Short-term uncertainty in investment models Pernille Seljom & Asgeir Tomasgard CREE 3rd research workshop 16.09.2013 30.11.2018

Introduction Research project founded by the Research Council of Norway (2011 – 2014) “The future Norwegian energy system in an European context” Long-term TIMES model of Northern Europe Some research questions: How will the different global and European energy scenarios affect Norway? What will be the future role of Norwegian hydropower? Important premise Proper modelling of unpredictable wind power = short-term uncertainty of supply Research project at IFE, PhD position at NTNU Today – Work from 1st paper presented – unpredictable characteristics of wind power in a case study of the Danish heat and electricity sector 30.11.2018

Introduction Short-term uncertainty = availability of wind power Mismatch supply and demand Back-up capacity, transmission, storage Long-term investment models as TIMES More wind power = more short-term uncertainty How to model? Stochastic modelling of short-term uncertainty Operational models - state of the art Investment models - not as common We use stochastic programming Case study: TIMES model of the Danish heat and electricity sector Operational models = Unit Commitment 30.11.2018

Denmark- some energy facts Wind power Combines Heat and Power (CHP) Highly interconnected Data: Energistyrelsen Interconnected to Norway, Sweden, Germany Two physical separated grids until August 2010 were the two Nord Pool regions were connected (600 MW) In EU, Denmark largest share if electricity production from CHP – 49 % in 2010, 12 % EU average Denmark is the country with largest share of electricity production from wind power, 28 % in 2011 Figure: Nord Pool Spot 07.06.2013 30.11.2018

Model description TIMES (The Integrated MARKAL-EFOM system) Modelling tool developed by ETSAP, an implementing agreement of IEA Used by individuals/ teams in 63 countries Work Shop twice a year Model support and continuous development Investment model of the Danish heat and electricity sector Linear Programming model Model regions Denmark East (DK-E) and Denmark West (DK-W) Model horizon 2010 – 2050 Model periods 2010, 2015, 2020, 2030, 2040, 2050 Period split 4 seasons, 12 two-hour periods Obj. function minimize total energy system costs Perfect foresight IEA – International Energy Agency Work shop : Share experiences and discuss needs for further improvement 30.11.2018

Model description 30.11.2018

Model description Traditionally in TIMES, wind power is modelled with a deterministic electricity peak reserve constraint Sufficient capacity such that wind power only can contribute to a part of peak electricity demand Possible wind contribution to the peak electricity demand 30 % typical value used 0 % most conservative value What is the right value ??? Deterministic electricity prices in external trading regions Germany, Sweden and Norway This constraint can ensure sufficient capacity. The right value is dependent on the energy system characteristics – how much wind power, how the wind power is correlated, and so on……. 30.11.2018

Model description We do Stochastic modelling of wind availability Stochastic modelling of electricity prices in trading regions Not previously done in TIMES! Future scenarios based on hourly historical data (2006- 2011) Hourly wind power production in DK-E and DK-W Wind turbine capacity in DK-E and DK-W Electricity prices in Germany, Sweden and Norway Stochastic sub-annual trade prices - Wind power data – include both onshore and offshore production and capacity data Danish electricity prices are endogenous generated in the model but the electricity prices outside Denmark are exogenously given Projected prices for biomass, fossil fuels, CO2 and annual electricity prices are from a Energinet study We use historical data to present possible future scenarios – not necessarily a valid assumption but it is considered the best available basis Not sufficient with annual electricity prices since the price tend to vary with season and time of day 30.11.2018

Modelling uncertainty Optimal investments given short-term uncertainty Wind availability = wind production / max theoretical production 1 stage= investment decisions wind availability and electricity price unknown 2 stage = operational decisions wind availability and electricity price in known Max theoretical production: 1 MW turbine can max produce 1 MWh in a given hour Two-stage scenario tree Scenario tree – discrete description of the outcomes of the uncertain parameter Stage – where new information is revealed Path – possible realisations of the uncertain parameter Model returns scenario independent investment decisions and scenario dependent operational decisions 30.11.2018

Wind power characteristics Average wind availability (2006 - 2011) Winter and spring shown in figure. Average is highest in winter and smallest in summer for both regions. Wind availability tend to be higher in the middle of the day compared to at night for all seasons except winter where the availability is more flat throughout the day. Figure spring show large variations within a season. 30/11/2018

Wind power characteristics Variance in wind availability (2006 - 2011) Variance show that wind availability is not predictable!!! 30.11.2018

Wind power characteristics Figure – Hourly wind availability of the first week of 2008 – 2011. Figure show 1. The wind is highly stochastic; 2. The wind is correlated in time; 3 The wind is correlated between the two model regions 30.11.2018

Electricity trade prices Average sub- annual electricity prices Germany (2006 - 2011) Price vary significant with time of day with peaks in the middle of the day and in the afternoon 30.11.2018

Electricity trade prices Average sub- annual electricity prices Norway/ NO2 (2006 - 2011) Norway – The price profile is more flat throughout the day. Higher prices in Winter than Summer - The prices are also stochastic!!! 30.11.2018

Modelling uncertainty Every second hour from historical data is used A scenario consist of 12 chronological two-hour wind availability data for DK-E and DK-W Correlation in time Correlation between model regions A scenario consist of 12 chronological electricity price data for Germany, Norway and Sweden Consistent electricity price and wind data All historical data = computational challenges Select a subset of historical data 1 day = 24 h and 1 model period = 12 daily periods Same period selected for wind and electricity price data - automatic correlation Subset – represent possible future scenarios! 30.11.2018

Scenario generation Model input: Subset of historical data Scenario reduction = solvable model T scenario trees are randomly generated Model input = best scenario tree Least deviation of the four moments between sub-set and the historical data on wind availability Scenario generation stability Stability on number of scenarios/ size of sub-set Sub-set selection, partly random process In-sample stability Out-if sample stability 30.11.2018

Results – model options 30.11.2018

Result 1 – In-sample stability Show ???? 30.11.2018

Result 2 – Electric capacity 2030 30.11.2018

Result 3 – Electric capacity 2050 30.11.2018

Result 4 – Electric trade 2050 30.11.2018

Result 5 – Exp. fuel consump. 2050 30.11.2018

Results 6 – Value of Stochastic solution « Cost of disregarding uncertainty» Fixed investment - Det_30 → Infeasible Fixed investment - Det_0 → More costly solution Stoch 5.0 % Stoch* 7.7 % VSS = (RP – EEV)/ EEV 30.11.2018

Conclusions Competiveness of wind power depend on the method used to model the unpredictable characteristics of wind power Deterministic model approach overestimate competiveness of wind power Deterministic investment decisions Can give infeasible solutions Or give more costly solutions Stochastic modelling of short-term uncertainty gives cost effective investment decisions Proper modelling of unpredictable wind power is essential to model the value of Norwegian hydro power 30.11.2018