Abstract

Demand flexibility is needed to manage the challenges of decarbonising the heating and transport sectors and integrating large shares of intermittent renewable generation. While existing literature has provided models for estimating the response potential of some flexible devices, they have not been applied to assess if the response in a location is sufficient to solve the grid issue. Grid issues such as constraint and congestions are geographical issues and hence can be studied through GIS analysis. This paper presents a methodology for the spatio-temporal assessment of demand flexibility opportunities, response potential and adequacy in solving various grid issues of a country. We provide a method that may be used to link the electrical network with socio-demographic spatial data when the low voltage network data is not available using the k-nearest neighbour classification algorithm. The proposed method was able to match neighbourhoods with their primary substation with an accuracy of 60–94%. By segmenting neighbourhoods based on various metrics, we perform a left-behind analysis to identify vulnerable consumer groups at risk of being left behind in the energy transition and propose a flexibility prioritisation model that ensures a fair distribution of flexibility opportunities across all locations. Finally, we present the Northern Ireland demand flexibility map, an interactive tool for use by system planners to help in developing an effective flexibility strategy as well as a flexibility implementation pathway for Northern Ireland.

Highlights

  • Buildings are a major source of greenhouse gas (GHG) emissions

  • This paper presents a methodology for the spatio-temporal assessment of demand flexibility opportunities, response potential and adequacy in solving various grid issues of a country

  • We have developed a methodology for the spatio-temporal assess­ ment of demand flexibility needs and opportunities

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Summary

Introduction

Buildings are a major source of greenhouse gas (GHG) emissions. They accounted for 38% of global emissions in 2019 [1]. 17% of the global emissions were from residential buildings: 6% from direct emis­ sions and 11% from indirect emissions (i.e. electricity) [1]. The energy system is changing due to the decarbonisation of heat and transport. The expected increase in electricity demand due to the electrification of heat and transport could lead to congestions in distribution networks. It could affect energy affordability due to increased investments in network infrastructure

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