Two-Area Control of Emergency Frequency Regulation Through Aggregation of Electric Vehicles

Abstract

Frequency control has become more of a concern for the reliable operation of interconnected power systems. Due to the integration of low inertia renewable energy sources into the grid and their volatility, there is a need for emergency frequency regulation and nonlinear control of the power system in case of large disturbances incorporating quick response loads such as Electric Vehicles (EV)s. Some published literature has studied the profits that EV owners can gain by participating in frequency regulation and focused more on the contract design among different entities. Some studies have addressed how much power the EVs can provide for the grid in case of the need for frequency stabilization. However, the contribution of EVs to emergency frequency regulation in a wider area network has not been studied yet, which is the main focus of this research. In this work, the control methods are primarily based on the aggregation of EVs injecting power into grid, and the frequency control system design is such that the grid-connected EVs aim at maximizing the reduction rate of the total kinetic energy of the system based on Lyapunov energy function. The performance of the control law was evaluated in a two-area network and with different locations of aggregated charging stations in the corners or in the middle of the interconnected network. At this stage, a big virtual EV is connected to each connected area and the control inputs are formulated with no constraint on the connected EVs. However, there is always a constraint on the maximum allowed penetration of EVs to the grid without any voltage violations on any nodes of the distribution network. In chapter 4, a model is proposed for aggregation of EVs in each area at distribution network level, in which the contribution of EVs for frequency regulation is proportional to the frequency deviation and EVs’ available energy. Then, the aggregate model is developed based on the probability distribution of number of EVs and their initial State of Charge (SOC) and energy. The simulation is initially done for two cases: 1- number of EVs to be fixed and their available energy to be random with a uniform distribution, 2- EVs’ energy to be fixed and random number of EVs with a binomial distribution. In the same chapter, the impact of aggregate model on distribution network is studied to find the safe penetration rate of EVs under the worst case scenario where all the points in a charging stations are connected to an EV. An EV-MPM algorithm is proposed to find the safe penetration rate of connected charging stations in a distribution network based on load-flow analysis and the voltage of any of the nodes not exceeding the maximum limit of 10% violation. The algorithm is tested on an IEEE 31 node 23 kV distribution system with five charging stations connected to the nodes. Given the day-ahead load profile, the daily safe penetration rate of EVs is estimated for a typical day. The derived penetration rate is then used in the aggregate model of virtual EV, and simulated in a two-are network to represent the emergency frequency control of connected EVs when contingency occurs in the system. The main contribution of this study is proposing a holistic layered structure to estimate the contribution of EVs from the distribution network level to a wider-connected areas, which hasn’t been addressed in the literature