Abstract
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.
Highlights
According to [2], load forecasting can be roughly divided in two main categories depending on the time horizon of forecasting: (a) short-term load forecasting (STLF) that mainly refers to time periods spanning from one day up to a few days and is dedicated for the operational planning of the distribution networks, and (b) long-term load forecasting (LTLF) that refers to time periods longer than three years and is necessary for network planning, expansion and maintenance
In [6], a novel method is proposed for multistage coordinated planning in active distribution networks considering high penetration of renewables and load scenarios based on LTLF
This paper proposes a novel probabilistic framework for spatial load forecasting (SLF) based on a hierarchical forecasting method that uses the well-known S-curves for fitting the historical load data
Summary
Load forecasting was always a topic of high priority for the distribution network operators (DNOs). Planning purposes have revealed the need for efficient load forecasting tools that cover the load growth for a time horizon from eight to twenty years [1]. In cities that are characterized by increased urban development, LTLF is getting more and more crucial for the network planners and reveals the need for more granular spatial forecasting. As defined in [1,7], spatial load forecasting (SLF) is a process of LTLF that geographically allocates the future electric loads to the service territory of a distribution network, i.e., a wider area (e.g., village, neighborhood, municipality, or city) that is supplied by one or more high voltage (HV)/medium voltage (MV) substations, which supply several MV/low voltage (LV) distribution substations. SLF in transmission planning uses the economic growth as the basic driver [9]
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