A novel energy system model to find the optimal balance of storage and transmission using social and weather drivers

1. A novel energy system model to find
the optimal balance of storage and
transmission using social and
weather drivers
Tiziano Gallo Cassarino, Mark Barrett, Ed Sharp
EnergySpaceTime group
t.cassarino@ucl.ac.uk
18.06.2018 – ETSAP, Gothenburg

2. Outline
• Analysis of the historic
demand in Europe
• Storage needs
• ESTIMO description

3. Social activity influences demand at different time resolutions
Off-peak heating
School closure
Electric heating
Winter peak
Christmas
/ New year
Activity profiles of electricity
hourly demand in France
(2=Wed, 6=Sun)
National electricity annual
demand in the countries
with the highest average
final energy consumption

4. Temperature sensitivity of electricity demand is country-specific
SolarW
/m2
Italy – Sensitivity of daily electricity
demand to population-weighted
temperature (2010-2015)
SolarW
/m2
UK – Sensitivity of hourly electricity
demand to population-weighted
temperature (2010-2015)

5. UK needs on average ~10% of electricity equivalent storage
Residual electricity demand peaks move along years:
~76 TWh
(26% of the annual demand in 2010)
Monthly electricity cumulative residual demand (= demand
– generation), assuming only wind and solar generation

6. Countries can balance seasonal residual demand
Monthly electricity cumulative residual demand in Europe, assuming only wind
and solar generation (2015)
GBR
DEU
ESP
FRA
ITA
POL
Unmet demand
Generation surplus
Balance

7. Fourier analysis of the residual demand shows hour to annual
periodicity patterns
2880h (4 months) ~ 3 GW
24h ~ 10 GW
12h ~ 9 GW
8000h (1y) ~ 5.5 GW
weeks < 3 GW
Periods of electricity residual demand in UK, de-trended (2010-2015)

8. ESTIMO - Energy Space Time Integrated Model Optimiser
Simulate and optimise (at hourly
resolution) multi-vector energy
systems with nodes (countries or
their aggregates)
What is the optimal mix of storage and interconnections assuming a highly electrified,
renewable system?
Initial tests with a star network
topology to balance
computability and accuracy

9. Simulation algorithm
1. Meet demands for heat and electricity internally in each node
2. Offset unmet demand with international trade (where possible)
3. Use a node’s internal dispatchable supply to meet remaining deficits
The following output is to illustrate model function using provisional data for nodes
and meteorology:

10. Energy demand module
• Engineering approach for energy service demands based on weather, human
activity, and building heat loss
• Focus on weather-dependent demands: space heating, air conditioning, lighting
• Weather-independent demands: appliances, cooking, hot water, transport,
synthetic fuels
• Preliminary tests for the UK in 2010-2014 (from IEA and Odyssee):
Demand % of simulated demand /
reported consumption
Delivered electricity 96%
Air conditioning 126%
Space heating 90%
Lighting 87%

11. Next steps
1. Complete first version of demand/renewable simulation modules including gas
2. Add costs
3. Collate/construct scenarios for the UK and each European country
4. Simulate system for future scenario years with different meteorology over
annual and peak cold/hot stress periods
5. Apply optimisation
6. Assess storage and transmission in different conditions with simulation and
optimisation
7. Other applications – system control markets, effect of heat waves on health
and demand, extend to Asia and other continents

12. Thank you!
Mark Barrett