Recent years have seen the rapid development of technologies in Smart Grids (SGs) to enhance electricity networks with digital and data communication technologies. SGs can proactively detect, react, and respond to dynamic changes in the network. SGs can also enhance the efficiency and reliability of electricity supplies and promote the integration of renewable energy sources. Smart Meters (SMs) are often seen as the first step to a successful implementation of SGs. While SMs enable Utility Providers and consumers to obtain near real-time information of energy consumption, they can also be exploited to infer sensitive consumer data. Therefore, privacy preservation in SMs is paramount in ensuring the widespread and successful deployment of SGs. In this paper, we present a comprehensive survey of the state-of-the-art SM privacy-preserving techniques published in the literature over the past decade. We categorize these techniques based on the attack types and their objectives. We aim to offer a unique perspective in this survey article through the lens of privacy preservation, cross-cutting the wide range of techniques presented in the literature. We conclude by identifying the challenges and highlighting key future research directions in the field.

Wireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop a novel adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations.

Future vehicle-to-grid (V2G) systems require more flexible scheduling to adjust and flatten the peak energy demand. For efficient scheduling and energy trading, the utility provider (UP) needs to keep track of the state of charge (SoC) of vehicle batteries (VBs). However, sharing of SoC of VBs from electric vehicles (EVs) to UP may compromise owner privacy by analyzing the electricity usage in EVs. Therefore, we propose Reinforcement learning (RL)-based demand-side energy management using a rechargeable battery (RB) for enhanced cost-friendly privacy of EVs, efficient scheduling, and accurate billing. With existing Q-Learning-based RL (using $\epsilon$ -greedy exploration and exploitation), we find that the reward maximization of efficient and private scheduling is often sluggish and incurs convergence issues. Therefore, we develop a genetic algorithm (GA)-based exploration and exploitation, which solves the convergence problems. We develop theoretical analysis and implement numerical results to demonstrate that the proposed GA-based RL framework accelerates convergence and enhances cost-friendly privacy considerably.

Due to the rapid growth of smart grids, use of smart meters (SMs) have increased in the recent days. The main problem with the use of SMs is that by observing the SMs reading, it is possible to infer the daily activities of the consumers. Therefore, protection of privacy is a major concern related to SMs. Using rechargeable batteries (RBs) is a popular method in protecting the privacy in SMs as these methods do not tamper with SM readings. The major problem in RB based mechanism is that the energy management unit (EMU) cannot protect privacy, if the demand is lower or higher for a longer period. To overcome this problem, in this paper a heuristic method has been proposed by considering time varying target output load based on the three major properties of artificial fish swarm optimization algorithm. For the optimal choice of the time varying target output load, RB constraints as well as reduction of the average cost of energy have been considered in our proposed method. We have proposed two privacy preserving mechanisms for both offline and online scenarios. The proposed method preserves privacy while reducing the cost of energy. Simulation results show that the proposed method is able to provide privacy by overcoming the problem identified in the existing methods.