Introducing Electricity Dispatchability Features in TIMES modelling Framework

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Introducing Electricity Dispatchability Features in TIMES modelling Framework

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Introducing Electricity Dispatchability Features in TIMES modelling Framework

  1. 1. Dispatching and Unit Commitment Features in TIMES 69th ETSAP Workshop, UCC, 30-31 May 2016 Update on project´s status Evangelos Panos, PSI Antti Lehtilä, VTT
  2. 2. The Unit Commitment problem (UC), is the optimisation problem for power plant scheduling, subject to a number of technical-economic constraints: maximum and minimum generation power limits start-up and shutdown power trajectories and costs ramp-up and ramp-down limitations and costs restrictions on online/offline times to minimise thermal stress partial load efficiency losses fuel and other variable costs Economic dispatch only captures the «fuel and other variable costs» The objective of this project is to improve the dispatch of power generation plants in TIMES, by taking into account notions of the UC problem Main objective of the project Page 2
  3. 3. Current status of the project Page 3 Tasks % of completion T1. Mathematical specification of the unit commitment problem * minor revision possible T2. Code writing and testing in GAMS T3. Implementation of the extension in the TIMES modelling framework * under testing T4. Documentation and demo model illustration * illustrative models created Total 95% 95% 95% 50% 85%
  4. 4. Integration within the TIMES objective function the costs arising from the power plant scheduling are included to the rest of the costs in the objective function this leads to a single framework that optimises in “one-shot” long-term investment decisions and short-term operational decisions Timeslices definition can be flexible the user is free to decide the intra-annual resolution, from 8760 hours to only few typical operational hours per day the timeslices do not have to be equal in length and do not necessarily have to correspond to 1h intervals Flexible cycling of the power plant schedule (from 1 to several days) user defined according to user’s timeslice tree structure allows modelling of power plant with long operating times (e.g. nuclear) Main features of the UC design in TIMES (1) Page 4
  5. 5. A generation unit does not necessarily correspond to a discrete plant A unit can change stand-by condition from hot to warm and then to cold depending on its non-operational time after shutdown this identifies three different start-up types: hot, warm, cold Start-up (and synchronisation) phase of a unit: its duration depends on the chosen start-up type load is linearly increased up to the minimum stable operation level start-up costs can occur:  capacity related  based on the started capacity  fuel related  due to partial load efficiency losses Main features of the UC design in TIMES (2) Page 5
  6. 6. Dispatching phase: load is between the minimum stable operation level and the maximum available (started) capacity load changes are subject to ramping limitations partial load efficiency losses can occur if unit operates below a level (above which no losses are assumed to occur anymore) Shutdown (and desynchronisation) phase: the load is linearly decreased from the minimum stable operation level  … based on the duration of the phase Shutdown costs can occur:  capacity related  based on the started capacity  fuel related  due to partial load efficiency losses All UC-related costs are entered into TIMES objective function Main features of the UC design in TIMES (3) Page 6
  7. 7. Partial load efficiency losses are modelled through a linearly approx. loss in activity, which is added to increase the fuel consumption: var_flo * act_eff = var_act + var_actlos Main features of the UC design in TIMES (4) Page 7 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 0% 20% 40% 60% 80% 100% Efficiency% Load % Calculated efficiency % vs load 0.2 2.2 4.2 6.2 8.2 10.2 12.2 14.2 16.2 0% 20% 40% 60% 80% 100% AdditionalActivity Load % Calculated loss in activity vs load 1) At the start-up load the activity loss is calibrated to the start-up efficiency 2) Then the activity loss decreases linearly to the losses at the minimum stable operation level 3) The loss at the minimum stable operation level equals to var_act 4) Then the activity loss is linearly decreased to 0
  8. 8. New TIMES user-defined parameters Page 8 Parameter Explanation Unit G_CYCLE (tslv) Number of cycles in an average year (ANNUAL=1, WEEKLY=365/7, DAYNITE=365) Number of cycles in each timeslice level ACT_MINLD (r,v,p) Minimum stable operation level % of online capacity ACT_UPS (r,v,p,bd) Ramp up (bd=UP) and ramp down (bd=LO) rates % of online capacity ACT_MAXNON (r,v,p,upt) Maximum non-operational time before start-up type (upt=HOT, WARM, COLD) Hours when DAYNITE Days when WEEKLY ACT_SDTIME (r,v,p,upt,bd) Duration of start-up and shut-down phases Hours ACT_CSTRAMP (r,v,p,bd,cur) Ramp up (bd=UP) and ramp down (bd=LO) costs Cost per unit of power output ACT_CSTSD (r,v,p,upt,bd,cur) Start up (bd=UP) costs per start-up type upt and shut down (bd=LO) costs Cost per unit of started-up capacity ACT_LOSPL (r,v,p,bd) Partial load efficiency loss at the dispatching phase % of increase in specific fuel consumption ACT_LOSSD (r,v,p,upt,bd) Partial load efficiency loss at the start- up/shut-down phases % of increase in specific fuel consumption
  9. 9. The standard UC problem is formulated as MIP, with binary variables representing offline and online decisions but with endogenous capacity this leads to non-linear equations: var_power <= var_cap * u , u=0,1 To break the non-linearity we may apply big-M formulation: var_power <= 0 + M * u var_power <= var_cap + M * (1 – u) … but it is difficult to chose appropriate M that is large enough and at the same time does not create numerical instability Therefore, we use the indicator constraint feature in CPLEX: u=0  var_power = 0 u=1  var_power <= var_cap Design and implementation challenges Page 9 The link between indicator and equation is established at the CPLEX.opt file
  10. 10. Binary variables with indicator constraints (UCO) Design based on a full-blown binary logic between status variables Indicator constraints used for both:  bounding the continuous variables  adding the costs into the objective function Continuous variables with indicator constraints (DUC) approach based on an LP formulation indicator constraints used for discretising the on-line capacity Motivation for the two different approaches validation of the design based on full binary logic together they also serve as an algorithmic benchmark dealing with performance issues Amount of binary variables and tightness of the LP relaxation appear critical for MIP performance Two draft implementations Page 10
  11. 11. The UCO implementation Page 11 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hours MW Dispatchable power Start-up power Shut-down power Minimum stable level t 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 x 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 u 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 x_indic 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 u_indic 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 d_indic 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0
  12. 12. The DUC implementation Page 12 0 20 40 60 80 100 120 140 160 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hour MW t 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 On-line 0 0 160 160 160 160 160 160 160 160 160 160 160 160 160 160 160 0 0 0 0 0 0 0 Slanted 0 20 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 27 13 0 0 0 0 On_indic 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0
  13. 13. Characteristic UCO DUC New binary variables ~(r,v,t,p,s) 11 1 New cont. variables ~(r,v,t,p,s) 1 7 New equations ~(r,v,t,p,s) 31 17 Indicator constraints ~(r,v,t,p,s) 18 2 Loads in the start-up / shut-down phases calibrated at: First startup, last shut-down hour Last start-up, first shut- down hour Mixing of simultaneous start-up types prevented Yes No Accuracy of SUD loads with general timeslice lengths Good Approximate Partial load efficiencies in start-up phase by start-up type Yes No (maybe possible) Differences between UCO and DUC Page 13
  14. 14. 0 400 800 1200 1600 2000 2400 H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 MW DUC (VAR_OBJ=18987987) ECOAL To demonstrate the features in TIMES a controlled example is presented In this example the ECOAL unit is forced to shutdown in H01 and start-up in H09 DUC and UCO equivalence & demonstration (1) Page 14 NCAP_ PASTI ACT_ MNLD Minimum stable operation load ACT_ UPS ACT_SDTIME (UP, HOT) ACT_SDTIME (LO, HOT) ECOAL 4000 40% 1600 10% 3 4 0 400 800 1200 1600 2000 2400 H01 H02 H03 H04 H05 H06 H07 H08 H09 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 MW UCO (VAR_OBJ=18987974) Note: in the graphs the other units have been ommitted ECOAL
  15. 15. Continuation of the previous example: part load efficiency losses DUC and UCO equivalence & demonstration (2) Page 15 ACT_ LOSSD (UP, HOT) ACT_ LOSSD (LO, HOT) ACT_LOSPL (UP, FX) ACT_ MINLD ACT_ EFF ECOAL 0.3 0.4 (0.68, 0.2) 0.4 33% The start-up load is defined as: 𝑀𝑖𝑛𝑆𝑡𝑎𝑏𝑙𝑒𝑂𝑝 𝑆𝑡𝑎𝑟𝑡𝑢𝑝𝑇𝑖𝑚𝑒 The shutdown load is defined as: 𝑀𝑖𝑛𝑆𝑡𝑎𝑏𝑙𝑒𝑂𝑝 𝑆ℎ𝑢𝑡𝑑𝑜𝑤𝑛𝑇𝑖𝑚𝑒 ECOAL (same solution in UCO and DUC) LOAD (%) EFF (%) H08 0.0% 0.0% H09 13.3% (=0.4/3) 25.4% = (0.33/(1+0.3) H10 26.7% 26.9% H11 40.0% 27.5%= 0.33/(1+0.2) H12 50.0% 29.4% H13 – H19 50.0% 29.4% H20 50.0% 29.4% H21 40.0% 27.5% H22 30.0% 27.0% H23 20.0% 26.1% H24 10.0% (=0.4/4) 23.6%= 0.33/(1+0.4)
  16. 16. Demonstration of different cycles (refers to ECOAL example of slide 14) DUC and UCO equivalence and demonstration (3) Page 16 ANNUAL DAYNITE H01…H24 G_CYCLE(‘DAYNITE’)=365 ANNUAL DAYNITE H01…H48 G_CYCLE(‘DAYNITE’)=365/2 0 500 1000 1500 2000 2500 H01 H03 H05 H07 H09 H11 H13 H15 H17 H19 H21 H23 0 500 1000 1500 2000 2500 3000 3500 4000 H01 H05 H09 H13 H17 H21 H25 H29 H33 H37 H41 H45
  17. 17. ECOAL 0 400 800 1200 1600 2000 H01 H02 H03 H04 H05 H06 MW UCO ECOAL 0 400 800 1200 1600 2000 H01 H02 H03 H04 H05 H06 MW DUC Accuracy of start-up/shutdown loads with general timeslice lengths Controlled example to demonstrate the difference: ECOAL is forced to start in H01 Differences between UCO and DUC (1) Page 17 GR_YRFR (h) H01 0.33 8 H02 0.08 2 H03 0.08 2 H04 0.08 2 H05 0.17 4 H06 0.25 6 VAR_CAP ACT_ MINLD Minimum stable operation load ACT_SDTIME (HOT,UP) Start- up/shutdown loads ECOAL 4000 40% 1600 3 = 1600/3 =533 * In DUC a start-up timeslice is ignored if less than half of it is included in the start-up phase
  18. 18. Differences between UCO and DUC (2) Page 18 Partial load efficiencies in start-up phase by start-up type Controlled example to demonstrate the difference: EGTCC is forced to a shutdown in H01 and then to a warm start-up in H11 ACT_ LOSSD (UP, HOT) ACT_ LOSSD (LO, HOT) ACT_ LOSSD (UP, WARM) ACT_ LOSPL (UP, FX) ACT_ MINL D ACT _ EFF ACT_ SDTIME (UP, HOT) ACT_ SDTIME (UP, WARM) ACT_ SDTIME (LO, HOT) EGTCC 0.2 0.3 0.4 (0.68, 0.15) 0.2 60% 2 3 3 LOAD EFF UCO EFF DUC VAR_ FLO UCO VAR_ FLO DUC DIFF IN VAR_ FLO H10 0.0% 0.0% 0.0% H11 6.7% 42.9% 48.0% 11.36 10.14 12% H12 13.3% 49.5% 51.1% 19.67 19.06 3% H13 20.0% 52.2% 52.2% 27.98 27.98 0% H14 33.2% 56.2% 56.2% 43.12 43.12 0% … Currently in DUC the start-up efficiency is not differentiated according to the start-up type: here the hot start-up efficiency is applied for a warm start-up duration
  19. 19. Example of run times with 288 timeslices of equal length , 6 power generation units , 1 storage unit , one period UCO needed 56 min, while DUC 17 min (compared to 5 min in normal LP case) The difference in objective function is practically zero DUC clearly outperforms UCO in run times Page 19 DUC: 17 minUCO: 56 min
  20. 20. 288 timeslices divided into 4 seasons, 3 typical days in a season, 24 operating hours 6 power generation units and 1 generic electricity storage device all processes operate at the DAYNITE level for thermal generation units only annual availability constraint (NCAP_AFA) no curtailment of renewables (NCAP_AF~FX at the DAYNITE level) hydro has seasonal (NCAP_AFS~LO, NCAP_AFS~UP) and annual (NCAP_AFA) availability constraints Two runs performed: UC_OFF: standard TIMES run without the UC implemented features UC_ON: TIMES run with the UC implemented features enabled An example to demonstrate insights gained (1) Page 20
  21. 21. An example to demonstrate insights gained (2) Page 21 The UC_ON case gives more realistic dispatch in TIMES The UC_ON case captures the value of flexibility by investing in storage options 0 1000 2000 3000 4000 5000 MW EGTOC ESOL EWND EHYD EGTTC ECOAL 0 1000 2000 3000 4000 5000 MW EGTOC ESOL EWND EHYD EGTTC ECOAL -400 -200 0 200 400 600 800 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139 145 151 157 163 169 175 181 187 193 199 205 211 217 223 229 235 241 247 253 259 265 271 277 283 MW Timeslices
  22. 22. The UC features make TIMES a unique tool that combines long-term and short- term decisions in a single and consistent framework The UCO and DUC implementations can have their own merits, but DUC seems to be more appropriate for TIMES models key factors influencing the performance are the tightness of the MIP and the optimality gap settings Some improvements in the design and implementation could be foreseen in the next 1.5 months, depending on outcomes from remaining testing The new features will be also available in full LP mode, should anyone interested in using the operational constraints in such a rough fashion: ramping constraints, minimum online/offline times, partial load efficiencies Special thanks to Antti for improving the UCO design & implementing DUC Conclusions Page 22
  23. 23. Thank you for your attention! Questions and suggestions are welcomed ! Page 23

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