A simulation study of the use of electric vehicles as storage on the New Zealand electricity grid

This paper describes a simulation to establish the extent to which reliance on non-dispatchable energy sources, most typically wind generation, could in the future be extended beyond received norms, by utilizing the distributed battery capacity of an electric vehicle fleet. The notion of exploiting the distributed battery capacity of a nation’s electric vehicle fleet as grid storage is not new. However, this simulation study specifically examines the potential impact of this idea in the New Zealand context. The simulation makes use of real and projected data in relation to vehicle usage, full potential non-dispatchable generation capacity and availability, taking into account weather variation, and typical daily and seasonal patterns of usage. It differs from previous studies in that it is based on individual vehicles, rather than a bulk battery model. At this stage the analysis is aggregated, and does not take into account local or regional flows. A more detailed analysis of these localized effects will follow in subsequent stages of the simulation

Supercharged? Electricity Demand and the Electrification of Transportation in California

The rapid electrification of the transportation fleet in California raises important questions about the reliability, cost, and environmental implications for the electric grid. A crucial first element to understanding these implications is an accurate picture of the extent and timing of residential electricity use devoted to EVs. Although California is now home to over 650,000 electric vehicles (EVs), less than 5% of these vehicles are charged at home using a meter dedicated to EV use. This means that state policy has had to rely upon very incomplete data on residential charging use. This report summarizes the first phase of a project combining household electricity data and information on the adoption of electric vehicles over the span of four years. We propose a series of approaches for measuring the effects of EV adoption on electricity load in California. First, we measure load from the small subset of households that do have an EV-dedicated meter. Second, we estimate how consumption changes when households go from a standard residential electricity tariff to an EV-specific tariff. Finally, we suggest an approach for estimating the effect of EV ownership on electricity consumption in the average EV-owning household. We implement this approach using aggregated data, but future work should use household-level data to more effectively distinguish signal from noise in this analysis. Preliminary results show that households on EV-dedicated meters are using 0.35 kWh per hour from Pacific Gas and Electric (PGE); 0.38 kWh per hour from Southern California Edison; and 0.28 kWh per hour from San Diego Gas and Electric on EV charging. Households switching to EV rates without dedicated meters are using less electricity for EV charging: 0.30 kWh per hour in PGE. Our household approach applied to aggregated data is too noisy to be informative. These estimates should be viewed as evidence that more focused analysis with more detailed data would be of high value and likely necessary to produce rigorous analysis of the role EVs are playing in residential electricity consumption

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

Smart Meter Privacy: A Utility-Privacy Framework

End-user privacy in smart meter measurements is a well-known challenge in the smart grid. The solutions offered thus far have been tied to specific technologies such as batteries or assumptions on data usage. Existing solutions have also not quantified the loss of benefit (utility) that results from any such privacy-preserving approach. Using tools from information theory, a new framework is presented that abstracts both the privacy and the utility requirements of smart meter data. This leads to a novel privacy-utility tradeoff problem with minimal assumptions that is tractable. Specifically for a stationary Gaussian Markov model of the electricity load, it is shown that the optimal utility-and-privacy preserving solution requires filtering out frequency components that are low in power, and this approach appears to encompass most of the proposed privacy approaches.Comment: Accepted for publication and presentation at the IEEE SmartGridComm. 201