Business model analysis for the interaction between smart grid and mobile network operators

Demand Response (DR) refers to the scenarios in which the energy consumer reduce its own consume for some financial purpose, or even during critical energy periods. It is notorious that DR devices are indispensable in Smart Grid operations. DR allows a smart load management, and, when added to the Smart Grid operation, makes possible the optimization of Distributed Generation, Energy Storage and still provide an efficient consumption for the customer. At the residential application, in order to become efficient, DR devices must work smart and autonomously, managing the loads keeping the good quality of the user consumption. The main objective of this paper is to show a new topology for residential load management, inside a Smart Grid concept, and its operation modes. The article will contain a detailed description of the operation logic algorithms, system autonomy and the interaction with the user for optimize its own consumption, preserving its quality. It will also contain simulations with load management devices to prove the concepts presented in the proposed model.

The penetration of information and communication technologies (ICT) and the Internet of Things (IoT) in the smart grid (SG) power network paradigm has widened the landscape of cyber threats aiming at disrupting the grid operations. The wider exposure of the communication infrastructure increases the vulnerability of smart grids towards cyber intrusions. The most common impactful intrusions (commonly referred to as false data injection attack (FDIA)) involve falsification of sensor measurement while it is being transmitted to the control centre. Among FDIAs, the load redistribution(LR) attack causes the maximum disruption on the grid operations without being detected at the control centre. During the LR attack, the intruder attempts to virtually manipulate the sensor information at different locations. Analyzing the cyber vulnerability of power networks demands identifying possible LR attack scenarios that disrupt the grid operations to the maximum extent. In this regard, the present work proposes monitoring metrics, Cyber Resiliency Index (CRI), Cyber Severity Index (CSI) and Cyber Market Index (CMI) to quantify the impact of measurement falsification through LR attack scheme, executed with minimum budget and resources. The attack design task has been framed as a multi-objective optimization based on the trade-off between the objectives of maximum disruption and minimum attack cost. A set of solutions (Pareto-front), each pertaining to a bus location for LR attack, is derived post-convergence of the optimization problem. The proposed metrics have been validated for different attack scenarios in a real-time environment.

A survey on applications of Alternating Direction Method of Multipliers in smart power grids

Chapter 4 - Frameworks for Big Data Integration, Warehousing, and Analytics

Smart grids are electric grids that utilize advanced technologies of monitoring, control, and communication to supply reliable and secure energy, improve the efficiency of the system, and provide flexibility to the prosumers. The beginning of the smart grid era and the development in modern infrastructures of metering, communication, and energy storage have revolutionized the power grid. Smart grids are developed with complex physical networks and cyber systems thus enabling smart grids for the Internet of Energy (IoE). IoE is the cloud where sources of power generation and loads of power consumption are embedded with intelligence. Modern electric power grids are integrated with sensors used to provide measurements. The sensor measurements and the complex applications of various sensors cause a need for Cyber-Physical System (CPS). CPS is a class of systems that integrates physical process, computation, and networking. The CPS model of the smart grid helps in modeling and simulation for evaluation of the system performance and characteristics. The CPS model of the smart grid must enable the smart grid to be robust, 2allow future extensions, and compatible with web service technologies. The power generation sources and loads such as smart buildings are the physical layers, the sensors used for measurements form the cyber-physical integration and the data storage and processing using IOE forms the cyber layer of the smart grid. The CPS model of the smart grid helps in the integration of intelligent devices and allied information and communication technologies for robust and reliable operation in smart grids. In the smart grid paradigm, the energy management system has a vital role to increase the efficiency and reliability of the system. This chapter presents a CPS model for smart grid and the challenges associated with the development of the CPS model. In addition, this chapter describes the energy management system model of the smart grid.

This study addresses a critical operational gap in smart grid infrastructure management: while hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have been theoretically proposed for simultaneous fault detection and cyber-threat classification in SCADA-based environments, no empirically validated quantitative framework exists to determine which hybrid architecture capability dimensions most strongly predict operational reliability, threat response effectiveness, and detection latency reduction in real-world deployment contexts. The purpose was to empirically validate a hybrid CNN-LSTM deep learning framework that links core architectural capabilities to smart grid operational outcomes within an enterprise-scale, case-based setting. Using a quantitative, cross-sectional, case-study design, data were collected from a purposive sample of N = 218 smart grid security and operations professionals including SCADA engineers, network security analysts, data scientists, grid operations managers, and protection relay engineers working in operational environments where AI-driven monitoring and anomaly detection platforms are actively deployed. Key independent variables were CNN Feature Extraction Capability (C), LSTM Temporal Sequence Modeling Capability (L), and Hybrid Architecture Integration Depth (H), alongside two domain-specific indices: the Hybrid Architecture Performance Index (HAPI) and the Real-Time Threat Detection Alignment (RTDA); key dependent variables were Fault Detection Accuracy (Y1), Cyber-Threat Classification Effectiveness (Y2), and Operational Continuity Performance (Y3). The analysis applied descriptive statistics, reliability and validity testing (Cronbach's alpha, EFA with KMO and Bartlett's test), Pearson correlations, and multiple regression models. Headline findings demonstrate strong measurement quality (alpha = .83-.89; KMO = .91; Bartlett's chi-squared = 2318.7, p < .001) and moderately high capability levels (CNN Capability M = 3.97, SD = 0.61; LSTM Capability M = 4.11, SD = 0.56; Hybrid Integration M = 3.84, SD = 0.67). All core relationships were positive and significant (p < .001), including Fault Detection Accuracy with HAPI (r = .66) and LSTM Capability (r = .63), Cyber-Threat Classification with RTDA (r = .68), and Operational Continuity with Hybrid Integration (r = .64). Regression results indicate substantial explained variance for Fault Detection Accuracy (R2 = .63; F(5,212) = 72.4, p < .001), with HAPI as the strongest predictor (beta = .37, p < .001), followed by LSTM Capability (beta = .25, p = .001).

The smart grid is a new type of energy-based cyber-physical system (CPS), which enables interactions between the utility provider and customers through smart meters and advanced metering infrastructures (AMI). Nonetheless, an adversary can inject misleading energy usage information to the utility provider through compromised smart meters and disrupt the grid and electricity market operations. To address this issue, in this paper, we propose an Energy Dispatching False Data Defense (EDF2D) approach, which can effectively detect the forged interactive information between customers and the utility provider with a great accuracy and mitigate the damage raised by attacks on grid operations. Particularly, EDF2D uses the historical interactive information of normal users to determine the conditional probabilities of data anomalies. Based on these conditional probabilities, a Bayesian network designed for detecting false data can be established by EDF2D, and this network is then used to confirm the authenticity of interactive information received by the utility provider originally transmitted from customers. Through a combination of theoretical analysis and performance evaluation, our experimental data shows that EDF2D can effectively detect harmful false interactive data forged by the adversary and mitigate false data injection attacks on smart grid operations.

The Internet of Agents (IoA) is an emerging field of research that aims to combine the advantages of multi-agent systems and Internet of Things (IoT), by adding autonomy and smartness to, traditionally, dummy things used in IoT. Multi-agent systems can be used to model distributed systems of smart grids, such as smart grid operations, power system control, electricity market, and monitoring and diagnostic. Trust management can be considered a key component for successful interactions between autonomous agents in IoA, especially when agents cannot assure that potential interactions’ partners share the same core beliefs, or make accurate statements regarding their competencies and abilities. When interactions are based on trust, trust establishment mechanisms can be used to direct trustees, instead of trustors, to build a higher level of trust and have a greater impact on the results of interactions. This paper presents a trust establishment model that uses a multi-criteria (multidimensional) approach to help trustees in IoA environment to adjust their behaviors to improve their perceived trustworthiness, to attract more interactions with trustors. It calculates the necessary improvement per criterion when only a single aggregated satisfaction value is provided per interaction, where the model attempts to predicted both the appropriate value per criteria and its importance. The proposed model is evaluated through simulation, and results indicate that trustees empowered with the proposed model have higher levels of trust and better chances to be selected as interaction partners when such selection is based on trust.

India is the defiant to meet electric power demand of the quick growing economy. Reconstruct of the power industries is only improves the several challenges for power system engineers. It is the proposed idea to introduce the workable Smart Grid (SG) at different level in the Indian power systems is to be suggested that an advanced automation mechanism is required. Smart Grid is introduce to build the grid operation smarter and quick. Smart grid operations, upon suitable deployment. It can be open up with new avenue and opportunities with important financial proposition. This work present in the various Smart grid initiatives and implication in the context of the power market development in India. This paper proposed the concert examination of the power flow controller for photovoltaic (PV), wind energy and fuel cell generation system. Typical grid joined with solar system includes Photovoltaic system, fuel cell, Dc to DC Converters, three phase inverter, wind energy, battery units, related power electronics devices and loads are required in the grid. This detailed model of smart grid connect with photovoltaic systems and fuel cell starts with PV source which is coupled with the boost dc-dc converter and then in turn joined to the three phase inverter which delivered the solar energy to the smart grid, in addition to fuel cell connects to inverter which deliver power to smart grid. This inverter is operated as the shunt active power filter (APF)and is adopted with the nonlinear control scheme in sort to compensates the voltage unbalance, the harmonics, are active power, and the load voltage and current variation.

The Internet of Things (IoT) promises many advantages in the control and monitoring of physical systems from both efficacy and efficiency perspectives. However, in the wrong hands, the data might pose a privacy threat. In this article, we consider the tradeoff between the operational value of data collected in the IoT and the privacy of consumers. We present a general framework for quantifying this tradeoff in the IoT, and focus on a smart grid application for a proof of concept. In particular, we analyze the tradeoff between smart grid operations and how often data are collected by considering a realistic direct-load control example using thermostatically controlled loads, and we give simulation results to show how its performance degrades as the sampling frequency decreases. Additionally, we introduce a new privacy metric, which we call inferential privacy. This privacy metric assumes a strong adversary model and provides an upper bound on the adversary’s ability to infer a private parameter, independent of the algorithm he uses. Combining these two results allows us to directly consider the tradeoff between better operational performance and consumer privacy.

The Smart Grid Operation Centre (SGOC) is established on 1st April, 2021, as part of the purpose-built facilities of the Haking Wong Campus of the Hong Kong Institute of Vocational Education (IVE), a Vocational Training Council (VTC) member institution. With the generous contributions and steadfast support from CLP Power Hong Kong Limited (CLP Power), SGOC is served as simulation and training facility available to support all Smart Grid development in Hong Kong. The SGOC has installed the Real-Time Digital Simulator (RTDS) and Smart grid-related equipment, including the Distributed Energy Resources (DERs), protection relay, and smart meter that can be looped into the simulator, for its capability and interoperability on real-time studies, including modeling and resolving the potential issues in realtime before they may impact the delivery or operation of the project or the smart grid network. Facing the up-coming challenges with penetration of renewable energy, digitalization, and increasing of electric vehicles, the SGOC provides a feasible range of smart grid related services in respect of real-time studies on the operation of smart grid system, operator training, and operational support that enable derisk deployment and operation of the related smart grid parties, and more practical and tailored training which increases familiarity and confidence of operators. This paper describes the practicalities of specifying, testing, installation and interfacing the platform, with a realtime simulator, at the SGOC, for future operational training and support use, including some uses on faults during operation, diagnosing alarms, scheme updates/upgrades and network interactions and range of smart grid focused training courses will be available and content enhancement into course delivery. Through these wide-ranging installations and systems, trainers will learn to apply the latest industry technologies. This will enrich their knowledge of smart grids and relevant new technologies, thereby helping to promote smart, clean energy development. This paper is also included the Power Hardware- In-the-Loop (PHIL) platform and a case study introduction for the frequency capability for microgrid and Battery Energy Storage System (BESS) response.

Smart grid technologies are expected to increase system efficiency and reliability using advancement in automation, communication, computation, and optimization. Several control algorithms have been developed or enhanced for operation and control of smart distribution grid. In recent years, volt/var control (VVC) algorithms have been proposed for energy saving in distribution systems due to advancement provided by smart grid automation and control. VVC helps to lower distribution feeder voltage to decrease energy consumption. However, quantification of this impact is hard due to several external factors such as weather and end users' behavior. On the other hand, existing evaluation methods of VVC algorithms are either not suitable for long-term study or intrusive. This paper addresses these issues by proposing a new technique to estimate “distribution system demand without smart grid implementation” utilizing “data measured from system with smart grid implementation.” Interdependence of voltage and load is considered based on estimated load characteristic. Our key contribution is developing a method for quantifying the real-time energy saving of smart grid automation and control focused on VVC and implementing in an electric utility in eastern Washington, USA. The algorithm is developed using both commercial and open-source software.

The operation of smart power grids is supported by interconnected sensors and devices that provide real-time monitoring. Smart grids provide more secure and dependable electricity than traditional grids because of constant monitoring. This paper reviews the technology used in smart grids, specifically sensors and communication technologies. It also talks about the challenges that smart grids face, such as providing power to the network's sensors, islanding, cyber-security, and issues that occur when using distributed generators. Current solutions for sensing changes and communication of sensed data in smart grid networks are also discussed.

India is truculent to meet the electric power demands of a fast expanding economy. Restructuring of the power industry has only increased several challenges for the power system engineers. The proposed vision of introducing viable Smart Grid (SG) at various levels in the Indian power systems has recommended that an advanced automation mechanism needs to be adapted. Smart Grids are introduced to make the grid operation smarter and intelligent. Smart grid operations, upon appropriate deployment can open up new avenues and opportunities with significant financial implications. This paper presents various Smart grid initiatives and implications in the context of power market evolution in India. Various examples of existing structures of automation in India are employed to underscore some of the views presented in this paper. It also reviews the progress made in Smart grid technology research and development since its inception. Attempts are made to highlight the current and future issues involved for the development of Smart Grid technology for future demands in Indian perspective.

The future power network will be designed to accommodate and integrate all types of distributed renewable energy resources, storage units, and flexible demand response loads in the existing conventional grid. Also it should be able to perform intelligent energy management utilizing advancement in computation and communication to cater the needs of ever growing energy demand in secure manner. This leads the transition of conventional power grid into smart grid. However, performance of the smart grid utilizing automated, intelligent, and integrated functional blocks with widely interconnected distributed energy resources is dependent on advanced communication network, sensors, computing, and information technologies. There is a need for reliable and efficient communication network and computing infrastructure for the robust, affordable, and secure supply of electric power through smart grid operation. This Special Section has accepted altogether seven research manuscripts depending on the novelties and problems that they address. The paper are briefly summarized here.