Abstract
The power system is undergoing a significant change as it adapts to the intermittency and uncertainty from renewable generation. Flexibility from loads such as electric vehicles (EVs) can serve as reserves to sustain the supply-demand balance in the grid. Some reserve markets have rules for participation that are computationally challenging for aggregators of such flexible loads: they are asked to bid both volume and price, and on top of this there is a minimum-volume requirement, a constraint currently under discussion both in the US and European markets. Several state-of-the-art methods to find a bidding strategy for the demand scheduling of large fleets of flexible loads in the day-ahead and reserve market are adapted to deal with such a shared constraint, and are compared based on costs, unscheduled demand, and running time. The experimental analysis shows that although such a shared constraint significantly affects scalability, some of the proposed adaptations can deal with this without much loss in quality. This comparison also shows the importance of including good uncertainty models for dealing with the risk of not meeting the users’ demands, and that it is possible to find an optimal single price per time unit for scheduling a fleet of EVs.
Highlights
The particular short-term market we consider in our model is a dayahead (DA) market, as those joint energy-reserve markets are simultaneously cleared by US independent system operators (ISO), but the concept and general models can be adapted to be used by ag gregators to bid to any reserve market
We offer adaptations of the state-of-the-art algorithms and models to such a shared constraint, and provide a comparison between them to inform such debate
This comparison shows the importance of good uncertainty models for dealing with the risk of not meeting the users’ demands
Summary
The intermittency and uncertainty of renewable energy sources complicate balancing supply and demand in power systems. Storage and flexible demand will likely play an increasingly important role in providing reserves for distribution and transmission grids [1] These services are typically traded via electricity markets. Some mar kets in the US [2] and Europe pose specific requirements on bids, such as a minimum volume and demanding the inclusion of both a price and a quantity in a bid simultaneously [3]. Without this requirement and when prices in these markets and deployment of reserves are known in advance, finding the minimum-cost schedule that meets all demand (e.g., of a fleet of electric vehicles) is relatively straightforward. Which algorithm to use to schedule and bid flexible de mand under these market conditions is an important open question
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