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|Title:||Optimal operation and planning of smart grid with electric vehicle penetration||Authors:||Zhao, Jian||Degree:||Ph.D.||Issue Date:||2017||Abstract:||Recent years have seen a fast development of electric vehicles (EVs) in the market, which can be attributed to the economic, environmental, and social benefits that can be potentially contributed by EVs. Specifically, these benefits include reduced national dependency on oils, reduced greenhouse gas emissions and reduced air pollution. As the transportation section accounts for large proportion in total energy consumption, the rapid shift to electrification of transportation will considerably increase the electricity usage. Thus, the current power infrastructure, especially the distribution network system, will suffer from some critical issues caused by large-scale EV charging demand. These issues include but are not limited to equipment overload, severe voltage fluctuation, power system reliability and so on. The investment in new generation capacity and upgrades of power infrastructures such as substation and transmission power line capacity may be urgently needed. Nevertheless, EV charging demand can be regarded as a flexible load and thus can be aggregated and managed so as to reduce the negative impact as well as to benefit to the power system. Besides, aggregated EVs also have the potential ability to provide ancillary service to power systems via vehicle to grid implementation in the future. In this regards, this thesis evaluates the challenges and opportunities introduced by large-scale EV charging in power system and developing new method to utilize EV charging flexibility to benefit power system economic and secure operation.
This thesis firstly focuses on power system distribution network operation planning, to hedge against negative impacts caused by large-scale random EV charging. The concept of an EV chargeable region is proposed to evaluate the distribution network EV hosting capacity, i.e., how much EV charging demand can be accommodated in a distribution network, within which the technical constraints of distribution network (e.g., voltage deviation) are guaranteed and EV owners' charging requests are maximally ensured. To further accommodate uncertain EV charging demand, a two-stage robust active distribution network planning model is then proposed. The distributed generator investment, location, and size are optimized in the first stage and the active distribution network operation feasibility in the worst-case scenario is checked in the second stage to prevent any constraint violations. Finally, a modified column-and-constraint generation algorithm is adopted to solve the distribution system operation and planning problems. Simulations on modified IEEE 123-node distribution network demonstrate the effectiveness of the proposed two models. Then this thesis proposes models for aggregated EV to provide ancillary service and bid in electricity market. To utilize the EV charging flexibility to benefit the grid, this thesis evaluates the potential ability of EVs in providing operating reserve, through optimizing day-ahead spinning reserve requirement with EV participation. Based on the probabilistic criteria, the cost of expected energy supplied by EV is formulated. The effects of EVs on system spinning reserve requirement quantification and unit commitment are comprehensively analyzed. At last, an information gap decision theory based EV scheduling method and bidding strategies are proposed. It aims at managing the revenue risk caused by the information gap between the forecasted and actual electricity prices. The proposed decision-making framework is used to offer effective strategies to either guarantee the predefined profit for risk-averse decision makers, or pursue the windfall return for risk-seeking decision makers considering the risks introduced by the electricity price uncertainty.
|Subjects:||Hong Kong Polytechnic University -- Dissertations
Electric vehicles -- Power supply
Smart power grids
|Pages:||xix, 129 pages : color illustrations|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/9187
Citations as of Jun 26, 2022
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