A Crowdsourcing Approach for the Inference of Distribution Grids

Nasirifard, Pezhman; Rivera, José; Zhou, Qunjie; Schreiber, Klaus Bernd; Jacobsen, Hans‐Arno

doi:10.1145/3208903.3208927

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…Planning and operation principles are taken into account and with an iterative algorithm the LV network is adjusted to meet these principles. In [18] a crowdsourcing approach for the collection of open data on DG devices with the aim of derivation of the grid topology is presented. Our approach enables an automated generation of simulation ready distribution grid models and offers a more realistic assessment of distribution system operators (DSO) operating principles by taking into account their financial interests.…”

Section: Related Workmentioning

confidence: 99%

Automated generation of large-scale distribution grid models based on open data and open source software using an optimization approach

Çakmak¹,

Luc²,

Hagenmeyer³

2022

Preprint

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The increasing share of renewable energy sources on distribution grid level as well as the emerging active role of prosumers lead to both higher distribution grid utilization, and at the same time greater unpredictability of energy generation and consumption. This poses major problems for grid operators in view of, e.g., voltage stability and line (over)loading. Thus, detailed and comprehensive simulation models are essential for planning future distribution grid expansion in view of the expected strong electrification of society. In this context, the contribution of the present paper is a new, more refined method for automated creation of large-scale detailed distribution grid models based solely on publicly available GIS and statistical data. Utilizing the street layouts in Open Street Maps as potential cable routes, a graph representation is created and complemented by residential units that are extracted from the same data source. This graph structure is adjusted to match electrical low-voltage grid topology by solving a variation of the minimum cost flow linear optimization problem with provided data on secondary substations. In a final step, the generated grid representation is transferred to a DIgSILENT PowerFactory model with photovoltaic systems. The presented workflow uses open source software and is fully automated and scalable that allows the generation of ready-to-use distribution grid simulation models for given 20 kV substation locations and additional data on residential unit properties for improved results. The performance of the developed method with respect to grid utilization is presented for a selected suburban residential area with power flow simulations for eight scenarios including current residential PV installation and a future scenario with full PV expansion. Furthermore, the suitability of the generated models for quasi-dynamic simulations is shown.

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Section: Related Workmentioning

confidence: 99%

Automated generation of large-scale distribution grid models based on open data and open source software using an optimization approach

Çakmak¹,

Luc²,

Hagenmeyer³

2022

Preprint

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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%

“…Residential and small commercial consumers account for about 40 percent of total electricity consumption in Germany [27], and their share will increase with the adaptation of electric vehicles [13]. They can contribute to a DR system by curtailing or shifting their demand loads to create a more reliable distribution and transmission grid [18,21]. Reducing the initial cost of joining the DR system is a significant incentive for residential and commercial consumers.…”

Section: Related Workmentioning

confidence: 99%

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

Nasirifard¹,

Jacobsen²

2022

Preprint

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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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“…Tackling a similar problem, in [22] the authors propose a crowdsourced framework to map electricity infrastructure using mobile devices and an algorithm that aims to collect open imagery data and the geographical location of grid infrastructure. However, this approach is bounded by the willingness of the crowd to participate and the availability of smartphone devices, both of which may be lacking in rural areas with recent electricity access.…”

Section: Related Workmentioning

confidence: 99%

This Little Light of Mine

Correa

Shah

Taneja

2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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The tracking of electricity infrastructure locations is crucial to making informed decisions on grid expansion and energy supply alternatives. However, in developing settings, these tasks are limited by technical and budget capacity constraints where the most recent data on the locations of low-and medium-voltage grids is outdated or even unknown. Currently, utilities in high-income economies monitor these lines using sophisticated sensing devices, airborne laser scanning, and field surveys which are unaffordable in emerging economies. In this work we aim to improve upon an existing open-source electricity mapping tool that uses night-time light data as the main proxy of electrification. Using ground-truth data from Kenya, we validate the performance of the existing tool and proposed a learning model to improve the detection of electrified sites. Our results show that our learning model is able to correctly identify ≈78% of those places which had electricity but were not identified before and improve the detection accuracy by up to ≈7%. Moreover, we show that using daily composites of nighttime data combined with other open-source data sources significantly helps the generation of accurate electricity access maps.

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A Crowdsourcing Approach for the Inference of Distribution Grids

Cited by 11 publications

References 10 publications

Automated generation of large-scale distribution grid models based on open data and open source software using an optimization approach

Automated generation of large-scale distribution grid models based on open data and open source software using an optimization approach

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

This Little Light of Mine

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