Vehicle energy consumption in python (Vencopy): Presenting and demonstrating an open-source tool to calculate electric vehicle charging flexibility

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised

Quantitive analysis of electric vehicle flexibility : a data-driven approach

The electric vehicle (EV) flexibility, indicates to what extent the charging load can be coordinated (i.e., to flatten the load curve or to utilize renewable energy resources). However, such flexibility is neither well analyzed nor effectively quantified in literature. In this paper we fill this gap and offer an extensive analysis of the flexibility characteristics of 390k EV charging sessions and propose measures to quantize their flexibility exploitation. Our contributions include: (1) characterization of the EV charging behavior by clustering the arrival and departure time combinations that leads to the identification of type of EV charging behavior, (2) in-depth analysis of the characteristics of the charging sessions in each behavioral cluster and investigation of the influence of weekdays and seasonal changes on those characteristics including arrival, sojourn and idle times, and (3) proposing measures and an algorithm to quantitatively analyze how much flexibility (in terms of duration and amount) is used at various times of a day, for two representative scenarios. Understanding the characteristics of that flexibility (e.g., amount, time and duration of availability) and when it is used (in terms of both duration and amount) helps to develop more realistic price and incentive schemes in DR algorithms to efficiently exploit the offered flexibility or to estimate when to stimulate additional flexibility. (C) 2017 Elsevier Ltd. All rights reserved

Capturing Aggregate Flexibility in Demand Response

Flexibility in electric power consumption can be leveraged by Demand Response (DR) programs. The goal of this paper is to systematically capture the inherent aggregate flexibility of a population of appliances. We do so by clustering individual loads based on their characteristics and service constraints. We highlight the challenges associated with learning the customer response to economic incentives while applying demand side management to heterogeneous appliances. We also develop a framework to quantify customer privacy in direct load scheduling programs.Comment: Submitted to IEEE CDC 201

Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage

Synergistic Interactions of Dynamic Ridesharing and Battery Electric Vehicles Land Use, Transit, and Auto Pricing Policies

It is widely recognized that new vehicle and fuel technology is necessary, but not sufficient, to meet deep greenhouse gas (GHG) reductions goals for both the U.S. and the state of California. Demand management strategies (such as land use, transit, and auto pricing) are also needed to reduce passenger vehicle miles traveled (VMT) and related GHG emissions. In this study, the authors explore how demand management strategies may be combined with new vehicle technology (battery electric vehicles or BEVs) and services (dynamic ridesharing) to enhance VMT and GHG reductions. Owning a BEV or using a dynamic ridesharing service may be more feasible when distances to destinations are made shorter and alternative modes of travel are provided by demand management strategies. To examine potential markets, we use the San Francisco Bay Area activity based travel demand model to simulate business-as-usual, transit oriented development, and auto pricing policies with and without high, medium, and low dynamic ridesharing participation rates and BEV daily driving distance ranges. The results of this study suggest that dynamic ridesharing has the potential to significantly reduce VMT and related GHG emissions, which may be greater than land use and transit policies typically included in Sustainable Community Strategies (under California Senate Bill 375), if travelers are willing pay with both time and money to use the dynamic ridesharing system. However, in general, large synergistic effects between ridesharing and transit oriented development or auto pricing policies were not found in this study. The results of the BEV simulations suggest that TODs may increase the market for BEVs by less than 1% in the Bay Area and that auto pricing policies may increase the market by as much as 7%. However, it is possible that larger changes are possible over time in faster growing regions where development is currently at low density levels (for example, the Central Valley in California). The VMT Fee scenarios show larger increases in the potential market for BEV (as much as 7%). Future research should explore the factors associated with higher dynamic ridesharing and BEV use including individual attributes, characteristics of tours and trips, and time and cost benefits. In addition, the travel effects of dynamic ridesharing systems should be simulated explicitly, including auto ownership, mode choice, destination, and extra VMT to pick up a passenger

Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning

Demand response (DR) becomes critical to manage the charging load of a growing electric vehicle (EV) deployment. Initial DR studies mainly adopt model predictive control, but models are largely uncertain for the EV scenario (e.g., customer behavior). Model-free approaches, based on reinforcement learning (RL), are an attractive alternative. We propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of charging stations. State-of-the-art algorithms either focus on a single EV, or control an aggregate of EVs in multiple steps (e.g., 1) make aggregate load decisions and 2) translate the aggregate decision to individual EVs). In contrast, our RL approach jointly controls the whole set of EVs at once. We contribute a new MDP formulation with a scalable state representation independent of the number of charging stations. Using a batch RL algorithm, fitted $Q$ -iteration, we learn an optimal charging policy. With simulations using real-world data, we: 1) differentiate settings in training the RL policy (e.g., the time span covered by training data); 2) compare its performance to an oracle all-knowing benchmark (providing an upper performance bound); 3) analyze performance fluctuations throughout a full year; and 4) demonstrate generalization capacity to larger sets of charging stations

Design and Analysis of Vehicle Sharing Programs: A Systems Approach

Transit, touted as a solution to urban mobility problems, cannot match the addictive flexibility of the automobile. 86% of all trips in the U.S. are in personal vehicles. A more recent approach to reduce automobile dependence is through the use of Vehicle Sharing Programs (VSPs). A VSP involves a fleet of vehicles located strategically at stations across the transportation network. In its most flexible form, users are free to check out vehicles at any station and return the vehicle at a station close to their destination. Vehicle fleets are comprised of bicycles, cars or electric vehicles. Such systems offer innovative solutions to the larger mobility problem and can have positive impacts on the transportation system as a whole by reducing urban congestion. This dissertation employs a network modeling framework to quantitatively design and operate VSPs. At the strategic level, the problem of determining the optimal VSP configuration is studied. A bilevel optimization model and associated solution methods are developed and implemented for a large-scale case study in Washington D.C. The model explicitly considers the intermodalism, and views the VSP as a `last-mile' connection of an existing transit network. At the operational level, by transferring control of vehicles to the user for improved system flexibility, exceptional logistical challenges are placed on operators who must ensure adequate vehicle stock (and parking slots) at each station to service all demand. Since demand in the short-term can be asymmetric (flow from one station to another is seldom equal to flow in the opposing direction), service providers need to redistribute vehicles to correct this imbalance. A chance-constrained program is developed that generates least-cost redistribution plans such that most demand in the near future is met. Since the program has a non-convex feasible region, two methods for its solution are developed. The model is applied to a real-world car-sharing system in Singapore where the value of accounting for inherent stochasticities is demonstrated. The framework is used to characterize the efficiency of Velib, a large-scale bicycle sharing system in Paris, France