Reasons for the drop of Swedish electricity prices

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1 Reasons for the drop of Swedish electricity prices Project for Svensk Energi Dr. Lion Hirth Neon Neue Energieökonomik GmbH Karl-Marx-Platz 12, Berlin, Germany Summary report Why did the Swedish whole power price drop by 65% since 2010? Based on fundamental power market modeling, the answer of this study is: because of an increase in electricity supply from renewable energy source, a decline in final electricity demand, and a wet year 2015 with much output of hydroelectricity plants. 1. Context and research question Between 2010 and 2015, wholesale electricity price in Sweden dropped by 65% in real terms (Figure 1). Figure 1. The wholesale electricity price in Sweden. The average, or base day-ahead price is show. After the split into different bidding areas, SE3 prices are displayed. Since 2010, the base price declined by two thirds. Source: own illustration based on data from Nordpool Spot. This study aims at answering a seemingly simple question: Why did the Swedish power price drop?, or, more precisely: Which factors contributed by how much to the drop of the Swedish electricity dayahead base price between 2010 and 2015? Several plausible explanations for the price drop are discussed in the public policy debate, including a decrease of final electricity demand; 1

2 increased supply from newly commissioned coal-fired and natural gas-fired plant; an increase in electricity supply from renewable sources, mostly wind, sun, and biomass; a decline of the prices for fossil fuels, most importantly hard coal; a decline of the price for greenhouse gas emission certificates; variation in the water inflow to Nordic hydropower; variation in the available of Swedish nuclear power; a decline in the available export capacity from the Nordic to the continent. Other factors must have had a positive impact on prices, including Germany s nuclear phase-out; decommissioning of old fossil fueled plants; the increase in natural gas prices. As price formation in Sweden, as elsewhere in the Nordic region, depends crucially on exports to the continent, changes on the continent, notably Germany, matter as well. 2. Methodology We assess the price decline with the fundamental power market modeling EMMA. The model has previously been used to study power markets. 1 EMMA is a techno-economic model of the integrated Northwestern European power system, covering France, Benelux, Germany, Poland, Sweden, and Norway. It models both dispatch of and investment in power plants, minimizing total costs with respect to investment, production and trade decisions under a large set of technical constraints. It calculates the long-term optima (equilibria) and estimates the corresponding capacity mix as well as hourly prices, generation, and cross-border trade for each market area. It models a primarily thermal power system; results might be quite different in a hydro-dominated system. 2 In economic terms, it is a partial equilibrium model of the wholesale electricity market with a focus on the supply side. The model is linear, deterministic, and solved in hourly time steps for one year. Details on EMMA, including open source code and input data, can be found on the following website: We use EMMA to model hourly electricity prices in Germany, Sweden, and surrounding countries for the year 2010 and the year The modeling process is as follows. 1. Using the entire input parameters set of the respective year (see Table 1 below), we replicate Swedish base prices for the year 2010 and We substitute 2010 parameters by the 2015 parameters one by one to estimate the impact of individual impacts of isolated parameters (individual rows in Table 1). This should give a rough indication about the size of the individual effects, such as the role of renewable growth vs. the role of declining coal prices. Because effects interact, the sum of individual effects will not exactly coincide with the joint effect. Kallabis et al. (2015) have conducted a similar exercise for Germany, but focused on future prices (rather than spot prices). They find that more than 50% of the price drop is explained by lower CO 2 prices, while only 11% of the price drop is caused by unexpected strong growth of renewable energy 1 Examples include the peer-reviewed publications by Hirth (2013, 2015a, 2015b) and Hirth & Ueckerdt (2013b), as well as Hirth (submitted) and Hirth & Steckel (submitted). 2 Hirth (submitted) expands the model to the Nordic regions, a power system dominated by hydroelectricity. 2

3 sources (Figure 2). The approach proposed here is slightly different, as Kallabis et al. model future prices, i.e., price expectations, while we model spot prices, i.e. realized equilibrium prices. Figure 2. Reasons for the drop of wholesale future prices (CAL-2014 base future) in Germany. Source: own illustration, based on Kallabis et al. (2015). A few remarks on the methodology: 1. Sum of individual effects does not equal joint effect. In 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, and an increase of CO2 prices rises the electricity price, but 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 2010 levels, 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) 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. Take a simple example: start with 2010 parameters, decrease demand first, increase RES supply then; start with 2010 parameters, increase RES supply first, decrease demand then. The two approaches will attribute a different effect to RES supply. 3. Data Table 1 lists the input parameters that are used in the project to explain the price drop. Table 1. Parameter assumptions for the model region. 3

4 Electricity demand Wind + solar generation Hydroelectricity output Net exports of model region Net demand (demand minus wind, solar, hydro, net imports) Coal 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 Natural gas price 21 /MWh 22 /MWh ENTSO-E Statistical factsheet Own calculation CO 2 price 16 /t 6 /t EUA price Electricity demand 1723 TWh 147 TWh 1647 TWh 134 TWh 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 prices are 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 flow-based market coupling. Figure 3 shows the volume changes in the model region. Net demand is electricity demand net of RES generation and net imports at the border of the model region. This gives a first hint about the relative size of effects: renewables generation expanded significantly, but also the role of declining demand was large. Figure 3. Changes to net demand. Reduced consumption, expansion of renewables, and more precipitation decreased net demand 2015 compared to Increased net exports compensated partly. Figure 4 shows prices for fossil fuels. Coal prices declined by 24%, but natural gas prices increased by 5% from 2010 to The prices for emission certificates declined by 63%. 4

5 Figure 4. Coal prices declined by 24%, but natural gas prices increased by 5% from 2010 to Replicating historical prices Figure 5 shows Swedish base prices as they materialized in reality and compares that to the modeled prices. Price levels are replicated fairly well, except in the year Also prices levels in Germany are replicated very well. The same is true for generation pattern (the electricity mix). Figure 5. Sweden spot prices are replicated fairly well. The modeled price drop is 33.0 /MWh, reality was 34.8 /MWh. 5. Factor decomposition Figure 6 shows the individual impacts of all factors tested in the Swedish electricity price. The largest factors that contributed to the decline of prices were the growth in renewable electricity generation and the decline in electricity demand. The wet year 2015 is third. The largest factors stabilizing the price were increased demands and, with much distance, the nuclear phase-out in Germany. European 5

6 RES growth decreased Swedish electricity prices by about 20 /MWh, of which Swedish RES growth alone contributed 60%. Driver Share in price drop Figure 6. The largest factors reducing Swedish prices were renewables and demand. Other factors stabilized the price. 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) The share in price drop is the effect of the individual effect relative to the total drop modeled. Renewables comprise wind power, solar PV, and biomass hydroelectricity is listed separately. 6. Conclusions and remarks A cost shock (e.g. a change in fuel or CO 2 prices) can have a lasting impact, if most (or all) price-setting technologies are affected. A volume shock (e.g. decrease of demand or increase of RES supply) decreases the wholesale electricity price, which triggers market exit, increasing prices again, and the long-term equilibrium price remains (nearly) unchanged. The crucial question is: how long is longterm? In power systems with long-living assets and little demand growth, reaching the long-term equilibrium can take decades. In a different study ( Market value of wind power in the Nordic region ), we have argued that the flexibility of hydroelectricity allows easy integration of large-scale wind power in the long-term. However, the sunk nature of hydro and nuclear assets makes the transition towards large-scale wind deployment less smooth. The Nordic system, a hydro-dominated system with large volumes of generation with low variable costs, is much more sensitive to volume changes than the continental system, dominated by thermal generators. Changes in net demand have a much larger price effect in the Nordics. Our model results indicate that in Germany, RES growth was largest downward driver on prices; demand, new investments and the CO 2 price were about half in size. The nuclear phase-out was the largest upward driver, followed by increased exports. In Sweden, RES growth and demand decline affect the electricity price about equally; followed by hydro inflow. Increased export helped strongly to stabilize the price. 6

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