Design Wilmar Planning tool

Transcription

1 Overview of presentation Design Wilmar Planning tool Peter Meibom PhD course: Energy System Analysis of Large-scale Wind Power Integration, Ålborg University 4 November Risø s mission is to promote environmentally responsible technological development that creates value in the areas of energy, industrial technology and bioproduction through research, innovation and consultancy. Overview of Wilmar project. The main idea of the Planning tool. Design of the Planning tool. Overview. Joint Market model. Generation of scenario trees for wind power production 4. Calculation secondary reserve need. Data handling WILMAR WP struktur 9 partners: Research institutions: DTU (DK), IER (D), KTH (S), Risø (DK), SINTEF (N), VTT(F), TSO: Elkraft (DK) Power producer: Elsam (DK) Power pool: Nord Pool Consulting (N), Resources: approximately man years Funding: Partly funded through EUs fifth framework programme,.4 MEuro from EU Duration years, finalised last month WP: Analysis of fluctuations and predictability WP: System stability analysis WP8: End-user testing of planning tool WP: Project management WP: Description of the electricity system in WP6: Development of a strategic planning tool WP9: Recommendations WP4: Analysis of emission-trading and green markets WP7: Distribution of the integration costs WP: Dissemination Main idea behind the Planning tool Improve decision making by using information contained in wind power production forecasts Information: Expected wind power production, but also precision of forecast, i.e. the distribution of the wind power production forecast errors Decisions before wind power is known: Trade on day-ahead market Decision after wind power is known (recourse actions): Activation of regulating power Main idea behind the Planning tool How: Build system-wide stochastic optimisation model with the wind power production as a stochastic input parameter Covering both day-ahead and intraday (regulating power) market Consequence: Model makes optimal unit dispatch on these markets that are robust towards wind power production forecast errors

2 Why is it relevant? Planning tool enables analysis of: Power prices (Day-Ahead and intraday) Operation patterns Reserve power need Feasibility of integration measures Value of wind power production (avoided costs) As a function of: Installed wind power Precision of wind power forecasting tools Market design Power system configuration Framework of Planning Tool Large-scale integration of wind power in a large liberalised electricity system Marginal costs determine unit dispatch, i.e. market power not analysed Market structure: Day-ahead market (Elspot at Nord Pool) Intraday market (Elbas at Nord Pool + Regulating power market run by Nordic TSOs) Market for primary (spinning) reserves Market for secondary (minute) reserves Heat markets Overview Planning tool Overview of the Planning Tool Going from deterministic to stochastic approach Design of Joint Market model Clarify decision structure: Time structure for new information arrival & decisions Number of stages and hours in each stage Introduce scenario tree: Equations node and time dependant Partitioning of decision variables: Rolling Planning Period : Day- ahead market cleared Rolling Planning Period P DAYAHEAD + INTRADAY INTRADAY i, s, t = Pi, t + Pi, s, t Pi, s, t Introduce rolling planning 8

3 Design Joint Market model Objective function F= Fuel costs + Variable O&M costs + Start-up costs Value at the end of optimisation period of heat and elec storage & hydro reservoir + Decrease in consumer surplus Increase in consumer surplus + CO & SO Taxes + Taxes on fuels used for heat production Support for renewable elec prod + Infeasibility penalties Restrictions Elec balance on day-ahead market Elec balance on intraday market Heat balance on each heat market Balance on primary reserve market Balance on secondary reserve market Production below capacity online Transmission restrictions Balance: heat and elect storage and hydropower reservoirs Storage restrictions (max load, max unload,..) Restrictions Restrictions Linear approximation of startup costs, partload efficiency, startup times and minimum load (C. Weber): Introduce additional real variable Capacity online Startup costs proportional to capacity put online in time step t Efficiency = Max_Eff*Elec_Prod + PartLoad_Eff_Factor*Cap_Online Restrictions Start-up times:. Decision about bringing capacity online has to be done before observing wind power production scenario Capacity online constant over the first LEADTIME hours of the wind power production scenarios. Capacity online in planning loop n in the first LEADTIME hours equal to capacity online found in planning loop n- in the corresponding hours Dispatch of unit group Available capacity Capacity online Realised production (Prod dayahead + Up regulation Down regulation) Minimum production (= Minimum load factor * Capacity online) Capacity reserved as primary positive reserve Capacity reserved as secondary positive reserve

4 Scenario Tree tool Data flow within the Scenario Tree Tool Task of the Scenario Tree Tool: Generation of n (currently n = ) wind power forecast scenarios based on measured wind speed and wind power data for the Planning Tool and further for the Stepwise Powerflow Model. Windspeed Forecast Error Module Scenario Reduction Module Reduced WP Scenarios (S,T,τ,R) Include-Files for Joint Market Model Scenario Tree Tool consists of the following models: - Wind speed forecast error model Distribution Forecast Error (S,t,τ,M) Wind Power Scenarios (S,T,τ,R) - Aggregated power curve model - Scenario reduction model Allocation of forecast error scenarios to windspeed data Wind Speed Scenarios (S,T,τ,M) Aggregated Power Curve All models are implemented and combined in MatLab. Needed data are stored in the Scenario Tree Tool Input Database (MS Access). Windspeed Data (t,m) Meteorological Input Database Reverse Aggregated Power Curve Real Wind Power Data (t,m) Scenario Tree Tool t:time T: Infotime (bid time) τ: Forecast Length R: Region M: Measurement station W: Week C: Case S: Non-reduced Wind Scenarios () S : Reduced Wind Scenarios Wind speed forecast error model Aggregated power curve model Based on work of Lennart Söder (KTH) and Rüdiger Barth (IER) : Based on wind speed data and historical forecast errors Simulation of wind speed forecast error using a multidimensional ARMA time series Including the autocorrelation of the wind speed forecast errors over the forecast length Including the correlations of the wind speed forecast errors between individual wind speed measurement stations for the individual forecast hours One sampling for determination of the average wind speed forecast error. One thousand samplings to describe the distribution of the wind speed forecast error. Scenario reduction model Wind speed forecast error model creates scenarios of wind speed forecast errors. Reduction of resulting wind power forecast scenarios to scenarios:. Scenario reduction model calculates the distances of the individual scenarios using as distance function the sum of squares.. similar scenarios are represented by one scenario.. While bundling the scenarios the probabilities of the individual remaining scenarios are calculated. 4. Reduced scenarios have to show the same variance as the original scenarios.. Creation of the scenario tree. d Output: Scenario tree for the Joint Market Model Scenario tree structure is predefined for usage within the Joint Market Model 4 - Number of branches and stages - Predecessors of individual nodes Results of the Scenario Tree Tool delivered to the Joint Market Model: - Wind power forecast scenarios with predefined node structure and consistent with wind forecasts - Probabilities for reaching each node 4

5 Interpretation of information in tree Calculation secondary reserve demand (). Expected amount of wind power sold on the dayahead market is based on the average (considering the individual probabilities) of the wind power values of the nodes 4 (stage of the scenario tree).. Realised wind power value of the successive time steps is described by the node (stage ) of the successive scenario trees.. Amount of needed up or down regulation is determined by the difference between. and Nordel criteria for minimum secondary reserve in each country based on N- criteria (outage of largest unit or transmission line) Wind power production forecast errors also consume secondary reserve New calculation of minimum secondary reserve taking both N- criteria and largest wind power forecast error into account Distribution of Outages and distribution of wind power forecast errors seen as two independent stochastic distributions Calculation secondary reserve demand () Percentiles in the two distributions can be added using (A +B ) ½ N- criteria representing some percentile in the outage distribution that the TSOs has agreed upon as expressing a reasonable level of system security Largest forecast error in unreduced scenarios (i.e. expected wind power production E(r,t) minus the lowest realised wind power production) used to represent wind power production forecast error distribution Data handling Using Access databases to handle both input and output data Combined with VBA code for automatic generation of input data and automatic inclusion of output data What have we learned Hard to treat both stochastic wind, CHP and dispatch of hydropower in one tool Complications in going from deterministic to stochastic: Generation of stochastic input parameter Rolling planning Calculation time Interpretation Data collection a large challenge Use of databases in handling of input and output data works very nice What have we learned Advantages: Endogenous treatment of wind power forecasts Inclusion of regulating power and regulating power market Thorough understanding of decision structure

6 Overview of analysed case scenarios () Results Wilmar Planning tool Peter Meibom PhD course: Energy System Analysis of Large-scale Wind Power Integration, Ålborg University 4 November Consideration of Germany and Scandinavian countries. Considered time period from Time-series are based on the year. Comparison of three cases: - Year configuration with base wind (_Base) - Year configuration with high wind development (_%) - Year configuration with very high wind development (_%) Wind power producers base their bid to the day-ahead market based on the expected wind power production. But the model allows the reduction of these bids. Overview of analysed case scenarios () _Base: For all countries, forecasted wind power capacities for are considered. _ %: For Denmark and Germany: Forecasted wind power capacities for (equal to cover ca. % and ca. 9 % of the annual electricity demand, respectively). For Finland, Norway and Sweden: Wind power capacities equal to cover % of the annual electricity demand. _ %: For Denmark and Germany: Forecasted wind power capacities for (equal to cover ca. % and ca. 9 % of the annual electricity demand, respectively). For Finland, Norway and Sweden: Wind power capacities equal to cover % of the annual electricity demand. Windpower capacity Overview of analysed case scenarios () Resulting wind power capacities for the different case scenarios: 8 _Base 6 _% _% DK_E DK_W FI_R NO_M NO_N NO_S SE_M SE_N SE_S Overview of analysed case scenarios (4) Time period: January and February : Case Name _Base _% _% Total Prod [TWh] Wind Prod. [TWh] Share Wind of Total [%] Overview of analysed case scenarios Assumptions () Annual electricity demand (in [TWh]): Year Growth Germany % Denmark % Finland % Norway.. 8. % Positive primary and secondary reserve demand (in ): Country Germany Denmark Finland Norway Sweden Positive primary reserve demand Sweden % Positive secondary reserve demand 6

7 Overview of analysed case scenarios Assumptions () Capacity development - Available capacity versus demand Overview of analysed case scenarios Assumptions () Fuel prices taken from the medium price scenario: 4 _Base Peak demand + Reserves WIND STRAW PEAT WOOD MUNI_WASTE WOOD_WASTE LIGHTOIL ELECTRIC FUELOIL LIGNITE NAT_GAS NUCLEAR COAL/Reserves WATER/Demand Fuel Nuclear Nat_Gas Coal Lignite Fueloil Lightoil Orimulsion Price Fuel [Euro/GJ].7 Shale 6.6 Peat. Muni_Waste. Straw 6.6 Wood 7.9 Wood (Waste). Price [Euro/GJ] CO emission allowance price for the medium price scenario: 7 Euro/MWh Value of wind power production () Value of wind power production () The integration of wind power leads to a change of the total system operation costs, consisting of: Fuel costs Operation and maintenance costs Start-up costs Transmission costs Change in CO emission allowance prices Use of taxes and tariffs The value of changed hydro reservoir levels The value of wind power production can be evaluated by the comparison of the use of different system configurations to cover the given demand. Change of the total system operation costs of the case _% and _% relatively to the case _Base during the time period.. 8..: [Mio EURO] _Base Total system operation costs Change of value of the hydro reservoir Value of wind power production () Price impacts of wind power production () Comparison of the total system operation costs with the wind power production: Case Name _Base _% _% Total Costs [Mio. Euro] Windpower Production [TWh] Change Costs [Mio. Euro] Avoided costs per MWh extra wind [Euro/MWh] 9..4 Average intraday prices for the time period.. 8..: Intraday _% 4 Intraday _% EURO/MWh 4 Intraday _Base DK_E DK_W FI_R NO_M NO_N NO_S SE_M SE_N SE_S 7

8 Price impacts of wind power production () Day-ahead prices in northern Germany during the time period for the cases _Base and _%: Price impacts of wind power production () Intraday prices in northern Germany during the time period for the cases _Base and _%: Euro/MWh Euro/MWh _% _Base _% _Base Technical impacts of wind power production () Capacity online for the time period and _Base: 8 ELECTRIC 6 WOOD_WASTE 4 WOOD STRAW Technical impacts of wind power production () Capacity online for the time period and _%: 8 ELECTRIC 6 WOOD_WASTE 4 WOOD STRAW PEAT LIGHTOIL FUELOIL NAT_GAS MUNI_WASTE PEAT LIGHTOIL FUELOIL NAT_GAS MUNI_WASTE COAL LIGNITE NUCLEAR WATER COAL LIGNITE NUCLEAR WATER Technical impacts of wind power production () Technical impacts of wind power production (4) Electricity export from SE_S to SE_M during the time period for the cases _Base and _%: 4 Average demand for secondary reserves (including the N- criterion and wind power fluctuations) for the time period.. 8..: _Base _% _% _% _Base DK_E DK_W FI_R NO_M NO_N NO_S SE_M SE_N SE_S 8

9 Technical impacts of wind power production () Amount of secondary reserve activated during the time period for the cases _Base and _%: _% _Base Earnings of wind power producers and penalties paid due to forecast errors () Average Dayahead price [Euro/MWh] Down regulation Average Intraday price [Euro/MWh] Share of production sold on intraday [%] _base _ _ Difference prices [%] Up regulation Average Dayahead Average Share of forecasted price Intraday price production bought on Difference [Euro/MWh] [Euro/MWh] intraday [%] prices [%] _base _ _ Earnings of wind power producers and penalties paid due to forecast errors () Production Production sold on dayahead sold on intra- market day market Revenue wind power producers [MEuro] Production bought on intraday Total market revenue Revenue if all production was sold to intraday prices Conclusions: Average prices paid to wind power producers decrease due to: - More wind power production decrease prices. - Imbalance penalties increase. Only up regulation causes imbalance penalties (this is caused by allowing strategic bidding of wind power producers) Difference [%] _base _ _ Wind power integration measures Transmission and storages Impact of extended transmission and storage capacities on electricity prices in Germany: Background of the case studies (will be published in IJGEI): Forecasts for indicate about GW of wind power capacity in Germany. These wind power extensions are mainly planned in the North of Germany as offshore wind power in the North and Baltic Sea ( and ). By contrast the main consumption areas are in the midland. Bottlenecks in the transmission network and therefore system stability problems and price deviations between the individual regions may occur. Analysis of: Utilisation of the transmission grid, price deviations and total system costs of an exemplarily week in with the existing transmission grid, with an extended grid and with extension of storage devices (CAES) in within the German system. Results for the existing transmission grid () Results for the existing transmission grid () Utilisation of the transmission grid between and the other regions: Transmission capacity [MW Resulting day-ahead prices: Day-ahead elec. price [ /MWh Hour -> -> Hour 9

10 Results for the extended transmission grid Resulting day-ahead prices for the extended transmission grid between and : Day-ahead elec. price [ /MWh] Hour Results for extended storage capacity Resulting day-ahead prices for the extended storage capacity in : Day-ahead Elec. Prices [ /MWh Hour Comparison of total system costs of the analysed week System operating costs [Mio. ] Analysed case Hour Without transmission or storage extension With transmission extension With storage extension Without extension Transmission extension Storage extension Total system operation costs [Mio. ] Value of electrical heat boilers and heat pumps for wind power integration Recent study with the Planning tool to be presented at EWEC 6 Introducing either heat boilers or heat pumps in three heat areas: Odense (DK_W), Copenhagen (DK_E) and Helsinki (FI) Analysing three cases during February: _% as reference case _% with heat boilers _% with heat pumps In each heat area: Electrical heat boilers and heat pumps have same heat production capacity. This heat production capacity is equal to half of the heat production capacity of CHP plants in the heat area. Electricity consumption capacity of heat measures: heat pumps: 76 MW, electrical heat boilers: MW Value of electrical heat boilers and heat pumps for wind power integration Resulting intraday prices Value of electrical heat boilers and heat pumps for wind power integration Resulting penalty payments Duration curve of intraday prices: Eastern Denmark Average Penalty Up Regulation Average Penalty Down Regulation Euro/MWh Euro/MWh DK_E DK_W FI Base Heat Pump ElecBoiler Euro/MWh DK_E DK_W FI Base Heat Pump ElecBoiler Base Elec Boiler Heat Pump

11 Value of electrical heat boilers and heat pumps for wind power integration Conclusions of the study: Heat measures replace production on heat boilers using fuel oil and CHP plants using different fuels. Heat pumps are used more than electrical heat boilers. Heat measures are beneficial for wind power producers in that: Heat measures use electricity to produce heat when power prices are low thereby increasing low power prices. Heat measures provide regulating power thereby decreasing the penalties connected to wind power production forecast errors. Value of electrical heat boilers and heat pumps for wind power integration Conclusions of the study: The revenue of wind power producers is increased from 8. MEuro in the base case to 88.7 MEuro (.%) in the case of electrical boilers and to 9.8 MEuro (.6%) in the case of heat pumps. Heat measures decrease the operational costs of the power system. The reduction in operational costs is probably enough to cover the annualised investment costs of heat pumps in DK_E, i.e. this measure increase social welfare, but extension of analysis to a full year is needed. Dissemination activities Public website: Planning tool available on website Half-yearly newsletter Conference presentations: EWEC 4, IAEE 4 &, Articles Recommendations The future More testing and calibration of models Analysis of the integration costs of wind power and the performance of integration measures Documentation and dissemination Public models put on the Wilmar homepage: New EU project: SUPWIND New EFP project with Bent Sørensen from RUC: Comparison of wind power integration measures: hydrogen production and international power trade