Reasons for the drop of Swedish wholesale electricity prices

Transcription

1 Reasons for the drop of Swedish wholesale electricity prices Lion Hirth Project for Svensk Energi Final report

2 Executive summary Swedish spot prices declined by 65% during Sweden: price drivers Declining demand and growth of renewable generation contributed most to the drop Large hydro inflows contributed also Increased export helped strongly mitigating the drop Power prices in the Nordic a hydro system with a lot of low variable cost generation react particularly sensitive to volume shocks Neon analysis. RES growth, reduced demand, and the wet year 2015 decreased Swedish prices most. Lion Hirth 2

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4 Neon: relevant project references Neonis a Berlin-based boutique consulting firm for energy economics. We combine expertise on economic theory with advanced modelling capabilities and extensive industry experience. Neon specializes in five areas: 1. Market value of wind power 2. System costs 3. Balancing power 4. Market design 5. Power market modeling System-friendly wind and solar power (IEA). Model-based study for the International Energy Agency, Paris. Neon assessed the market and system benefits of low-wind speed wind turbines and east-and west-oriented PV based on its power market model EMMA The study is published in Energy Economics. More Integration costs (Agora Energiewende). Literature-based study for Agora Energiewende, Berlin. Neon advised Agora and helped implementing workshops in Berlin and Paris The report has been published by Agora. More Whole system costs (DECC). Neon advised the UK Department of Energy and Climate Change in a project on whole system costs of wind and solar power Open Power System Data (BMWi). Construction of an open platform for European power system data for the German Ministry of Economic Affairs an Energy. Neon coordinates a team of three research institutes More Electricity market design (IEA-RETD). Project on power market design under very high shares of variable renewables. Wholesale, balancing, and retail markets are covered in different markets, ranging from liberalized to vertically integrated. Neon is conducting the project in cooperation with FTI CL Energy More Model development (Vattenfall). Neon supported Vattenfall in model development Wind market value in the Nordic region (Energiforsk ( Energiforsk). Model-based assessment of the market value of wind power in the hydro-dominated power system of the Nordic region. Neon design the study, developed the model, and wrote the report Power market trainings. Neon trained staff at IRENA, ERRA, Vattenfall, JRC, UFZ, Clean Air Task Force, IG Windkraft in topics such as power markets, energy economics, and electricity policy. More Lion Hirth 4

5 Context

6 Motivation: the electricity price plunge Swedish base price (yearly) SWE vs. GER prices (monthly) Neon analysis. Neon analysis. Swedish wholesale electricity prices declined by 65% from 2010 to 2015 (dayahead base price, inflation-adjusted). German spot prices declined, but not as much as prices in Sweden. Lion Hirth 6

7 The price structure has changed as well German spot price structure Change The diurnal structure of German day-ahead spot price during summer months Neon analysis based on data from TSOs and power exchanges. The change of price structure between 2002 and Neon analysis based on data from TSOs and power exchanges. The price structure of German prices changed dramatically with the rise of solar. Sunny hours became relatively much cheaper, and night hours more expensive. Lion Hirth 7

8 Three drivers of falling prices: thermal system Demand Demand Demand Demand Variable cost ( /MWh) GT GT Previous price Reduced price CHP Hydro RES Nuclear Coal OCGT Nat gas CHP Hydro RES Nuclear Coal OCGT nat gas CHP Hydro RES Nuclear Coal OCGT Nat gas GT Capacity(MW) Capacity(MW) Capacity(MW) Reduced demand Increased low-variable cost supply Reduced variable cost Lion Hirth 8

9 Three drivers of falling prices: hydro system Demand Demand Demand Demand Variable cost ( /MWh) GT GT Previous price Reduced price CHP Hydro RES Nuclear Coal OCGT Nat gas CHP Hydro RES Nuclear Coal OCGT nat gas CHP Hydro RES Nuclear Coal OCGT Nat gas GT Annual energy(twh) Annual energy(twh) Annual energy(twh) Reduced demand Increased low-variable cost supply Reduced variable cost Lion Hirth 9

10 Potential drivers in detail Reduced demand Increased low-cost supply Reduced variable cost Declined final demand for electricity Reduced export (ATC) capacity to other countries, particularly outside the Nordic region Additional thermal capacity (mostly coal-fired plants on the Continent) Year-to-year variation of hydro inflow in the Nordics Additional wind, solar, and biomass capacity Availability of Swedish nuclear power Decommissioning of conventional plants Nuclear phase-out in Germany Declining coal price Declining CO 2 price Improved fleet efficiency (heat rate) Increased natural gas price Lion Hirth 10

11 New coal plants in Germany Coal plants: new and under construction. Plant name Fuel Capacity BoA 2 lignite 1050 BoA 3 lignite 1050 Emsland gas 887 Gemeinschaftskraftwerk Irsching gas 846 GKM coal 843 Rheinhafen-Dampfkraftwerk coal 842 Moorburg B coal 766 Westfalen coal 765 Westfalen coal 765 Trianel Kohlekraftwerk Lünen coal 746 KW Walsum coal 725 Boxberg lignite 640 Ulrich Hartmann(Irsching) gas 545 Kopswerk II PHS 525 Knapsack Gas II gas 430 Trianel Gaskraftwerk gas 421 Trianel Gaskraftwerk gas 417 Rodundwerk II PHS 295 GKL gas 230 Dow Stade gas 173 Rheinkraftwerk Iffezheim hydro 146 Zellstoff Stendal GmbH biomass 139 GuD Tiefstack gas 127 Emsland gas 116 Emsland gas 116 Total (incl. < 100MW) New and retrofitted power plants in Germany, Lion Hirth 11

12 Which electricity prices are we interested in? One can analyze spot or financial markets. On average, they should be identical, but in the past years they often deviated significantly. Spot (day-ahead) ahead) markets How did realized prices develop? How did market fundamentals (supply, demand, costs) change? Financial (future) markets How did expectations develop? A spot market analysis is easier to interpret, and data availability is better (expectations are private information). Lion Hirth 12

13 Research question In short: Why did the Swedish power price drop? More precisely: Which factors contributed by how much to the drop of the Swedish electricity day-ahead base price between 2010 and 2015? Lion Hirth 13

14 The EU ETS carbon price

15 CO 2 emission certificates: an intertemporal market Electricity spot prices are (almost) fully explained by instantaneous factors (residual demand and variable costs) Certificate markets work very different (both emission certificates and green certificates) The intertemporaldemand-supply balance determines the current price (= the demand-supply balance over the entire period the ETS is in effect) Anticipated (expected) changes in future abatement costs or the stringency of future emission caps determines the price today The intertemporal (aggregated) demand-supply balance is mostly affected by Aggregated business-as-usual emissions Aggregated cap Mitigation costs Lion Hirth 15

16 Price setting in an intertemporal ETS Allowance Price Courtesy Nicolas Koch. Time 2050 Cumulative Emissions Lion Hirth 16

17 Why are EU ETS prices so low? EUA prices are much lower than many expected (or hoped or feared). There are a range of possible reasons (or a combination of these): 1. Demand shock: baseline emissions decreased because of the macroeconomic recession and renewable support policies 2. Supply shock: more certificates are available than anticipated 3. Supply shock & expectations: market participants believe (or speculate) that the long-term cap will be less stringent than announced 4. Demand shock & myopia: market participants do not take the long-term stringency of the system into account 5. Demand shock & high risk-adjusted discount rate: current prices are low because long-term (high) prices are discounted at a high rate (the Hotelling price path is very steep) Lion Hirth 17

18 Methodology

19 Methodology 1. Replicate prices for the years 2010 and 2015 With a fundamental power market model Using the full set of input factors of the respective year (electricity demand, RES generation, hydro inflow, fuel prices,...) Model check: can prices be replicated? 2. Quantify impact of individual factors Substitute one individual factor (e.g. electricity demand) from 2010 with 2015 values Leave all other factors (e.g., RES generation, hydro inflow, fuel prices,...) unchanged at 2010 values Replicate this procedure for each factor one-by-one Estimate the impact of individual factors on price drop Lion Hirth 19

20 Kallabis et al. (2015): German futures Reasons for the German price drop 16% 11% 10% Share in total effect 52% Neon illustration based on Kallabis et al. (2015). Lion Hirth 20

21 Conceptual remarks on the methodology (1) 1. Sum of individual effects does not equal joint effect On a non-linear system like power markets, in general the sum of individual effects does not equal the joint effect. Take an extreme example: An increase of coal prices rises the electricity price An increase of CO2 prices rises the electricity price An increase of both prices might not rise the electricity price, if all coal plants are driven out of the money 2. Alternative benchmarks The two following questions are not identical What would be reduction of the electricity price if all parameters are at 2010levels, only RES supply is increased to 2015 levels? (2010 benchmark) What would be the increase of the electricity price of all parameters are at the 2015 level, only RES supply is decreased to 2010 levels? (2015 benchmark) Lion Hirth 21

22 Conceptual remarks on the methodology (2) 3. Individual ( separate ) vs. cumulative ( added ) effect We test factors individually, starting always with the 2010 parameter set In other words, we test each effect individually, always holding all other effects at 2010 levels A different approach would be to add changes on top of each other 4. Cumulative ( added ) effect: order matters If effects are added one on the other, order of effects impacts their size For example: Start with 2010 parameters, decrease demand first, increase RES supply then Start with 2010 parameters, increase RES supply first, decrease demand then This is the reason we do not follow such an approach Lion Hirth 22

23 The Electricity Market Model EMMA Numerical partial-equilibrium model of the European interconnected power market Objective: minimize system costs Capital costs Fuel and CO2 costs Fixed and variable O&M costs... of thermal and hydro power plants, storage, interconnectors Decision variables Hourly dispatch Yearly investment... of plants, storage, interco s Constraints Energy balance Capacity constraints Volume constraints of storage/hydro Balancing reserve requirement CHP generation (No unit commitment, no load flow) Resolution Temporal: hours Spatial: bidding areas (countries) Technologies: eleven plant types Input data Wind, solar and load data of the same year Existing plant stack Equilibrium Short-/mid-/long-term model (= dispatch / capacity expansion / greenfield) Equilibrium ( one year ) rather than a transition path ( up to 2030 ) Economic assumptions Price-inelastic demand No market power Carbon price Implementation Linear program GAMS / cplex Applications Four peer-reviewer articles Various consulting projects Copenhagen Economics Open source

24 Model extensions for this project Backcasting(replicating) historical prices requires high precision and more detailed input parameters than long-term modeling This is even more true for hydro-dominated power systems, where small changes in the yearly energy balance can have dramatic effects on power prices Amendments to EMMA (examples) Market power modeling of EDF in French nuclear power dispatch Low availability of Polish plants due to old equipment Improved fleet efficiency over time due to new investments and retirement of old plants Net imports to model regions from neighboring countries Empirically calibrated average availability Demand elasticity / price spikes Lion Hirth 24

25 Data

26 Crucial parameters 2010 vs in the model region Parameter Data source Electricity demand of which Sweden Wind + solar generation of which Sweden Hydroelectricity output of which Sweden Net exports of model region Netdemand (demand minus wind, solar, hydro, net imports) of which Sweden Coal price Natural gas price 1723 TWh 147 TWh 75 TWh 4 TWh 282 TWh 66 TWh 38 TWh -3 TWh 1404 TWh 77 TWh 92 $/t 8.4 /MWh 1647 TWh 134 TWh 193 TWh 16 TWh 302 TWh 76 TWh 90 TWh 18 TWh 1246 TWh 43 TWh 59 $/t 6.4 /MWh 21 /MWh 22 /MWh IEA Monthly electricity statistic IEA Monthly electricity statistic IEA Monthly electricity statistic ENTSO-E Statistical factsheet Own calculation CO 2 price 16 /t 6 /t EUA price IHS McCloskey Northwest Europe Marker Price IMF German border import price Conventional capacity includes nuclear and hydro power as well as all fossil fuel generators. Numbers are shown for the entire model region (Sweden, Norway, Germany, France, Poland, Belgium, The Netherlands). Electricity consumption and wind/solar generation is estimated based on Nov 2015 data, because Dec data are not published yet. All pricesare nominal values (not inflation-adjusted). Dollar-denominated prices were converted into Euro using exchange rate data from the ECB. ATC values are used until the introduction of flowbased market coupling. Lion Hirth 26

27 First observations: volume changes Electricity demand from power plants with positive marginal costs (thermal plants) declined by 158 TWh (9%). RES growth had largest effect 76 TWhreduced electricity demand (176 TWhif linear trend used for comparison) 118 TWh increased generation from wind and solar 16 TWh higher hydroelectricity generation 52 TWhincrease in net exports from the model region (mostly SWE-FIN and FRA to ITA/GBR/CHE) Increase in RES explains largest share of volume change. Lion Hirth 27

28 First observations: volume changes Changes to net demand Neon analysis. Reduced consumption, expansion of renewables, and more precipitation decreased net demand 2015 compared to Increased net exports compensated partly. Lion Hirth 28

29 First observations: price changes Fuel prices fluctuated widely, but net change is pretty small. The carbon price declined strongly during the same period. Some fuel prices declined, while others remained stable Coal -24% Natural gas + 5% CO 2-63% (Fuel prices in nominal terms denominated in Euro) It is pretty obvious that a 24% decline in coal prices can, by itself, not explain a 65% decline in electricity prices. Neon analysis. Coal and nat. gas prices Lion Hirth 29

30 Replicating historical prices (Step 1)

31 Factors modeled that vary from year to year Fuel prices Coal price Natural gas price CO2 price Investments in thermal capacity Change in total capacity Improvement of average heat rate Electricity demand Consumption Net exports at model area border Electricity generation by renewables Wind Solar Biomass Hydro inflow Nuclear power Phase-out in Germany Fluctuating availability in Sweden Lion Hirth 31

32 The model is able to replicate historical prices well Sweden Neon analysis. Sweden spot prices are replicated fairly well. The modeled price drop is 33.0, reality was 34.8 /MWh. Lion Hirth 32

33 The model is able to replicate historical prices well Germany Norway Neon analysis. Neon analysis. German prices are replicated quite well as well as Norwegian prices. Lion Hirth 33

34 ... as well as historical generation pattern Real world (GER) Model results (GER) Neon analysis. Neon analysis. Observed generation mix in Germany. Modeled mix. The model overstates coal generation somewhat, but replicates structural shifts well. Lion Hirth 34

35 Factor decomposition (Step 2)

36 The impact of individual factors: Germany Germany Driver Renewables growth 54% Electricity demand 25% Fuel and CO 2 prices 23% Hydro inflow 10% Share in price drop Other factors modeled -50% (increasing) Neon analysis. The sharein price drop is the effect of the individual effect relative to the total drop modeled. Neon analysis. The largest factors reducing German prices were renewables and demand. Other factors stabilized the price (see below). If RES grew as they did, but everything else remained unchanged, the price drop would be 54% of the actual drop. Lion Hirth 36

37 The impact of individual factors: Germany Germany sum of individual effects joint effect Neon analysis. The non-linear interaction effect is the difference between the sum of individual effects and the joint impact if all effects are modeled simultaneously. The interaction is relatively small. Lion Hirth 37

38 The impact of individual factors: Sweden Sweden Driver Renewables growth 61% Electricity demand 55% Fuel and CO 2 prices 6% Hydro inflow 33% Share in price drop Other factors modeled -119% (increasing) Neon analysis. The sharein price drop is the effect of the individual effect relative to the total drop modeled. Neon analysis. RES growth, reduced demand, and the wet year 2015 decreased Swedish prices most. Compared to Germany, declining demand and hydro inflow plays a larger role. Lion Hirth 38

39 The impact of individual factors: Sweden Sweden Neon analysis. Swedish price are much more sensitive to changes in fundamentals. This is the nature of a hydro system where small changes in the yearly energy balance can lead to large shifts of prices. An additive decomposition leads to a significant residual (non-linear interaction). Lion Hirth 39

40 Factor decomposition (Step 2): more details

41 More details than above In the follow slides, we decompose the aggregate category other factors modeled Nuclear availability in Sweden Nuclear phase-put in Germany Exports and imports at the border of the model region Lion Hirth 41

42 The impact of individual factors: Germany Neon analysis. Germany Driver Share in price drop Renewables growth 54% Electricity demand 25% Coal/gas invest 24% CO 2 price 24% Hydro inflow 10% Coal price 8% Nuclear availability SWE -1% (increasing) Nat. gas price -8% (increasing) Imports/Exports -31% (increasing) Nuclear phase-out GER -41% (increasing) Neon analysis. The sharein price drop is the effect of the individual effect relative to the total drop modeled. Increased exports and the nuclear phaseout stabilized prices most. If the only changes was the decline in CO 2 prices, the electricity price drop would have been a quarter of the actual. Lion Hirth 42

43 The impact of individual factors: Germany Germany Price-depressing effects Price-stabilizing effects Neon analysis. Six factors reduced the electricity price, four increased it. The additive decomposition into individual effects works quite well: the non-linear interaction term is small. Lion Hirth 43

44 The impact of individual factors: Sweden Neon analysis. Sweden Driver Share in price drop Renewables growth 61% Electricity demand 55% Hydro inflow 33% Coal/gas invest 14% CO2 price 13% Coal price 0% Nuclear availability SWE -5% (increasing) Nat. gas price -7% (increasing) Nuclear phase-out GER -12% (increasing) Imports/Exports -105% (increasing) Neon analysis. The sharein price drop is the effect of the individual effect relative to the total drop modeled. RES growth, reduced demand, and the wet year 2015 decreased Swedish prices most. Lion Hirth 44

45 The impact of individual factors: Sweden Sweden Price-depressing effects Price-stabilizing effects Neon analysis. Swedish price are much more sensitive to changes in fundamentals. This is the nature of a hydro system where small changes in the yearly energy balance can lead to large shifts of prices. An additive decomposition leads to a significant residual (non-linear interaction). Lion Hirth 45

46 The impact of RES: two perspectives ( plus & minus ) Germany Sweden Neon analysis. If nothing changed since 2010 except renewables, prices would have dropped by 10.2 /MWh. If 2015 would materialize in all aspects, but renewables remain at 2015 levels, prices would increase by 11.5 /MWh. In Sweden as in Germany, the two perspectives lead to very similar estimates. This increases the confidence in robustness of the analysis. Lion Hirth 46

47 The impact of Swedish renewables Above, we reported the impact of renewables growth in the entire model region Sweden This is the joint price impact of increasing renewables in all countries on Swedishelectricity prices Alternative, we could ask: What is the impact of Swedish RES growth on Swedish prices? Swedish RES alone represents 60% of the joint RES impact If only Swedish RES grew, Swedish prices would have dropped by 11 /MWh. Lion Hirth 47

48 Conclusions

49 Most impacts are transitory but might take a while A cost shock (e.g. a change in fuel or CO 2 prices) can have a lasting impact, if most (or all) pricesetting technologies are affected A volumeshock (e.g. decrease of demand or increase of RES supply) decreases the wholesale electricity price This triggers market exit, increasing prices again The long-term equilibrium price remains (nearly) unchanged Crucial question: how long is long-term? In the long term, we are all dead John Maynard Keynes In power systems with long-living assets and little demand growth, this can be decades Lion Hirth 49

50 Renewables in the Nordic region: long vs. short term In a different study, we have reported that the Nordic region, thanks to the large amount of highly flexible hydro power, is well suited to integrate large amounts of variable renewables, such as wind power The market value of wind power (average spot revenue) drops less in the Nordics than on the Continent This study reports that the average (base) electricity price in Sweden was much more depressed by renewables expansion than the German price This leads us to the following interpretation The flexibility of hydroelectricity allows easy integration of large-scale wind power The sunk nature of hydro and nuclear assets makes the transition towards large-scale wind deployment less smooth Lion Hirth 50

51 Summary and conclusions Wholesale power prices throughout Europe have declined substantially Several factors depressed, several increased the price; the former dominated The Nordic system, a hydro-dominated system with large volumes of generation with low variable costs, is much more sensitive: changes in fundamentals have a much larger price effect Germany: important price drivers Downward: RES growth was largest driver; demand, new investments and the CO 2 price were about half in size Upward: nuclear phase-out, followed by increased exports Sweden: important price drivers Downward: RES growth and demand decline about the same size; followed by hydro inflow Upward: increase exports (very large effect) Lion Hirth 51

52 Reasons for the drop of Swedish wholesale electricity prices