Abstract
The penetration rate of renewable energy source generation in power systems continues to increase in recent years. However, due to the intermittent nature of renewable energy source (RES) generation, more frequency regulation resources with faster response time and larger capacities are required in the system in order to maintain the system frequency stability. Electric vehicles (EVs) can provide frequency regulation capacities to the system with their batteries when idle, but first they need to be aggregated to enter the ancillary service market for frequency regulation. In this paper, a model predictive control scheme is proposed for the EV aggregators. With the proposed control scheme, an EV aggregator can receive more payment through participation in system frequency regulation while not violating the EV users' convenience. A prediction method based on a seasonal-autoregressive-integral-moving-average (SARIMA) model on the regulation capacity price is also implemented to further boost the EV aggregator's payment. Simulation based on actual frequency regulation market price is conducted to examine the performance of the proposed control scheme. Compared with the simple prediction method on the regulation capacity price used in the existing literature, the proposed MPC scheme with SARIMA prediction increases the payments from the ancillary service market by 4.3% for the EV aggregator.
Highlights
Integration of a large amount of renewable energy source (RES) generation has brought many challenges to the traditional power systems
The SARIMA model is implemented in a way that cooperates with the proposed Model predictive control (MPC) scheme
OPTIMAL SCHEDULING AND DISPATCHING CONTROL The resources participating in the frequency regulation market need to submit a regulation capacity offer to the market, stating how much regulation capacity they would like to provide in each time-step
Summary
Integration of a large amount of renewable energy source (RES) generation has brought many challenges to the traditional power systems. Many control and schedule strategies concerning users’ convenience have been proposed in the literature [9]–[13] These methods control EVs based on the real-time state-of-charge (SOC) of the EV batteries and the expected SOC of each EV so that there will be enough energy in the EV battery for the travel when plug-out. Optimal bidding strategies for EVs entering both regulation market and energy market are proposed in [17], [18] These EV scheduling methods are in an MPC scheme so that the EV aggregator can change its bidding strategy in a real-time market according to the current states of plug-in EVs. In this paper, an MPC scheme for the EV aggregators participating in the frequency regulation market is proposed.
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