Final report: Workshop on: Integrating electric mobility systems with the grid infrastructure

EXECUTIVE SUMMARY: This document is a report on the workshop entitled “Integrating Electric Mobility Systems with the Grid Infrastructure” which was held at Boston University on November 6-7 with the sponsorship of the Sloan Foundation. Its objective was to bring together researchers and technical leaders from academia, industry, and government in order to set a short and longterm research agenda regarding the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. The report is a summary of their insights based on workshop presentations and discussions. The list of participants and detailed Workshop program are provided in Appendices 1 and 2. Public and private decisions made in the coming decade will direct profound changes in the way people and goods are moved and the ability of clean energy sources – primarily delivered in the form of electricity – to power these new systems. Decisions need to be made quickly because of rapid advances in technology, and the growing recognition that meeting climate goals requires rapid and dramatic action. The blunt fact is, however, that the pace of innovation, and the range of business models that can be built around these innovations, has grown at a rate that has outstripped our ability to clearly understand the choices that must be made or estimate the consequences of these choices. The group of people assembled for this Workshop are uniquely qualified to understand the options that are opening both in the future of mobility and the ability of electric utilities to meet the needs of a highway transportation system powered primarily by electricity. They were asked both to explain what is known about the choices we face and to define the research issues most urgently needed to help public and private decision-makers choose wisely. This report is a summary of their insights based on workshop presentations and discussions. New communication and data analysis tools have profoundly changed the definition of what is technologically possible. Cell phones have put powerful computers, communication devices, and position locators into the pockets and purses of most Americans making it possible for Uber, Lyft and other Transportation Network Companies to deliver on-demand mobility services. But these technologies, as well as technologies for pricing access to congested roads, also open many other possibilities for shared mobility services – both public and private – that could cut costs and travel time by reducing congestion. Options would be greatly expanded if fully autonomous vehicles become available. These new business models would also affect options for charging electric vehicles. It is unclear, however, how to optimize charging (minimizing congestion on the electric grid) without increasing congestion on the roads or creating significant problems for the power system that supports such charging capacity. With so much in flux, many uncertainties cloud our vision of the future. The way new mobility services will reshape the number, length of trips, and the choice of electric vehicle charging systems and constraints on charging, and many other important behavioral issues are critical to this future but remain largely unknown. The challenge at hand is to define plausible future structures of electric grids and mobility systems, and anticipate the direct and indirect impacts of the changes involved. These insights can provide tools essential for effective private ... [TRUNCATED]Workshop funded by the Alfred P. Sloan Foundatio

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Foresighted Demand Side Management

We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators' costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators' states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators' states or the ISO's state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregator's energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities

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Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.\u