Smart Grid Power System Research Articles

IoT Based Smart Energy Meter

In Modern day smart grid technology relies heavily on communication networks for two way communication between load, generation, transmission, and control centre. As a part of the smart grid, smart meters use advanced metering infrastructures (AMI) that are widely distributed and interconnected to the communication network. In modern Technology the smart grid plays very important role for two way communication between Supplier and consumer. Smart meter is main component of smart grid. Currently we are paying electricity bill like one person came to home to read the electricity board and handover the bills to the owner of that house. The drawback of this it requires more man power, time, and many times it has error in bill amount. To eliminate this error smart energy meter is introduced. This system also helps in detecting and control over power theft. this smart energy meter helps us in home automation using IoT and enabling wireless communication which is a great step towards Digital India .Efficient energy utilization plays a very vital role for the development of smart grid in power system By providing real-time monitoring and control of energy consumption, it can help users to reduce their energy usage, save money, and promote sustainable energy consumption. As a first step towards such cyber-attack related researches and searching for possible mitigation techniques, it is necessary that a smart meter be developed that has the feature of network integration. The present contribution reports the design, fabrication, and operation of an Internet of Things (IoT) based smart meter using Arduino to serve as an integral part of a smart grid system.

Deep-Block Network for Cyberattack Mitigation and Assessment in Smart Grid Power System with Resilience Indices

Distributed renewable energy sources, with wide communication components in a microgrid infrastructure, make cyber security assessment and mitigation a developing cyber-physical system study. Extensive cybersecurity threads are prevailing in modernized smart grid. Hence, to detect and mitigate cyber threads an advanced cost-effective resilience cyber risk assessment and mitigation mechanism is needed. To enhance cyber-physical security in smart grids, a secured deep learning algorithm with blockchain technology (BlockDeepNet) is proposed. Distributed secured data analysis is carried by using deep learning approach, while blockchain helps in the implementation of secured decentralized resilient control. To validate, real-time cosimulation on IEEE 15 bus system was conducted. Also, for evaluating cyber security breach, four types of cyberattacks were introduced to validate the effectiveness of proposed security assessment and resilience operation. We obtained normalized resilience index of 2.36 for grid communication failure, 0.91 for replay attack, 1.34 for false data injection, and 1.74 for DoS attack. The obtained results on simulation case study by real-time hardware in the loop implementation showed that the proposed BlockDeepNet accurately reduce load loss for various cyberattack and provide robust resiliency. Overall, this research provides a platform for cybersecurity assessment and enhanced resilience operation of cyber-physical power energy system.

Special issue on: AI/data‐based modeling, control, and operation of smart grid power systems

Special issue on: <scp>AI</scp>/data‐based modeling, control, and operation of smart grid power systems

Fundamentals of State-Space Based Load Flow Calculation of Modern Energy Systems

Our current energy landscape is ever-changing, resulting from the ongoing energy transition and introducing a massive expansion of volatile generation feed-in and energy consumption (due to electrification). In turn, the energy supply’s requirements are also being affected. In this context, the energy system’s optimisation across all sectors will only grow in significance, especially in terms of future developments. Facilitating and researching methods for carrying out the aforementioned optimisation creates new demands pertaining to load flow simulation programmes. The volatility of specific participants and their interactions within existing power grids must be evaluated, wherefore the consideration of a large number of time steps but also dynamic simulations becomes inevitable. Carrying out such simulations is possible but very time-consuming. This article compared a variety of conventional load flow simulations such as the current iteration and Newton–Raphson methods and also introduced a novel, state-space based calculation approach, which boasts the potential of structurally increasing simulation speeds. Each of the method’s underlying principles, requirements, and mathematical correlations will be discussed and explained. In the second part of this article, the most important state-space equivalent circuit models of critical operating equipment are introduced. These models are essential for carrying out load flow simulation tests for an exemplary test network but could also be used for dynamic purposes. The previously showcased load flow methods were applied to a test network, with which the simulation methods should be validated. The results show that the state-space simulation has high accuracy while also being very flexible. All in all, the load flow calculation in state-space offers many advantages that could be an interesting alternative to conventional load flow simulations, especially for the analysis of complex, time series-based, and intelligently controlled smart grid power systems. In this context, the direct application of system theoretical methods for stability calculations, controllability, and dynamic system studies is to be mentioned. Optimisation options regarding the processes within the state-space calculation software still promise further significant increases in performance.

A Novel Renewable Smart Grid Model to Sustain Solar Power Generation

The stability performance of smart grid power systems is critical and requires special attention. Additionally, the combination of Battery Energy Storage (BES) systems, Solar Photovoltaic (SPV), and wind systems in the intelligent grid model provides utilities with excellent efficiency and dependability. However, a coordination grid with PV and other resources frequently results in severe issues, such as outages or power disruptions. A power outage in the grid might result in a power loss in the delivery system. As a result, the distributed grid model’s dependable performance is intended for integrated wind energy, SPV arrays, and BE systems. This paper proposes a renewable intelligent grid model to sustain solar power generation. The model incorporates a boost converter to optimize the performance of solar panels by converting the DC power generated by the panels into AC power for use in the grid. The boost converter is optimized using a novel Horse Herd Optimization Algorithm (HOA) method. In this case, the HOA method is used to optimize the control parameters of the boost converter, such as the duty cycle and the inductor and capacitor values. According to the final results, the proposed method has reduced the Total Harmonic Deformation (THD) and power loss. Additionally, the proposed method outperformed existing strategies related to the Expected Energy Not Supplied (EENS), Loss of Load Probability (LOLP), and Loss of Load Expected (LOLE), indicating the sustainability of power generation.

Investigating the impact of cyber security risks and reliability scenarios under the influence of IoT on the Smart Grid environment

The modern infrastructure of the smart grid minimises the power losses and maintain the electric power flow closer to nominal values. It enables the bidirectional flow of energy between consumer end and generation end along with a communication flow. Due to increasing number of smart grid equipment, it is important to investigate security protection and appropriate recovery measures for the smart grid application. Cyber security measures are important to consider for improving the reliability and intelligence features of the smart grid. This paper investigated the security measurements and different standards to counter the future security threats to the smart grid power systems. False data injection and risk analysis is carried out along with an internet of thing (IoT) based security solution to increase the reliability of data flow and communication on the smart grid. The investigations are performed by creating a power system model on a MATALB/Simulink environment.

Secure advance metering infrastructure protocol for smart grid power system enabled by the Internet of Things

The incorporation of Advanced Metering Infrastructure (AMI) into Smart Grid applications has piqued the interest of Internet of Things (IoT) experts, who have aided smart city initiatives. One of the fundamental building components of AMI for information sharing is wireless communication networks. Wireless communication networks are prone to a variety of security threats, which causes issues when developing the AMI system in smart city projects. Confidentiality, integrity, availability, and privacy are the primary criteria of cyber-physical systems such as AMI. To meet the needs of cyber-physical systems, we worked on a wireless communication protocol that solves data transmission dependability and data security while consuming little power and overhead. We proposed a novel Secure and Reliable AMI (SRAMI) protocol in this paper using a lightweight cryptographic technique and trust-based mechanism for reliability for Wireless Sensor Network (WSN)-assisted AMI. The trust-based approach presents a trustworthy data transfer technique between two communicating AMI entities. In this regard, the next-hop is chosen based on trust-evaluation characteristics such as energy expenditure, geographical distance, and bandwidth availability. Furthermore, a lightweight hybrid approach based on Elliptic Curve Cryptography (ECC) is introduced to meet the data integrity and privacy requirements in the AMI network. SRAMI's performance was improved compared to state-of-the-art protocols in terms of throughput, Packet Delivery Ratio (PDR), average energy consumption, latency and communication overhead.

Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System.

Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem is to build mechanisms to selectively send out communications, which allow distributors to send information at certain moments and key states. The distributors in the power grid are modeled as reinforcement learning agents, and the communication bandwidth in the power grid can be reduced by optimizing the communication frequency between agents. Therefore, in this paper, we propose a model for deciding whether to communicate based on the causal inference method, Causal Inference Communication Model (CICM). CICM regards whether to communicate as a binary intervention variable, and determines which intervention is more effective by estimating the individual treatment effect (ITE). It offers the optimal communication strategy about whether to send information while ensuring task completion. This method effectively reduces the communication frequency between grid distributors, and at the same time maximizes the power distribution effect. In addition, we test the method in StarCraft II and 3D environment habitation experiments, which fully proves the effectiveness of the method.

Building construction based on video surveillance and deep reinforcement learning using smart grid power system

New trendy neighborhoods require trimming scientific and technological methods and equipment. Smart buildings (SB) use resources efficiently, save energy, and provide services to the community more easily for their occupants while reducing their environmental footprint. Smart cities have benefited from this growth in terms of smart buildings. Maximum accuracy and reduced latency are both required for smart building monitoring systems. Poor scheduling rules can lead to network congestion and latency that is too high for real-time monitoring on construction sites, which have restricted computing and networking capabilities. These devices can collect the data on on-site actions, achievements, and circumstances and send it back to the central dashboard for analysis. Model predictive control and Deep Reinforcement Learning (DRL) have significant drawbacks, and DRL addresses some drawbacks. Researchers are intrigued by DRL, a brand-new approach to quality control. The most important considerations for developing smart power grid systems are energy conservation, renewable energy integration, and a streamlined control system. Experiments have shown that the new video surveillance has a low loss rate and a consistent latency. The DRL-SB-IoT technique can successfully track multiple cameras in a wide monitoring situation. This technique results in excellent tracking performance and meets the criteria for developing an intelligent campus in the best way possible. Researchers analyzed studies using supervised learning to solve common building issues, such as health monitoring, security on building sites, accommodation modeling, and energy consumption prediction. Reinforcement learning has been used to solve these issues. The proposed method advances the smart gateway channel of 97.5%, the energy storage ratio of 96.9%, and the overall surveillance performance ratio of 98.6%.

A Reinforcement Learning Approach for Defending Against Multiscenario Load Redistribution Attacks

The accelerated digitalization of today’s electric power infrastructure has highlighted the gravity of cyber resilience in the electricity ecosystem. Due to the expanded attack surface inherited in the digitalized operational and information technologies, the electricity sector is facing a continually escalating cyberthreat landscape and must act to prepare for more frequent and sophisticated cyberattacks as the new normal. To aid in this effort, this paper presents a novel approach to identify critical branches to strengthen and by doing so, shield the smart-grid power system from the threat of load redistribution attacks (LRAs) under a wide range of operating scenarios. Compared with conventional critical branch identification approaches, we propose a new concept, namely, chain of defense, that empowers the system operator to incorporate existing cyber protections and develop branch strengthening strategy in a more dynamic, adaptive, and flexible way. We then propose a novel reinforcement learning framework to identify the most effective chain of defense. A cross-updating search strategy is developed to specifically ensure that the identified chain of defense can safeguard the network from the two-fold damaging effects of the LRAs on operation economics and security simultaneously. The effectiveness of the proposed approach is evaluated on the IEEE 14-bus system and the European 89-bus system. Simulation results indicate that the proposed approach provides more comprehensive and effective mitigation against the damaging effects of LRAs compared with the conventional approaches.

Overview of Power Electronic Devices &amp; its Application Specifically Wide Band Gap and GaN HEMTs

The review paper presents a prologue of power electronics devices and its industrial applications including wide band gap semiconductor devices in range of 600V to 1700V and latest evaluation of Gallium Nitride High Electron Mobility Transistor technology (GaN HEMT) in range of 30V to 650V with applications. The study emphasizes advance power electronic applications in renewable energy system, smart grid power system, power saving, electric vehicles and energy storage system. Similarly, comparison of latest WBG semiconductors as SiC, SiC JFETs, SJ MOSFETs and GaN-HEMTs discussed in terms of different parameters e.g static losses, dynamic losses and temperature impact, thus further signify latest GaN technology evolution and its related challenges with detailed static as well as dynamic characterization. This paper demonstrates all types of electronics devices in power sector and its practical usage mentioned in detail, it includes tradeoff between different WBG devices and also explain GaN functions and applications in various circuits of high efficiency and density.

AUTOMATED MONITORING OF TIME SYNCHRONISATION DEVICES AND DIGITAL PROCESSING OF VECTOR MEASUREMENTS OF DYNAMIC CHARACTERISTICS OF SMART GRID POWER SYSTEMS

Currently, customers of energy companies use devices to generate electricity (solar panels, windmills, etc.) and return energy to the grid to save costs or generate income. Therefore, it is urgent to solve the problem of creating an automated system for diagnosing the quality of electricity for consumers who use renewable sources of electricity. The purpose of this study was to improve the efficiency of management in intelligent SMART Grid power systems. To fulfil the said purpose, it was proposed to use an automated system for monitoring synchro signals of PMU microprocessor equipment and software tools for digital processing and statistical analysis of the results of synchronised vector measurements of dynamic characteristics of power grids. Software tools for digital processing and statistical analysis of the obtained vector measurement data were developed. Linear regression, Random Forest Regression, and Ridge regression methods were employed to analyse and predict electricity quality indicators. The results of a comparative analysis of the obtained results of the predictive model were presented. To conduct experimental studies, a laboratory sample of a quality control device for generating synchro signals was developed and manufactured, which directly affects the reliability of digital data from distributed synchronised vector measurements in transient modes (in real time). The multichannel nature of the device increases the performance and reliability of the automated monitoring system. The paper presents the results of experimental studies of the device as a part of the developed laboratory stand, which is connected to the current local IP network of the National University of Life and Environmental Sciences of Ukraine. The practical significance of the conducted experimental studies lies in the establishment of the technical possibility of simultaneous monitoring of time synchronisation signals generated by two GPS receivers. In this case, it is obvious to increase the performance of the developed and manufactured device for monitoring the quality of clock signal generation, compared to a single-channel one. This material can be useful for engineers who design electrical systems connected to renewable energy sources.

Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review

Flexible AC Transmission System (FACTS) controller has got unanimous acceptance in the electric power industry due to their power control capability especially in transmission and sub transmission networks. These FACTS devices play a decisive role in stability enhancement and effective asset utilization. The controllers suffer from grave drawbacks such as colossal size, poor reliability, high cost, and unavailability of experienced personnel for operation and maintenance, which makes them unapt to be used in smart grid and modern power system applications. These inadequacies are effectively assuaged by the distributed FACTS devices, which are proliferated along the transmission lines, are said to be portable, cost-effective, highly reliable, and easy to maintain. This paper explores an exhaustive review of the smart wires and modular FACTS controllers with their application in smart power networks.

Smart Wires and Modular FACTS Controllers for Smart Grid Applications: A Review

Flexible AC Transmission System (FACTS) controller has got unanimous acceptance in the electric power industry due to their power control capability especially in transmission and sub transmission networks. These FACTS devices play a decisive role in stability enhancement and effective asset utilization. The controllers suffer from grave drawbacks such as colossal size, poor reliability, high cost, and unavailability of experienced personnel for operation and maintenance, which makes them unapt to be used in smart grid and modern power system applications. These inadequacies are effectively assuaged by the distributed FACTS devices, which are proliferated along the transmission lines, are said to be portable, cost-effective, highly reliable, and easy to maintain. This paper explores an exhaustive review of the smart wires and modular FACTS controllers with their application in smart power networks.

Adaptive robust control design to enhance smart grid power system stabilization using wind characteristics in Indonesia

&lt;span lang="EN-US"&gt;This paper is interested to study power system stability in smart grid power system using wind characteristic in south of Yogyakarta, Indonesia. To overcome the intermittent of wind characteristics, this paper presents adaptive robust control design to enhance power system stabilization. The online identification system is used in this research, which updated whenever the estimated model mismatch exceeds predetermined bounds. Then genetic algorithm (GA) is applied to re-tune parameters controller based on the estimated model. The structure of controller is proportional integral (PI) controller due to the most applicable in industry, simple structure, low cost and high reliability. Robustness of controller is guaranteed by taking system uncertainties into consideration. The performance of the proposed controller has been carried out in a hybrid wind-diesel power system in comparison with previous work controller. Simulation results confirm that damping effect of the proposed controllers are much better that of the conventional controllers against various operating.&lt;/span&gt;

Combined fuzzy PID regulator for frequency regulation of smart grid and conventional power systems

In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing a CFPID controller using the proposed SCiWOA technique for a smart grid system having inexhaustible sources like sun oriented, wind, photovoltaic and capacity gadgets like a battery, flywheel just as module electric vehicles. The first advantages of the SCiWOA tuned CFPID controller over hybrid-particle-swarm-optimization and pattern-search (hPSO-PS) adjusted fuzzy proportional-integral (FPI) controller, hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) adjusted proportional-integral (PI) controller, genetic algorithm (GA) tuned proportional and integral (PI) controller, BFOA adjusted PI controller, jaya algoritm (JA) tuned PID with derivative filter (PIDN) controller and teaching learning based optimization (TLBO) tuned proportional-integral-derivative (PID) controller are demonstrated for the two-area non-reheat thermal power system. The second advantages of the SCiWOA tuned CFPID controller over artificial-bee-colony (ABC) tuned PID controller, SOSA tuned PID controller and Firefly algorithm (FA) tuned PID controller are demonstrated for two-area reheat thermal power system. It is seen that SCiWOA based CFPID controller is more effective in controlling the recurrence comparative with PID regulator.

AN OVERVIEW OF THE COMPOSITE DESIGN OF SMART GRID DUE TO POWER DEMAND,PRODUCTION AND POWER DEFICIT IN NIGERIA

The general aim and objectives of the research is to demonstrate an overview of the composite design and emphasis of the implementation of an effective and efficient smart grid power system that will address the challenges of epileptic power due to power demand, power production and power deficit in Nigeria.

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Optimal Model Predictive Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.

Optimal Frequency Control Management of Grid Integration PV/Wind/FC/Storage Battery Based Smart Grid Using Multi Objective Particle Swarm Optimization MOPSO and Model Predictive Control (MPC)

This article forecasts the performance of smart-grid electrical transmission systems and integrated battery/FC/Wind/PV storage system renewable power sources in the context of unpredictable solar and wind power supplies. The research provided a hybrid renewable energy sources smart grid power system electrical frequency control solution using adaptive control techniques and model predictive control (MPC) based on the Multi-Objective Practical Swarm Optimization Algorithm MOPSO. To solve the problems of parameter tuning in Load Frequency Control, the suggested adaptive control approach is utilized to accomplish on-line adjustment of the Load Frequency Control parameters. During the electrical grid's integration, the system under investigation is a hybrid Wind/PV/FC/Battery smart grid with variable demand load. To achieve optimal outcomes, all of the controller settings for various units in power grids are determined by means of a customized objective function and a particle swarm optimization method rather than a regular objective function with fluctuating restrictions. To suppress the consumption and generation balance, MPCs were designed for each of the Storage Battery, Wind Turbine Generation, and the model Photovoltaic Generation. In addition, demand response (real-time pricing) was used in this scheme to reduce the load frequency by adjusting the controlled loads. The suggested control strategy is evaluated in the Simulink /MATLAB environment in order to analyse the suggested approach's working in the power system, as well as its effectiveness, reliability, robustness, and stability. The simulation findings show that the proposed control method generally converges to an optimal operating point that minimises total user disutility, restores normal frequency and planned tie-line power flows, and maintains transmission line thermal restrictions. The simulation results further indicate that the convergence holds even when the control algorithm uses inaccurate system parameters. Finally, numerical simulations are used to illustrate the proposed algorithm's robustness, optimality, and effectiveness. In compared to previous methodologies, the system frequency recovers effectively and efficiently in the event of a power demand disturbance, as demonstrated. A sensitivity test is also performed to assess the suggested technique's effectiveness.