Carbon problems: Carbon problems: • Higher priced energy Cost problems: Cost problems: • PV & Wind Renewables mis-match Renewables mis-match • Inefficient use of resources; • ‘Local’ (LV network) overload; Infrastructure problems: Infrastructure problems: 5 Filling the trough Filling the trough Peak load Peak load Depends on hydro levels in Feb – April Khan et al (2018) 10.1016/j.jclepro.2018.02.309 Net-Zero economy by 2050 ? -> ~$50 billion new investment under BAU
demand? What might be reduced? Who might respond? And what are the local network consequences? What to do? Storage Storage • Just reducing it per se Demand Reduction Demand Reduction • Shifting it somewhere else in time (or space and time) Demand Response Demand Response 6 Key Questions
+ kW AU 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) R package: ipfp Blocker (2016) Weights Census ‘constraint’ tables
~ 30 monitored households • Hawke’s Bay & Taranaki Using NZ GREENGrid Data Using NZ GREENGrid Data • ~ 600 households per AU • For Hawke’s Bay & Taranaki At NZ Area Unit level At NZ Area Unit level • IPF re-weighting of survey cases (Ballas et al, 2005) Spatial Microsimulation Method Spatial Microsimulation Method
Variables used: · N rooms · N people · Presence children Potential future variables: · Main heating fuel · Dwelling type · Income band · Age of adults/children Data: NZ Census 17 Matches GREENGrid sample ~ 90,000 households Some are not in GREENGrid data Because they correlate with demand
with ‘constraint’ variables + kW AU 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) R package: ipfp Blocker (2016) Weights Census ‘constraint’ tables
with ‘constraint’ variables + kW AU 1 Iterative Proportional Fitting Deming and Stephan 1940; Fienberg 1970; Wong 1992 Birkin & Clarke, 1989; Ballas et al, 1999 Ballas et al (2005) R package: ipfp Blocker (2016) Weights Census ‘constraint’ tables GREEN Grid sample
data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] We’re replicating a lot of households Each dot = 1 unit area so weird stuff can happen…
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] Simulated household counts work OK Each dot = 1 unit area
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] Simulated household counts in categories used as constraints work OK Each dot = 1 unit area
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://nzdotstat.stats.govt.nz/wbos/Index.aspx] Simulated household counts in categories NOT used as constraints work quite well (sometimes) Each dot = 1 unit area
Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], unweighted] Small n… As measured (mean) Why this day? • Dry summer • No wind • Unusually cold • Gas pipeline broke • Avoid burning coal
Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], unweighted) Real world heterogeneity As measured Households
As modelled Source: Author’s calculations using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://archive.stats.govt.nz/Census/2013-census/data-tables/meshblock-dataset.aspx] Each line = 1 area unit Shiftable demand? Areas with biggest potential to shift?
using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], unweighted) Real world heterogeneity As measured Small n… Why? • Avoid burning coal in dry years • Reduce local lines peaks (congestion)
using NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://archive.stats.govt.nz/Census/2013-census/data-tables/meshblock-dataset.aspx] As modelled Each line = 1 area unit Shiftable demand? Met by solar PV? But is this ‘weird stuff’?
NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], unweighted) Real world heterogeneity As measured VERY small n… Why? • Avoid burning coal in dry years • Reduce local lines peaks (congestion)
NZ GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334], weighted), NZ Census 2013 small area tables [http://archive.stats.govt.nz/Census/2013-census/data-tables/meshblock-dataset.aspx] Where might LEDs reduce demand? As modelled Each line = 1 area unit Highest lighting Lowest lighting
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334] ‘Sheep-dip’ model – Take our 22 households – Replace non-LEDs with LEDs Problems – Based on ‘most of your bulbs’ survey item – We seem to have more low-energy already · So our savings will be underestimates – We have VERY small numbers: · Only 2 Taranaki households in the lighting data!
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334] • Before spatialisation • As you’d expect: demand reduction is large Observed LED model But: We need 100* this
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334] • Before spatialisation • As you’d expect: demand reduction is large Observed LED model But: We need 100* this
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334] After spatialisation: Power savings vary from place to place LED model Each line = 1 area unit Big savings here Smaller savings here
GREENGrid data [https://dx.doi.org/10.5255/UKDA-SN-853334] After spatialisation: Power savings vary from place to place LED model Each line = 1 area unit Big savings here Smaller savings here
But the GREENGrid data is insufficient – The results are probably garbage We need to: – Gather better kW data – Impute demand from Time Use Surveys Where have we got to? 39 N * 100 Representative
But the GREENGrid data is insufficient – The results are probably garbage We need to: – Gather better kW data – Impute demand from Time Use Surveys Where have we got to? 40 N * 100 Representative
year EU Global Fellowship @Otago CfS – 2017-2019 https://git.soton.ac.uk/ba1e12/spatialec “This research was supported by a Marie Sklodowska-Curie Individual Global Fellowship within the H2020 European Framework Programme (2014 -2020) under grant agreement no. 700386.” 41 pixabay.com