atw 2019-02

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atw Vol. 64 (2019) | Issue 2 ı February FEATURE | MAJOR TRENDS IN ENERGY POLICY AND NUCLEAR POWER 72 Real Exchange Rates Annual Inflation Rates US$ per £1 US$ per €1 £ per €1 US UK Eurozone 2018 1.30 1.17 0.87 2.2% 2.5% 1.5% 2019 1.35 1.23 0.88 2.2% 2.1% 1.6% 2020 1.40 1.22 0.84 2.1% 2.1% 1.8% 2021 & 2022 1.46 1.22 0.84 2.0% 2.0% 2.0% | | Tab. 1. Inflation and real exchange rate (2016 Money). dollar- denominated oil and coal price projections Euros. Table 1 shows the real exchange rates and the annual inflation rates for the US, UK and the Eurozone that are assumed in the modelling. Nominal exchange rates for 2018-2020 are based on the median composite Bloomberg forecast from up to 50 financial institutions. The farthest (2020) nominal exchange rate is taken as the assumed long-term forecast and kept constant for the following years. Inflation rates for 2018–2019 are derived using the median composite CPI forecasts from Bloomberg. In 2020 the inflation rate trends between the 2019 value and the long-term (from 2021 onwards) assumption of 2 % in all three economic areas. The real exchange rates therefore fluctuate until 2020 in line with the inflation rate and the nominal exchange rate differentials that occur to 2020. pp generation capacity The projected generation capacity for Germany is based on three main components: plant which already exist or is under construction, generic new power plant (in the longterm, based on need and economic viability); and renewable development (based primarily on policy and targets). With regards to plant existing or under construction, the status is taken from several sources including company’s annual reports, the German federal agency for power grid (Bundesnetzagentur) power plant list and Pöyry’s own market intelligence. A more rapid phase-out of coal power plants as being currently considered by the German government in 2018 is not modelled. The construction of new fossil generation as generic new power plant results has a negligible impact on the rather short time period study considered here. With regards to renewable energy sources (RES) development the current renewable capacity plans are considered. pp electricity demand The projections for electricity demand in Germany are produced using Pöyry’s demand model. This is an [TWh] 2020 2021 2022 Demand DE 551.8 551.5 550.9 | | Tab. 2. Projected annual electricity consumption. econometric model which assumes a long-term relationship between electricity demand and Gross Domestic Product (GDP). The base demand in Germany for each future year is calculated by using annual GDP growth assumptions based on International Monetary Fund (IMF) projections [3]. In addition to the underlying demand development due to economic growth or recession, the demand model also captures the impact of energy efficiency measures and the shift of energy demand from other fuels in the transport and heat sectors into electricity. Temperature corrections are also applied to historical demand values to mitigate the impact of extreme (cold or warm) weather years on future demand projections. The reference weather year of 2013 is selected as basis for this study. The choice of this weather year is explained in §2.1.3. The resulting electricity demand development (average year) can be found in Table 2. pp interconnectors Because of its geographical location within Europe, the German power grid is interconnected with nine neighboring countries: Austria, Czech Republic, Denmark, France, Luxembourg, Poland, Sweden, Switzerland and the Netherlands. The current interconnectors as well as the projects planned for realization in time frame up to 2023 are considered in this study according to ENTSO-E data. Regarding intra-German transmission capacities, the assumptions of the German TSO’s on the German grid are used in this study, which are in line with ENTSO-E assumptions for the investigated period 2020–2022. Specifically, the Elbe 2 grid project planned for realization in 2019 is considered operational. pp fuel and CO 2 prices Fuel and CO 2 prices are exogenous parameters inserted into Pöyry’s power market modelling tool BID3. These prices are determined with the help of models specific to each commodity, expect for lignite. For lignite no model exists as fuel costs depend only on the production costs of this energy source. An overview of all projected fuel prices as well as historic prices for reference can be found in Figure 2. pp storage Batteries are not modelled in this study. Although they are being built in Germany, they are only operating in the ancillary services market so far and there is no evidence showing the introduction of batteries into the day-ahead market in the modelled time frame. | | Fig. 2. Fuel prices overview. Dashed line represents historical prices, solid line projections. Sources: Historical prices – MCIS (Coal), Thompson Reuters (CO 2 ), ICIS Heren (Gas), EIA (HSFO); Projections – Pöyry Management Consulting interpolated with forwards from API2 (Coal), Thompson Reuters (CO 2 ), ICIS Heren (Gas), ICE (HSFO); Lignite based on fundamental production economics 2.1.2 NPPs phase-out scenarios According to the German atomic low, the seven NPPs operating today, with a total installed capacity of 9,509 MW, will be shut-down successively by the end of 2022 at the latest, as highlighted in Table 3. The plant closures are modelled according to the German Atomic law latest NPPs shut-down dates. This scenario is referred to as “Reference” case in this study. The reference scenario is used to benchmark the outcomes obtained from the “NPP Out” case. The “NPP Out” case is the alternative scenario considered. It assumes the premature closure of all remaining seven NPP as of 01.01.2020, as highlighted in Table 3. Feature | Major Trends in Energy Policy and Nuclear Power Contribution of Nuclear Power Plants to the Energy Transition in Germany ı Denis Janin, Eckart Lindwedel, Volker Raffel, Graham Weale, James Cox and Geir Bronmo
atw Vol. 64 (2019) | Issue 2 ı February MW 2019 2020 2021 2022 2023 Reference 9,509 8,107 8,107 4,049 0 NPP Out 9,509 0 0 0 0 | | Tab. 3. Nuclear capacity per scenario. 2.1.3 Selection of reference weather year A reference weather year is required to model both the demand and the generation from RES. The weather choice is rather sensitive as it could impact substantially the study outcomes. The weather year 2013 is selected from the available set of weather years (2010–2014). This choice is motivated by 2013 “average behavior”: 2013 is close to average with regards to wholesale price, renewable gen eration and peak prices. An overview of the two parameters for the years under consideration can be found in Figure 3 and Figure 4 with the deviation from average given in Table 4. By not selecting a more extreme weather year such as 2011 the authors aim to remain as objective and robust as possible in the study’s outcomes. 2012 is a second candidate as both wholesale price and renewable generation deviation are in a reasonable range. Due to the cold winter spells end of January and early February however, the year shows atypical price peaks and would therefore distort the overall results too much. An overview of the effect of cold spells on the 200 highest prices per year can be found in Figure 4. To assess the effects of a change in weather year on the results of this study, a sensitivity analysis with 2012 weather year is performed. 2.2 Modelling tools A multistage process described in this section is followed in this work to properly assess the role of NPPs in the German power system. The two scenarios described in §2.1 are modelled using Pöyry’s proprietary fundamental market modelling software BID3 combined with grid modelling and analysis via PSS/E. 2.2.1 Power market modelling and BID3 Pöyry’s in-house, fundamental model BID3 [6] [7], models the market dispatch of all generation facilities in Europe. BID3 can model the behavior of individual power plants of all fuel types as well as renewable generators. It simulates all 8760 hours per year, generating hourly wholesale prices. An overview is shown in Figure 5. The output of all generators is jointly optimized for economic costs for each hour of the modelled time period. The result of the process is a fundamental view of what the market prices, power plant dispatch, cross-zonal interconnection flows and total cost of generation in each scenario will be on an hourly resolution. In this modelling process, price zones are optimized jointly such that for Germany the entire price zone is optimized disregarding any internal transmission capacity restrictions while for instance Sweden is split into four price zones. All zones are optimized simultaneously and so is the market flow between them. All evaluations are realized at the European scale. 2.2.2 System modelling and PPS/E PSS/E is a transmission system planning and analysis software developed by Siemens Power Technologies International (Siemens PTI). The Siemens PTI PSS/E software product is an integrated program providing power flow, short circuit and dynamic simulation. In this study PSS/E is applied to the European Network of Transmission System | | Fig. 3. Weather years 2010–2014 wholesale price and RES generation. | | Fig. 4. Peak Prices in Weather Years 2010 – 2014 [EUR/MWh]. | | Fig. 5. Pöyry BID3 Overview. Weather year Wholesale price Operators for Electricity (ENTSO-E) high voltage system data of the Central European synchronous area. The software simulates substations as nodes to which power lines, loads, generators, and auxiliary devices such as shunt reactors/capacitors are connected. For the load flow calculations performed in this study, power lines are modelled as impedances with loss-causing resistance and power-factor altering reactance. Generators are modelled by providing maximum and minimum real power deliverable as well as available range in terms of reactive power. The maximum real power is provided by BID3 and is a result of market modelling. Loads are modelled as constant active and reactive power based on the ENTSO-E Ten-Year Network Development Plan 2016 (TYNDP2016) dataset. Loads and generators connected below 220 kV voltage level are aggregated to loads and/or generators at the buses where they are connected to the high voltage grid. Flows to countries outside the synchronous areas, i.e. through DC lines, are set as fixed flows using hourly flow data from BID3. Renewable Generation 2010 +9.9 % -6.5 % 2011 -3.5 % +7.7 % 2012 +4.2 % +2.2 % 2013 -1.6 % -2.7 % 2014 +9.0 % -0.8 % | | Tab. 4. Deviation from average of the weather years 2010–2014. FEATURE | MAJOR TRENDS IN ENERGY POLICY AND NUCLEAR POWER 73 Feature | Major Trends in Energy Policy and Nuclear Power Contribution of Nuclear Power Plants to the Energy Transition in Germany ı Denis Janin, Eckart Lindwedel, Volker Raffel, Graham Weale, James Cox and Geir Bronmo
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