Market Drivers in India’s Smart Grid:Responsibilities and Roles of Stakeholders

The emergence of smart grids in India is propelled by an intricate interaction of market dynamics,regulatory structures,and stakeholder obligations.This study analyzes the primary factors that are driving the widespread use of smart grid technologies and outlines the specific roles and obligations of different stakeholders,such as government entities,utility companies,technology suppliers,and consumers.Government activities and regulations are crucial in facilitating the implementation of smart grid technology by offering financial incentives,regulatory assistance,and strategic guidance.Utility firms have the responsibility of implementing and integrating smart grid infrastructure,with an emphasis on improving the dependability of the grid,minimizing losses in transmission and distribution,and integrating renewable energy sources.Technology companies offer the essential hardware and software solutions,which stimulate creativity and enhance efficiency.Consumers actively engage in the energy ecosystem by participating in demand response,implementing energy saving measures,and adopting distributed energy resources like solar panels and electric vehicles.This study examines the difficulties and possibilities in India’s smart grid industry,highlighting the importance of cooperation among stakeholders to build a strong,effective,and environmentally friendly energy future.

AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy protection.Current research emphasizes data security and user privacy concerns within smart grids.However,existing methods struggle with efficiency and security when processing large-scale data.Balancing efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent challenge.This paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data modalities.The approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user privacy.It also explores the application of Boneh Lynn Shacham(BLS)signatures for user authentication.The proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

Self-Attention Spatio-Temporal Deep Collaborative Network for Robust FDIA Detection in Smart Grids

False data injection attack(FDIA)can affect the state estimation of the power grid by tampering with the measured value of the power grid data,and then destroying the stable operation of the smart grid.Existing work usually trains a detection model by fusing the data-driven features from diverse power data streams.Data-driven features,however,cannot effectively capture the differences between noisy data and attack samples.As a result,slight noise disturbances in the power grid may cause a large number of false detections for FDIA attacks.To address this problem,this paper designs a deep collaborative self-attention network to achieve robust FDIA detection,in which the spatio-temporal features of cascaded FDIA attacks are fully integrated.Firstly,a high-order Chebyshev polynomials-based graph convolution module is designed to effectively aggregate the spatio information between grid nodes,and the spatial self-attention mechanism is involved to dynamically assign attention weights to each node,which guides the network to pay more attention to the node information that is conducive to FDIA detection.Furthermore,the bi-directional Long Short-Term Memory(LSTM)network is introduced to conduct time series modeling and long-term dependence analysis for power grid data and utilizes the temporal self-attention mechanism to describe the time correlation of data and assign different weights to different time steps.Our designed deep collaborative network can effectively mine subtle perturbations from spatiotemporal feature information,efficiently distinguish power grid noise from FDIA attacks,and adapt to diverse attack intensities.Extensive experiments demonstrate that our method can obtain an efficient detection performance over actual load data from New York Independent System Operator(NYISO)in IEEE 14,IEEE 39,and IEEE 118 bus systems,and outperforms state-of-the-art FDIA detection schemes in terms of detection accuracy and robustness.

Fortifying Smart Grids: A Holistic Assessment Strategy against Cyber Attacks and Physical Threats for Intelligent Electronic Devices

Intelligent electronic devices(IEDs)are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions.In the context of the heightened security challenges within smart grids,IEDs pose significant risks due to inherent hardware and software vulner-abilities,as well as the openness and vulnerability of communication protocols.Smart grid security,distinct from traditional internet security,mainly relies on monitoring network security events at the platform layer,lacking an effective assessment mechanism for IEDs.Hence,we incorporate considerations for both cyber-attacks and physical faults,presenting security assessment indicators and methods specifically tailored for IEDs.Initially,we outline the security monitoring technology for IEDs,considering the necessary data sources for their security assessment.Subsequently,we classify IEDs and establish a comprehensive security monitoring index system,incorporating factors such as running states,network traffic,and abnormal behaviors.This index system contains 18 indicators in 3 categories.Additionally,we elucidate quantitative methods for various indicators and propose a hybrid security assessment method known as GRCW-hybrid,combining grey relational analysis(GRA),analytic hierarchy process(AHP),and entropy weight method(EWM).According to the proposed assessment method,the security risk level of IEDs can be graded into 6 levels,namely 0,1,2,3,4,and 5.The higher the level,the greater the security risk.Finally,we assess and simulate 15 scenarios in 3 categories,which are based on monitoring indicators and real-world situations encountered by IEDs.The results show that calculated security risk level based on the proposed assessment method are consistent with actual simulation.Thus,the reasonableness and effectiveness of the proposed index system and assessment method are validated.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management.However,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of secrecy.Motivated by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the RL.By integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms.This can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the network.We compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network conditions.The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence trend.For our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation dataset.This was accompanied by the lowest RMSE(0.95),indicating very precise predictions with minimal error.

Optimization of Stealthwatch Network Security System for the Detection and Mitigation of Distributed Denial of Service (DDoS) Attack: Application to Smart Grid System

The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.

Development and Challenges of Smart Grids

The development of smart grids marks a pivotal transformation in the global energy landscape.As traditional power grids face inefficiencies,high costs,and challenges related to renewable energy integration,smart grids offer a solution through the incorporation of advanced information and communication technologies(ICT),automation,and real-time data analytics.These technologies enhance the monitoring,control,and optimization of energy systems,enabling better integration of renewable sources,efficient energy distribution,and two-way communication between consumers and utilities.Despite the promising benefits,the widespread deployment of smart grids is hindered by technological,economic,regulatory,and social barriers.This paper explores the technological advancements,current applications,challenges,and future prospects of smart grids,emphasizing the need for global collaboration,innovation,and adaptable policies.The successful implementation of smart grids is essential for achieving a sustainable and resilient energy future,requiring concerted efforts across multiple sectors to overcome existing obstacles.

Optimal gateway deployment under different queuing mechanisms in smart grid

By optimizing the network topology, this paper proposes a newmethod of queuing theory clustering algorithm based on dynamic programming in a home energy management system（ HEMS）. First, the total cost of the HEMS system is divided into two parts, the gateway installation cost and the data transmission cost. Secondly, through comparing two kinds of different queuing theories, the cost problem of the HEMS is converted into the problem of gateway deployment. Finally, a machine-to-machine（ M2M） gateway configuration scheme is designed to minimize the cost of the system. Simulation results showthat the cost of the HEMS system mainly comes from the installation cost of the gateways when the gateway buffer space is large enough. If the gateway buffer space is limited, the proposed queue algorithm can effectively achieve optimal gateway setting while maintaining the minimal cost of the HEMS at desired levels through marginal analyses and the properties of cost minimization.

An evaluation model for smart grids in support of smart cities based on the Hierarchy of Needs Theory

Smart cities depend highly on an intelligent electrical networks to provide a reliable,safe,and clean power supplies.A smart grid achieves such aforementioned power supply by ensuring resilient energy delivery,which presents opportunities to improve the cost-effectiveness of power supply and minimize environmental impacts.A systematic evaluation of the comprehensive benefits brought by smart grid to smart cities can provide necessary theoretical fundamentals for urban planning and construction towards a sustainable energy future.However,most of the present methods of assessing smart cities do not fully take into account the benefits expected from the smart grid.To comprehensively evaluate the development levels of smart cities while revealing the supporting roles of smart grids,this article proposes a model of smart city development needs from the perspective of residents’needs based on Maslow’s Hierarchy of Needs theory,which serves the primary purpose of building a smart city.By classifying and reintegrating the needs,an evaluation index system of smart grids supporting smart cities was further constructed.A case analysis concluded that smart grids,as an essential foundation and objective requirement for smart cities,are important in promoting scientific urban management,intelligent infrastructure,refined public services,efficient energy utilization,and industrial development and modernization.Further optimization suggestions were given to the city analyzed in the case include strengthening urban management and infrastructure constructions,such as electric vehicle charging facilities and wireless coverage.

Smart Grid: Future of Electrical Transmission and Distribution

For century, the need of change in the system of power grid is being felt and being considered as the most important change that we need in the modern era electrical system but before moving towards a new change the things in past should be kept in mind so that there are better chances of great and beneficial change. The purpose of this research is to investigate the changes need to be incorporated in a conventional system to make a system self-sufficient and automated in order to make Electrical Power system more reliable. This paper assesses the current one way power system that is needed to be changed and has tried to provide an overview of the changes that we need in the system. The paper has focused more on the smart grid system and has explained the importance of smart grid system in terms of efficiency, automation and decision making capability in case of faults occurred on primitive grids with the help of comparative studies. The paper also highlighted the results in form of comparison with conventional grids and threw some light on the vision, control and the application of the smart grid system that will provide a two way system to the electrical network of the country and will make the distribution and consumption of energy more efficient also which is going to increase the reliability and accuracy in the system.

Smart Grids with Intelligent Periphery:An Architecture for the Energy Internet

A future smart grid must fulfill the vision of the Energy Internet in which millions of people produce their own energy from renewables in their homes, offices, and factories and share it with each other. Electric vehicles and local energy storage will be widely deployed. Internet technology will be utilized to transform the power grid into an energysharing inter-grid. To prepare for the future, a smart grid with intelligent periphery, or smart GRIP, is proposed. The building blocks of GRIP architecture are called clusters and include an energy-management system （EMS）-controlled transmission grid in the core and distribution grids, micro-grids, and smart buildings and homes on the periphery; all of which are hierarchically structured. The layered architecture of GRIP allows a seamless transition from the present to the future and plug-and-play interoperability. The basic functions of a cluster consist of （1） dispatch, （2） smoothing, and （3） mitigation. A risk-limiting dispatch methodology is presented; a new device, called the electric spring, is developed for smoothing out fluctuations in periphery clusters; and means to mitigate failures are discussed.

Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services.However,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy threats.To address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA.Specifically,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart grids.Subsequently,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous authentication.In particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation.Finally,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

Dynamic GM（1,1） Model Based on Cubic Spline for Electricity Consumption Prediction in Smart Grid

Electricity demand forecasting plays an important role in smart grid expansion planning.In this paper,we present a dynamic GM(1,1) model based on grey system theory and cubic spline function interpolation principle.Using piecewise polynomial interpolation thought,this model can dynamically predict the general trend of time series data.Combined with low-order polynomial,the cubic spline interpolation has smaller error,avoids the Runge phenomenon of high-order polynomial,and has better approximation effect.Meanwhile,prediction is implemented with the newest information according to the rolling and feedback mechanism and fluctuating error is controlled well to improve prediction accuracy in time-varying environment.Case study using the living electricity consumption data of Jiangsu province in 2008 is presented to demonstrate the effectiveness of the proposed model.

Preserving scheme for user’s confidential information in smart grid based on digital watermark and asymmetric encryption

As an essential part of the industrial Internet of Things(IoT)in power systems,the development of advanced metering infrastructure(AMI)facilitates services such as energy monitoring,load forecasting,and demand response.However,there is a growing risk of privacy disclosure with the wide installation of smart meters,for they transmit readings and sensitive data simultaneously.To guarantee the confidentiality of the sensitive information and authenticity of smart meter readings,we proposed a privacy-preserving scheme based on digital watermarking and elliptic-curve cryptography(ECC)asymmetric encryption.The sensitive data are encrypted using the public key and are hidden in the collected readings using digital watermark.Only the authorized user can extract watermark and can decrypt the confidential data using its private key.The proposed method realizes secure end-to-end confidentiality of the sensitive information.It has faster computing speed and can verify the data source and ensure the authenticity of readings.The example results show that the proposed method has little influence on the original data and unauthorized access cannot be completed within a reasonable time.On embedded hardware,the processing speed of the proposed method is better than the existing methods.

An efficient data aggregation scheme with local differential privacy in smart grid

By integrating the traditional power grid with information and communication technology, smart grid achieves dependable, efficient, and flexible grid data processing. The smart meters deployed on the user side of the smart grid collect the users' power usage data on a regular basis and upload it to the control center to complete the smart grid data acquisition. The control center can evaluate the supply and demand of the power grid through aggregated data from users and then dynamically adjust the power supply and price, etc. However, since the grid data collected from users may disclose the user's electricity usage habits and daily activities, privacy concern has become a critical issue in smart grid data aggregation. Most of the existing privacy-preserving data collection schemes for smart grid adopt homomorphic encryption or randomization techniques which are either impractical because of the high computation overhead or unrealistic for requiring a trusted third party.

Genetic Algorithm-Based Redundancy Optimization Method for Smart Grid Communication Network

This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network.

Costs and Benefits of Smart Grids on Liberalized Markets

The discussion about smart grid （SG） implementation is mostly focused on pilot projects. These projects are necessary for mapping of particular technical devices of advanced metering management （AMM） which is needed for successful SG and whole functional SG system operation. According to our opinion, for the next step of SG implementation, the participation of effective market design would be quite necessary. In other words, pilot project which is operated regardless to the market conditions and special SG tariff is incomplete and could be irrelevant for further evaluation of feasibility. With regard to above mentioned facts, the detailed cost-benefit-analysis （CBA） is needed to establish the correct methodology for evaluation of SG implementation effectiveness. Related aspects are mentioned and discussed in this paper, in which the particular cost and benefits as well as feedback that occurs as the reaction on implementation are summarized and quantified.

Load distribution model and voltage static profile of Smart Grid

Voltage profiles of feeders with the connection of distributed generations(DGs) were investigated.A unified typical load distribution model was established.Based on this model,exact expressions of feeder voltage profile with single and double DGs were derived and used to analyze the impact of DG's location and capacity on the voltage profile quantitatively.Then,a general formula of the voltage profile was derived.The limitation of single DG and necessity of multiple DGs for voltage regulation were also discussed.Through the simulation,voltage profiles of feeders with single and double DGs were compared.The voltage excursion rate is 7.40% for only one DG,while 2.48% and 2.36% for double DGs.It is shown that the feeder voltage can be retained in a more appropriate range with multiple DGs than with only one DG.Distributing the total capacity of DGs is better than concentrating it at one point.