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

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

Optimizing plug-in electric vehicle charging in interaction with a small office building

This paper considers the integration of plug-in electric vehicles (PEVs) in micro-grids. Extending a theoretical framework for mobile storage connection, the economic analysis here turns to the interactions of commuters and their driving behavior with office buildings. An illustrative example for a real office building is reported. The chosen system includes solar thermal, photovoltaic, combined heat and power generation as well as an array of plug-in electric vehicles with a combined aggregated capaci-ty of 864 kWh. With the benefit-sharing mechanism proposed here and idea-lized circumstances, estimated cost savings of 5% are possible. Different pricing schemes were applied which include flat rates, demand charges, as well as hourly variable final customer tariffs and their effects on the operation of intermittent storage were revealed and examined in detail. Because the plug-in electric vehicle connection coincides with peak heat and electricity loads as well as solar radiation, it is possible to shift energy demand as desired in order to realize cost savings. --Battery storage,building management systems,dispersed storage and generation,electric vehicles,load management,microgrid,optimization methods,power system economics,road vehicle electric propulsion

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †

The design and implementation of management policies for plug-in electric vehicles (PEVs) need to be supported by a holistic understanding of the functional processes, their complex interactions, and their response to various changes. Models developed to represent different functional processes and systems are seen as useful tools to support the related studies for different stakeholders in a tangible way. This paper presents an overview of modeling approaches applied to support aggregation-based management and integration of PEVs from the perspective of fleet operators and grid operators, respectively. We start by explaining a structured modeling approach, i.e., a flexible combination of process models and system models, applied to different management and integration studies. A state-of-the-art overview of modeling approaches applied to represent several key processes, such as charging management, and key systems, such as the PEV fleet, is then presented, along with a detailed description of different approaches. Finally, we discuss several considerations that need to be well understood during the modeling process in order to assist modelers and model users in the appropriate decisions of using existing, or developing their own, solutions for further applications

Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles

Plug-in electric vehicles (PEVs) are expected to balance the fluctuation of re-newable energy sources (RES). To investigate the contribution of PEVs, the availability of mobile battery storage and the control mechanism for load man-agement are crucial. This study therefore combined the following: a stochastic model to determine mobility behavior, an optimization model to minimize vehicle charging costs and an agent-based electricity market equilibrium model to esti-mate variable electricity prices. The variable electricity prices are calculated based on marginal generation costs. Hence, because of the merit order effect, the electricity prices provide incentives to consume electricity when the supply of renewable generation is high. Depending on the price signals and mobility behavior, PEVs calculate a cost minimizing charging schedule and therefore balance the fluctuation of RES. The analysis shows that it is possible to limit the peak load using the applied control mechanism. The contribution of PEVs to improving the integration of intermittent renewable power generation into the grid depends on the characteristic of the RES generation profile. For the Ger-man 2030 scenario used here, the negative residual load was reduced by 15 to 22 percent and the additional consumption of negative residual load was be-tween 34 and 52 percent. --Plug-in electric vehicles,demand-side management,variable prices,intermittent generation