen-Bradley—Micro820/2080-LC20-20QBB PLC to demonstrate control algorithms aiming for efficient control, energy management, and resilient microgrid automation. The design and construction of a prototype controller test bench, along with the PLC configuration, wiring diagrams, and control logic, constitute the significant contributions of this research. The proposed controller test bench’s performance is evaluated through similar condition simulations in the presence of disturbances induced by the reduced contributions of renewable energy sources, such as solar and wind. The results illustrate the effectiveness of the developed control algorithm implemented in the proposed controller, ensuring the stable operation of the microgrid. Consequently, this work advances the simulation-based validation of microgrid control algorithms and provides insights into the practical application of a PLC-based hardware test bench under various environmental conditions.

Performance analysis of machine learning based prediction models in assessing optimal operation of microgrid under uncertainty

Of late, the exponential rise in the global population is driving higher energy demand. However, the rapid depletion of conventional fossil fuels and growing environmental concerns have prompted the evolution of alternative energy sources. To this end, Microgrid (MG) with Renewable Energy Sources (RES) has emerged as popular means of small-scale localized power grid. However, planning of MG operation poses challenges due to the inherent variability and stochasticity in RES power output and energy demand. On account of this, the present study introduces a Stochastic Energy Management Strategy (SEMS) for a grid-connected MG incorporating Micro-Turbine, Fuel-Cell, RES, Battery Energy Storage, and electrical and heat energy demand. The stochasticity of RES is forecasted through a hybrid prediction model (sARIMA-GRU) and the uncertain demand is estimated via 'Monte Carlo Simulation.' The proposed problem is formulated as a dynamic non-linear stochastic optimization problem. It seeks to minimize the expected value of MG operational cost satisfying the practical constraints. Addressing this, a newly developed ‘Artificial Electric Field Algorithm (AEFA)' is utilized. Several case studies are performed to assess MG operation under varied operating conditions. Moreover, the present study analyses the impact of uncertainty on energy contribution from DER, grid dependency, and MG operation cost. Comparative analysis reveals that sARIMA-GRU outperforms other contemporary prediction models. It is noteworthy that the superior prediction accuracy of sARIMA-GRU leads to lower MG operation costs. Moreover, statistical analysis and convergence confirm the proficiency of applied AEFA over state-of-the-art Grey Wolf Optimization and Firefly Algorithm in solving the proposed problem.