Baselining Flexibility from PV on the DSO-Aggregator Interface

Abstract

Flexibility can be used to mitigate distribution network overloading. Distribution system operators (DSOs) can obtain this flexibility from market parties connected to the distribution network. After flexibility has been delivered to the DSO, it needs to be settled. This is typically done by comparing load measurements with a baseline. This baseline describes an asset’s power profile in case no flexibility would have been delivered. Until recently, baselining research mainly focused on large-scale, predictable and controllable assets. The flexibility used by DSOs however typically comes from small-scale, less predictable and less controllable assets. This paper addresses the baselining problem for photo-voltaic systems. Three existing baselining methods are selected based on their simplicity and transparency and their limitations with respect to application towards photo-voltaic systems are evaluated. Based on this, a proof-of-concept for a new, fourth method is provided. It overcomes some of the limitations of the three existing ones, while still ensuring simplicity and transparency in order to promote market acceptance and practical applicability. All four methods are subjected to two different curtailment strategies: curtailing all peaks above a threshold and curtailing based on a day-ahead flexibility request. Using weather data from three summer weeks in 2019, it is shown that the newly developed method is able to provide a more accurate baseline than the existing methods.

Network-Aware Flexibility Requests for Distribution-Level Flexibility Markets

“…The theoretical worst-case market clearing outcome is not feasible (4). By definition, non-feasible market clearing outcomes are worse than the unconstrained outcome by exactly the social welfare.…”

Challenges for large-scale Local Electricity Market implementation reviewed from the stakeholder perspective

“…Baselining, determining the baseline, is not mentioned in the studies included in the literature overview, likely due to the extra complexity, and baselining is, therefore, not considered in the presented LEMs. However, several baselining methods apply to LEMs, and a detailed description of baselining methods can be found in [70]. It is necessary, however, to highlight a few in this section.…”

“…Determining the baseline with either the window before method or using historical data is transparent and explainable, but the accuracy depends on the magnitude and frequency of flexibility [70]. Machine learning, however, is complex and opaque because a machine-learned model taught itself the connection and relation between input and output.…”

A Methodology for Exploiting Smart Prosumers’ Flexibility in a Bottom-Up Aggregation Process

“…In [13], the baselining challenge on the interface between the DSO and an aggregator is faced in the context of utilizing flexibility in distribution networks. In [14], the authors show how an aggregator can fulfill a flexibility request by re-scheduling the home-appliances loads of 20 residential end-users for the next 24-h horizon while minimizing the costs associated with the remuneration given to end-users.…”