Data-driven demand response characterization and quantification

Controllable Distributed Energy Resources (DERs) in Active Distribution Grids (ADGs) provide operational flexibility to system operators thereby offering the means to address various challenges. Existing local controllers for these resources are communication-free, robust, and cheap, but with sub-optimal performance compared to centralized approaches that heavily rely on monitoring and communication. Data-driven local controls can bridge the gap by providing customized local controllers designed from historical data, off-line optimization, and machine learning methods. These local controllers emulate the optimal behavior, under expected operating conditions, without the use of communication. However, they exhibit high implementation overhead with the need of individual programming of DER controllers, especially when there are many DERs or when new units are installed at a later stage. In this paper, we propose a clustering method to decrease the implementation overhead by reducing the individual DER controls into a smaller set while still achieving high performance. We show the performance of the method on a three-phase, unbalanced, low-voltage, distribution network.

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Identification of Typical and Anomalous Patterns in Electricity Consumption

Fidalgo

Macedo

2022

Applied Sciences

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Nontechnical losses in electricity distribution networks are often associated with a countries’ socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.

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Data-driven demand response characterization and quantification

Cited by 3 publications

References 11 publications

Smart Meter Data Characterization and Clustering for Peak Demand Targeting in Smart Grids

Smart Meter Data Characterization and Clustering for Peak Demand Targeting in Smart Grids

Clustering Data-Driven Local Control Schemes in Active Distribution Grids

Identification of Typical and Anomalous Patterns in Electricity Consumption

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