Smart Grid Control Research Articles

Advanced smart grid controller: a next-gen alternative to Fuzzy-logic for UPFC

A very valuable component of flexible AC transmission systems (FACTs) is the Unified Power Flow Controller (UPFC). By simultaneously regulating both active and reactive power on transmission lines, it maintains voltage stability on linked buses. This study devised a thorough control strategy for the Unitary Power Flow Controller (UPFC), combining traditional and artificial intelligence (AI)-based methods. To see how the system would react in different situations and how well artificial intelligence control worked compared to more traditional proportional-integral power control, we ran a lot of simulations. The study found that the control strategy based on artificial intelligence (AI) was better than the proportional-integral (PI) control at managing power flow, keeping the voltage stable, and responding to changes in the environment. The results of this study emphasize the capacity of artificial intelligence methods to improve the performance and stability of FACT components such as the UPFC. The research provides important insights into power system control and optimization, indicating that strategies based on artificial intelligence could greatly enhance power generation. In order to enhance the AI control strategy, future research should focus on its practical application in actual power systems.

Optimizing Smart Power Grid Stability Based on the Prediction of a Deep Learning Model

A smart grid is an electricity transmission system that uses digital technology to control getting and dispatching electricity from all generation sources to satisfy end users' fluctuating electricity demands. It achieves this through deploying technologies such as technology and smart grids, which are pivotal in increasing the power supply's efficiency, reliability, and sustainability to the public. Decentralized Smart Grid Control (DSGC) is a system where the control and decision-making functions are distributed to different grid points instead of in one central place. This paradigm is critical for the fault resistance and efficiency of the grid because it enables the local regions to carry on by themselves, manage electric power flows, respond to changes, and integrate many kinds of energy sources successfully. The grid frequency is monitored via the DSGC to ensure dynamic grid stability estimation. All parties, from users to energy producers, may take advantage of the price of power tied to grid frequency. The DSGC, a vital component of this research, gathered information about clients' consumption and used several assumptions to predict the behavior of the consumers. It establishes a method to assess against current supply circumstances and the resultant recommended pricing information. This research proposes a long short-term memory (LSTM) model to analyze data gathered regarding smart grid characteristics and predict grid stability. The results show a strong capacity for the LSTM model, achieving an accuracy of 96.73% with a loss of just 7.44%. The model also achieves a precision of 96.70%, recall of 98.18%, and F1-score of 97.43%.

A review of integrated modeling and simulation of control and communication systems in Smart Grid

In order to provide effective, safe, and dependable grid operation, the use of information and communication technologies (ICT) for real-time monitoring and control of the Smart Grid (SG) has grown in recent years. With such technologies, our system evolved into a complex cyber-physical system (CPS) in which communications networks play an important role in the dynamic performance of SG. Therefore, it is imperative to perform integrated modeling and simulation (IMS) of control and communication systems to evaluate the SG CPS for new control strategies or select appropriate communication technologies for specific applications before implementing them on a larger scale. This paper provides a state-of-the-art literature review about co-simulation, one of the key methods to perform an IMS. The paper starts with a brief review of the SG communication infrastructure, communication, and SG simulators used for co-simulations to give the readers an understanding of the requirements and issues for modeling and simulating SG communication systems. Then, detailed literature about the co-simulation platform covers the individual simulators used, the key features, test case advantages, and the limitations of different co-simulation platforms. In the discussion section, comparative analyses of co-simulation platforms are provided. The review summarizes critical challenges and opportunities in co-simulation, emphasizing the potential of IMS for advancing SG design and operation.

Review on Advanced Storage Control Applied to Optimized Operation of Energy Systems for Buildings and Districts: Insights and Perspectives

In the context of increasing energy demands and the integration of renewable energy sources, this review focuses on recent advancements in energy storage control strategies from 2016 to the present, evaluating both experimental and simulation studies at component, system, building, and district scales. Out of 426 papers screened, 147 were assessed for eligibility, with 56 included in the final review. As a first outcome, this work proposes a novel classification and taxonomy update for advanced storage control systems, aiming to bridge the gap between theoretical research and practical implementation. Furthermore, the study emphasizes experimental case studies, moving beyond numerical analyses to provide practical insights. It investigates how the literature on energy storage is enhancing building flexibility and resilience, highlighting the application of advanced algorithms and artificial intelligence methods and their impact on energy and financial savings. By exploring the correlation between control algorithms and the resulting benefits, this review provides a comprehensive analysis of the current state and future perspectives of energy storage control in smart grids and buildings.

A Lightweight Sequential Convolutional Neural Network for Smart Grid Stability Analysis

A Smart grid stability analysis is essential for ensuring modern power systems' reliable and secure operation. This approach helps identify potential instabilities and disturbances that can lead to blackouts or equipment failures. By analyzing the stability of the grid, operators can take proactive measures to maintain a stable and resilient power infrastructure. Monitoring smart grid data from various sources and analyzing how to control the stability of the grid are challenging tasks. A convolutional neural network (CNN) can effectively capture spatial dependencies and patterns from grid data and can help in enabling accurate prediction and classification of stability-related events in a power system. However, developing a CNN that has fewer learnable parameters and provides high accuracy is challenging. This paper presents a sequential CNN architecture to detect the stability of the Decentral Smart Grid Control (DSGC) system. A mathematical model of the 4-node start architecture of a smart grid was presented. Later, 12 parameter-based grid datasets from the UCI repository were used to validate the proposed network. The proposed CNN accepts sequential data to capture temporal dependencies in the data. The sequential process in a single dimension offers fewer learnable parameters, making the network more compact and computationally efficient. The proposed 11-layered CNN has a total of 12.7K learnable parameters. The detailed analysis of the proposed CNN using ambiguity and the t-SNE score suggested that the model can identify discriminative features for classifying data into stable and unstable classes. A comparison analysis of the quantitative parameters revealed that the model performed well, with 98.82%, 98.55%, 98.88%, and 98.77% accuracy, precision, recall, and F1 score, respectively.

Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid

The growing popularity of plug-in hybrid electric vehicles (PHEVs) is due to their environmental advantages. But uncoordinated charging of a large number of PHEVs can lead to a significant surge in peak loads and higher charging costs for PHEV owners. To end this, this paper introduces an innovative approach to address the issue by proposing a multi-objective weighting control for coordinated charging of PHEVs in a future smart grid, which aims to find an economically optimal solution while also considering load stabilization with large-scale PHEV penetration. Technical constraints related to the owner’s demand and power limitations are considered. In the proposed approach, the charging behavior of PHEV owners is modeled by a normal distribution. It is observed that owners typically start charging their vehicles when they arrive home and stop charging when they go to their workplace. The charging cost is then calculated based on the tiered electricity price and charging power. By adjusting the cost weighting factor and the load stability weighting factor in the multi-objective function, the grid allows for flexible weight selection between the two objectives. This approach effectively encourages owners to actively participate in coordinated charging scheduling, which sets it apart from existing works. The algorithm offers better robustness and adaptability for large-scale PHEV penetration, making it highly relevant for the future smart grid. Finally, numerical simulations are presented to demonstrate the desirable performance of theory and simulation.

Wind and photovoltaic systems in sustainable energy mixes: Cost-effective integration approaches

This study introduces an innovative methodology for optimizing the renewable energy sources (RES) mix, specifically wind-based distributed generation (WDG) and photovoltaic distributed generation (PVDG), in smart grids to enhance sustainability and efficiency. Our primary aim is to minimize total system costs, encompassing capital, operational, and maintenance expenses for RES units, alongside grid energy purchases. This methodology incorporates probabilistic models for each generation technology, energy prices, and demand. These models are integrated into a comprehensive multi-state generation-load-price model, addressing uncertainties and enhancing smart grid operations through real-time control. By leveraging the communication infrastructure of smart grids, the approach optimizes investments and boosts RES penetration during the planning phase. Simulation results from a representative distribution system demonstrate the efficacy of this approach in augmenting RES integration within the energy mix and maximizing investment returns. Compared to traditional RES planning, our methodology achieves a significant 2.47-fold increase in RES intake with minimal energy curtailment (8.38 %) and a notable 28.3 % reduction in total costs. These outcomes highlight the potential of our planning approach to significantly enhance the sustainability and economic viability of smart grid systems, proving effective in managing all conceivable system conditions and optimizing the energy mix. This study underscores the benefits of incorporating advanced probabilistic models and real-time control in smart grid planning to meet sustainability goals effectively.

A Machine Learning-Based Smart Grid Protection and Control Framework Using Kalman Filters for Enhanced Power Management

A Machine Learning-Based Smart Grid Protection and Control Framework Using Kalman Filters for Enhanced Power Management

IoT Security Framework Optimized Evaluation for Smart Grid

Modern systems' needs may be satisfied by smart grid technologies. Since we frequently struggle to effectively manage security, the smart grid's capacity is frequently underutilized. Despite the fact that a variety of solutions have been offered for securing the smart grid, the problem still exists that no single solution can entirely protect the environment. We provide a protection architecture for the IoT-connected smart grid. The proposed framework to secure IoT devices for the smart grid includes three complementary approaches. By conducting a rigorous comparative analysis of our proposed solution alongside four existing models, we contribute to the ongoing discourse on bolstering the security infrastructure of the smart grid IoT environment. Our optimized evaluation provides valuable insights into the strengths, weaknesses, and unique attributes of each model, offering a comprehensive understanding of their respective applicability and efficacy within the intricate realm of sensor-based applications. Two testing configurations were used to evaluate the Threat Mitigation Framework. It demonstrated superior performance in recognizing attacks like XSS across all testing configurations. In each of the two test sets, we also assessed the device management functions, and we found that they accurately recognized and presented IoT for the smart grid controller.

Monitoring Energy Flows for Efficient Electricity Control in Low-Voltage Smart Grids

Modern low-voltage distribution lines, especially those linked with renewable energy sources, face technical hurdles like unaccounted and illegal electricity use, increased power losses, voltage control issues, and overheating. Tackling these challenges effectively requires continuously monitoring power flows and identifying problematic network spots. This study introduces a method involving ongoing energy flow monitoring from distribution transformers and other sources to end-users through auxiliary facilities. The algorithm seamlessly integrates with consumers’ existing smart power meters and supporting infrastructure, eliminating the need for extra equipment or data. Deployed in several distribution networks totaling about 40 GWh/year over two years, this diagnostic system showed promising results. It notably cut total power consumption by around 6% by detecting and mitigating illegal energy waste and addressing technical issues. Additionally, it reduced technical personnel involvement in operational tasks by approximately twentyfold, significantly enhancing network profitability overall.

Cyber Security of Smart-Grid Frequency Control: A Review and Vulnerability Assessment Framework

Smart Grid involves application of Information and Communication Technology (ICT) for monitoring, protection, operation, and control of interconnected power systems under various scenarios. Smart Grid Control (SGC) is an important aspect that is constantly subjected to various vulnerabilities, threats, and attacks under central and distributed control architectures. Cyber Security of smart grid control, especially, Load Frequency Control (LFC) is an important issue that is addressed in this article. The state-of-the-art in cyber security and attacks on smart grid control and intensive literature review is discussed with a comprehensive list of references on LFC. The authors present a part of their own work carried out on a systematic Vulnerability Assessment (VA) framework that can be used to identify weak points in the LFC system. The proposed methodology is explained for Vulnerability Assessment of the standard 39-bus New England test system and the 96 bus reliability test system to illustrate the concept of cyber security and Vulnerability Assessment of smart grid LFC.

Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms

Distributed energy generation increases the need for smart grid monitoring, protection, and control. Localization, classification, and fault detection are essential for addressing any problems immediately and resuming the smart grid as soon as possible. Simultaneously, the capacity to swiftly identify smart grid issues utilizing sensor data and easily accessible frequency and voltage data from PMU devices is a prerequisite of this task. Therefore, this paper proposes new methods using fuzzy logic and adaptive fuzzy neural networks as well as machine learning and meta-heuristic algorithms. First, line voltage is used by a fuzzy thresholding method to estimate when a transmission line defect would develop in less than 1.2 clock cycles. Next, features taken from frequency signals in the real-time interval are utilized to classify the type of error using machine learning systems (decision tree algorithm and random forest algorithm) optimized with wild horse meta-heuristic algorithm. To locate the precise problem location, we finally use a neural fuzzy inference system that is capable of adapting to new data. We employ a simulated power transmission system in MATLAB to test our proposed solutions. Mean square error (MSE) and confusion matrix are used to assess the efficiency of a classifier or detector. For the decision tree algorithm method, the detector attained an acceptable MSE of 2.34e−4 and accuracy of 98.1%, and for the random forest algorithm method, an acceptable MSE of 3.54e−6 and accuracy of 100%. Furthermore, the placement error was less than 153.6 m in any direction along the line.

Providing a Control System for Charging Electric Vehicles Using ANFIS

Frequency control, especially when incorporating distributed generation units such as wind and solar power plants, is crucial for maintaining grid stability. To address this issue, a study proposes a method for controlling the connection status of electric vehicles (EVs) to prevent frequency fluctuations. The method utilizes an adaptive neural-fuzzy inference system (ANFIS) and a whale optimization algorithm to regulate the charging or discharging of EV batteries based on frequency fluctuations. The objective is to minimize and adjust the frequency fluctuations to zero. The proposed method is evaluated using a real microgrid composed of a wind power plant, a solar power plant, a diesel generator, a large household load, an industrial load, and 711 electric vehicles. The ANFIS system serves as the primary controller, taking inputs such as electric vehicle and battery status and generating outputs that determine the charging or discharging of the electric vehicles. Several investigations are conducted to assess the effectiveness of this model, and the results obtained are compared with the normal state where electric vehicles only consume power. By implementing this method, it is expected that the connection status of electric vehicles can be optimized to help stabilize the grid and minimize frequency fluctuations caused by the integration of distributed renewable energy sources. This study highlights the importance of automatic frequency control in smart grids and offers a potential solution using ANFIS and the whale optimization algorithm.

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid—integrating multiple Microgrids with a battery management system—can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results, Voltage variation of PI, Fuzzy, and LSTM-ANN is 370V,180V,and 70V . The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

Low computational cost convolutional neural network for smart grid frequency stability prediction

In the smart grid, it is critical to collect dynamic and time-dependent information on energy demand and consumption and compare it to current supply conditions. The decentral smart grid control (DSGC) system manages frequency, a Smart grid element. It connects energy costs to grid frequency, allowing access to both consumers and producers. This work proposes a pruning of the convolution layers and neurons of 1-dimensional time-aware convolutional neural network (1D CNN) analysis of grid frequency stability to determine efficient energy costs. The proposed solution evaluated augmented grid stability datasets in addition to two other publicly available datasets to ascertain the approach’s feasibility in various scenarios; the simulation demonstrated a minimal train and prediction time of 124.37 s and 17.67 s efficiency over compared models, with prediction accuracy of 99.79% and 0.01 MFLOPs. Matthew’s correlation coefficient was applied to evaluate further the performance of the proposed 1D CNN to ascertain its applicability in various scenarios.

Fuzzy Logic-Based Energy Storage Control in Smart Grids for Grid Stability

This study studies the usefulness of fuzzy logic-based control systems for improving energy storage control inside smart grids to promote grid stability. The study combines empirical data analysis, including energy storage system (ESS) specifications, smart grid operational data, fuzzy logic-based control rules, and ESS state variables, to demonstrate the suitability and efficiency of using fuzzy logic-based control mechanisms in dynamic grid environments. The examination of ESS specs revealed a wide range of maximum capacities, spanning from 100 kWh to 200 kWh. Additionally, the charge and discharge efficiencies exhibited variations, ranging from 85% to 96%. An analysis of operational data from the smart grid revealed significant variations in grid frequency, ranging from 50.0 Hz to 50.3 Hz. Voltage levels also exhibited fluctuations, ranging from 229 kV to 232 kV. Additionally, renewable energy generation from solar and wind sources showed fluctuations between 1400 kW to 1650 kW and 800 kW to 850 kW, respectively. The incorporation of linguistic factors and fuzzy rules based on grid parameters facilitated the adaptive control of ESS units in the construction of fuzzy logic-based control rules. The analysis of ESS state variables revealed dynamic changes in the state of charge, which ranged from 60% to 90%. Additionally, oscillations in available energy were observed across different timestamps and ESS units. An investigation of in state variables, revealed adaptive changes percentage change demonstrating varying degrees of variations in state of charge, available energy, and operational states at various timestamps. The results emphasize the flexibility and efficiency of control systems based on fuzzy logic in improving energy storage operations in smart grids, highlighting their capacity to improve grid stability and efficiently handle changing grid characteristics.

Load Forecasting with Hybrid Deep Learning Model for Efficient Power System Management

Aim: Load forecasting with for efficient power system management Background:: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method:: 1D CNN BI-LSTM model incorporating convolutional layers. Result:: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion:: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.

The Demand-Side Management and Control of Smart Grids Based on Weighted Network Congestion Games

The Demand-Side Management and Control of Smart Grids Based on Weighted Network Congestion Games

A cyber-layer based on weighted average consensus in blockchain environment for accurate sharing of power systems’ dynamic states

Sharing the dynamic states among power system generators is crucial for wide-area monitoring, decentralized control, and protection of the prospective smart grid. However, this sharing process is subject to cyber–physical contingency conditions, which limits its accuracy. This paper proposes a framework that incorporates a cyber-layer with the power system to ensure accurate sharing of the generators’ dynamic states. The proposed framework utilizes a weighted average consensus mechanism within a blockchain environment. The consensus ensures that the communication links/data among generator buses are reliable in both directions and that any link/data failure is detected to guarantee the validity, and hence accuracy of the shared generators’ states. Moreover, a smart contract at each generator applies the proposed consensus mechanism and credits the validator generator bus that creates the next block in the states’ blockchain after achieving the consensus. Finally, the proposed framework is tested on the standard IEEE 3-generator 9-bus power system under data contingency conditions. The generators’ speed states are tested, while the other states can be verified in a similar manner. Simulation results show the ability of the proposed framework to ensure accurate sharing of the generators’ speed states and to detect possible cyber-attacks, compared to the case without using the proposed framework.

IoT-based monitoring and control of substations and smart grids with renewables and electric vehicles integration

The integrated renewable energy resources (RERs) based smart grid in the power distribution network (PDN) has financial and ecological benefits. However, the emergence of RER-based microgrids and substations without real-time monitoring of their power parameters leads to various challenges in the PDN, such as suboptimal resource allocation, poor load management, grid instability, and lack of real-time decision-making capabilities. To mitigate these challenges, increase the system's visibility, and make efficient decisions, intelligent monitoring and control system is required for both smart grid and power substation. This proposed study develops IoT-based monitoring and control of power substations and associated distributed smart grids to make effective decisions of integration/segregation into the PDN. The proposed IoT-based integration/segregation of smart grids and load management can mitigate the stated challenges effectively. Using the HOMER Grid®, the research also investigates the annualized power production pattern of smart grids and the power consumption pattern of integrated loads to enable proactive decisions about energy management. The proposed study implements IoT technology for power parameters monitoring of substations and smart grids for their effective use, as it considers four types of load management, including industrial, domestic, commercial, and electric vehicles, with the aid of IoT technology to avoid power fluctuations and contingencies. Effective load management has been adopted based on annualized energy consumption, load patterns and real-time monitoring, and active decisions making. The results highlight that the proposed IoT-based approach helps advance smart grid integration into the PDN and enhance energy load management, consequently reducing energy costs and suppressing carbon emissions. The validation of the proposed model is verified by the constructed prototype, where the achieved real-time monitoring and control of power substations and smart grids into PDN is performed to make effective decisions related to energy and load management. Moreover, it effectively allows power distribution companies to manage loads during high demand or crises and enhances grid stability and energy efficiency.