Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented

Pairing-based authentication protocol for V2G networks in smart grid

[EN] Vehicle to Grid (V2G) network is a very important component for Smart Grid (SG), as it offers new services that help the optimization of both supply and demand of energy in the SG network and provide mobile distributed capacity of battery storage for minimizing the dependency of non-renewable energy sources. However, the privacy and anonymity of users¿ identity, confidentiality of the transmitted data and location of the Electric Vehicle (EV) must be guaranteed. This article proposes a pairing-based authentication protocol that guarantees confidentiality of communications, protects the identities of EV users and prevents attackers from tracking the vehicle. Results from computing and communications performance analyses were better in comparison to other protocols, thus overcoming signaling congestion and reducing bandwidth consumption. The protocol protects EVs from various known attacks and its formal security analysis revealed it achieves the security goals.Roman, LFA.; Gondim, PRL.; Lloret, J. (2019). Pairing-based authentication protocol for V2G networks in smart grid. Ad Hoc Networks. 90:1-16. https://doi.org/10.1016/j.adhoc.2018.08.0151169

Cognitive Radio for Smart Grid with Security Considerations

In this paper, we investigate how Cognitive Radio as a means of communication can be utilized to serve a smart grid deployment end to end, from a home area network to power generation. We show how Cognitive Radio can be mapped to integrate the possible different communication networks within a smart grid large scale deployment. In addition, various applications in smart grid are defined and discussed showing how Cognitive Radio can be used to fulfill their communication requirements. Moreover, information security issues pertained to the use of Cognitive Radio in a smart grid environment at different levels and layers are discussed and mitigation techniques are suggested. Finally, the well-known Role-Based Access Control (RBAC) is integrated with the Cognitive Radio part of a smart grid communication network to protect against unauthorized access to customer’s data and to the network at large

PAP: A Privacy-Preserving Authentication Scheme with Anonymous Payment for V2G Networks

Vehicle-to-grid (V2G) networks, as an emerging smart grid paradigm, can be integrated with renewable energy resources to provide power services and manage electricity demands. When accessing electricity services, an electric vehicle(EV) typically provides authentication or/and payment information containing identifying data to a service provider, which raises privacy concerns as malicious entities might trace EV activity or exploit personal information. Although numerous anonymous authentication and payment schemes have been presented for V2G networks, no such privacy-preserving scheme supports authentication and payment simultaneously. Therefore, this paper is the first to present a privacy-preserving authentication scheme with anonymous payment for V2G networks (PAP, for short). In addition, this scheme also supports accountability and revocability, which are practical features to prevent malicious behavior; minimal attribute disclosure, which maximizes the privacy of EV when responding to the service provider\u27s flexible access policies; payment binding, which guarantees the accountability in the payment phase; user-controlled linkability, which enables EV to decide whether different authentication sessions are linkable for continuous services. On the performance side, we implement PAP with the pairing cryptography library, then evaluate it on different hardware platforms, showing that it is essential for V2G applications

Demand Response Management and Control Strategies for Integrated Smart Electricity Networks

Demand Response (DR) programs are being introduced by some electricity grid operators as resource options for curtailing and reducing the demand of electricity during certain time periods for balancing supply and demand. DR is considered as a class of demand-side management programs, where utilities offer incentives to end-users to reduce their power consumption during peak periods. DR is, indeed, a promising opportunity for consumers to control their energy usage in response to electricity tariffs or other incentives from their energy suppliers. Thus, successful execution of a DR program requires the design of efficient algorithms and strategies to be used in the utility grid to motivate end-users to actively engage in residential DR. This thesis studies DR management using machine learning techniques such as Reinforcement Learning (RL), Fuzzy Logic (FL) and Neural Networks (NN) to develop a Home Energy Management System (HEMS) for customers, construct an energy customer behaviour framework, investigate the integration of Electrical Vehicles (EVs) into DR management at the home level and the provision of ancillary services to the utility grid such as Frequency Regulation (FR), and build effective pricing strategies for Peer-to-Peer (P2P) energy trading. In this thesis, we firstly proposed a new and effective algorithm for residential energy management system using Q-learning method to minimise the electricity bills and maximise the user’s satisfaction. The proposed DR algorithm aims to schedule household appliances considering dynamic electricity prices and different household power consumption patterns. Moreover, a human comfort-based control approach for HEMS has been developed to increase the user’s satisfaction as much as possible while responding to DR schemes. The simulation results presented in this Chapter showed that the proposed algorithm leads to minimising energy consumption, reducing household electricity bills, and maximising the user’s satisfaction. Secondly, with the increasing electrification of vehicles, emerging technologies such as Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, EVs become as distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Therefore, we proposed an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations studies are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue. In addition, the potential benefits of EVs battery and V2G technology to provide grid frequency response services have also been investigated. We have designed an optimal real-time V2G control strategy for EVs to perform supplementary frequency regulation using Deep Deterministic Policy Gradient (DDPG). The main feature that distinguishes the proposed approach from previous related works is that the scheduled charging power of an individual EV is optimally tracked and adjusted in real-time to fulfil the charging demand of EV's battery at the plug-out time without using the forced charging technique to maximise the frequency regulation capacity. Finally, a Peer-to-Peer (P2P) model for energy transaction in a community microgrid has been proposed. The concept of P2P energy trading can promote the implementation of DR by providing consumers with greater control over their energy usage, incentivising them to manage their energy consumption patterns in response to changes in energy supply and demand. It also stimulates the adoption of renewable energy sources. The proposed P2P energy-sharing mechanism for a residential microgrid with price-based DR is designed to engage individual customers to participate in energy trading and ensures that not a single household would be worse off. The proposed pricing mechanism is compared with three popular P2P energy sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and Bill Sharing (BS) considering different types of peers equipped with solar Photovoltaic (PV) panels, EVs, and domestic energy storage systems. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network

A Case Study on Application of Fuzzy Logic based Controller for Peak Load Shaving in a Typical Household\u27s Per Day Electricity Consumption

The cost of electricity for consumers depends on the cost of generation, transmission, and distribution of power. The electrical load consumed by consumers per day is not constant throughout the day. The utilities must be capable of meeting the load demand, which means they must have enough electricity generation potential and necessary infrastructure. This cost is significant. However, the revenue they generate will only be for the actual use of electricity by the consumers. In general, the electrical power generation is done in stages, always generating a base load. As demand changes throughout the day, additional stages of power generation are brought online to meet the changes in demand. This approach of management is known as supply-side management. Theoretically, if it is possible to manage the load such that there is lower peak demand and the difference between peak load and base load were minimized, the generation capability and grid infrastructure required to provide reliable power would be reduced resulting in lower costs for utility companies and ultimately consumers. This management strategy is referred to as demand-side management or demand response. In this research, a small-scale smart grid is modeled in Simulink to mimic the electrical grid. A Smart controller based on fuzzy logic is developed to control charging and discharging of an electric vehicle battery to provide extra power during peak times and to act as load (storing energy) during off-peak time to provide a more manageable and balanced load as seen by the grid. A comparative study is presented of electricity consumption throughout the day with or without the smart controller. The results show the significant reduction in peak demand, much smoother load curve for the grid, and a decrease in per kilowatt cost of electricity for the given day when newer pricing structures are applied

AI Enabled Crypto Mining for Electric Vehicle Systems

A virtual grid (VG) is an interconnected system that includes a decentralized power plant, flexible loads, and energy storage facilities. During peak demand, a VG can distribute the power provided by several interconnected units in an equitable manner, ensuring that the grid burden is spread out evenly. Electric vehicles (EVs) and other demand-side energy devices can help keep the energy market supply and demand in harmony with proper use. However, it might be difficult to maintain a consistent power balance due to the inherent unpredictability of the power units. Furthermore, the issue of protecting the privacy of communications between a VPP aggregator and the final facilities has not been thoroughly explored. In this paper, we provided detailed analytics on optimization-based crypto mining for electric vehicle systems. The simulation is conducted to test the efficacy of the model, and the results show that the proposed method has a higher rate of accuracy than other methods