Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Autonomous Mobility and Energy Service Management in Future Smart Cities: An Overview

With the rise of transportation electrification, autonomous driving and shared mobility in urban mobility systems, and increasing penetrations of distributed energy resources and autonomous demand-side management techniques in energy systems, tremendous opportunities, as well as challenges, are emerging in the forging of a sustainable and converged urban mobility and energy future. This paper is motivated by these disruptive transformations and gives an overview of managing autonomous mobility and energy services in future smart cities. First, we propose a three-layer architecture for the convergence of future mobility and energy systems. For each layer, we give a brief overview of the disruptive transformations that directly contribute to the rise of autonomous mobility-on-demand (AMoD) systems. Second, we propose the concept of autonomous flexibility-on-demand (AFoD), as an energy service platform built directly on existing infrastructures of AMoD systems. In the vision of AFoD, autonomous electric vehicles provide charging flexibilities as a service on demand in energy systems. Third, we analyze and compare AMoD and AFoD, and we identify four key decisions that, if appropriately coordinated, will create a synergy between AMoD and AFoD. Finally, we discuss key challenges towards the success of AMoD and AFoD in future smart cities and present some key research directions regarding the system-wide coordination between AMoD and AFoD.Comment: 19 pages, 4 figure

Unsplittable Load Balancing in a Network of Charging Stations Under QoS Guarantees

The operation of the power grid is becoming more stressed, due to the addition of new large loads represented by Electric Vehicles (EVs) and a more intermittent supply due to the incorporation of renewable sources. As a consequence, the coordination and control of projected EV demand in a network of fast charging stations becomes a critical and challenging problem. In this paper, we introduce a game theoretic based decentralized control mechanism to alleviate negative impacts from the EV demand. The proposed mechanism takes into consideration the non-uniform spatial distribution of EVs that induces uneven power demand at each charging facility, and aims to: (i) avoid straining grid resources by offering price incentives so that customers accept being routed to less busy stations, (ii) maximize total revenue by serving more customers with the same amount of grid resources, and (iii) provide charging service to customers with a certain level of Quality-of-Service (QoS), the latter defined as the long term customer blocking probability. We examine three scenarios of increased complexity that gradually approximate real world settings. The obtained results show that the proposed framework leads to substantial performance improvements in terms of the aforementioned goals, when compared to current state of affairs.Comment: Accepted for Publication in IEEE Transactions on Smart Gri

Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions

To enhance environmental sustainability, many countries will electrify their transportation systems in their future smart city plans. So the number of electric vehicles (EVs) running in a city will grow significantly. There are many ways to re-charge EVs' batteries and charging stations will be considered as the main source of energy. The locations of charging stations are critical; they should not only be pervasive enough such that an EV anywhere can easily access a charging station within its driving range, but also widely spread so that EVs can cruise around the whole city upon being re-charged. Based on these new perspectives, we formulate the Electric Vehicle Charging Station Placement Problem (EVCSPP) in this paper. We prove that the problem is non-deterministic polynomial-time hard. We also propose four solution methods to tackle EVCSPP and evaluate their performance on various artificial and practical cases. As verified by the simulation results, the methods have their own characteristics and they are suitable for different situations depending on the requirements for solution quality, algorithmic efficiency, problem size, nature of the algorithm, and existence of system prerequisite.Comment: Submitted to IEEE Transactions on Smart Grid, revise

On the Evaluation of Plug-in Electric Vehicle Data of a Campus Charging Network

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities.Comment: Accepted by IEEE Energycon 201