OPTEC Miniworkshop on "Optimal Control for Solar Thermal Power Plants at K.U. Leuven Revenue-optimal Scheduling of Solar Thermal Power Plants with integrated Thermal Storage Michael Wittmann, DLR
Overview Motivation Current situation Electricity Market in Spain Storage Operation Optimization System Boundaries Constraints Results Forecast horizon Economic Potentials Slide 2
Motivation Slide 3
Motivation Current Operation Strategies Actual Operation Strategies are based on a flat electricity price structure known from feed-in tariffs efficiency optimization in order to increase electricity production and thereby revenue (Reference) no electricity price prediction is considered no forecast based operation strategy Aim of the work Elaboration of a methodology to optimize a forecast based plant operation strategy of STPP with integrated storage technology Advantages / Potentials G Possibility to supply dispatchable solar energy Raise in revenue due to market participation Slide 4
Motivation 7 Electricity market in Spain6 Premium model a lower base tariff is paid in comparison to the feed-in tariff (FT) the plant operator is allowed to sell its electricity on the market operator acts as normal market 1 agent and looses the feed-in right Electricity Price [ct/kwh] 9 8 5 4 3 2 Electricity Price in 1st half of 2007 0 0 500 1000 1500 2000 2500 3000 3500 4000 Hour of Year [h] price Day-Ahead-Prices Min = 0.500 ct/kwh Max = 8.479 ct/kwh Mean = 3.616 ct/kwh Gap = 1.537 ct/kwh current legal foundation RD 661/2007 feed-in tariff 26.9375 ct/kwh premium 25.4000 ct/kwh FT PR Slide 5
Storage Operation Optimization Slide 6
Storage Operation Optimization Plant scheme target function J DNI G Pel s control function Q Slide 7
Storage Operation Optimization Optimization boundaries high velocity in DNI changes non-deterministic, i.e. forecasted values with certain confidence intervals 2002 Slide 8
300 Q sol [MW] 200 100 1000 900 Q PB,max 0 0 6 12 18 24 1100 Zeit t [h] 0 Q d Q dt Q cap Q & sol Wärmemenge Tank load Q Q [MWh] 800 700 600 500 400 300 200 100 0 0 6 12 18 24 Zeit t [h] Time [h] & d Qmin Q, dt where Q& min, i = Q& Q & defoc = 0 Q 0 = 0 sol, i Q& PB,max Slide 9
Storage Operation Optimization Optimization problem with J[ x] min! x dq0 Q i Qi Qi 1 cap dt Q & d t Q Q& min d t Q & defoc = 0 sol follows where Q & J[x] x = min, i t E = t 0 P el s dt ( Q Q..., ) T & 1, 2, LC : Ax tb & = Qsol, i Qdefoc, i Q n Q & PB, max Slide 10
Storage Operation Optimization Model Predictive Control Approach time op op schedule op schedule op schedule op schedule schedule 24h time forecast horizon Slide 11
Results Slide 12
Results Assumptions plant model oriented on Andasol-1 plant real meassured data set for 2002 (normal) and 2005 (extraordinary year) power block with part-load behavior solar field characteristic of LS-2 collectors Limitations: steady-state components energetic and exergetic ideal storage ideal forecasts assumed Slide 13
Results Forecast horizon jump in stock exchange revenue from 1 to 2 days of schedule basis <= weather changes are considered early saturation starting at 2 days of forecast horizon <= limitation of storage size with forecast horizon also uncertainty increases => horizon of two days most promising Slide 14
Results Optimization potentials [M ] 63 70 61 60 59 50 57 55 40 53 30 51 20 49 47 10 450 2002 2005 0,6 0,4 8,1 0,7 0,3 4,3 T/R P/R P/O1 P/O2 T/R P/R P/O1 P/O2 Premium/Fixed Tariff Stock Exchange Axis-Legend: T P R O1 O2 tariff option premium option reference operation optimized operation based on 1 day optimized operation based on 2 days Slide 15
Thank you for your attention. OPTEC Miniworkshop on "Optimal Control for Solar Thermal Power Plants at K.U. Leuven Revenue-optimal Scheduling of Solar Thermal Power Plants with integrated Thermal Storage Michael Wittmann, DLR
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Strommarkt OMEL Slide 18