Chapter 16 - Multistage and decentralized operations of electric vehicles within the California demand response markets

Abstract

Electric vehicle (EV), as a significant type of distributed energy resources, provides extra flexibilities for microgrid operators to achieve a myriad of objectives. In the commercial microgrids, EVs are considered as controllable mobile batteries in the global optimization problem together with other microgrid components, such as the building load, renewable generations, and battery energy storage system. However, the patterns of the EV charging behaviors, including the public and the fleet EVs, result in further complexity of the microgrid management plans. We introduced a demonstration project with real-world EV driver behaviors collected in north California and integrated the EV charging control with the microgrid energy management problem with the objective to minimize the monthly energy bills. This chapter will analyze the cost-saving performance of smart charging controls, provided the tariff structures and the patterns of other microgrid components from the historical datasets. As an evaluation approach, we provide deterministic problem formulations to optimize the monthly energy bills under multiple energy and demand response markets. In addition, a more realistic day-by-day operation strategy is proposed and implemented with the real-world EV usage data and the building load data. The day-by-day algorithm adaptively minimizes the impact of demand charges caused by the monthly load peaks in a decentralized fashion based on the alternating direction method of multipliers (ADMM), which is later extended to an asynchronous version. In summary the highlights of this chapter are threefold: (1) introduction of a real-world smart charging project in a real-world demonstration site, which is AlCoPark; (2) comprehensive modeling and decision-making framework for commercial microgrids with EVs under multiple California demand response markets; (3) distribute and asynchronous operations in the real-time energy-allocation stage(s) to preserve the customers’ privacy and improve the system robustness.