A polymorphic heterogeneous security architecture for edge-enabled smart grids

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.

With the rapid advancement of technology and the growing reliance on renewable energy sources, smart grids have emerged as a transformative solution for enhancing the efficiency and reliability of power distribution systems. As the smart grid ecosystem evolves, security concerns become paramount due to the interconnectedness of various components, including advanced metering infrastructure, communication networks, and distributed energy resources. This paper presents a comprehensive analysis of smart grid security, focusing on the challenges, vulnerabilities, and potential threats that must be addressed to ensure the resilience of these intelligent infrastructures. It explores the diverse attack vectors that malicious actors may exploit to compromise smart grid operations and highlights the potential consequences of such security breaches, including power disruptions, data breaches, and financial losses. To counteract these risks, this paper delves into various state-of-the-art security measures and strategies that can be implemented to safeguard smart grids effectively. Moreover, the importance of collaboration between various stakeholders is described, including utility companies, government agencies, researchers, and technology vendors, to foster a holistic approach to smart grid security. By emphasizing the significance of information sharing and collaboration, this paper advocates for a robust and adaptive security framework to respond promptly to emerging threats and vulnerabilities. Furthermore, the paper explores emerging technologies such as artificial intelligence, blockchain, and intrusion detection systems, showcasing their potential contributions in fortifying smart grid defenses. It was noted that the integration of these innovative technologies can enhance anomaly detection, facilitate secure data exchange, and reinforce the resilience of the smart grid against sophisticated cyber-attacks. Towards the end of this paper, insights into future research directions and policy implications to foster a more secure and sustainable smart grid environment is provided. By acknowledging the evolving threat landscape and the dynamic nature of the smart grid domain, this work encourages continuous research and development to stay ahead of adversaries and establish a secure foundation for the future of energy distribution.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Guest Editorial

With the exponential growth of science, Internet of Things (IoT) innovation, and expanding significance in renewable energy, Smart Grid has become an advanced innovative thought universally as a solution for the power demand increase around the world. The smart grid is the most practical trend of effective transmission of present-day power assets. The paper aims to survey the present literature concerning predictive maintenance and different types of faults that could be detected within the smart grid. Four databases (Scopus, ScienceDirect, IEEE Xplore, and Web of Science) were searched between 2012 and 2020. Sixty-five (n = 65) were chosen based on specified exclusion and inclusion criteria. Fifty-seven percent (n = 37/65) of the studies analyzed the issues from predictive maintenance perspectives, while about 18% (n = 12/65) focused on factors-related review studies on the smart grid and about 15% (n = 10/65) focused on factors related to the experimental study. The remaining 9% (n = 6/65) concentrated on fields related to the challenges and benefits of the study. The significance of predictive maintenance has been developing over time in connection with Industry 4.0 revolution. The paper’s fundamental commitment is the outline and overview of faults in the smart grid such as fault location and detection. Therefore, advanced methods of applying Artificial Intelligence (AI) techniques can enhance and improve the reliability and resilience of smart grid systems. For future direction, we aim to supply a deep understanding of Smart meters to detect or monitor faults in the smart grid as it is the primary IoT sensor in an AMI.

This article investigates and analyzes the security aspects of 5G specifications from the perspective of IoT‐based smart grids. As the smart grid requires high‐speed and reliable communication to enable real‐time grid monitoring via Internet of Things (IoT) devices, 5G can be considered a catalyst to transform the current power grid infrastructure into a smart grid. Thus, an understanding of what 5G can bring in terms of cyber security in IoT‐based smart grids is important for design decisions and future risk analysis efforts. In this article, we explore a smart grid use case on automatic voltage control—a use case utilizing 5G as a wireless communication infrastructure with edge support. We identify the benefits 5G brings to several security aspects, and show how 5G security techniques are applicable to the smart grid, thus providing a foundation for future security analysis of 5G enabled smart grid systems. Future research should extend this work to additional smart grid use cases.

The smart grid is widely considered as an efficient and intelligent power system. With the aid of communication technologies, the smart grid can enhance the efficiency and reliability of the grid system through intelligent energy management. However, with the development of new energy sources, storage and transmission technologies together with the heterogeneous architecture of the grid network, several new features have been incorporated into the smart grid. These features make the energy trading more complex and pose a significant challenge on designing efficient trading schemes. Based on this motivation, in this paper, we present a comprehensive review of several typical economic incentive approaches adopted in the energy-trading control mechanisms. We focus on the technologies that address the challenges specific to the new features of the smart grid. Furthermore, we investigate the energy trading in a new cloud-based vehicle-to-vehicle energy exchange scenario. We propose an optimal contract-based electricity trading scheme, which efficiently increases the generated profit.

The integration of cybersecurity measures, specifically Intrusion Detection Systems (IDS), into grid modernization initiatives is essential to protect critical energy infrastructures from evolving cyber threats. This paper presents a conceptual model for embedding cybersecurity frameworks into the modernization of power grids, focusing on the seamless integration of IDS within the grid architecture. With the increasing digitization of energy systems, traditional grids face heightened vulnerabilities, making robust cybersecurity strategies a priority. Through an in-depth review of existing cybersecurity frameworks and IDS models, this paper identifies the gaps in current integration approaches and proposes a layered security architecture that integrates AI and real-time monitoring for proactive threat detection. The proposed model also emphasizes scalability, interoperability, and compliance with regulatory frameworks. In addition to offering a phased implementation roadmap, the paper discusses the strategic, regulatory, and institutional challenges associated with integrating advanced cybersecurity systems into grid infrastructure. Furthermore, it explores future research areas, including quantum-safe cryptography, autonomous threat responses, and the potential use of blockchain for secure grid transactions. By addressing both the theoretical foundations and practical aspects of cybersecurity in grid modernization, this paper contributes to the development of more secure, resilient, and future-proof energy grids.

Smart Grid is a promising power delivery infrastructure integrated with communication and information technologies. By incorporating monitoring, analysis, control and communications facilities, it is possible to optimize the performance of the power system, allowing electricity to be delivered more efficiently. In the transmission and distribution sector, online monitoring of transmission lines and primary equipments is of vital importance, which can improve the reliability of power systems effectively. Optical fiber sensors can provide an alternative to conventional electrical sensors for such applications, with high accuracy, long term stability, streamlined installation, and premium performance under harsh environmental conditions. These optical fiber sensors offer immunity to EMI and extraordinary resistance to mechanical fatigue and therefore they will have great potential in on-line monitoring applications in Smart Grid. In this paper, we present a summary of the on-line monitoring needs of Smart Grid and explore the use of optical fiber sensors in Smart Grid. First, the on-line monitoring needs of Smart Grid is summarized. Second, a review on optical fiber sensor technology is given. Third, the application of optical fiber sensors in Smart Grid is discussed, including transmission line monitoring, primary equipment monitoring and substation perimeter intrusion detection. Finally, future research directions of optical fiber sensors for power systems are discussed. Compared to other traditional electrical sensors, the application of optical fiber sensors in Smart Grid has unique advantages.

Building an efficient, smart, and multifunctional power grid while maintaining high reliability and security is an extremely challenging task, particularly in the ever-evolving cyber threat landscape. The challenge is also compounded by the increasing complexity of power grids in both cyber and physical domains. In this article, we develop a stochastic Petri net based analytical model to assess and analyze the system reliability of smart grids, specifically against topology attacks under system countermeasures (i.e., intrusion detection systems and malfunction recovery techniques). Topology attacks, evolving from false data injection attacks, are growing security threats to smart grids. In our analytical model, we define and consider both conservative and aggressive topology attacks, and two types of unreliable consequences (i.e., system disturbances and failures). The IEEE 14-bus power system is employed as a case study to clearly explain the model construction and parameterization process. The benefit of having this analytical model is the capability to measure the system reliability from both transient and steady-state analysis. Finally, intensive simulation experiments are conducted to demonstrate the feasibility and effectiveness of our proposed model.

EPPSQ: Achieving efficient and privacy-preserving statistics queries over encrypted data in smart grids

With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the stability and reliability of smart grids. This study explores the performance of various machine learning classifiers in predicting the stability of smart grid systems. Utilizing a smart grid dataset obtained from the University of California’s machine learning repository, classifiers such as logistic regression (LR), XGBoost, linear support vector machine (Linear SVM), and SVM with radial basis function (SVM-RBF) were evaluated. Evaluation metrics, including accuracy, precision, recall, and F1 score, were employed to assess classifier performance. The results demonstrate high accuracy across all models, with the Deep Neural Network (DNN) model achieving the highest accuracy of 99.5%. Additionally, LR, linear SVM, and SVM-RBF exhibited comparable accuracy levels of 98.9%, highlighting their efficacy in smart grid stability prediction. These findings underscore the utility of machine learning techniques in enhancing the reliability and efficiency of smart grid systems.

Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.

This paper is the result of an extensive study, comparison, and analysis of academic articles and research related to smart grids and the solutions they offer to enhance the efficiency, reliability, and sustainability of modern electrical power systems. It presents a comprehensive analysis of the progress made in recent years in smart grid technologies, including architecture, communication infrastructure, control strategies, and the integration of renewable energy sources. Additionally, the benefits and advantages of adopting smart grids are explored and presented, along with the challenges arising from their use. There is also a reference to the future prospects of using smart grids and how they can impact human life. Following this, there is an analysis of the two most critical elements of smart grids, namely Energy Management Systems (EMS) and Advanced Metering Infrastructure (AMI). Finally, some conclusions are drawn regarding the use of smart grid systems, and a personal opinion is expressed.

Smart Grid as a Dynamical System of Complex Networks: A Framework for Enhanced Security