Smart EV Energy Management System to Support Grid Services
- Author(s): Wang, Bin
- Advisor(s): Gadh, Rajit
- et al.
Under smart grid scenarios, the advanced sensing and metering technologies have been applied to the legacy power grid to improve the system observability and the real-time situational awareness. Meanwhile, there is increasing amount of distributed energy resources (DERs), such as renewable generations, electric vehicles (EVs) and battery energy storage system (BESS), etc., being integrated into the power system. However, the integration of EVs, which can be modeled as controllable mobile energy devices, brings both challenges and opportunities to the grid planning and energy management, due to the intermittency of renewable generation, uncertainties of EV driver behaviors, etc. This dissertation aims to solve the real-time EV energy management problem in order to improve the overall grid efficiency, reliability and economics, using online and predictive optimization strategies.
Most of the previous research on EV energy management strategies and algorithms are based on simplified models with unrealistic assumptions that the EV charging behaviors are perfectly known or following known distributions, such as the arriving time, leaving time and energy consumption values, etc. These approaches fail to obtain the optimal solutions in real-time because of the system uncertainties. Moreover, there is lack of data-driven strategy that performs online and predictive scheduling for EV charging behaviors under microgrid scenarios. Therefore, we develop an online predictive EV scheduling framework, considering uncertainties of renewable generation, building load and EV driver behaviors, etc., based on real-world data. A kernel-based estimator is developed to predict the charging session parameters in real-time with improved estimation accuracy. The efficacy of various optimization strategies that are supported by this framework, including valley-filling, cost reduction, event-based control, etc., has been demonstrated.
In addition, the existing simulation-based approaches do not consider a variety of practical concerns of implementing such a smart EV energy management system, including the driver preferences, communication protocols, data models, and customized integration of existing standards to provide grid services. Therefore, this dissertation also solves these issues by designing and implementing a scalable system architecture to capture the user preferences, enable multi-layer communication and control, and finally improve the system reliability and interoperability.