Control and Optimization of Future Electric Grid Integrating Plug-In Electric Vehicles and Wind Power.

Plug-in Electric Vehicle;Wind Power;Electric Grid Integration;Charging Control;Valley Filling;CO2 Emissions;Mechanical Engineering;Engineering;Mechanical Engineering

Li, Chiao-TingTilbury, Dawn M. ;

University of Michigan

关键词: Plug-in Electric Vehicle;    Wind Power;    Electric Grid Integration;    Charging Control;    Valley Filling;    CO2 Emissions;    Mechanical Engineering;    Engineering;    Mechanical Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/99824/ctli_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This dissertation studies the integration and control problems that will arise when plug-in electric vehicles (PEVs) and wind power are introduced to the electric grid. This dissertation harnesses the synergy between them via various control and optimization techniques.This dissertation first presents a PEV charging control algorithm. The algorithm adopts a partially-decentralized structure to address the different battery state of charge and plug-off time of individual PEVs. This allows most PEVs to be fully charged. Also, ;;valley filling” and grid frequency regulation are achieved.Secondly, this dissertation adopts model predictive control (MPC) on battery energy storage system (BESS) to mitigate wind intermittency. The MPC controller is derived using realistic objective functions that capture the reserve costs to cover wind intermittency. The capacity sizing of BESS is also investigated.Next, a three-level hierarchical control algorithm is proposed to integrate PEVs and wind power on the grid. The top-level controller solves a scheduling optimization problem to minimize the costs of electricity generation. The middle- and bottom-level controllers are based on the control algorithms previously developed for PEV charging and wind power scheduling. The hierarchical structure allows the features in the different control algorithms to be preserved.Exerting on the scheduling optimization framework, the scope of study is expended to consider grid CO2 emissions. A carbon disincentive policy is proposed to promote the use of low-carbon power plants for electricity generation. The tradeoff between the generation costs and grid CO2 emissions is investigated using optimal Pareto fronts.Lastly, a cost evaluation is proposed for generation planning. The evaluation considers the evolutions in both the supply and demand on the electric grid. The wind intermittency and reserve-related costs are also considered. The evaluation show that wind power will still be expensive in the next two decades owing to the high construction cost, although the wind intermittency can be addressed by BESS or PEVs on the operation stage.This dissertation shows more than one piece of evidence that PEVs and wind power are good complements to each other, and a proper integration is needed to bring the best out of them.