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Introduction to PPSGen, an eRisk Group model

Introduction to PPSGen, an eRisk Group model. December 2013. PPSGen: Overview model - three-step approach. CHP must-run Obligations. Hourly dispatch power plant Plant valuation Generation of price scenarios. Supply. Demand. Demand. MC. Fuel Prices. Three-step Approach:

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Introduction to PPSGen, an eRisk Group model

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  1. Introduction to PPSGen, an eRisk Group model December 2013

  2. PPSGen: Overview model - three-step approach • CHP must-run Obligations • Hourly dispatch power plant • Plant valuation • Generation of price scenarios Supply • Demand Demand MC • Fuel Prices • Three-step Approach: • Determination of cross border bid ladders for France and the UK • Assessment of marginal costs for each hour for the Region Germany & the Benelux countries being one ‘copper plate’ • Assessment of marginal costs for each hour for a specific country using cross border bid ladders • Carbon Prices • Renewable Energy Supply • Commissioning/ Decommissioning • Cross Border Capacity • Pump and Storage eRisk Group

  3. PPSGen: Power plant database Power plant database • Database of power plant is developed internally • Database is up to date and includes recent outlook for commissioning and decommissioning • Data include • Capacity • Thermal efficiency • O&M costs • Transport and handling cost • heat supply, if any • potential for co-firing biomass, if any • Database is based on • Elia, TenneT, RTE, DECC, and Deutsche Bundesnetzagentur • CITL • Company websites • Internal research “The model includes an up-to-date and extensive database of power plants in Europe” eRisk Group

  4. PPSGen: Sorting merit order Merit order is determined by including • Fuel prices (including seasonality) • Handling and transport costs, in case of coal • Transport costs of natural gas is optional • Carbon prices • VariableO&M costs (optional) • CHP and BFG plants are split into two plants to cover ‘must-run obligations’. Avoided gas costs for stand alone and revenues from ‘blue certificates’ or exemptions schemes are deducted from marginal costs of the plant Dealing with biomass potential • Co-firing of biomass is taken into account by including: • Lower thermal efficiency • Higher marginal costs due to prices wood pellets • Lower energy input and thus lower output, in case of voluntary scheme • Revenues from green certificates, if any “The impact of co-firing of biomass and CHP can be studied in a detailed manner with the model ” eRisk Group

  5. PPSGen: Determination of net demand (1) Net Hourly Demand • Gross demand data is derived from Entsoe • Demand data for the Netherlands is adjusted to include own consumption of industrial CHP • Demand data for Germany is adjusted to include demand from railways, own consumption of industrial CHP and PV • Adjustments are in line with findings of Entsoe • Net demand is determined by deducting supply of renewable energy per hour • Wind production is determined by applying actual hourly wind speeds data for 9 regions in case of onshore wind capacity and for 6 regions in case of offshore wind capacity • PV production is determined by taking into account actual hourly solar radiation and temperature data for 9 regions • Weather data is derived from KNMI, and DeutscherWetterdienst, http://meteo.infospace.ru,JRC, and Met Office • Data is fitted against actual plant data “Model uses net demandby subtracting renewable energy supply from gross demand ” eRisk Group

  6. PPSGen: Determination of net demand (2) Adjustment of net demand for ‘peak shaving’ • Pump and storage capacity is assumed to be dispatched to smoothen demand curve as prices are assumed to behave accordingly • Hydro plants near lakes are assumed to be dispatched when the net demand is high • It is assumed that cross border capacity is used in a similar manner as pump and storage given the power fleet in the adjacent countries, with the exception of Poland, Spain and Italy • For Poland, Spain and Italy import and export data are based on actual data from ENTSO-E “It is assumed that cross border capacity and pump and storage are mostlyutilised to smoothen demand” eRisk Group

  7. Hydro, storage, and Demand Side Management (DSM) • Hydro plants near lakes (France) • Hydro plants near lakes are assumed to be dispatched when the net demand is the highest (based on top percentile) • The usage is matched by the overall production as published by the grid operator • Hydro plants: pump and storage • Pump and storage capacity is assumed to be dispatched to smoothen the demand curves on a weekly and daily basis as prices are assumed to correlate with demand • Minimum and maximum demand levels are determined on a daily and weekly basis • Subsequently, the storage devices will load during these minimum levels and offload during the maximum levels • New storage devices • New storage devices are modeled in a similar way as pump and storage • The load and off-load capacity are assumed equal (in practice a wide range of alternatives exist) • Two types of demand side management types are distinguished • short cycle: consumers are willing to postpone demand for a number of hours. The model assumes that this type of demand side response is used to reduce the maximum annual demand. Subsequently, the reduced power demand spread over the next 4 hours • Long cycle: consumers are flexible in the power demand and are willing to move the power use from potential periods with high prices to low prices. Consequently, this type of demand side management is modeled similarly to pump and storage eRisk Group

  8. Cross borders within the Northwest Europe region • The cross border flows between UK, France, Germany, the Netherlands and Belgium/Luxembourg are determined endogenously in the model • For each country, the total cross border capacity is divided into small blocks • Through the multi step approach, the model determines a bid ladder for the total cross border capacity of each country • These bid ladders represent at what price these blocks are offered to the market; i.e. at what price each country is willing to import less/ export power • Herewith, the cross border capacity of these countries are taken into account in the merit order in a similar manner as the conventional power plants • These bid ladders are determined for each hour as the available capacity depends on net demand (obviously the higher the net demand the higher the bid ladder) eRisk Group

  9. Cross borders with the other adjacent countries • Contrary to the cross border flows within the region of France, Germany, UK and Benelux, the cross border flow with the other countries is not determined endogenously in the model • In case of Switzerland and Scandinavia the cross border capacity is used in to smoothen the demand curve, given the power fleet • For other countries import and export data are based on actual data from ENTSO-E eRisk Group

  10. Order of determing net demand • The hourly demand curve of 2010 as published by ENTSO-E is used as starting point • Adjustment of demand to include own consumption and PV as indicated by ENTSOE • Assessment of demand in target year by applying a growth rate to the reference year • Subtraction of the hourly renewable production curves (PV, wind, Hydro RoR, biomass and waste) • Change in demand due to cross border flow (note that this mainly applies to cross border flow with adjacent countries outside UK, France, Germany and Benelux region, as the cross border flow within the region is determined endogenously) • Inclusion of demand side management (short cycle) to reduce maximum load on an annual basis • Inclusion of hydro near lakes (in case of France) to reduce (subsequent) maximum load on an annual basis • Inclusion of pump and storage (including demand side management (long cycle) and other storage devices) to smoothen load on weekly an daily basis • Finally this net demand is met by the merit order of conventional power plants (including cross border capacity within the region) eRisk Group

  11. PPSGen: Model output • PPSGen is a flexible and transparent tool to generate • power price scenarios • Plant / portfolio valuation • Plant hourly dispatch • Analyses can be performed by changing the (key) variables • The above allows for the development of a set of scenarios in a timely manner • Consequently,it makes PPSGen an ideal toolidealto perform sensitivity analyses with respect to regulatory changes • Next to regulatory changes, PPSGen will also provide clear insight in the market consequences of the commissioning or decommissioning of power plants, the penetration of renewable energy and other market developments “PPSGenis a flexible and transparent tool which can be used for multiple analyses, including the impact of market changes and regulation on prices, portfolio value, and plant dispatch” eRisk Group

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