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

Economics of Electric Vehicle Charging: A Game Theoretic Approach

In this paper, the problem of grid-to-vehicle energy exchange between a smart grid and plug-in electric vehicle groups (PEVGs) is studied using a noncooperative Stackelberg game. In this game, on the one hand, the smart grid that acts as a leader, needs to decide on its price so as to optimize its revenue while ensuring the PEVGs' participation. On the other hand, the PEVGs, which act as followers, need to decide on their charging strategies so as to optimize a tradeoff between the benefit from battery charging and the associated cost. Using variational inequalities, it is shown that the proposed game possesses a socially optimal Stackelberg equilibrium in which the grid optimizes its price while the PEVGs choose their equilibrium strategies. A distributed algorithm that enables the PEVGs and the smart grid to reach this equilibrium is proposed and assessed by extensive simulations. Further, the model is extended to a time-varying case that can incorporate and handle slowly varying environments

Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

Exploiting Mobile Energy Storages for Overload Mitigation in Smart Grid

The advancement of battery and electronic technologies pushes forward transportation electrification, accelerating the commercialization and prevalence of plug-in electric vehicles (PEVs). The development of PEVs is closely related to the smart grid as PEVs are considered as high power rating electric appliances that require frequent charging. As PEVs become regular transportation options, charging stations (CSs) are also extensively deployed in the smart grid to meet the PEV charging demand. During peak traffic hours, the increasing PEV charging demand could exceed the loading capacities of CS-connected transformers, causing heavy charging overload in-station. Without proper overload mitigation, the energy imbalance issues will result in severe feeder degradation and power quality issue. Therefore, solutions for CS overload mitigation are in urgent demand. Considering the rechargeable nature of PEV batteries, PEVs can serve as potential mobile energy storages (MESs) to carry energy from power nodes with excess energy to overloaded CSs to compensate the overloads. Compared to infrastructure upgrade and installing stationary energy storages at CSs, the utilization of PEVs not only minimizes the additional upgrade/installation expenditure, but also maximizes the energy utilization in the smart grid with high flexibility. However, the PEV utilization for overload mitigation is confronted with a variety of challenges due to vehicular mobility and the fear of battery degradation. Because of vehicular mobility, the CS operation dynamics become stochastic processes, increasing the difficulty of the CS demand estimation. Without accurate demand estimation, the overload condition cannot be timely predicted and controlled. Moreover, the stochastic on-road traffic could impair the time-efficiency of the PEV overload mitigation service. Further, as the overload mitigation service demands frequent charging and discharging, the fear of battery degradation could impede PEV owners from providing the service, making the overload mitigation tasks harder to fulfill. In this thesis, we address the above challenges to effectively utilize PEVs for overload mitigation in the smart grid. In specific, different approaches are designed according to the PEV properties at different commercialization stages. First, at the early PEV commercialization stage, power utility company purchases large battery capacity PEVs as utility-owned MESs (UMESs) whose only responsibility is fulfilling the energy compensation task. The fleet of UMESs is rather small due to the company's limited budget, and therefore UMESs priorly serve the CSs with large energy imbalance (e.g., 500-1000kWh). Thus, the stochastic CS charging demand needs to be accurately estimated and then UMESs can be scheduled to these CSs for overload mitigation. To achieve this objective, we develop a two-dimensional Markov Chain model to characterize the stochastic process in-station so that the CS charging demand can be precisely estimated. Based on the estimated CS demand status, a two-tier energy compensation framework is designed to schedule UMESs to the heavily overloaded CSs in a timely and cost-efficient manner. Second, at the medium stage of PEV commercialization, vehicle-fleet based companies are motivated by legislation to purchase a large fleet of PEVs which can be served as potential MESs, referred to as legislation-motivated MESs (LMESs). To deliver energy to overloaded CSs using LMESs would introduce a large amount of additional traffics to the transportation network. When injecting these LMES traffics into an already busy transportation network, unexpected traffic delay could occur, delaying the overload mitigation service. To avoid the potential traffic delay incurred by LMES service, we develop an energy-capacitated transportation network model to measure the road capacity of accommodating additional LMES traffics. Based on the developed model, a loading-optimized navigation scheme is proposed to calculate the optimal navigation routes for LMES overload mitigation. To stimulate LMESs following the optimal navigation, we propose a dynamic pricing scheme that adjusts the service price to align the LMES service routes with the optimal routes to achieve a time-efficient service result. Third, when PEVs are prevalent in the automobile market and become regular transportation options for every household, on-road private-owned PEVs can be efficiently used as energy porters to deliver energy to overloaded CSs, named as private MESs (PMESs). As the primary objective of PMESs is to reach their planned destinations, the monetary incentive is demanded to stimulate them actively participating in the overload mitigation tasks. Therefore, a hierarchical decision-making process between the utility operator (UO) and PMESs is in demand. Moreover, considering PMESs have different service preferences (e.g., the fear of battery degradation, the unwillingness of long service time, etc.), individual PMES decision making process on the task should be carefully modelled. Thus, we propose to characterize the price-service interaction between the operator and PMESs as a Stackelberg game. The operator acts as the leader to post service price to PMESs while PMESs act as followers, responding to the posted price to maximize their utility functions. In summary, the analysis and schemes proposed in this thesis can be adopted by the local power utility company to utilize PEVs for overload mitigation at overloaded power nodes. The proposed schemes are applicable during different PEV commercialization stage and present PEVs as a flexible solution to the smart grid overload issue