Macro Demand Spatial Approach (MDSA) at Spatial Demand Forecasting for Transmission System Planning

Sasmono, Sudarmono; Sinisuka, Ngapuli I.; Atmopawiro, Mukmin W.; Darwanto, Djoko

doi:10.15676/ijeei.2015.7.2.3

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…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%

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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Section: Motivationmentioning

confidence: 99%