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Title: Optimization and control of energy storage in a smart grid
Author: Wang, Lu
ISNI:       0000 0004 6347 8028
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2017
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Environmental issues such as global warming, limited storage of fossil fuels and concerns about cost and energy efficiency are driving the development of the future smart grid. To reduce carbon emissions, it is expected that there will be a large-scale increase in the penetration of renewable generators (RGs), electric vehicles (EVs) and electrical heating systems. This will require new control approaches to ensure the balance of generation and consumption and the stability of the power grid. Energy storage can be used to support grid operations by controlling frequency and voltage, and alleviating thermal overload. This thesis makes three novel contributions to the field: optimal battery sizing; optimal dispatch of vehicle-to-grid batteries; and optimal coordination of EV batteries and RGs. Appropriate sizing of the energy storage is very important when using it to support the power system. In this thesis, an approach has been proposed to determine the capacity of a battery storage providing support during N-1 contingencies to relieve transmission line thermal overload. In addition, as the increasing use of EV is an inevitable trend in the future smart grid, the system's peak demand may increase significantly due to EV charging, causing serious overloading of some power system facilities such as transformers and cables in the grid if an effective EV battery dispatch strategy is not used. Therefore, this report presents a dispatch strategy for EV batteries based on the Analytic Hierarchy Process taking into account both vehicle users' and power system requirements and priorities, as well as the constraints of the battery system. However, using renewable power to charge EVs is the prerequisite of realizing clean transport. EVs can store the extra renewable power and feed it into the grid when needed via vehicle-to-grid operations to increase the utilization and integration of RGs in the power grid. Thus, the optimal dispatch of EVs and RGs to realize the synergy between them will be one of the key challenges. Two optimal agent-based coordinated dispatch strategies are developed in this thesis, respectively using dynamic programming and the A* search procedure (comparisons between these two algorithms are made and discussed), for the synergistic integration of EVs and RGs, so that the benefits of both EV users and power grid are maximized. Each of the proposed approaches was tested on an IEEE Reliability Test System or a modified UK generic distribution system (UKGDS) using MATLAB. The simulation results demonstrate the feasibility and efficacy of the proposed approaches.
Supervisor: Chipperfield, Andrew Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available