6533b870fe1ef96bd12cefa9

RESEARCH PRODUCT

Load Demand Analysis of Nordic Rural Area with Holiday Resorts for Network Capacity Planning

Morten GoodwinMohan KolheNils Jakob Johannesen

subject

Transport engineeringCapacity planningElectrical loadPeak demandComputer sciencebusiness.industryDistributed generationRural areaDemand forecastingGridbusinessEnergy storage

description

Most of the Nordic holiday resorts are in rural area with low capacity distributed network. The rural area network is weak and needs capacity expansion planning as the load demand of this area are going to increase due to penetration of electric vehicles and heat pumps. Such type of rural network can also be operated as a micro-grid, and therefore load analysis is required for appropriate operation. The load analysis will also be useful for finding proper sizing of distributed energy resources including energy storage. In this work, load demand analysis of a typical Nordic holiday resorts, connected in rural grid, is presented to find out the load variation during the usage periods. The load analysis is targeted for demand prediction. The demand forecasting has been considered through integrating Regression Tools with Artificial Neural Networks due to the low amount of data available from the Holiday Resorts. Collected data is from a rural area in Norway consisting of 125 holiday cabins, with maximum load of 478 kW in the period of 2014 to 2018. This work is presenting the analysis on the total electric load consumption of cabins during typical short and long term holidays. It is observed, during the longer time holiday period, the loads are significantly higher compared to shorter time holiday period. Prediction analysis shows that the MAPE is relatively higher compare to predicted results in higher load area. Through analysis, it is observed that the curvature of the maximum peak demand is unfitting the predictive outcome. To overcome this problem the finite gradient by autoregression, has been used in this work.

https://doi.org/10.23919/splitech.2019.8783029