Data Integrity and Cost Efficiency in Smart Grids: Block-chain Integration Approach

The smart grid is an upgraded concept of electricity network with tight coupling among information, control, and bi‐directional communication technologies. Along with the silent features of the on‐going smart grid, cyber–physical security appears to be a deep concern due to its significant dependence on sensing technologies, complex networks of computers, intelligence, and wide‐area communication infrastructures. Moreover, the smart grid is an extensive critical infrastructure and vulnerable to coordinated cyber–physical attacks. As a result, cyber–physical attacks cause significant threats to the confidentiality and integrity of the power data, including power outages, cascading failures, and unnecessary expenditure. In this study, security issues of smart grid, including cyber–physical interdependency, attack varieties, detection methods, requirements, standards, challenges, and future prospects, are taken into consideration for both cyber and physical systems, aiming to provide an extensive understanding of vulnerabilities and solutions for the smart power grid. By revealing the inherent features of cyber–physical security in the smart grid, this survey study is addressed to facilitate future research in these two areas.

Since the appearance of the revolutionary field on the domain of the power industry called smart grid, many challenges are proposed. However, the main challenge is how to secure a large amount of data using smart grid. In a smart grid, the power consuming data are collected to optimize energy utilization. However, the collection of electricity usage data may cause the leakage of user privacy-sensitive information that threatens the user's privacy. In fact, we propose, a secure energy prototype for smart grids with data confidentiality, data integrity, intrusion detection and filtering. In fact, in the proposed prototype, we achieve data source authentication, data aggregation, where the data confidentiality is based on the AES-CBC (Advanced Encryption Standard -Cipher Block Chaining) algorithm and the data integrity is based on the SHA (Secure Hash Algorithm) algorithm. The proposed prototype delivers energy related information safely throughout the data transfer in a chain of a smart grid. Therefore, we propose a filtering process to prevent remote attacks. We have noticed from the python implementation results of a prototype that the proposed security mechanisms do not introduce a significant overhead for all the process.

A Residential Load Scheduling Based on Cost Efficiency and Consumer's Preference for Demand Response in Smart Grid

To improve the economical efficiency of the residential electricity consumption, in this paper we introduce a novel concept of cost efficiency-based residential load scheduling framework. The cost efficiency is defined as a ratio of consumer's total consumption benefit and its total electricity payment during a certain period. We develop a cost-efficient load scheduling algorithm for the demand-side's day-ahead bidding process based on a fractional programming approach. We found that the proposed scheduling algorithm can effectively reflect and affect user's consumption behavior and achieve the optimal cost-efficient energy consumption profile. For practical consideration, we also take into account the service fee in calculating the cost-efficiency in practical smart grid applications, and analyse its effect on the cost efficiency. Simulation results confirm that the proposed algorithm significantly improves consumer's cost efficiency compared with the conventional payoff maximization algorithm. It also shows that a higher service fee will considerably decrease the cost efficiency.

In this paper, we propose an adaptive cyber-enabled parametric feedback linearization (PFL) control scheme for transient stability of smart grids. Based on feedback linearization control theory, the distributed PFL controller utilizes a distributed energy storage system to modify the dynamics of the power system during transients. We consider cyber attacks on data integrity and availability in the smart grid, and propose to adapt the PFL controller's parameter to the cyber state of the smart grid. Specifically, the PFL control scheme adapts its aggressiveness parameter to the level of noise, communication latency, and data injection attacks. Further, depending on the severity of the physical disturbance, the controller adjusts the value of its parameter to speed up the stabilization process. The performance of the proposed control scheme is validated on the IEEE 68-bus test power system, where the adaptive PFL controller is shown to efficiently stabilize the power system during physical and cyber disturbances.

The future energy internet for utility energy service and demand-side management in smart grid: Current practices, challenges and future directions

Smart grids allow automated meter readings and facilitate two-way communications between the smart meters and utility control centers. As the smart grid becomes more intelligent, it becomes increasingly vulnerable to cyber-attacks. Smart grid security mainly focuses on mutual authentication and key management techniques. An impeding factor in grid security is the memory and processing constraints of the smart meters. The aim of this thesis is to propose a lightweight mutual authentication protocol with an effective key renewal mechanism between a residential smart meter and a gateway. The authentication protocol proposed in the thesis, guarantees source authentication, data integrity, message confidentiality, as well as non-repudiation. The security analysis renders this protocol robust against several attacks. Furthermore, its performance analysis provides meticulous results as to how the proposed protocol is efficient in terms of computation overhead, average delay and buffer occupancy at the gateway.

Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects

Smart grids incorporate IoT devices that enhance energy management, monitoring, and overall grid efficiency. However, this interconnectivity also increases vulnerability to cybersecurity threats, posing risks to critical infrastructure. This research investigates the implementation of blockchain technology to secure data transactions within IoT-based smart grids. By leveraging blockchain's decentralized, tamper-resistant characteristics, the study demonstrates improvements in data integrity and cybersecurity for smart grids, providing a potential framework for resilient and secure energy infrastructures.

In smart grids, smart meters periodically collect users' fine-grained multi-dimensional energy data, which poses great concerns on users' privacy and security. Existing privacy preserving multi-dimensional aggregation schemes suffer from heavy computational burdens, especially for smart meters with limited computational resources. To address these limitations, in this paper we propose an efficient and flexible multi-dimensional data aggregation scheme called MDA-SMuSha, by which smart meters employ the Shamir's multi-secret sharing to generate a set of shared secrets, with the first one kept locally, while the remained ones are packaged and then uploaded to a control center via an aggregator. By the MDA-SMuSha scheme, aggregation results of smart meters' multi-dimensional energy data during multiple periods can be obtained, with only one time of Paillier encryption conducted on the smart meters. In addition, it allows the control center to send query requests flexibly, i.e., at a pre-specified frequency or whenever it wants to obtain statistical data of interests. Rigorous security analyses show that the MDA-SMuSha scheme satisfies security requirements of privacy-preservation, authenticity and data integrity as well as fault-tolerance. Both theoretical analyses and experiment results show that the MDA-SMuSha scheme outperforms state-of-the art methods in terms of computation costs, with comparable communication costs.

The rapid growth of smart grid systems has resulted in an exponential increase in data volume. This has led to significant challenges for efficient storage and transmission, thereby necessitating the development of advanced data compression techniques. Traditional techniques often struggle to maintain a balance between compression efficiency and data integrity, particularly when dealing with diverse and large datasets. To address this issue, this paper presents an adaptive data compression algorithm based on discrete wavelet transform (DWT) and Horse Herd Optimization (HHO). The proposed technique significantly improves storage by dynamically optimizing key performance metrics, such as signal-to-noise ratio (SNR), mean square error (MSE), and compression ratio (CR). By employing a robust optimization algorithm, it effectively addresses the computational challenges of processing large-scale and real-time data. This adaptability ensures the algorithm is well-suited for dynamic smart grid environments, providing a scalable and reliable solution to modern data management demands. This technique uses HHO to find the optimal thresholding for smart grid data compression. In general, DWT-based data compression is carried out using a universal threshold for ignoring particular wavelet coefficients. But, the performance of data compression varies for different threshold values. Hence, selecting an optimal threshold is a challenging task for data compression. Therefore, to solve this issue, an effective optimization algorithm is needed. In this work, a Multi-Objective Horse Herd Optimization (MO-HHO) algorithm is proposed to find the optimal threshold. The suggested MO-HHO algorithm accurately determines the global optimum threshold. Hence, it maintains a good compromise between SNR, MSE and CR. The effectiveness of the proposed algorithm is examined using three different data sets. Various datasets from the IEEE Power quality wave data, Household Electric Power Consumption data and Real-time dataset from an experimental set-up were used to test the proposed algorithm. The outcome demonstrates that the suggested MO-HHO algorithm performs better than the other conventional methods.

Different from the traditional grid, smart grid builds a real-time connection network between the user and the grid company by smart terminals, which can achieve bidirectional data transmission and information control. In smart grid, the smart meters send various information to the power generators and substations. Frequent data collection meets real-time management, but it tends to raise privacy concerns from the users about privacy information leakage. Based on the blind signature and the key distribution scheme, an efficient and privacy-preserving data collection (EPPDC) scheme is proposed for smart grid to cope with the above problems. In EPPDC scheme, the users' data information is transmitted to the local aggregator by building gateway with privacy preserving. In addition, the security analysis indicates that EPPDC scheme not only can resist replay attack, but also has source authentication and data integrity, confidentiality, unforgeability, nonrepudiation, and evolution of shared keys. Furthermore, performance analysis shows that EPPDC scheme has less computation cost than existing scheme.

With the developing of the Smart Grid, false data injection attacks (FDIAs) as a typical data integrity attack successfully bypass the traditional bad data detection and identification, has a serious influence on the power system safe and reliable operation. State estimation, which is an important process in smart grid, is used in system monitoring to get optimally estimate the power grid state through analysis of the monitoring data. However, FDIAs compromising data integrity will lead to wrong decision makings in power dispatch or electric power market transactions. In this paper, focusing on the power property, we introduce an index to quantitatively measure the node voltage stability and reflect the influence of FDIAs on the power system. Then, we use an improved clustering algorithm to identify the node vulnerability level, which helps operators take measures and detect the false data injection attacks timely. Besides, one effective state forecasting detection method is proposed, which is meaningful for real-time detection of false data injection attacks. Finally, the simulation result verifies the effectiveness and performance of the proposed method.

Objectives: Providing efficient energy management for smart home through appropriate scheduling of household appliances is addressed in this paper, with two objectives namely, electricity peak demand minimization and electricity cost minimization. Methods/Statistical Analysis: The residential load scheduling problem requires the prior knowledge about the residential electricity demand and electricity price information, for scheduling the appliances. Two different algorithms namely, Discrete Non-dominated Multi-objective Particle Swarm Optimization (DNMPSO) algorithm and Manhattan distance based Non-dominated Multi-objective Genetic Algorithm (MNMGA) are proposed to solve the problem in this paper. Findings: Both the algorithms were simulated in order to evaluate their performance. The Peak-To-Average Ratio value is used as the measure to assessing the peak load in electricity demand. Based on the results, it is observed that the DNMPSO algorithm obtains significant cost reduction with the acceptable Peak-To-Average Ratio value than the MNMGA algorithm. In addition to that, the DNMPSO algorithm provides better diversity than the MNMGA algorithm. Application/Improvements: The proposed algorithm can be used to enhance the energy management for residential load in smart grid. Further, the cost efficiency can be improved by incorporating renewable energy resource.

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.