|Title:||Optimization for power system planning and state estimation with stochastic EV user behavior|
|Advisors:||Chung, C. Y. (EE)|
|Subject:||Electric vehicles -- Power supply.|
Hong Kong Polytechnic University -- Dissertations
|Department:||Department of Electrical Engineering|
|Pages:||xviii, 110 pages : color illustrations|
|Abstract:||Environmental protection has become a global priority as people are getting increasingly aware of the importance of sustainable development. Replacing traditional internal combustion engine (ICE) vehicles with electric vehicles (EVs) is regarded as one effective way to reduce hazardous particulate matter and greenhouse gas emissions. Under the green grid paradigm, the redesigned load forecast, system state estimation and system planning methods proposed in this thesis will play an important role in monitoring, control and expansion of the future smart grid considering the increasing EV penetration with stochastic user behavior. The stochasticity of EV user behavior mainly lies in two aspects: EV travel behavior and EV charging behavior. EV can be regarded as a kind of mobile load and this thesis first provides a general model for simulating the daily routes with multiple travel purposes considering the geographical and temporal distribution of EVs. The EV charging demand at each location is estimated by the distance traveled earlier and the distance to be traveled for the next trip. This thesis strives to make a more practical forecast of EV charging demand with realistic model and data because a reasonable forecast is the cornerstone for monitoring, control and planning of the power systems. System monitoring is of great importance in providing useful information to regulators for fault detection and control scheme design. Compared to the complete data of travel surveys in various countries, there is no such credible statistics on EV charging preferences. As the aggregated stochastic charging power puts additional pressure to the peak load, the importance of having an accurate system state estimation (SSE) arises as the EV charging behavior can only be estimated in a range. In this thesis, an effective SSE based on quasi-Newton (QN) method and Armijo line search (ALS) is proposed to obtain a faster, more accurate and yet more reliable state estimation under potential forecast and measurement errors. The estimation accuracy and computation time required are compared with the widespread weighted least square (WLS) method and extended Kalman Filter (EKF) method. It is shown that the QN method has the best performance under most scenarios.|
Considering the increasing penetration of EVs, upgrading and reconstruction of the power system infrastructure should be planned ahead to satisfy the growth of load demand. One of the focuses is the construction of public charging stations while battery charging and battery swapping are two feasible technical options. This thesis proposes the distributed swapping and centralized charging (DSCC) battery-swap system by improving the operation and logistics among stations. Firstly, the traffic conditions are formulated such that the swapping stations and other supporting facilities can be deployed. Secondly, the real-time available batteries and demand of batteries are investigated with the improved (s, S) inventory management to guarantee adequate supply of recharged batteries. Finally, suitable optimization schemes are derived to attain the objectives of maximum battery inventory turnover or minimum impact of EV charging on power system. This thesis also proposes the planning optimization within the scope of local distribution systems where EVs are charged at the homes of customers rather than at specialized charging or swapping stations. With vehicle to grid (V2G) technology, the increasing integration of EVs is raising the future potentials of smart grid because the residual energy stored in EV batteries can be discharged to support grid when needed. However, the stochasticity of EV user behavior poses challenges to the regulators of distribution systems. How regulators decide upon a control strategy for V2G and how EV users respond to the strategy will significantly influence the variation of load profiles in the planning horizon. In this thesis, a comprehensive cost analysis is performed to obtain the optimal planning scheme considering the variation of EV penetration, charging preference and customer damage cost (CDC). The economics and stability of the planned distribution system are assessed with real-world travel records and cost statistics to show the effectiveness of the optimization algorithm.
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