Peer to peer energy trading with electric vehicles

This paper presents a novel peer-to-peer energy trading system between two sets of electric vehicles, which significantly reduces the impact of the charging process on the power system during business hours. This trading system is also economically beneficial for all the users involved in the trading process. An activity-based model is used to predict the daily agenda and trips of a synthetic population for Flanders (Belgium). These drivers can be initially classified into three sets; after discarding the set of drivers who will be short of energy without charging chances due to their tight schedule, we focus on the two remaining relevant sets: those who complete all their daily trips with an excess of energy in their batteries and those who need to (and can) charge their vehicle during some daily stops within their scheduled trips. These last drivers have the chance to individually optimize their energy cost in the time-space dimensions, taking into account the grid electricity price and their mobility constraints. Then, collecting all the available offer/demand information among vehicles parked in the same area at the same time, an aggregator determines an optimal peer-to-peer price per area and per time slot, allowing customers with excess of energy in their batteries to share with benefits this good with other users who need to charge their vehicles during their daily trips. Results show that, when applying the proposed trading system, the energy cost paid by these drivers at a specific time slot and in a specific area can be reduced up to 71%

Management Strategies for Electric Vehicle Fleets

The research leading to these results has received funding from the EU 7th FP under the project DATA science for SIMulating the era of electric vehicles (DATASIM, FP7-ICT-270833). DATA SIM aims at providing an entirely new and highly detailed spatial-temporal microsimulation methodology for human mobility with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles. The objective of this work is focused in the development of charging management strategies for electric vehicle (EV) fleets. Its purpose is to maximize the integration of EVs in the current electric grid considering their consumption and their charging limits, both temporal and spatially. The main contribution of this work is the development of a novel Peer to Peer Energy Trading System (P2PETS) between EVs in order to reduce the impact of charging EVs over the electric grid

Investigation of Electric Vehicles Contributions in an Optimized Peer-to-Peer Energy Trading System

The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market solutions. This paper investigates the effect of EVs and shiftable loads on P2P energy trading with enhanced Vehicle to Home (V2H) mode, and proposes an optimized Energy Management Systems aimed to reduce the net energy exchange with the grid. Mixed-integer linear programming (MILP) is used to find optimal energy scheduling for smart houses in a community. Results show that the V2H mode reduces the overall energy costs of each prosumer by up to 23% compared to operating without V2H mode (i.e., EVs act as a load only). It also reduces the overall energy costs of the community by 15% compared to the houses operating without the V2H mode. Moreover, it reduces the absolute net energy exchanged between the community and the grid by 3%, which enhances the energy independence of the community

Blockchain and artificial intelligence enabled peer-to-peer energy trading in smart grids

Peer-to-peer (P2P) energy trading allows smart grid-connected parties to trade renewable energy with each other. It is widely considered a scheme to mitigate the supplydemand imbalances during peak-hour. In a P2P energy trading system, users (e.g., prosumers, Electric Vehicles (EV)) increase their utility by trading energy securely with each other at a lower price than that of the main grid. However, three challenges hinder the development of secured P2P energy trading systems. First, there is a lack of implicit trust and transparency between trading participants because they do not know each other. Second, P2P energy trading systems cannot offer an intelligent trading strategy that could maximize users’ (agents’) utility. This is because the agents may lack previous trading experience data that enable them to select an optimal trading strategy. Third, the current energy trading platforms are mainly centralized, which makes them vulnerable to malicious attacks and Single point of failure (SPOF). This may interrupt the transaction validation mechanism when the system is compromised, and the central database is unavailable. [...

Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid. Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating. In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023

Future energy retail markets: stakeholder views on multiple electricity supplier models in the UK

In the transition to smart, low-carbon energy systems, the energy retail market is evolving. Many non-traditional actors are beginning to offer services that can help accommodate distributed supply intermittency. At the same time, they provide greater choice for consumers through new electricity products, such as specialised supply for assets such as EVs and smart appliances, or democratising energy supply, e.g. through peer-to-peer energy trading and community energy schemes. This represents a shift from a supplier-centric energy system to one placing greater emphasis on the role of energy end-users. However, under the current ‘supplier hub principle’ governing the UK market, domestic consumers’ interaction with the energy system is mediated by a single licensed supplier, creating barriers for non-traditional business models. This paper shares findings from eight semi-structured interviews conducted in summer 2020 with regulators, innovators, energy suppliers, and consumer advocacy groups on the future of the UK’s energy retail market and consumers’ relationship with it. The research focuses on one alternative to the supplier hub principle; a ‘multiple supplier model’, which would enable consumers to have multiple electricity suppliers at the same time, engaging with non-traditional models whilst keeping their national-level supplier. Interviewees highlighted peer-to-peer energy trading, and community energy, as well as the ability to bundle supply with technologies such as electric vehicles or smart appliances, as the most transformational use cases that multiple supplier models could facilitate. Although most interviewees felt that the current supplier hub model is not fit to support the energy transition, contention remains around how best to replace it. Findings offer insight into the challenges posed by the supplier hub principle; the advantages and disadvantages of permitting multiple suppliers; and the key aspects of interactions with multiple energy suppliers from the consumer’s perspective. This work contributes towards understanding the landscape of future supplier models and the challenges faced in transforming the energy retail market