This article presents a blockchain-based scheme for energy trading between electric vehicles (prosumers) and critical load (consumer) in a microgrid. Unlike traditional wholesale energy markets where retailers sell energy to consumers, our proposed model directly connects prosumers with consumers to meet temporary energy demands. We exploit blockchain technology to establish a trusted energy trading ecosystem. The article provides an overview of the blockchain enabled architecture that facilitates peer-to-peer energy trade in a logical network, using the building blocks provided by 5G network. An energy trading prototype is developed to remotely monitor energy trading activities between prosumers and consumer, using graphical user interface. Experimental results based on a real map of Paisley illustrate that the energy trading system is effective in finding, associating and routing prosumers to consumers, while protecting privacy of entities. Numerical results show a favorable performance of our optimization model in comparison to traditional frameworks.

Intelligent vehicular communication is fundamental to manage vehicle-to-grid (V2G) interaction, where electric vehicles (EVs) provide energy to balance demand of critical loads (CLs). We propose resource efficiency (RE) to exploit the tradeoff between spectral efficiency (SE) and cost efficiency (CE) of EVs in a V2G communication network. The CE is the data rate of the V2G channel between EVs and base station (BS) over the operating cost of EVs to supply energy to CLs. We consider maximizing the RE in the downlink of a V2G communication network, where EVs are served by a BS and associated with CLs, while satisfying energy demand and charging station constraints. As the proposed RE problem is inherently non-convex and known to be NP-hard, we develop a suboptimal scheme based on a two-phase algorithm. Phase 1 derives optimum EV-CL association using a heuristic approach, while phase 2 finds optimum power allocation using geometric programming. We then derive upper and lower bounds to the optimal RE as a benchmark to study the performance gap of the suboptimal scheme. Simulation results demonstrate that the proposed suboptimal scheme is close to the optimal solution, while its complexity is relatively low, making it promising for V2G applications.

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system.

Under vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed to meet additional energy demand of critical load (CL) in a microgrid. In this article, an incentivized energy trading approach is introduced to study the interaction between EVs and CL. EV mobility and battery degradation are studied to ensure they do not deter EV participation. Bidder satisfaction is introduced which allows EV owners to enforce their energy trading conditions. EV-CL association and discharging scheduling are considered in a two-phase model. In the first phase, EV-CL association is modeled as a single auction to determine the winning bids and corresponding payments. Successful bidders are determined by solving a mixed integer non-linear programming (MINLP) problem, while Vickery-Clarke-Groves (VCG) payment rule is applied to pay the auction winners. In the second phase, EV discharging scheduling determines the operating cost and discharging power of associated EVs at each time slot. Simulation results show that the proposed approach achieves comparable performance with reference schemes and guarantees bidder satisfaction. Theoretical analysis on economic properties of truthfulness and individual rationality are verified as well.

As electric vehicles (EVs) are currently under‐utilised, the features of deploying EVs as distributed energy resources (DERs), based on an EV as a service (EVaaS) framework, are exploited and a resource allocation scheme is proposed for optimum association of dispersed EVs with critical load for demand fulfilment in microgrids. The proposed approach is based on a combined economic emission (CEE) optimisation model where both energy costs and carbon emissions are taken into account. The CEE optimisation problem is then formulated as a bi‐objective optimisation problem, considering a number of practical constraints, such as energy demand, cost budget, emission limit and charging station limit. Carbon price is introduced to convert the bi‐objective problem into a single objective function. The authors included EV battery degradation cost to ensure EV owners are not worse off after EVaaS participation. The feasibility of the proposed model is demonstrated in simulation studies. The approach has been extended to evaluate the trade‐off between EVaaS and conventional DERs. Numerical results demonstrate the efficiency of the proposed resource allocation scheme.