Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points |
| |
| Affiliation: | 1. Department of Electrical Engineering, University of the State of Sao Paulo – UNESP, Ilha Solteira, SP, Brazil;2. Center for Engineering and Mathematical Sciences – CECE, State University of West Parana – UNIOESTE, Iguaçu, PR, Brazil;1. Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, United States;2. Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, United States;3. Department of Neurosurgery, Medical College of Georgia at Augusta University, United States;1. Center of Excellence for Power Systems Automation and Operation, Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran;2. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran;1. Electrical Energy Systems, Eindhoven University of Technology, Den Dolech 2, Eindhoven, The Netherlands;2. Liander N.V., Utrechtseweg 86, Arnhem, The Netherlands;1. Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ 85721, USA;2. Center for Energy, Environmental, and Economic Systems Analysis, Argonne National Laboratory, Argonne, IL 60439, USA |
| |
| Abstract: | The paper presents a spatial analysis of points especially suited to estimate a preference map for new consumers, which is then used as an analytical tool in spatial electric load forecasting. This approach is an exploratory spatial data analysis used to discover useful point patterns in the spatial location of distribution transformers to calculate a preference value for each area, rating it with respect to a hypothetical load change that may occur. We consider the locations of distribution transformers occupied land. Random points are generated in the study area where the new loads are expected; these points are referred to as unoccupied land. The method uses a generalized additive model (GAM) to estimate the probability of unoccupied land becoming occupied land. We test the approach with data from a real distribution system in a mid-size city in Brazil; the result is a preference map that shows the areas where new consumers are most likely to be allocated. The main advantage of this method is the ability work with a small-scale resolution, which enables the use of a resolution suitable for spatial load forecasting method chosen. We test the calculated probabilities in a spatial load forecasting simulation, yielding results with lower spatial error when compared with the heuristic technique. |
| |
| Keywords: | Distribution planning Land use Spatial points patterns analysis Spatial load forecasting |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
