Electric Vehicle Storage Management in Operating Reserve Auctions

Carsharing operators, which rent out electric vehicles for minutes or hours, lose money on idle vehicles. We develop a model that allows carsharing operators to offer the storage of these vehicles on operating reserve markets (market for quickly rampable back-up power sources that replace for instance failing power plants). We consider it a dispatch and pricing problem with the tradeoff between the payoffs of offering vehicles for rental and selling their storage. This is a problem of stochastic nature taking into account that people can rent electric vehicles at any time. To evaluate our model we tracked the location and status of 350 electric vehicles from the carsharing company Car2Go and simulated the dispatch in the Dutch market. This market needs to be redesigned for optimal use of storage. We make recommendations for the market redesign and show that carsharing operators can make substantial additional profits in operating reserve markets

Load Balancing in the Smart Grid: A Package Auction and Compact Bidding Language

Distribution system operators (DSOs) are faced with new challenges from the continuous integration of fluctuating renewable energy resources and new dynamic customer loads such as electric vehicles, into the power grid. To ensure continuous balancing of supply and demand, we propose procurement package auctions to allocate load flexibility from aggregators and customers. The contributions of this research are an incentive-compatible load flexibility auction along with a compact bidding language. It allows bidders to express minimum and maximum amounts of flexibility along with unit prices in single bids for varying time periods. We perform a simulation-based evaluation and assess costs and benefits for DSOs and balancing suppliers given scenarios of varying complexity as well as computational aspects of the auction. Our initial findings provide evidence that load flexibility auctions can reduce DSO costs substantially and that procurement package auctions are well-suited to address the grid load balancing problem

Improving the Market for Flexibility in the Electricity Sector. Report of a CEPS Task Force. CEPS Task Force Report

Electricity will play a greater role in the transport and building sectors and all decarbonisation scenarios point to the increasing electrification of the energy system. To reach EU climate change targets, however, electricity will need to come increasingly from low carbon sources, especially (but not only) from variable renewable energy sources. Both trends − the electrification of sectors and the need to integrate electricity from variable renewables − mean that the electricity sector should become more flexible. This report reflects the discussions held in the CEPS Energy Climate House Task Force on Creating a Market Design for Flexibility in EU Electricity Markets, which met between April and September 2017. The Task Force formulated a number of recommendations in the areas of short-term and balancing markets; grid reinforcement and cross-zonal capacity allocation; aggregation; priority dispatch; DSOs (distribution system operators); and sectoral integration

Road to 2020: IS-Supported Business Models for Electric Mobility and Electrical Energy Markets

Electric mobility is on the rise, as many countries such as the United States of America, Germany, and China intend to bring one million electric cars onto their streets by 2020. This opens up perspectives on new business models, including aggregators for electric vehicles that enter ancillary services energy markets. These intermediaries are of particular interest to the IS discipline, since their market position links research on decision support, electronic markets and energy informatics. In this paper we present a research agenda to analyze IS-supported business models that cover the entire value chain of such an intermediary. We argue that IS can enhance decision making in the auctions for ancillary energy by improving forecasts of maximum energy prices. Additionally, IS facilitates electronic auctions and scheduling mechanisms that enable the intermediary to aggregate numerous electric vehicles

Multiple Vickrey Auctions for Sustainable Electric Vehicle Charging

Electric vehicles (EVs) are important contributors to a sustainable future. However, uncontrolled EV charging in the smart grid is expected to stress its infrastructure, as it needs to accommodate extra electricity demand coming from EV charging. We propose an auction mechanism to optimally schedule EV charging in a sustainable manner so that the grid is not overloaded. Our solution has lower computational complexity, compared to state-of-the-art mechanisms, making it easily applicable to practice. Our mechanism creates electricity peak demand reduction, which is important for improving sustainability in the grid, and provides optimized charging speed design recommendations so that raw materials are not excessively used. We prove the optimal conditions that must hold, so that different stakeholder objectives are satisfied. We validate our mechanism on real-world data and examine how different trade-offs affect social welfare and revenues, providing a holistic view to grid stakeholders that need to satisfy potentially conflicting objectives

An Artificial Intelligence Framework for Bidding Optimization with Uncertainty inMultiple Frequency Reserve Markets

The global ambitions of a carbon-neutral society necessitate a stable and robust smart grid that capitalises on frequency reserves of renewable energy. Frequency reserves are resources that adjust power production or consumption in real time to react to a power grid frequency deviation. Revenue generation motivates the availability of these resources for managing such deviations. However, limited research has been conducted on data-driven decisions and optimal bidding strategies for trading such capacities in multiple frequency reserves markets. We address this limitation by making the following research contributions. Firstly, a generalised model is designed based on an extensive study of critical characteristics of global frequency reserves markets. Secondly, three bidding strategies are proposed, based on this market model, to capitalise on price peaks in multi-stage markets. Two strategies are proposed for non-reschedulable loads, in which case the bidding strategy aims to select the market with the highest anticipated price, and the third bidding strategy focuses on rescheduling loads to hours on which highest reserve market prices are anticipated. The third research contribution is an Artificial Intelligence (AI) based bidding optimization framework that implements these three strategies, with novel uncertainty metrics that supplement data-driven price prediction. Finally, the framework is evaluated empirically using a case study of multiple frequency reserves markets in Finland. The results from this evaluation confirm the effectiveness of the proposed bidding strategies and the AI-based bidding optimization framework in terms of cumulative revenue generation, leading to an increased availability of frequency reserves

Determining Utility System Value of Demand Flexibility From Grid-interactive Efficient Buildings

This report focuses on ways current methods and practices that establish the value to electric utility systems of distributed energy resource (DER) investments can be enhanced to determine the value of demand flexibility in grid-interactive efficient buildings that can provide grid services. The report introduces key valuation concepts that are applicable to demand flexibility that these buildings can provide and links to other documents that describe these concepts and their implementation in more detail.The scope of this report is limited to the valuation of economic benefits to the utility system. These are the foundational values on which other benefits (and costs) can be built. Establishing the economic value to the grid of demand flexibility provides the information needed to design programs, market rules, and rates that align the economic interest of utility customers with building owners and occupants. By nature, DERs directly impact customers and provide societal benefits external to the utility system. Jurisdictions can use utility system benefits and costs as the foundation of their economic analysis but align their primary cost-effectiveness metric with all applicable policy objectives, which may include customer and societal (non-utility system) impacts.This report suggests enhancements to current methods and practices that state and local policymakers, public utility commissions, state energy offices, utilities, state utility consumer representatives, and other stakeholders might support. These enhancements can improve the consistency and robustness of economic valuation of demand flexibility for grid services. The report concludes with a discussion of considerations for prioritizing implementation of these improvements