Optimization of an Energy Storage System for Electric Bus Fast-Charging Station

Abstract

To relieve the peak operating power of the electric grid for an electric bus fast-charging station, this paper proposes to install a stationary energy storage system and introduces an optimization problem for obtaining the optimal sizes of an energy buffer. The charging power demands of the fast-charging station are uncertain due to arrival time of the electric bus and returned state of charge of the onboard energy storage system can be affected by actual traffic conditions, ambient temperature and other factors. The introduced optimization is formulated as a stochastic program, where the power matching equality of the total charging demands of connected electric buses is described as a chance constraint by denoting a satisfaction probability, then a stochastic supremum for the operating power of the electric grid is defined by actual data and the problem finally can be solved by convex programming. A case study for an existing electric bus fast-charging station in Beijing, China was utilized to verify the optimization method. The result shows that the operation capacity cost and electricity cost of the electric grid can be decreased significantly by installing a 325 kWh energy storage system in the case of a 99% satisfaction probability.

Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning

“…Fast charging technology is a rapidly developing area, driven by demand to make the EV charging experience as similar as possible to refuelling ICE vehicles. Most developments to date have targeted light EVs; however, future developments are expected to extend to ultra-high-power chargers (>1 MW), e.g., for heavy EVs [35]. Further development and use of communication technologies in smart grids are likely to enable additional functionality and better management of charging technologies and systems [36].…”

Review of Solid-State Transformer Applications on Electric Vehicle DC Ultra-Fast Charging Station

“…In order to predict the charging station's load profile, curve fitting through standard exponential load model [232], conventional optimization [233], and machine learning techniques have been used in the literature [234]. By means of these methods, the optimal sizing of the resources in DC ultra-fast charging stations is achievable [14], thereby leading to maximizing third-party interest [235].…”

“…The limit is due to the high temperature [12] and state of health (SOH) issues of the battery [13]. However, it is foreseen to reach 1200 kW maximum delivered DC power [14] as a result of advancements in battery structure design, vehicle electrical architecture, and material in the near future [15].…”

Distributed Nonlinear AIMD Algorithms for Electric Bus Charging Plants

“…The optimal management of EV charging has been widely investigated in the literature, where centralized, decentralized or distributed control approaches have been proposed (see, e.g., [4][5][6][7][8][9][10][11][12][13][14], among many others).…”

“…In [4], for instance, an optimization problem is solved to coordinate the choice of EV charging station in the case of long-distance trips, while reducing the waiting time for charging. In [5], instead, electric bus fast-charging stations with a stationary energy storage system are considered, and an optimization problem is solved to achieve the optimal sizes of the energy buffer to cope with uncertainties on arrival time and state of charge due to different factors such as actual traffic conditions. In [6], a method to coordinate the charging of EVs and shift the load demand from the peak period to the valley period is introduced, taking also into account indicators on different charging demand urgency for ensuring some charging priority among vehicles.…”