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Xia, Bainan; Ming, Hao; Lee, Kiyeob; Li, Yuanyuan; Zhou, Yuqi; Bansal, Shantanu; Shakkottai, Srinivas; Xie, Le

doi:10.1145/3077839.3077862

Cited by 24 publications

(6 citation statements)

References 11 publications

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“…Simplicity, or its opposite complexity, is a criterion describing how easy or hard it is to implement and understand a baselining methodology. This criterion is addressed extensively in the literature (e.g., by [3,[17][18][19]). In particular [20,21] consider this to be a crucial criterion.…”

Section: Simplicitymentioning

confidence: 99%

“…Proneness to manipulation, also known as integrity, describes the extent with which a baselining methodology is prone to manipulation. Two examples of manipulation are described by [19]. The first example is called user dilemma, which refers to flexibility providers (users) influencing the future baseline by actively delivering flexibility.…”

Section: Proneness To Manipulationmentioning

confidence: 99%

“…These measurements also reflect flexibility delivered in the past, thus affecting future baselines. Secondly, [19] discusses gaming, or baseline cheating, which refers to the baseline being influenced by intentionally increasing consumption (or production) in the days before flexibility is delivered. Gaming possibilities are also identified as a factor by [3,18].…”

Section: Proneness To Manipulationmentioning

confidence: 99%

“…However, for highly variable flexibility sources the accuracy of a single measurement before activation may be insufficient. This baselining methodology is commonly used and can be found in for example references [11,12,15,17,19].…”

Section: Window Beforementioning

confidence: 99%

“…The accuracy however quickly drops when flexibility assets are volatile and have different profiles every day. This baselining methodology is commonly referred to in the literature, and can be found in for example references [11,[15][16][17]19].…”

Section: Historicalmentioning

confidence: 99%

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Baselining Flexibility from PV on the DSO-Aggregator Interface

et al. 2021

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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.

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