Due to increasing numbers of intermittent and distributed generators in power systems, there is an increasing need for demand responses to maintain the balance between electricity generation and use at all times. For example, the electrification of transportation significantly adds to the amount of flexible electricity demand. Several methods have been developed to schedule such flexible energy consumption. However, an objective way of comparing these methods is lacking, especially when decisions are made based on incomplete information which is repeatedly updated. This paper presents a new benchmarking framework designed to bridge this gap. Surveys that classify flexibility planning algorithms were an input to define this benchmarking standard. The benchmarking framework can be used for different objectives and under diverse conditions faced by electricity production stakeholders interested in flexibility scheduling algorithms. Our contribution was implemented in a software toolbox providing a simulation environment that captures the evolution of look-ahead information, which enables comparing online planning and scheduling algorithms. This toolbox includes seven planning algorithms. This paper includes two case studies measuring the performances of these algorithms under uncertain market conditions. These case studies illustrate the importance of online decision making, the influence of data quality on the performance of the algorithms, the benefit of using robust and stochastic programming approaches, and the necessity of trustworthy benchmarking.@en

The growing capacity of intermittent energy sources causes more frequent system imbalances as well as congestion. Demand flexibility is a valuable resource that can be used to resolve these. Unfortunately, flexibility can also contribute to congestion, particularly when used to balance the grid. Using flexibility to solve grid problems without creating new ones requires well-designed financial incentives. Congestion management mechanisms (CMMs) are a primary example of such incentives. The question is which of these is most effective in preventing congestion with minimal impact on trading on the imbalance market. This question is answered by comparing traditional CMMs such as grid tariffs to a local flexibility market on their impact on the load in the grid and the lost value of flexibility on the imbalance market. This analysis shows that energy tariffs are not suited for preventing congestion. Capacity tariffs are able to prevent congestion but they impose limitations on the consumer which significantly reduce the value of flexibility on the imbalance market. The flexibility market, an example of a local market, is effective if aggregators do not have a position day ahead or if the distribution system operator limits the buying of flexibility a day before delivery.

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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.

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