The future energy system in Longyearbyen - A modelling study Hans-Kristian Ringkjøb, PhD-Candidate Renewable Energy, Geophysical Institute

Illustration: www.colourbox.com Longyearbyen

• Arctic climate (78.2° N)

• Largest settlement on Svalbard

• About 2100 year-round residents

• Heavily influenced by the coal industry The energy system on Longyearbyen

• Only coal-fired power plant in Norway

• Built in 1982

• Electricity and heat

o About 70 GWh district heat per year

o About 40 GWh electricity

• 25 000 tonnes of coal

• Reserve diesel generators and oil boilers MATEVANNSTANK

46 BAR 450 OC

TURBIN 1 5,5 MW =19% 40 TONN DAMP/TIME =63% G OLJEFYRT KJEL 22 MW TRAFO 7 MW 10,6kV NETT FJERNVARME 6,6kV 10 MW 5 MW

DUMPEKONDENSER 15 MW KJELE 1 FJERNVARMEVEKSLERE

46 BAR 450 OC TURBIN 2 5,5MW =27% G TRAFO 7 MW 10,6kV NETT 40 TONN DAMP/TIME 6,6kV

KONDENSER

KJELE 2 P

SJØVANNSINNTAK

TIL INNTAKSKUM RENSEVERK

Figure: Longyearbyens energisituasjon i dag og i fremtiden, Kim Rune Røknes (Longyearbyen Lokalstyre) Current situation

• Ageing infrastructure

o Recent upgrades extends lifetime for about another

20 years

• Emissions

• Coal supply for ten more years? ? How can we transition the energy system in Longyearbyen to one based on renewable energy sources? Motivation

• AGF 353/853 Sustainable Arctic Energy Exploration and Development • Developed a very simple model of the energy system in Longyearbyen • Obtained promising and interesting results

Excursion in the Russian settlement Barentsburg. Photo: Lars Henrik Smedsrud/UNIS. Modelling approach

• TIMES-Longyearbyen • Built from the TIMES (The Integrated MARKAL-EFOM System) framework • The model aims to provide energy services at the lowest cost possible • Makes optimal decision regarding investments in infrastructure, operation of the system and imports of energy carriers • Linear program (deterministic, i.e. only one operational scenario): Model structure

• Model horizon: 2050 • Base-year: 2015 • Currency: NOK • Discount rate: 4 % Temporal Resolution

2015 2020 2025 2030 2045 2050 Investment Period 1 Period 2 Period 3 Period 7 Years Decisions

SP SU AU WI Seasons

Operational WD WE WD WE WD WE WD WE Weeks Decisions

1 24 1 24 1 24 1 24 1 24 1 24 1 24 1 24 Hours Demand projection scenarios

160000

• High, low and status-quo 140000 scenario 120000 100000 • Results from the status-

80000 GWh quo scenario will be 60000

40000 presented 20000

0 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Heat (Historic) Heat (Status-quo) El (Historic) El (Status-quo) Demand for electricity and heat

Seasonal cycle Daily profiles Electricity Heat 0.008 0.008 8,000,000 Fall WD 7,000,000 El prod. KWh Fall WE 6,000,000 (Diesel + Coal) 0.006 0.006 Winter WD 5,000,000 Winter WE

4,000,000 0.004 KWh 0.004 Heat prod. Spring WD 3,000,000 KWh

(Coal only)

Normalised Normalised Demand Normalised Normalised Demand 2,000,000 Spring WE 0.002 0.002 1,000,000 Summer WD

0 Summer WE -3 2 7 12 0 0 Month 0 6 12 18 24 0 6 12 18 24 Hours Hours Wind and Solar Resources

• 17 years of hourly data (01.01.2000 – 31.12.2016) • Based on MERRA reanalysis data run through the GSEE (Global Solar Energy Estimator) model and the VWF model (Virtual Wind Farm) – Web application renewables.ninja Wind and Solar Resources Solar and wind input profiles

Solar Wind Calibration of existing system

Statistics 01.-14.01 2017 Reserve Diesel Power Condensation Turbine Back-Pressure Turbine 8,000

6,000

4,000 kWh

2,000

0 01.01.2017 04.01.2017 07.01.2017 10.01.2017 14.01.2017 00:00 - 01:00 06:00 - 07:00 12:00 - 13:00 18:00 - 19:00 00:00 - 01:00

Model simulation of 2015 150 Back-Pressure turbine Condensation Turbine Reserve Diesel Power PV Airport 100 0.3 0.25 GWh 50 0.2 0 0.15

Simulation Statistics GWh 0.1 BP - District Heat Oil Boiler - District Heat 0.05 Back-Pressure Turbine Condensation Turbine 0

Reserve Diesel Power PV Airport

SP… SP… SP… SP… SP… SP… SP…

FA… FA… FA… FA… FA… FA… FA…

WI… WI… WI… WI… WI… WI… WI…

SU… SU… SU… SU… SU… SU… SU… Deterministic Model Results 2050

Installed Capacities 2050 Yearly Operation in 2050 (Electricity)

Battery - Out Battery - In Photovoltaic Onshore Wind Demand

90

80 Electric Boiler 0.6 70 Heat Pump 60

Capacity 50 Ground mounted photovoltaics 0.3

40 Onshore Wind Installed 30 GWh

Discharge capacity batteries MW 20 Charge capacity batteries 10 0 0

-0.3 SP_01 SP_25 SU_01 SU_25 FA_01 FA_25 WI_01 WI_25 Stochastic Modelling Approach

• The deterministic model treats wind power as a base-load generator • A better representation of solar and wind variability is needed • Treat solar and wind inputs as uncertain parameters, described by 60 operational scenarios • Apply a two-stage stochastic model:

o First stage involves investment decisions

o Second stage deals with the operation of the system

o Investments are feasible for all operational scenarios (Important for security of supply) Stochastic Modelling Approach

Stage 1: Investment Decisions Stage 2: Operation Decisions

2015 2050

S1

2015 2050

S2

S3 Solar and wind scenarios

Solar Wind Stochastic modelling results

• Some modelling challenges that needs to be solved will change these results dramatically

250

200 Electric Boiler Heat Pump PV Airport 150 Ground mounted PV

Onshore Wind MW Hydrogen Fuel Cell 100 Hydrogen Electrolyser Discharge Capacity Batteries

50 Charge Capacity Batteries

0 Discussion

• A combination of solar and wind with energy storage shows promise for Longyearbyen • Results still need to be solidified through the stochastic modelling approach • There are a lot of technologies not included that should be discussed (Geothermal, CCS, hydropower, tidal, other storage options etc.) • Future work can expand to look at the whole energy system

o Decarbonisation of the transport sector (snowmobiles on hydrogen, emission-free tourism) Thank you for your attention!