Blockchain-Enabled Cyber Security Framework for Enhanced Transactions in Distributed Smart Grid Infrastructure with Wind Farm Energy Source

The increasing reliance on smart grid infrastructures, particularly for critical services such as hospitals, necessitates highly secure, efficient, and resilient energy management systems. In response to the vulnerabilities exposed during the COVID-19 pandemic, particularly in Gujarat where power failures disrupted ventilator operations, this paper introduces CryptoGrid, a novel hybrid cryptographic ledger framework for smart grid cyber security and energy redistribution. It is proposed to achieve grid security against advanced cyber threats using the proposed architecture that incorporates blockchain-powered identity authentication, federated learning, and GAN-based anomaly detection. The agent-based control systems and optimization of energy management with Multi-Agent Reinforcement Learning (MARL) framework with PSO-GA hybrid credit pricing and redistribution have been used to improve energy management. Priority nodes are critical infrastructures like hospitals and are provided in case of an outage automatically by mobile EV energy banks and the decentralized microgrid systems. The experimental findings indicate that CryptoGrid is efficient in providing secure and life-saving energy solutions with an anomaly detection accuracy of 98.7, a 72% drop in the energy wastage, and a 29% increase in emergency energy supply.

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 (SG) infrastructure is an energy network connected with computer networks for communication over the internet and intranets. The revolution of SGs has also introduced new avenues of security threats. Although Digital Certificates provide countermeasures, however, one of the issues that exist, is how to efficiently distribute certificate revocation information among Edge devices. The conventional mechanisms, including certificate revocation list (CRL) and online certificate status protocol (OCSP), are subjected to some limitations in energy efficient environments like SG infrastructure. To address the aforementioned challenges, this paper proposes a scheme incorporating the advantages and strengths of the fog computing. The fog node can be used for this purpose with much better resources closer to the edge. Keeping the resources closer to the edge strengthen the security aspect of smart grid networks. Similarly, a fog node can act as an intermediate Certification Authority (CA) (i.e., Fog Node as an Intermediate Certification Authority (FONICA)). Further, the proposed scheme has reduced storage, communication, processing overhead, and latency for certificate verification at edge devices. Furthermore, the proposed scheme reduces the attack surface, even if the attacker becomes a part of the network.

In smart grid, critical data like monitoring data, usage data, state estimation, billing data etc are regularly being talked among its elements. So, security of such a system, if violated, results in massive losses and damages. By compromising with security aspect of such a system is as good as committing suicide. Thus in this paper, we have proposed security mechanism in Advanced Metering Infrastructure of smart grid, formed as Mesh-Zigbee topology. This security mechanism involves PKI based Digital certificate Authentication and Intrusion detection system to protect the AMI from internal and external security attack.

The growth of wind farms in Brazil brings new challenges to the Brazilian electrical system, such as the adequacy of regulation for system operation, the supply-demand balance, the quality of the supplied power, and the system stability. New technologies associated with smart grids will enable an increase in intermittent renewable sources connected to the power distribution system. D-STATCOM (Distribution Static Synchronous Compensator) provides the necessary resources to prevent technical problems in systems with high penetration of wind power plants. It makes possible the wind farms to meet the requirements of network connection as power factor, voltage output and fault ride through requirements, as well as increases the transmission capacity and the network stability. This paper presents the dynamic behavior obtained by computer simulation of a distribution grid at 34.5 kV with D-STATCOM and wind farms. The use of D-STATCOM contributed to increasing the stability margin of the system, allowing the permanence of the three wind farms during fault and post-fault conditions.

Smart grid is an evolving new power system framework with ICT driven power equipment massively layered structure. The new generation sensors, smart meters and electronic devices are integral components of smart grid. However, the upcoming deployment of smart devices at different layers followed by their integration with communication networks may introduce cyber threats. The interdependencies of various subsystems functioning in the smart grid, if affected by cyber-attack, may be vulnerable and greatly reduce efficiency and reliability due to any one of the device not responding in real time frame. The cyber security vulnerabilities become even more evident due to the existing superannuated cyber infrastructure. This paper presents a critical review on expected cyber security threats in complex environment and addresses the grave concern of a secure cyber infrastructure and related developments. An extensive review on the cyber security objectives and requirements along with the risk evaluation process has been undertaken. The paper analyses confidentiality and privacy issues of entire components of smart power system. A critical evaluation on upcoming challenges with innovative research concerns is highlighted to achieve a roadmap of an immune smart grid infrastructure. This will further facilitate R&D and associated developments.

Smart grid has been an active research area in recent years because almost all the technologies required to build smart grid are mature enough. It is expected that not only can smart grid reduce electricity consumption, but it can also provide a more reliable and versatile service than the traditional power grid can. Although the infrastructure of smart grid all over the world is far from complete yet, there is no doubt that our daily life will benefit a lot from smart grid. Hence, many researches are aimed to point out the challenges and needs of future smart grid. The question is, how do we use the massive data captured by smart meters to provide services that are as “smart” as possible instead of just automatically reading information from the meters. This paper begins with a discussion of the smart grid before we move on to the basic computational awareness for smart grid. A brief review of data mining and machine learning technologies for smart grid, which are often used for computational awareness, is then given to further explain their potentials. Finally, challenges, potentials, open issues, and future trends of smart grid are addressed.

A smart city is an urban agglomeration which capitalises on information technologies (IT) and artificial intelligence (AI) facilities to offer special services to its community and visitors. These range from more complex services such as energy production, road traffic management, civil security and territorial administration, to simpler services such as household waste management or free Internet in parks, libraries, shopping centres and other public places. To achieve this integration of management, an important step is to install sensors that are essential for collecting data. The collection of information to manage sensitive infrastructure via IT services comes with security risks, however. The impact of cyber security breach risks linked to these data collection activities and storage can be dramatic, depending on the type of service: power plants, water management systems, automated or driverless trains, vehicle traffic management systems, etc. These types of service, due to IT and AI evolution, are increasingly the victim of attacks by cyber criminals, especially the industrial control system (ICS)/supervisory control and data acquisition (SCADA) and smart grid networks. These networks are nowadays prime targets for professional hackers, cybercriminal organisations, or even competitors who want access to inside privileged information. To support interest in this type of attack for these networks, we can cite malicious programs such as Stuxnet, Dragonfly and BlackEnergy3, which damaged the Ukraine SCADA network with major consequences for energy production and distribution. Ukrainian energy private companies were victims of the attack, which resulted in total blackouts in at least eight regions of the country. Stuxnet has also been used to destroy a large part of the centrifuges fleet used by Iran for its uranium enrichment programme, etc. The rise of electric vehicle (EV) smart cities is based on smart grid infrastructure and IT — a combination that can lead to increased cyber security risks to the electricity network as well as for fast-charging stations. The purpose of this paper is to identify the most sensitive systems in smart cities, describe potential risks associated with these systems, and propose mitigation mechanisms to reduce associated cyber security risks.

Accessibility for operation and maintenance tasks in co-located wind and wave energy farms with non-uniformly distributed arrays

IT security management in distributed Smart Grids has been discussed in a number of recent works, mainly from the viewpoint of how to prevent unauthorized access, either organizationally or cryptographically. In this paper, we aim to extend this viewpoint by introducing the concept of emergent security in cyber-physical energy systems. The main contribution of this paper is the description of four architectural layers, providing a structured approach for security assessment and evaluation. The proposed emergent security layer shifts the perspective of IT security towards incorporating real-time measurements, decoupling feedback of components, increasing topology-awareness, and implementing distributed counter-measures and participant reputation systems. We propose an iterative process for designing resilient distributed control algorithms for Smart Grids. This process acknowledges the new challenges for security management in critical infrastructures due to the recent advances in inter connectivity of systems. The proposed approach is validated using an implementation of Smart Grid control and a co-simulation of a cyber-physical energy system, indicating the importance of the introduced emergent security layer.

In real-time large-scale optimization problems, such as in smart grids, centralized algorithms may face difficulties in handling fast-varying system conditions, such as high variability of renewable-based distributed generators (DGs) and controllable loads (CLs). Further, centralized algorithms may encounter computation and communication bottlenecks while handling a large number of variables. To tackle these issues, consensus-based distributed strategies have been proposed recently. However, distributed computational intelligence (CI)-based techniques can provide a much better near-optimal solution within fewer iterations of the algorithm, which is a critical requirement in smart grids. Therefore, in this paper, a consensus-based dimension-distributed CI technique is proposed for real-time optimal control in smart distribution grids in which a large number of DGs and CLs are present. The proposed approach considers each DG or CL as a separate private entity, which is more relevant from the perspective of smart grid optimization. In the proposed consensus-based framework, each DG or CL is associated with an agent, and each agent is allowed to communicate only with its neighboring agents. The effectiveness of the proposed approach in terms of convergence, adaptability, and optimality with respect to a centralized algorithm and a benchmark algorithm is shown through simulations on 30-node and 119-node distribution test systems.

This work analyzes different grid resolution and domains for numerical forecasts of wind energy injected in the electric grid by the wind farms “Emanuele Cambilargiu” and “Kentilux”, in Uruguay. The analysis shows that the optimal horizontal resolution for the wind farm installed in a flat plate terrain (Kentilux wind farm) is 3.3 km, and for more steep terrain (Emanuelle Cambilargiu wind farm), the optimal horizontal resolution is 1 km.

As wind-turbine arrays continue to be installed and the array size continues to grow, there is an increasing need to represent very large wind-turbine arrays in numerical weather prediction models, for wind-farm optimization, and for environmental assessment. We propose a simple analytical model for boundary-layer flow in fully-developed wind-turbine arrays, based on the concept of sparsely-obstructed shear flows. In describing the vertical distribution of the mean wind speed and shear stress within wind farms, our model estimates the mean kinetic energy harvested from the atmospheric boundary layer, and determines the partitioning between the wind power captured by the wind turbines and that absorbed by the underlying land or water. A length scale based on the turbine geometry, spacing, and performance characteristics, is able to estimate the asymptotic limit for the fully-developed flow through wind-turbine arrays, and thereby determine if the wind-farm flow is fully developed for very large turbine arrays. Our model is validated using data collected in controlled wind-tunnel experiments, and its usefulness for the prediction of wind-farm performance and optimization of turbine-array spacing are described. Our model may also be useful for assessing the extent to which the extraction of wind power affects the land–atmosphere coupling or air–water exchange of momentum, with implications for the transport of heat, moisture, trace gases such as carbon dioxide, methane, and nitrous oxide, and ecologically important oxygen.

The global energy landscape has always been dominated by fossil fuels, but with the intensification of climate change and environmental issues, people realize that the traditional energy system must be transformed. In order to integrate renewable energy and improve the sustainability and resilience of the energy system, smart grids have emerged. Smart grid is a modern grid system based on advanced communication, control and information technology, which can integrate renewable energy more effectively. As one of the renewable energy sources, solar energy plays an important role in the smart grid. It has the advantages of wide distribution, environmental protection, and renewable, and with technological progress and cost reduction, solar power generation is widely used in smart grids. Similarly, wind energy, another important renewable energy source, also plays an important role in the smart grid. The continuous development of wind turbine technology has made wind power generation more efficient and reliable, and the integration with smart grid infrastructure has further improved the efficiency of wind energy utilization. In addition, hydrogen energy has great potential as a clean energy carrier, but the storage and distribution of hydrogen energy in smart grid systems still face challenges.

Assessment of wind and wave power characteristic and potential for hybrid exploration in Australia