DYNAMICS  OF A PV/BATTERY FOR ELECTRIC VEHICLE CHARGING IN A MICROGRID 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The world’s Plug-in-Electric Vehicles (PEVs) fleet and sales have been growing at fast rates and eventually evolving as a major load demand. In parallel, Microgrids are emerging to meet the growing demand for reliability and resiliency in the electricity market. Similar to the utility grid, microgrids face challenges of the intermittency associated with a high-penetration of renewable power generation (e.g., PV) and randomly occurring loads associated with electric vehicle charging. Characterizing and understanding the dynamics of these challenges are required in order to establish the smart controls and other distributed energy resources that will be required for microgrids to remain stable. 

 

For two reasons, a secondary circuit on the University of California, Irvine (UCI) Microgrid serves as the case study for this research.  First, it is a heavily loaded circuit and an integral component of a highly characterized and metered microgrid.  Second, a unique “next-generation” distributed energy resource has been deployed at the end of the circuit that integrates photovoltaic power generation, battery storage, and EV charging as part of a U.S. Department of Energy “Irvine Smart Grid Demonstration” project.  

 

On the roof of a parking structure, 48kW of PV panels have been deployed.  On the first floor of the structure, 20 monitored EV chargers have been installed.  Outside but immediately adjacent to the parking structure, a 100kW battery energy storage system (BESS) has been integrated into a combined system. The system has an internal inverter that converts the photovoltaic as well as the battery power outputs from direct current (DC) to alternating current (AC).  A Princeton Power Systems site controller switches the inverter between two control modes:  the demand response mode and the distributed generation mode. The modes determine to which control signals the inverter responds while the other modes contain the algorithms for overall system behavior.  The demand response mode allows the control of the BESS minimum state of charge. This means that if the battery is at or below this set-point, no further discharge can occur. The distributed generation mode allows the use of PV power without curtailment. The BESS is immediately charged using either PV or grid power.  

 

The battery has four different control modes. The first is a demand response event mode that allows the system to operate at a specified power until the battery limit is reached. The second is a peak shaver mode that causes the BESS to charge and discharge in order to bring the power imported from the microgrid to a specified level. In the third mode, a PV smoother algorithm uses the battery system to lower the fluctuation in the PV power output. In the fourth mode, the chargers are powered from the UCI microgrid for power when energy is not available from the BESS. 

 

In my research, the effects of intermittent renewable solar PV generation and random EV charging on secondary microgrid circuits are analyzed in the presence of a controllable battery in order to characterize and better understand the dynamics associated with  intermittent power production and random load demands in the context of the microgrid paradigm. In order to analyze this system and evaluate the impact of the DER on the secondary circuit, a model was developed to provide a real-time load flow and power quality analysis. The research aims to develop a power management system applicable to similarly integrated systems. 

 

Performing such analysis allows utilities to become efficiently equipped to solve potential operational issues that RES and PEVSs can cause to other system components to eventually enable a higher penetration of these technologies into the current electric distribution grid. Both technologies have a great potential for enabling a sustainable development and meeting current environmental goals, and certainly will have a significant growth in market share in the near future.