1,194 research outputs found

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

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    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

    Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

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    With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

    Mathematical optimization techniques for demand management in smart grids

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    The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies
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