A Game-Theoretic Approach to Energy Trading in the Smart Grid

Electric storage units constitute a key element in the emerging smart grid system. In this paper, the interactions and energy trading decisions of a number of geographically distributed storage units are studied using a novel framework based on game theory. In particular, a noncooperative game is formulated between storage units, such as PHEVs, or an array of batteries that are trading their stored energy. Here, each storage unit's owner can decide on the maximum amount of energy to sell in a local market so as to maximize a utility that reflects the tradeoff between the revenues from energy trading and the accompanying costs. Then in this energy exchange market between the storage units and the smart grid elements, the price at which energy is traded is determined via an auction mechanism. The game is shown to admit at least one Nash equilibrium and a novel proposed algorithm that is guaranteed to reach such an equilibrium point is proposed. Simulation results show that the proposed approach yields significant performance improvements, in terms of the average utility per storage unit, reaching up to 130.2% compared to a conventional greedy approach.Comment: 11 pages, 11 figures, journa

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

Electric vehicle as a service (EVaaS):applications, challenges and enablers

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

A multi-layer market for vehicle-to-grid energy trading in the smart grid

In this paper, we propose a novel multi-layer market for analyzing the energy exchange process between electric vehicles and the smart grid. The proposed market consists essentially of two layers: a macro layer and a micro layer. At the macro layer, we propose a double auction mechanism using which the aggregators, acting as sellers, and the smart grid elements, acting as buyers, interact so as to trade energy. We show that this double auction mechanism is strategy-proof and converges asymptotically. At the micro layer, the aggregators, which are the sellers in the macro layer, are given monetary incentives so as to sell the energy of associated plug-in hybrid electric vehicles (PHEVs) and to maximize their revenues. We analyze the interaction between the macro and micro layers and study some representative cases. Depending on the elasticity of the supply and demand, the utility functions are analyzed under different scenarios. Simulation results show that the proposed approach can significantly increase the utility of PHEVs, compared to a classical greedy approach.postprin

Electrical Vehicles in the Smart Grid: A Mean Field Game Analysis

In this article, we investigate the competitive interaction between electrical vehicles or hybrid oil-electricity vehicles in a Cournot market consisting of electricity transactions to or from an underlying electricity distribution network. We provide a mean field game formulation for this competition, and introduce the set of fundamental differential equations ruling the behavior of the vehicles at the feedback Nash equilibrium, referred here to as the mean field equilibrium. This framework allows for a consistent analysis of the evolution of the price of electricity as well as of the instantaneous electricity demand in the power grid. Simulations precisely quantify those parameters and suggest that significant reduction of the daily electricity peak demand can be achieved by appropriate electricity pricing.Comment: submitted to IEEE Journal on Selected Areas in Communications: Smart Grid Communications Serie

A Stackelberg Game for Multi-Period Demand Response Management in the Smart Grid

This paper studies a multi-period demand response management problem in the smart grid where multiple utility companies compete among themselves. The user-utility interactions are modeled by a noncooperative game of a Stackelberg type where the interactions among the utility companies are captured through a Nash equilibrium. It is shown that this game has a unique Stackelberg equilibrium at which the utility companies set prices to maximize their revenues (within a Nash game) while the users respond accordingly to maximize their utilities subject to their budget constraints. Closed-form expressions are provided for the corresponding strategies of the users and the utility companies. It is shown that the multi- period scheme, compared with the single-period case, provides more incentives for the users to participate in the game. A necessary and sufficient condition on the minimum budget needed for a user to participate is provided.Comment: Accepted for Proc. 54th IEEE Conference on Decision and Contro

A Bayesian Demand-Side Management Strategy for Smart Micro-Grid

In this manuscript a novel strategy for distributed and autonomous demand-side energy management among users of a low-voltage micro-grid is developed. Its derivation is based on: a) modelling the energy consumption scheduling of the shiftable loads that belong to a given user as a noncooperative two-player game of incomplete information, in which the user itself plays against an opponent collecting all the other users of the same micro-grid; b) assuming that each user is endowed with statistical information about its behavior and that of its opponent, so that it can choose actions maximising its expected utility. Numerical results evidence the efficacy of the proposed strategy when employed to manage the charging of electric vehicles in a micro-grid

Integrated PHEV Charging Loads Forecasting Model and Optimization Strategies

In this dissertation, an integrated Plug-in Electric Vehicle (PHEV) charging loads forecasting model is developed for regular distribution level system and microgrid system. For regular distribution system, charging schedule optimization is followed up. The objectives are 1. Better cooperation with renewable energy sources (especially wind). 2. Relieving the pressure of current distribution transformers in condition of high penetration level PHEVs. As for microgrid, renewable energy power plants (wind, solar) plays a more important role than regular system. Due to the fluctuation of solar and wind plants\u27 output, an empirical probabilistic model is developed to predict their hourly output. On the other hand, PHEVs are not only considered at the charging loads, but also the discharging output via Vehicle to Grid (V2G) method which can greatly affect the economic dispatch for all the micro energy sources in microgrid. Optimization is performed for economic dispatch considering conventional, renewable power plants, and PHEVs. The simulation in both cases results reveal that there is a great potential for optimization of PHEVs\u27 charging schedule. Furthermore, PHEVs with V2G capability can be an indispensable supplement in modern microgrid