Optimal charging of an electric vehicle using a Markov decision process

Abstract

The combination of electric vehicles (EVs) and renewable energy is taking shape as a potential driver for a future free of fossil fuels. However, the efficient management of the EV fleet is not exempt from challenges. It calls for the involvement of all actors directly or indirectly related to the energy and transportation sectors, ranging from governments, automakers and transmission system operators, to the ultimate beneficiary of the change: the end-user. An EV is primarily to be used to satisfy driving needs, and accordingly charging policies must be designed primarily for this purpose. The charging models presented in the technical literature, however, overlook the stochastic nature of driving patterns. Here we introduce an efficient stochastic dynamic programming model to optimally charge an EV while accounting for the uncertainty inherent to its use. With this aim in mind, driving patterns are described by an inhomogeneous Markov model that is fitted using data collected from the utilization of an EV. We show that the randomness intrinsic to driving needs has a substantial impact on the charging strategy to be implemented.

Dispatch of Vehicle-to-Grid Battery Storage Using an Analytic Hierarchy Process

“…where Sev C is the severity of overloading caused by contingency C and Sev max is the severity of the severest overload caused by the severest contingency, which is derived by running the simulation of power flow analysis for all possible N-1 contingencies within the network, calculating the severity of overload they caused using (14) and then selecting the maximum.…”

“…An algorithm was proposed in [14] for optimal charging of EVs formulated as a stochastic dynamic programming problem taking into account the intrinsic uncertainty. The algorithm considers electricity procurement cost with an added inconvenience cost penalty term set by the EV owner to control the availability of the EV.…”

Optimal coordination of vehicle-to-grid batteries and renewable generators in a distribution system

“…In this work, a novel agent-based coordinated dispatch strategy for EVs and RGs is developed for real-time application, which aims at satisfying the concerns and requirements of both EV users and the grid, including 1. cost, as saving charging cost is a very common request of EV users, which are taken into account in many publications [13,14,18,19]; 2. sufficient SOC for the next journey, which is important because mobility is the basic function of an EV, as considered in many researches [1,11,13,19,21]; 3. improved utilization of renewable energy, because it is crucial for carbon emission reduction, as discussed earlier and in [1,5,7,8]; 4. load levelling, which is one of the main grid operational support that EV batteries can provide and is also discussed in many papers [9,15,19,24]. In this strategy, each node in the network is represented by an software agent which is only aware of the elements that are locally connected to it and manages the dispatch of EVs and RGs connected to it, based on information received from the agents of other nodes that are directly linked to it, so that the stability of the network is ensured and all the objectives of dispatch are best achieved.…”

“…An algorithm was proposed in [11] for optimal charging of EVs formulated as a stochastic dynamic programming problem taking into account the intrinsic uncertainty of their travel pattern. A charging optimization approach was proposed in [12] to maximize the total electric energy that all EVs absorb from the grid while avoiding violations of voltage limits and components' loading.…”

Electricity costs for a Level 3 electric vehicle fueling station integrated with a building

“…Once EVSE sites and layout have been determined, other research has examined how to control PEV refueling to improve grid performance [22,23], minimize electricity cost [24], or both [25,26]. If special EVSE with bi-directional capabilities are installed, a PEV battery can be discharged for the purposes of supplying electricity in support of improving the performance of a building [27][28][29], micro-grid [30][31][32], or macro grid [33] as well as reducing the need for electric energy storage in systems with high renewable penetrations [34].…”