Candidate: Nan Chen
Title: Exploiting Mobile Energy Storages for Overload Mitigation in Smart Grid
Date: July 26, 2019
Time: 1:30 PM
Place: EIT 3142
Supervisor(s): Shen, Sherman X.
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. Due to the limit of aged power infrastructure and increasing PEV charging demand, some CSs could encounter heavy charging overload during peak hours and 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 redundant energy to overloaded CSs to mitigate their overload issues. Compared to power 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 process, increasing the difficulty of the CS demand estimation. Without the accurate demand estimation, the overload condition cannot be timely predicted and controlled. Moreover, the vehicular mobility could not guarantee the 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 smart grid. In specific, different approaches are designed according to the PEV properties at different commercialization stages. First, at the early PEV commercialization stage, utility-owned MESs (UMESs) whose sole functionality is overload mitigation are utilized by power utility companies to deliver energy to overloaded CSs. The fleet of UMESs is rather small due to the company's budget and should be scheduled to heavily overloaded CSs priorly. Therefore, 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 bust transportation network, unexpected traffic delay could occur, delaying the overload mitigation service. To avoid the potential traffic delay incurred by LMES service, an energy-capacitated transportation network model is developed 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 overload mitigation. Third, when PEVs are prevalent in 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, sufficient monetary incentive is demanded to stimulate them actively participating in the overload mitigation tasks. Therefore, a hierarchical decision making process between the smart grid operator 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 overload mitigation task should be carefully modeled. Thus, we proposed to characterize the price-service interaction between the operator and PMESs as a Stacklberg 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 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.
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