Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

Abstract

Abstract:The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%.

Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links

“…Effective energy coordination and control could alleviate these challenges and realize a more stable and reliable energy grid [3]. Some centralized approaches have been discussed in the literature, for instance, [4,5], that require a single component to assess the whole system and make decisions for other peers and gradient update, the delay cost function associated with maximum capacity utilization is maximally reduced;…”

Effects of Traffic Characteristics on Energy Consumption of IoT End Devices in Smart City

“…These challenges have motivated city councils, policy makers, the research community and other stakeholders to consider new ways of managing cities to make them compliant with 21st century citizen's needs. One key approach is to increase digital presence through deployment of various smart technologies such as Internet of things (IoT) [3], [4].…”

“…Sigfox (US) at 600bps, Tx current= 49mA, PA current=300mA, sleep current=6µA at 3.3V Payload size vs battery life for various number of daily messages in Sigfox access network Battery life in LoRaWAN transceiver with various payload sizes, SF=10, coding rate4 6 and CRC enabled. Other parameters are 8 symbols preamble and bandwidth 125kHz.…”

An EV Charging Scheduling Mechanism Based on Price Negotiation

“…Currently, research on EV charging prices keeps growing with the increasing development of EVs. Most research mainly focuses on the reasonable charging price model and strategy by considering the profits of charging station operators and the EV user, the cost and travelling requirements of EV users, and the utilization of renewable energy resources [11][12][13][14][15][16][17][18][19][20].…”

“…Another paper [13], analyzes the charging pricing space in centralized charging and discharging mode, and analyzes the benefit levels of charging station operators at different charging price levels. The authors of [14] proposed a decentralized optimization algorithm for plug-in vehicles (PEVs) during grid-vehicle (G2V) and vehicle-grid (V2G) operations by using the economic model predictive control technique. It considers the state-of-charge (SoC) at a given time as a discrete state space to investigate PEV performance and the schedule of V2G/G2V cycles for the maximum economic benefits of EV users.…”