Carbon Price Transfer in Norway

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1 Public ISDN nr THEMA Report Carbon Price Transfer in Norway The Effect of the EU-ETS on Norwegian Power Prices Commissioned by Energy Norway, Federation of Norwegian Industries and Industri Energi March 2011 FINAL

2 CONTENT 1 INTRODUCTION AND SUMMARY THEORETICAL EVIDENCE EMPIRICAL EVIDENCE MODEL BASED EVIDENCE The Econ Pöyry BID power market model Short-Term Carbon Price Transfer Long-Term Carbon Price Transfer APPENDIX 1: MODEL VALIDATION AND BACK-TESTING APPENDIX 2: USING BID TO ESTIMATE TRANSFER FACTORS of 42 THEMA Consulting Group

3 1 INTRODUCTION AND SUMMARY The transfer factor for CO 2 costs in power prices is measured by the increase in the power price due to the CO 2 price, i.e. in /MWh per /ton CO 2. This is equivalent to the marginal CO 2 emissions in power generation, i.e. ton CO 2 /MWh. All in all the average transfer factor in Norwegian power prices is found to be similar to the transfer factors in other market areas in North West Europe, and the estimate is quite robust. The normal year transfer factor is 0.6 ton CO 2 /MWh, with a range across the sensitivity analyses from 0.5 to 0.7 CO 2 /MWh. This estimate is in line with theory and with historic market data. The longer term transfer factor is more uncertain, in particular due to the uncertainty surrounding the development in the continental generation mix. CO 2 Prices Have a Significant Impact on Norwegian Power Prices This report analyses the price effect of the EU ETS on Norwegian power prices, i.e. the transfer factor of the CO 2 price in Norway. The background for the analysis is the proposed carbon price compensation scheme. The design and financing of such a compensation scheme and the general power price level are topics beyond the focus of the presented analysis. Although Norwegian power is almost 100% hydro-based, we find that CO 2 pricing leads to a significant power price increase in Norway. Generally, the transfer factor differs between markets, but the Norwegian transfer factor is on par with the transfer factors in adjacent, thermal based markets. This observation is supported by economic theory, analysis of historical data, as well as quantitative model simulations. Overall, our estimate for the carbon transfer factor lies around 0.6 ton CO 2 /MWh with a narrow confidence interval. This means that a carbon price of 10/ton CO 2 leads to an increase of Norwegian power prices of 6 /MWh. With the current EU ETS price of around 15 /MWh, the CO 2 price increases the Norwegian power price by around 9 /MWh compared to a situation without CO 2 pricing in Europe. Norway Is Not Isolated Norwegian power generation is almost 100 % hydro-based and as such, practically CO 2 free. However, the Norwegian power price is not CO 2 free. This is due to the interconnectivity of the Norwegian power market with neighboring markets. There is a common Nordic market between Norway, Sweden, Finland and Denmark with extensive power exchange between market areas, and substantial interconnector capacity between the Nordic market and the Continent. Thus, price formation in the Norwegian market has to be regarded in the context of the wider market area. In the Nordic market area, hydro power generation represents between 50 and 60% of annual generation, as illustrated in Figure 1 (based on data for 2008 which was a wet year). Hence, the Nordic market is hydro dominated, but with substantial thermal generation as well. Figure 1: Nordic electricity generation mix, Source: norwatt.no 3 of 42 THEMA Consulting Group

4 Theoretical Evidence Norwegian hydro power is characterized by large storage capacity and ample installed effect capacity. This means that Norwegian hydro power is very flexible. Maximum generation in a given period is limited by the reservoir filling and the inflows during that period. The hydro generators must decide how much to produce within the period and how much to save for future periods, and how to distribute the generation during the period in question. These decisions are of course linked. Figure 2 is a simplified illustration of how the generation decision determines the water value and the price level in a hydro dominated power system with thermal generation capacity. Figure 2: Price formation and water value in a hydro dominated system, illustrative. Price p H Low load High load Water Value Coal/Gas p L Hydro Nuclear CHP Volume The supply curve in any given hour is given by the capacity (in MWh) for each type of generation and the marginal (variable) cost of generation. Demand (consumption) varies by time of day, indicated by the two demand curves, Low load and High load. Nuclear power has lower variable costs than coal and gas generation. Hydropower has low variable costs, the water is basically free. Hence we place hydro power at the lower left-hand side of the supply curve. However, since the availability of water is limited, water has an alternative value. If the hydropower producer bids in full generation in every hour at the variable cost, he will run out of water before the end of the period. In order to optimize the value of the water, the hydropower producer will reduce generation in low load and increase generation in high load. If the reservoir levels and installed effect capacities are not binding, this will continue until the price is equal in all hours. With a simplified system as illustrated in the figure, this means that the water value is determined by the cost of coal and gas power capacity. This basic mechanism holds in the real market as well, but here the optimization problem is complicated by the uncertainty about future market conditions such as inflow, fuel prices, plant availability, demand, etc. The implication is that changes in the marginal cost of coal and gas power capacity will change the water value as well. Hence, a CO 2 cost will be reflected in power prices in a hydro dominated system. In a system as illustrated in the figure, the hydro system and the thermal system are perfectly integrated, i.e. there are no bottlenecks between market areas in the (combined) system. In practice, when looking at the integrated North European system, the hydro power generation is dominating in some parts of the system, particularly in the north, and the reservoir capacity is predominantly located in Norway. The different parts of the system are connected through a few transmission lines with limited capacity and there are frequent bottlenecks. Bottlenecks between the systems mean that prices are not fully equalized in all hours in all parts of the system, and that the transfer factor varies between market areas. 4 of 42 THEMA Consulting Group

5 Figure 3: CO 2 transfer with bottlenecks between a hydro dominated system and a thermal system, duration curves of prices during one year, illustrative. Price Thermal system Prices must adjust in order to preserve power balance Hydro System Export Import Figure 3 illustrates how the CO 2 cost is transferred from the thermal system to the hydro system when there are bottlenecks between the hydro system and the thermal system. The figure shows typical price structures in a hydro system and a thermal system. There are exports from the hydro system to the thermal system when prices are high in the thermal system, and imports to the hydro system when prices are low in the thermal system. The price level in the hydro system reflects the energy balance during the year: Exports imports = Hydro generation domestic consumption. For simplicity we assume that domestic consumption is given. The CO 2 cost increases the prices in the thermal system, and in order to maintain the export-import balance, the price level in the hydro system must increase, reflecting the increased alternative value of the available water. The annual transfer factor in a thermal system is the average of the marginal CO 2 cost for each hour during the year, and varies accordingly between hours. The annual transfer factor in a hydro system reflects the alternative value of water, and varies much less between hours. Empirical Evidence The transfer factor is defined as the increase in the annual average power price associated with a CO 2 cost of 1 /ton CO 2, i.e. the transfer factor is measured as /MWh per /ton CO 2 (or ton CO 2 /MWh). The average transfer factor in a thermal power system is the average of the marginal CO 2 cost for all hours during the year. In a thermal system with a mix of different technologies and fuels, we would expect the average transfer factor to lie in-between the transfer factor for gas power generation (0.4 ton CO 2 /MWh) and coal power generation ( ton CO 2 /MWh). However, if the share of renewable electricity generation increases or the system has a high share of older, less efficient, coal generation facilities, the average transfer factor may be outside this range. We have estimated the historical transfer factor in the Norwegian power price using a regression model with coal price, CO 2 price and deviation from normal reservoir filling as explanatory parameters. The model has a high explanatory value and the confidence interval of the estimated transfer factor is small. 5 of 42 THEMA Consulting Group

6 The regression analysis based on historical data indicates an historical average transfer factor in the Norwegian power price of 0.67 ton CO 2 /MWh. Model Evidence Model simulations: To analyze future transfer factor and the drivers affecting it, we have used a numerical simulation model of the North West European power market and simulated the market under different assumptions, focusing on the year The model employed is a state-of-the art power market model with hourly time resolution, specifically developed to capture exchange and market effects of interconnection between a hydro-based and a thermal power system. The model has a proven track-record from back-testing against historical power prices and has been used extensively to analyze the economics of new interconnectors between the Nordic market and the Continent in recent years. Employing a model allows us to study how the transfer factor may vary with variations in different market drivers. The main scenario (Base scenario) used in the analysis describes the expected market situation for the North European power market in the year The scenario is thus based on today s expectations of demand, fuel prices, new capacities and interconnectors according to known and perceived developments for The model simulation of the Base scenario yields a transfer factor of 0.6 ton CO 2 /MWh for Norway. In addition we carried out a number of sensitivity analyses to test how the transfer factor is affected by changes in different, individual market drivers. The analyzed market drivers are fuel prices, CO 2 price, inflow variations (Nordic energy balance), increased interconnectivity and increased gas power generation in Germany. The sensitivity analysis reveals that The average transfer factor in all NWE market areas is in-between the coal and gas transfer factor, but varies between market areas In years with normal inflow and temperatures, the average Norwegian transfer factor is in line with the average transfer factor in Finland, and in-between e.g. the average transfer factor of Germany and the Netherlands. The results from the Base scenario and sensitivity simulations are summarized in Figure 4. The lower line and the upper shaded area represent the transfer factors (emission factor in ton CO 2 /MWh) for gas and coal power generation respectively. The red bars show the Norwegian transfer factor in the different cases, and the blue band shows the maximum and minimum transfer factor in the NW European market areas for the same cases (min and max of the Nordic areas, Germany, Netherlands and UK). 6 of 42 THEMA Consulting Group

7 Figure 4: Model estimated average transfer factors for Norway compared to coal and gas transfer factors and transfer factors in other NW Europe market areas, for different scenarios. Model simulations based on 2013 fuel and CO 2 prices. The results of the sensitivity analyses show that the most significant impact on the transfer factor is made by an increase in the (relative) gas price, and by an increase in the energy surplus (i.e. power exports) in the Norwegian market, particularly in very wet years. The effect of changes in fuel prices is found to vary somewhat between market areas, but is largely the same in all the Nordic market areas: A higher relative gas price increases the transfer factor, while a higher coal price reduces the factor. All of the other changes in the transfer factor are less than +/- 0.1 ton CO 2 /MWh. Notably, a higher CO 2 price hardly has any impact on the transfer factor, implying a close to linear relationship between CO 2 price and increase in power price. In the longer term, the magnitude of the transfer factor is more uncertain. This is particularly related to the uncertainty surrounding the long-term capacity mix in adjacent market areas. 7 of 42 THEMA Consulting Group

8 2 THEORETICAL EVIDENCE Water Values and Prices Are Determined by Thermal Generation Costs The Nordic power market (Norway, Sweden and Finland) is special in the sense that the system is largely composed of generation from hydro (Norway and Sweden) and nuclear generation (Sweden and Finland). Only a small fraction of the system is conventional thermal generation emitting CO 2, implying that the majority of the system is CO 2 free. Nevertheless, CO 2 prices play a significant role in the price formation in the Nordic markets. This is due to the fact that the price of hydro resources, the so-called water value, is not determined and bid into the market according to short-run marginal costs, but according to the alternative cost of generation in the market. Water values as such are difficult to calculate, but in essence they reflect the alternative costs of generation on the margin, i.e. the cost of replacing the last unit of hydro generation with other conventional generation. As the total amount of water is given by nature, the hydro power producer must decide whether to produce a unit of water today or store it for the future. If a hydro producer produced 1 MWh less hydro power, this 1 MWh would have to be replaced by some other type of generation. Assuming other water and nuclear generation fixed, this would typically imply that Norway would have to import this 1 MWh from e.g. Denmark or Finland, where it would (as of today) be produced in a thermal coal power plant. Alternatively, exports have to be reduced by this 1 MWh, implying that generation in Denmark or Finland would have to increase. In practice, it is therefore often argued that as a rule of thumb the water values in Norway are given by the short-run marginal cost of coal generation in Denmark or Finland. But since the short-run marginal costs of these types of generation contain a significant costs element associated with CO 2 prices, the CO 2 costs are transferred into Norwegian or Nordic prices. Hydro Is a Flexible, Renewable, but Limited Resource: Determination of Water Values The actual determination of water values is an intertemporal problem under uncertainty. Norwegian hydro power is characterized by large storage capacity and ample installed effect capacity. This means that Norwegian hydro power is very flexible. Maximum generation in a given period is limited by the reservoir filling and the inflows during that period. The hydro generator must decide how much to produce within the period and how much to save for future periods, and how to distribute the generation during the period in question. These decisions are of course linked. Figure 5 is a simplified illustration of how the generation decision determines the water value and the price level in a hydro dominated power system with thermal generation capacity. 8 of 42 THEMA Consulting Group

9 Figure 5: Price formation and water value in a hydro dominated system, illustrative. Price p H Low load High load Water Value Coal/Gas p L Hydro Nuclear CHP The supply curve in any given hour is given by the capacity (in MWh) for each type of generation and the marginal (variable) cost of generation. Demand (consumption) varies by time of day, indicated by the two demand curves, Low load and High load. Nuclear power has lower variable costs than coal and gas generation. Hydropower has low variable costs as the water is basically free. Hence we place hydro power at the lower left-hand side of the supply curve. However, since the availability of water is limited, water has an alternative value. If the hydropower producer bids in full generation in every hour at the variable cost, he will run out of water before the end of the period. In order to optimize the value of the water, the hydropower producer will reduce generation in low load and increase generation in high load. If the reservoir levels and installed effect capacities are not limiting, this will continue until the price is equal in all hours. With a simplified system as illustrated in the figure, this means that the water value is determined by the cost of coal and gas power capacity. This basic mechanism holds in the real market as well, but here the optimization problem is complicated by the uncertainty about future market conditions such as inflow, fuel prices, plant availability, demand, etc. The implication is that changes in the marginal cost of coal and gas power capacity will change the water value as well. Hence, a CO 2 cost will be reflected in power prices in a hydro dominated system. Interconnection is Important Volume In a system as illustrated in the figure, the hydro system and the thermal system are perfectly integrated, i.e. there are no bottlenecks between market areas in the (combined) system. In practice, when looking at the integrated North European system, the hydro power generation is dominating in some parts of the system, particularly in the north, and the reservoir capacity is predominantly located in Norway. The different parts of the system are connected through a limited number of transmission lines and there are frequent bottlenecks. Bottlenecks between the systems mean that prices are not fully equalized in all hours in all parts of the system, and that the transfer factor may vary between market areas. 9 of 42 THEMA Consulting Group

10 Figure 6: CO 2 transfer with bottlenecks between a hydro dominated system and a thermal system, duration curves of prices during one year, illustrative. Price Thermal system Prices must adjust in order to preserve power balance Hydro System Export Import Figure 6 illustrates how the CO 2 cost is transferred from the thermal system to the hydro system when there are bottlenecks between the hydro system and the thermal system. The figure shows typical price duration curves in a hydro system and a thermal system, for example Germany. Price duration curves sort the prices according to size and display them in descending order. They easily reveal information about how many hours within a year a price has been larger or lower than a certain threshold. Typically, prices in a thermal system vary from hour to hour, and from week to week, depending on fuel prices, demand load, availability of plants, etc. This is illustrated in Figure 7, showing how demand at different loads crosses the supply curve at different locations, which implies different prices at these different loads. This is the reason why the price duration curve for a thermal system spreads over a very large price range (bold yellow line, the tilted S in Figure 6). In contrast, the price duration curve for the hydro system is rather flat (bold blue horizontal line). This is due to the fact that the flexibility in the hydro system typically limits large price variations between hours (cf. Figure 5). If there were price differences between hours, hydro producers would shift generation to the high price hours, hereby limiting price variations. In a hydro system, price variations typically only occur between seasons as reservoir capacities and run-of-river constraints are temporarily binding, and in cases of inflow extremes, or if the fundamental of the markets (e.g. fuel prices) change and hence the expected alternative value of water. 10 of 42 THEMA Consulting Group

11 Marginal Costs Report Carbon Pass Through in Norway Figure 7: Price formation in a thermal system: Simplified merit order for Germany. 140 Low load High load Capacity GT Hydro Wind Nuclear CHP Lignite Extraction Coal Condensing Coal CCGT GT Comparing the two price duration curves illustrates the export/imports between the hydro and the thermal system, although the high price in the thermal system does not always correspond with the high prices in the hydro system. By looking at the intersection of the two curves, one can approximate the number of hours with export and import: There are exports from the hydro system to the thermal system when prices are high in the thermal system, and imports to the hydro system when prices are low in the thermal system. Thus, the price level in the hydro system reflects the energy balance during the year: Exports imports = Hydro generation domestic consumption (for simplicity we assume that domestic consumption is given). But the energy balance basically does not change with the introduction of a CO 2 cost, assuming inelastic demand, while the marginal costs in the thermal system do. The latter is reflected by an upward shift of the price duration curve for the thermal system in Figure 6: The dotted yellow line reflects the duration curve without CO 2 costs, while the bold yellow line reflects the duration curve with CO 2 costs. In the merit order depicted in Figure 7, the CO 2 costs are indicated by the shaded areas on top of the columns, showing that CO 2 costs change the merit ( height ) and the order ( sorting ) of the merit order. Since the available amount of water in hydro generation does not change, the price in the hydro system (or the water values) must increase correspondingly in order to preserve the import/export balance. This increase in the water values is reflected in Figure 6 by the upwards shift of the price duration curve for the hydro system: The dotted blue line reflects the price duration curve without CO 2 costs, while the bold blue line reflects the duration curve with CO 2 costs. In order to restore the import/export balance, the price duration curve in Norway must shift upwards as well. The Transfer Factor is a priori Expected to Be Between 0.4 and 0.9 ton CO 2 /MWh There are two major technologies that play an important role for the CO 2 transfer factor in thermal systems. Coal fired generation that fires steam turbines in a condensing power plant, and CCGT, combined cycle gas turbines that use natural gas to fire a gas turbine in combination with a steam turbine. 11 of 42 THEMA Consulting Group

12 Looking at current efficiencies for coal and CCGT plants, the marginal CO 2 cost in coal units lies somewhere between 0.8 and 0.9 ton CO 2 /MWh (depending on energy efficiency) and at around 0.4 ton CO 2 /MWh in gas power units. This means, for example, that a EUA price of 10 per ton CO 2 increase short-run marginal costs of coal generation by 8-9 per MWh, while they increase short-run marginal costs in CCGTs by around 4 per MWh. One would therefore expect the transfer factor in the average annual price within a thermal system to lie somewhere between those boundaries, which should at the same time also be the boundaries for the transfer factor in hydro dominated systems. In a coal dominated system we would expect the average transfer factor to be higher, and in a gas dominated system it would be lower. Actual Transfer Factor Calculations Require Solid Quantitative Analysis In practice, the calculation of water values is not trivial as it depends on the expectations of the market participants and is affected by changes in market conditions and market outlooks. There are a number of reasons why the calculations are rather advanced, and more challenging than the simplified illustrations above suggest: Power prices in Norway are not always correlated with power prices in Germany or Denmark, or other surrounding countries as suggested in Figure 6. Hydro power generation is not perfectly flexible and sometimes run-of-river and reservoir capacities are binding. That means that the high price hour in Germany is not necessarily the same as the high price hour in Norway. Thus, Figure 6 is a true simplification. The merit order in thermal systems is not static, and the short-run marginal costs also include start-up costs that are dependent on whether a unit is already running or not. Norway is not only connected to one country, but to several countries, which are also interlinked with each other. A simple illustration like Figure 6 cannot account for this complexity. This interconnection also implies that all variables are somehow linked. That is, the prices in say Germany or Denmark are not entirely independent on the power prices and water values in Norway. Thus, one cannot simply take outside prices as given when calculating water values. Uncertainties in the market about fuel prices, demand, inflow, and other parameters, should be reflected in the determination of prices and water values. In order to estimate the actual transfer factor of CO 2 costs in the Norwegian prices, we therefore employ two approaches: First, we use statistical methods to estimate the historic transfer factor as observed in the market (see Chapter 0); second, we use a power market simulation model to estimate the transfer of CO 2 costs that can be expected in the future (see Chapter 4). Using the model we also explore how changes in fundamental market conditions affect the transfer factors. In both cases our findings are in line with the theory presented in this chapter. 12 of 42 THEMA Consulting Group

13 3 EMPIRICAL EVIDENCE One way to analyze and evaluate the actual carbon transfer factor is to analyze historic prices. The challenge, however, is that hydro power generators and other generators do not explicitly report the part in their marginal costs or bidding that is related to carbon prices. Therefore, we ran a (linear) regression of historic weekly prices from 2004 to 2008 and tried to estimate the statistical significance of certain input parameters on power prices. In a regression, one analyses the statistical relationship and significance of factors for the variable to be explained, in this case prices. The task involves finding a formula that explains the prices as a function of relevant input factors. In this case, we found that a formula including the following factors had a high explanatory value: Short-run marginal costs of coal without CO 2 ( /MWh): As coal units typically set the water values, one would expect a strong relationship with water values and Nordic prices. Furthermore, as coal prices are typically correlated with gas and oil prices, they also reflect to some extent marginal costs in other units. Carbon prices ( /ton): As carbon prices enter the marginal costs of coal and other thermal units, they should also be an explanatory factor in the water values for the same reason as fuel prices explain water values. Deviation from mean reservoir level in that week (measured in percentage points): The hydrological conditions and the reservoir fillings seem to significantly impact power prices. For example, the dry spell in the Winter 2010/2011 period lead to very low reservoir fillings and hence very high prices. Thus, essentially, we tried to obtain a formula that explains water values and prices in Norway as a function of short-run marginal costs and CO 2 prices, and the reservoir situation. Of course, we could have added a number of other factors, such as forward fuel prices to capture expectations about future fuel prices, plant outages, or other factors. These factors are however more difficult to define (which forward price to use, no direct data revealing generators perception of outage risks, etc.), and often, these factors are already to some extent captured by the variables above (for example, low nuclear generation in Sweden yields lower reservoir fillings). And as mentioned above, the specified formula has a very high explanatory value, and including additional variables would probably not improve the explanatory power of the regression. Table 1: Results for the linear regression analysis based on historic prices from SRMC CO 2 Reservoir Lower (5%) Estimated Upper (95%) The results from the regression analysis are found in Table 1. The explanatory power of these variables is very strong, with an adjusted R-Square of 95%. Colloquially speaking, this essentially means that the values and variations in these three variables explain 95% of the price level and variations. Furthermore, all factors are significant, with fairly limited variation and narrow confidence intervals. As for the carbon cost component the estimated pass through factor is estimated at 0.67 ton CO 2 /MWh. The confidence interval is estimate between 0.57 and 0.77 ton CO 2 /MWh. Colloquially speaking, this means that with 90% likelihood the actual carbon transfer factor lies between 0.57 and 0.77 ton CO 2 /MWh. These values correspond well with the hypothesis that the 13 of 42 THEMA Consulting Group

14 Input Prices and Power Prices ( per MWh and per ton) Deviation from Mean Storage Level (% points) Report Carbon Pass Through in Norway transfer factor will be somewhere between the marginal carbon cost of coal and gas power generation. Figure 8: The relationship between short-run marginal costs for coal generation and power prices in Norway from 2004 to SRMC Coal CO2 Price NO Price NO Regressed Res.Deviation Zero Res Dev We also see that the short-run marginal costs for coal have a strong explanatory power for the power prices, with an almost one to one relationship between short-run marginal costs and power prices (value at 0.95 with confidence interval between 0.89 and 1.01). Also the reservoir deviation has a strong and significant impact on the results. For example, 10 percentage points lower reservoir filling would, according to the estimates, increase power prices by 10.8 per MWh. The historic prices, short-run marginal costs, carbon prices, and reservoir deviations are visualized in Figure 5. They also show the estimated or explained price by our regression (Price NO Regressed), indicating a good fit between observed and estimated prices. We have also tested the results and the significance of the carbon prices for alternative regressions, for example the squared reservoir deviation (as typically larger deviations from mean filling would imply stronger than linear reactions in prices). But, in all cases, the estimates for the carbon transfer factor were strong and significant at around 0.65 ton CO 2 /MWh. From the beginning of the EU-ETS in 2005 until the end of 2008, the average carbon price was ca. 15 /ton CO 2 with large variations. Our estimates suggest that the average impact of the EU ETS on power prices in Norway has been around 10 / MWh in that period (0.64 ton CO 2 /MWh multiplied by 15 /ton CO 2 = 9,6 /MWh). 14 of 42 THEMA Consulting Group

15 4 MODEL BASED EVIDENCE Historical data indicate a carbon transfer factor in Norwegian power prices between 0.57 and 0.77 ton CO 2 /MWh. Analysis of historical data cannot however, reveal how the transfer factor is affected by changes in other market drivers. Employing a model allows us to analyze whether and how the future transfer factor may depend on changes in various fundamental market conditions. In this chapter we present model results for the short term, represented by 2013, and the long term, represented by The Econ Pöyry BID power market model The model simulations are made using the Econ Pöyry BID model, which is an advanced, fundamental power market model for North West Europe. The BID model optimizes power generation and trade across Northern Europe based on detailed input of demand, generation capabilities, transmission capacities, fuel and CO 2 prices and availability of wind power. The BID model was developed in cooperation with some of the TSOs in NW Europe and is specifically designed to capture the interaction between the Nordic hydro system and the thermal systems on the Continent. It was the first model of its kind to integrate sophisticated water value modeling with detailed modeling of thermal generation plant including part-load efficiencies and start-up costs. The model has been extensively used to analyze the economics of new interconnectors between the Nordic market and thermal markets like Germany, the Netherlands and the UK. In addition, the model has been tested against historical market data, and is found to replicate market behavior quite well. A more detailed documentation of the BID model s capabilities and methodology, back-testing of price formation in Norway, and the scenario assumptions are provided in Appendix 1: Model validation and back-testing. 4.2 Short-Term Carbon Price Transfer In order to estimate the impact of CO 2 prices in the near future, we modeled the year 2013 with our best-guess assumptions for fuel prices, demand, etc. for the year Relative to the situation in 2010 the Base scenario for 2013 represents a further transition towards the 2020 climate and energy targets. Some of the low efficiency thermal plants are phased out and the share of renewable generation is increased. The interconnector capacity between Germany and Denmark is increased by 500 MW. More detailed assumptions can be found in Appendix 2: USING BID to Estimate transfer Factors. The short term transfer factor is analyzed further by a number of sensitivity estimates, where one input factor is varied at the time. The sensitivity of the transfer factor to changes in the gas, coal and CO 2 prices, hydro inflow levels, continental capacity mix, and interconnector capacity is analyzed. In the 2013 Base case we find a transfer factor of 0.6 for Norway. Moreover, the Norwegian transfer factor is similar to the transfer factor in adjacent market areas. The results are quite robust across the range of sensitivity analysis. The minimum and maximum transfer factor for Norway is 0.5 and 0.7 respectively, thus, very much in line with the results of the analysis of historical data. The lowest transfer factor is found in wet years, and the highest transfer factor is found when the gas price is increased relative to coal. In this section we explain the results in more detail. Results for the 2013 Base Case The estimated transfer factors for the power market areas in North West Europe (Nordics, Germany and the Netherlands) are illustrated in Figure 9 shows the estimated annual transfer 15 of 42 THEMA Consulting Group

16 Transfer factor (t/mwh) Report Carbon Pass Through in Norway factors for the Nordic countries, Germany and the Netherlands. The transfer factor for Norway is estimated to 0.6 ton CO 2 /MWh, which is close to the historically estimated transfer factor of 0.67 ton CO 2 /MWh. As expected, all transfer factors lie between the typical emission factors for coal ( ton CO 2 /MWh) and gas (0.4 ton CO 2 /MWh) generation (cf. page 11), shown by the horizontal lines in the figure. Furthermore, the transfer factor for Norway lies in-between the transfer factors of the other market areas in the region. As expected, we find the highest transfer factor in Denmark, which has the highest share of coal power generation, and the lowest transfer factor in the Netherlands, with the highest share of gas power generation. Figure 9: Estimated transfer factors for the 2013 base case, ton CO 2 /MWh Coal bound (42% efficiency) Gas bound (52% efficiency) Norway Sweden Jutland Zealand Finland Germany Netherlands In the model simulations we have used a carbon price of 16 /ton CO 2. This implies that the price increase caused by the carbon price in Norway is almost 10 /MWh (equals 0.6 ton CO 2 /MWh multiplied with 16/ton CO 2 ). Results of Sensitivities Even though 2013 is fairly close in time, there are a lot of uncertainties about the framework conditions in 2013: Fuel prices and CO2 prices are rather uncertain, so is the inflow for these years, etc. We therefore modeled a number of sensitivities to test how the transfer factor varies with different assumptions. An overview of the sensitivity results is given in Figure 10. Here the red bars show transfer factors for Norway whereas the blue band shows the minimum and maximum transfer factors found in the NWE market areas in each sensitivity simulation. The transfer factors and associated power price increases for Norway are listed in Table of 42 THEMA Consulting Group

17 Figure 10: Model estimated average transfer factors for Norway compared to coal and gas transfer factors and transfer factors in other NW Europe market areas, for different scenarios, 2013, ton CO 2 /MWh. Below we explain the sensitivities modeled and the results in some detail. An overview of the input variations and results is presented in Table 2: CO 2 price: The CO 2 price makes coal relatively more expensive than gas. A higher CO 2 price will therefore induce a larger shift in the merit order curve than a low price coal becomes relatively less competitive compared to gas. With low CO 2 prices, coal will dominate in the low end of the merit order curve, whereas for extremely high CO 2 prices gas may become the dominating fuel. Depending on the load variations, the transfer factor may hence be reduced as the CO 2 price increases. A different CO 2 price may therefore potentially lead to a different transfer factor. In order to analyze this, we have calculated the power price for a high and a low CO 2 price, 8 and 32 /ton, respectively. Result: We find that the transfer factor is largely insensitive to the CO 2 price level, implying a linear relationship between the CO 2 price and the power price increase. In the case of the high CO 2 price, the transfer factor is 0.58, yielding a power price increase of 18.5 /MWh. Fuel prices: To analyze the effect of changes in fuel prices, we ran two sensitivity simulations where we increase the gas and coal prices by 20%, one at the time. The fuel prices constitute a large proportion of the total operational costs for thermal power plants and affect what thermal technology will be on the margin. Changes in relative fuel prices alter the merit order curve as well. Result: We find that higher gas prices leads to a shift from gas to coal, hence increasing the transfer factor, while higher coal prices favor gas powered generation and reduce the transfer factor. Changes in fuel prices, if they are perceived to be lasting, may change the CO 2 price as well. Generally, a higher gas price would be associated with a higher CO 2 price in order to induce sufficient fuel switching to comply with the cap. For the same reason a higher coal price would be associated with a lower CO 2 price. Our sensitivity analysis here mainly focus on short term variations in (relative) fuel prices, and as such, keeping the CO 2 price unchanged, is justified. Interconnectivity: New interconnectors between Norway and Germany are planned to be commissioned between 2014 and 2020 (Nord.Link and/or NorGer). In order to analyze the 17 of 42 THEMA Consulting Group

18 effect of increased interconnector capacity, we ran a sensitivity simulation with one additional 1400 MW cable between Norway and Germany. Result: The transfer factor in Norway increases somewhat when the interconnector capacity increases. Germany has a high share of power generation based on fossil fuels, in particular coal and lignite, and these capacities often sets the mid-merit price level which is most relevant for the Norwegian water values. When the interconnector capacity increases, capacity with slightly lower efficiency (higher in the merit order) becomes more important for the Norwegian power price. Hence, in a market situation where gas power constitutes the high end of mid-merit capacity, the result could be a reduction in the transfer factor. Capacity mix: Because of the high amount of hydro power in the Nordics (Norway, Sweden), the power prices in these regions largely depend on the value of water (water values). One of the factors that set these water values is the marginal cost of (mid-merit marginal) thermal generation on the Continent, especially in Denmark and Germany. The power park composition (especially coal vs. gas capacity) will therefore have an impact on the power prices and the transfer factors. To analyze the implication of this, we estimated the transfer factor with a higher share of CCGT plants in Germany and the Netherlands than the base scenario. Result: The results are similar to the high coal price sensitivity, i.e. due to a larger share of gas fired generation in the generation mix, the transfer factor is reduced. Inflow: Hydro inflow can change dramatically from one year to another, and as we have seen in the regression analysis above, deviations from normal reservoir fillings (associated with variations in inflows) impact price formation in the Nordics. For hydro dominated countries, imports based on alternative energy sources increase in dry years and exports are increased in wet years (equivalent to a shift up or a shift down in the price duration curve, cf. Figure 6). To see what effect different hydro inflows have on the transfer factors, we have run the model with five additional inflows in the Nordic area, ranging from a very dry to an extremely wet year. Result: The results reveal that the average transfer factor in the Nordic area is significantly reduced in wet years, and that the reduction is greatest in Norway. The reduction in the transfer factor in wet years is mainly due to the price effects of high inflows in the summer months. In order to avoid spilling, and to make room for additional inflows during the fall, hydro generation and exports are high and prices low during the summer. During the rest of the year the transfer factor is pretty much unaffected. However, the annual average transfer factor does not fall below the Dutch average transfer factor, or the marginal CO 2 emission factor of gas power generation. It should also be noted that the model probably somewhat overestimates the price reduction during the summer hence, the effect on the Norwegian transfer factor is probably somewhat overestimated. In summary the sensitivity analyses show that the short term Norwegian transfer factor is fairly robust around 0.6. The estimates vary between 0.5 and 0.7 ton CO 2 /MWh in the sensitivities. Thus, in all cases, the transfer factor is significant and substantial, and lies within the band that power market theory and historic observations would suggest. 18 of 42 THEMA Consulting Group

19 Table 2: Transfer factor and absolute price increase for base case and all sensitivities for Norway. Transfer factor CO 2 price Absolute increase ton CO 2 /MWh /ton CO 2 /MWh Medium CO 2 price Low CO 2 Price High CO 2 Price High Gas Price High Coal Price Capacity Mix Increased Transmission Very dry Dry Wet Very wet Extremely wet Long-Term Carbon Price Transfer The model simulations are based on the market situation expected in 2013, i.e. the first year of the EU ETS third trading period. It is however likely that the European electricity sector will be substantially transformed between 2013 and 2020 following the implementation of the EU 2020 Energy and Climate Policy package. In addition, the uncertainty pertaining to several important input parameters is more uncertain in the longer run. In order to analyze how possible changes may affect the transfer factor in the longer run, we have simulated the carbon cost transfer in 2020 based on four different scenarios: Politics Work: High share of renewable generation, but moderate demand growth. Moderate fuel and CO 2 price levels. Strong increase in interconnectivity between the Nordic market and the Continent. Green Growth: High share of renewables compared with strong demand growth and conversion to electricity. High fuel and CO 2 price levels. Strong increase in interconnectivity between the Nordics and the Continent. Stagnation: Low growth in renewable generation capacity and in demand. Low fuel and CO 2 price levels. Small increase in interconnectivity. Supply Worries: Moderate increase in renewable generation, but relatively strong increase in demand. Some nuclear capacity is phased out in Sweden, and fuel and CO 2 prices are high. Small increase in interconnectivity. These scenarios are based on the study Challenges for Nordic power: How to handle the renewable energy surplus, a scenario study published by THEMA Consulting and Econ Pöyry. 1 The transfer factors in the different scenarios are shown in Figure 11. Two main conclusions can be drawn. First, the Norwegian transfer factor is between 0.5 and 0.6 ton CO 2 /MWh, i.e. quite similar to the 2013 base case. Hence, the transfer factor is still substantial and significant. Second, in some scenarios, e.g. Green Growth or Stagnation, the transfer factor in Norway is higher the transfer factor in Germany. Thus, the price burden of the CO 2 prices would be higher in Norway than in Germany. The main explanation is that, although 1 See for a more detailed scenario description. 19 of 42 THEMA Consulting Group

20 Transfer factor (t/mwh) Report Carbon Pass Through in Norway the share of renewable generation increases in all markets, gas generation becomes the predominant marginal source of generation in Germany in while the marginal source of generation in the Nordics is still to a large extent coal fired generation. Figure 11: The transfer factor for the 2020 scenarios, ton CO 2 /MWh Norway Denmark Sweden Germany Politics Works Green Growth Stagnation Supply Worries 20 of 42 THEMA Consulting Group

21 APPENDIX 1: MODEL VALIDATION AND BACK-TESTING In order to ensure that our modeling of CO 2 price transfer give realistic results, it is important to verify that the BID model is able to simulate actual price formation with a reasonable degree of accuracy. In this project, such verification is done by so-called back-testing : Back-testing implies that the model is populated with historical data in order to simulate the power market of a previous period. The resulting output prices can then be compared directly with corresponding historical prices. For the German market, the BID model has previously been found to model the historical average price level with good accuracy, although it somewhat underestimates the highest prices in the peak hours. In this work, we focus on the model s ability to reproduce prices in the hydro-dominated Nordic market for the years Results from Back-Testing and Evaluation Back-Testing - Challenges in a Hydro Dominated System Due to the complex dynamic of hydro operation captured by the concept of water values, modeling a hydro system is more challenging than to model a predominately thermal system like Germany. In a thermal system, power is bid at the marginal costs of generation, and there is a more or less direct relationship between marginal fuel costs and the power price. For a hydro dominated system, it is the water value which determines the power price. The water value reflects the alternative value of an available unit of water. The water may be produced today at the current market price or be stored and sold in the future. If it is produced today, it cannot be produced later. Hence, the water value is essentially the highest price that hydro producers think they can get for their hydro power, now or in the future. Therefore, water values also depend on the market s expectations for the future, for instance with respect to fuel prices, plant availabilities, storage capacities and inflows. While expectations and uncertainty about future inflow is well handled by BID, the model works with full foresight with respect to fuel prices, demand and plant availabilities. In reality, expectations are prone to change during the year due to changes in fuel markets, outages and changes in demand (e.g. colder or warmer than normal weather conditions) and other unusual events affecting the market. In order to obtain accurate results, the data that the model sees for future weeks should therefore ideally be updated to reflect changes in the market s expectation at any time. This means that the model cannot be run for an entire year at the time. Instead, the model must be run for shorter periods and fuel prices and availabilities updated at regular intervals. Another challenge is to determine the starting storage of the hydro reservoirs in the Nordic countries. In BID, a starting storage level (in % filling grade) must be set at the start of each simulation. The model then predicts the development in the reservoir filling through the year, based on the defined inflow and the release determined by the model. While the information on actual storage levels are readily available in the public domain, the storage levels used in BID do not always perfectly correspond to the actual values. If the actual storage level is used directly, the model may produce unrealistically much or too little water. In normal BID use, this is handled by a calibration procedure where the starting storage is determined so that for a year of normal inflow, the ending storage should be the same as the starting storage. This assumption is adequate when modeling expectations for a future year, but in reality no given year is perfectly normal, and the hydro producers will usually produce less than the inflow in wet years and more in dry years. In other words, adjustments to deviations from normal inflows are distributed over a longer time period than one year. For these reasons, and because no model can capture every factor that impact power prices, we cannot expect that the model will reproduce historical power prices perfectly. 21 of 42 THEMA Consulting Group

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