Inference of Distribution Grids Based on Crowdsourced Grid Data and Drone Imagery

Jacobsen, Hans‐Arno; Nasirifard, Pezhman; Rivera, José; Baruah, Prerona Ray

doi:10.1109/tsusc.2019.2961763

“…For the characterization of electrical components, we use international standard types [25], considering the operation of MV grids at 20 kV and LV at 0.4 kV. Table I shows the line types 4 and Table II shows the transformer types. We use cables for LV grids, setting the line type 1 for rural and type 2 for urban and peri-urban zones [42].…”

Section: Case Studymentioning

confidence: 99%

“…Additionally, some of these frameworks can only be adopted for small-scale areas, as they entail significant computation effort [10]. Others offer a light computational burden but generate grids with insufficient detail about the electrical components [4], [11]. In summary, no existing work has covered the four characteristics necessary for a large-scale inference of PDGs using open data: combining both LV and MV levels, geo-referencing all PDG components, being computationally efficient, and providing electrical components' details.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Inference of Geo-Referenced Power Distribution Grids Using Open Data

Oneto,

Gjorgiev,

Tettamanti

et al. 2023

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0

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<p>Power distribution grids host an increasing amount of distributed renewable generators, electric vehicles, and heat pumps worldwide. Distribution grids, however, were not designed with the goal of incorporating large shares of these technologies. These soaring challenges demand accurate and realistic grid models to assess the need for operation strategies and reinforcements that ensure reliable and economic management. Nevertheless, real models are often unavailable due to privacy and security concerns or a lack of digitized data from distribution system operators. To address this issue, we present a framework for large-scale inference of geo-referenced low- and medium-voltage grid models using publicly accessible information on power demand and transport infrastructure. First, we develop a clustering algorithm, which detects load areas served by distribution grids. Then, we obtain the graphical grid layout, i.e., a graph with the street and pathway geometries and the load point connections inside the load area. Next, we introduce a selection method for line types that assigns cost-effective conductors to grid lines while ensuring operational constraints. We demonstrate the effectiveness of our approach by inferring all the low- and medium-voltage infrastructure in Switzerland. Remarkably, the inferred grids present overall power requirements and line lengths statistically aligned with reference grids.</p>

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“…RES accounts for 28% of electricity production in Germany, and it has been steadily increasing over the past two decades [27]. Nevertheless, the fluctuating nature of RES, commonly the wind and the sun, causes many concerns with the irregularity, uncertainty, and unreliability of supplies [11,18,29]. One solution for increasing the predictability of the RES is a weather forecast which is not without errors [19].…”

Section: Related Workmentioning

confidence: 99%

“…We evaluated the four generated power models by using the test dataset of the corresponding model based on different metrics including Adjusted R-Squared, MAPE, and Min/Max Accuracy of constructed models and also the Correlation Accuracy between the actual and predicted values, as Table 2 summarizes. The Adjusted R-Squared 11 represents the proportion of the variation in the measured real power explained by the model when taking the number of features in the model into consideration. A higher Adjusted R-Squared implies a better model.…”

Section: Power Models Evaluationmentioning

confidence: 99%

i13DR: A Real-Time Demand Response Infrastructure for Integrating Renewable Energy Resources

Nasirifard¹,

Jacobsen²

2022

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With the ongoing integration of Renewable Energy Sources (RES), the complexity of power grids is increasing. Due to the fluctuating nature of RES, ensuring the reliability of power grids can be challenging. One possible approach for addressing these challenges is Demand Response (DR) which is described as matching the demand for electrical energy according to the changes and the availability of supply. However, implementing a DR system to monitor and control a broad set of electrical appliances in real-time introduces several new complications, including ensuring the reliability and financial feasibility of the system. In this work, we address these issues by designing and implementing a distributed real-time DR infrastructure for laptops, which estimates and controls the power consumption of a network of connected laptops in response to the fast, irregular changes of RES. Furthermore, since our approach is entirely software-based, we dramatically reduce the initial costs of the demand side participants. The result of our field experiments confirms that our system successfully schedules and executes rapid and effective DR events. However, the accuracy of the estimated power consumption of all participating laptops is relatively low, directly caused by our software-based approach.

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“…Therefore, concerns such as voltage stability, reverse power flows [2], and overloading of transformers [3] are becoming increasingly pressing. Tackling these issues requires implementing and evaluating PDG models for computational assessments [4]. However, publicly available information about PDGs is minimal because of privacy and security issues [5], added to the lack of digitized grid data [6] of distribution system operators (DSOs).…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Inference of Geo-Referenced Power Distribution Grids Using Open Data

Oneto,

Gjorgiev,

Tettamanti

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>Power distribution grids host an increasing amount of distributed renewable generators, electric vehicles, and heat pumps worldwide. Distribution grids, however, were not designed with the goal of incorporating large shares of these technologies. These soaring challenges demand accurate and realistic grid models to assess the need for operation strategies and reinforcements that ensure reliable and economic management. Nevertheless, real models are often unavailable due to privacy and security concerns or a lack of digitized data from distribution system operators. To address this issue, we present a framework for large-scale inference of geo-referenced low- and medium-voltage grid models using publicly accessible information on power demand and transport infrastructure. First, we develop a clustering algorithm, which detects load areas served by distribution grids. Then, we obtain the graphical grid layout, i.e., a graph with the street and pathway geometries and the load point connections inside the load area. Next, we introduce a selection method for line types that assigns cost-effective conductors to grid lines while ensuring operational constraints. We demonstrate the effectiveness of our approach by inferring all the low- and medium-voltage infrastructure in Switzerland. Remarkably, the inferred grids present overall power requirements and line lengths statistically aligned with reference grids.</p>

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Inference of Distribution Grids Based on Crowdsourced Grid Data and Drone Imagery

Cited by 3 publications

References 26 publications

Large-Scale Inference of Geo-Referenced Power Distribution Grids Using Open Data

Large-Scale Inference of Geo-Referenced Power Distribution Grids Using Open Data

i13DR: A Real-Time Demand Response Infrastructure for Integrating Renewable Energy Resources

Large-Scale Inference of Geo-Referenced Power Distribution Grids Using Open Data

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