On the Emerging Role of Spatial Load Forecasting in Transmission/Distribution Grid Planning

Heymann, Fabian; Melo, Joel D.; Martínez, Pablo Dueñas; Soares, Filipe; Miranda, Vladimiro

doi:10.1049/cp.2018.1861

Cited by 13 publications

(7 citation statements)

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“…Knowing the detailed spatial distribution of power demand at the microscopic spatial (e.g., at every single house or 100 m grid level) and temporal (i.e., at tight time intervals) scales is essential, especially for power system planning and the construction of substations (Yi et al, 2016). Some studies used spatial load forecasting methods to reveal the characteristics of electricity demand (Heymann et al, 2018; Ye et al, 2019). But these methods have low simulation accuracy and mainly focus on estimating future load growth.…”

Section: Related Workmentioning

confidence: 99%

A site selection framework for urban power substation at micro‐scale using spatial optimization strategy and geospatial big data

Yao,

Feng,

Xie

et al. 2023

Transactions in GIS

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The world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations and optimizing the location of existing ones are necessary to address the energy crisis. The current site selection lacks consideration of spatial and temporal heterogeneity in urban power demand, which results in unreasonable energy transfer and waste, leading to power outages in some areas. Aiming to maximize the grid coverage and transformer utilization, we propose a multi‐scene micro‐scale urban substation siting framework (UrbanPS): (1) The framework uses multi‐source big data and the machine learning model to estimate fine‐scale power consumption for different scenarios; (2) the region growing algorithm is used to divide the power supply area of substations; and the (3) location set coverage problem and genetic algorithm are introduced to optimize the substation location. The UrbanPS was used to perform siting optimization of 110 kV terminal substations in Pingxiang City, Jiangxi Province. Results show that the coverage and utilization rate of the optimization results under different power consumption scenarios are close to 99%. We also found that the power can be saved by dynamic regulation of substation operation.

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Section: Related Workmentioning

confidence: 99%

A site selection framework for urban power substation at micro‐scale using spatial optimization strategy and geospatial big data

Yao,

Feng,

Xie

et al. 2023

Transactions in GIS

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“…MAF studies do not currently consider regional or highly granular values of temperature but use aggregated and population weighted data grouped per market zone. In addition, in such power system studies, load patterns and their evolution are modelled on an aggregated level, neglecting the spatial heterogeneity of load growth 32 and other impacting factors such as technology diffusion, for example distributed energy resources 33 , or policy changes 34 . Therefore, in this work, we identified three different ways to assess the effect of climate change on demand using a post-processing approach.…”

Section: The Impact Of Temperature Changes On Electric Loadmentioning

confidence: 99%

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method

Harang

Heymann

Stoop

2020

Sustainable Energy, Grids and Networks

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“…With SLF, the magnitude of future loads (mainly the forecasted peak demand) is allocated to the small areas or the distribution equipment (distribution transformers and LV feeders) of a distribution network's service territory [1,2,7]. The latest developments and trends in SLF are summarized by the authors of [8] to highlight the emerging role of SLF in planning of transmission and distribution networks. SLF in distribution networks is mainly driven by land use change, whereas SLF in transmission planning uses the economic growth as the basic driver [9].…”

Section: Motivationmentioning

confidence: 99%

“…The spatial resolution varies depending on the application, the planning needs of the distribution network and the type of equipment that is studied to be installed. The granularity of the service territory varies from 0.25 km 2 to 2.5 km 2 for distribution network planning, according to [8]. Typically, electricity distribution companies use small areas of 0.63 km 2 for planning studies of distribution substations and small areas of 0.28 km 2 for planning the installation of new LV feeders in the service territory [1,7].…”

Section: Spatial Resolutionmentioning

confidence: 99%

Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

Evangelopoulos¹,

Karafotis²,

Georgilakis³

2020

Energies

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The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.

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On the Emerging Role of Spatial Load Forecasting in Transmission/Distribution Grid Planning

Cited by 13 publications

References 0 publications

A site selection framework for urban power substation at micro‐scale using spatial optimization strategy and geospatial big data

A site selection framework for urban power substation at micro‐scale using spatial optimization strategy and geospatial big data

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method

Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

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