Apr 2015
Electricity Distribution
Development of An Improved Distribution Load Estimates Methodology
Apr 2015
Nov 2015
Northern Powergrid
Alan Creighton (Alan.Creighton@Northernpowergrid.com)
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Network Innovation Allowance
Modelling and Distributed Generation
The scope of analysis may identify observed trends in the following, but not definitive, list of areas:

Trends influenced by internal factors:

  • Consistent increase / decrease in demand

  • Increased variation in the change of demand

  • Flattening of demand profiles

  • Increasing / reducing diversity

  • Demand at satellite and source substations

  • Increasing / reducing power factor

  • Changes in seasonal demand variations

  • Increasing influence of generation

Trends influenced by external factors:

  • Demand and economic growth/downturn

  • Demand and geographic location

  • Demand and temperature or weather conditions

  • Demand and geographic location

  • Demand and customer type (domestic, industrial, commercial)

The nature of this research based study means that scope is likely to be altered, although probably in a relatively subtle manner, as the study proceeds. Any such changes will be agreed with the Northern Powergrid contact at the regular review & feedback meetings.

The key objective is to assess the feasibility of the development and delivery of a revised DLE methodology that materially increases the accuracy and robustness of the demand forecasts compared with the current Northern Powergrid and industry standard methodologies. This will facilitate enhanced robustness of investment planning decisions both internally and externally in the future.

Where this is feasible, to then deliver a fully documented new tool / model and process for forecasting demand which is materially more accurate / more robust to internal and external challenge than the present process. 

The project success criteria are defined as production and publication of the following:

  • Review of the state of the art and analysis options

  • Report describing the findings from the data analysis

  • Improved algorithm identification and associated documentation

  • Case study reports, methodology refinement report, sensitivity analysis report using the improved algorithm

  • Evaluation and benchmarking report

Development of spreadsheet model(s) together with supporting documentation and user guidance which can be used to apply the algorithms.