The (Rocky) Path to 80 percent Renewables STEP Seminar Series Princeton University April 14, 2014 Warren B.
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The (Rocky) Path to 80 percent Renewables STEP Seminar Series Princeton University April 14, 2014 Warren B. Powell Hugo P. Simao PENSA Laboratory Princeton University http://energysystems.princeton.edu Slide 1 Lecture outline 80 percent from wind and solar? The uncertainties of energy A spreadsheet model The PJM grid and planning process SMART-ISO – Modeling the PJM energy markets © 2010 Warren B. Powell Slide 2 Lecture outline 80 percent from wind and solar? The uncertainties of energy A spreadsheet model The PJM grid and planning process SMART-ISO – Modeling the PJM energy markets © 2010 Warren B. Powell Slide 3 Hydroelectric production Biomass production Wind in the U.S. 99.9 percent from renewables! Wind & Solar Battery Storage Fossil Backup 750 GWhr battery! 20 GW News flash – Oct 29, 2013 Dealing with uncertainty Available at energysystems.princeton.edu Lecture outline 80 percent from wind and solar? The uncertainties of energy A spreadsheet model The PJM grid and planning process SMART-ISO – Modeling the PJM energy markets © 2010 Warren B. Powell Slide 12 © 2013 Warren B. Powell Energy from wind Wind farms on the PJM system Energy from wind Wind power from all PJM wind farms 1 year Jan Feb March April May June July Aug Sept © 2010 Warren B. Powell Oct Nov Dec Slide 17 Energy from wind Wind from all PJM wind farms 30 days © 2010 Warren B. Powell Slide 18 Modeling wind Forecast vs. actual for a single wind farm Actual Forecasted Wind energy in PJM Forecast vs. actual for all wind farms in PJM Forecasting wind Rolling 24-hour forecast of PJM wind farms Solar energy Princeton solar array Solar energy Princeton solar array PSE&G solar farms Solar output over entire year (all farms) Sept Oct Nov Dec Jan Feb March April May June July Aug Solar from PSE&G solar farms Solar from a single solar farm Solar from PSE&G solar farms Within-day sample trajectories Solar from PSE&G solar farms Solar from all PSE&G solar farms Rainfall Foz do Iguaçu (Brazil) – 2011 through 2013 2011 2012 2013 Commodity prices The price of natural gas » Reflects global and local economies, competing global commodities (primarily oil), policies (e.g. toward CO2), and technology (e.g. fracking). $120 /mmBTU! $4 /mmBTU LMPs – Locational marginal prices Locational marginal prices on the grid $58.47/MW LMPs – Locational marginal prices Locational marginal prices on the grid $977/MW !!! LMPs – Locational marginal prices Locational marginal prices on the grid $328/MW ! Locational marginal prices on the grid $52/MWhr Uncertainty It is important to separate: » Predictable variability PJM load • Diurnal cycles • Large weather patterns • Major human events (Super bowl) Aggregate solar » Stochastic uncertainty • Temperature deviations from forecast • Late/early arrival of a storm • Generator failures • Wind shifts Lecture outline 80 percent from wind and solar? The uncertainties of energy A spreadsheet model The PJM grid and planning process SMART-ISO – Modeling the PJM energy markets © 2010 Warren B. Powell Slide 37 Wind energy in PJM Total PJM load plus actual wind (July) 53 wind farms Wind energy in PJM Total PJM load plus actual wind (July) Wind ~ 37 percent of total load 100GW 101,000 MWhr battery $50 billion!! Solar energy Solar from all PSE&G solar farms Solar energy Total PJM load plus factored solar (July) Solar ~ 15 percent of total load Combining wind and solar Mixture of wind and solar to meet July load 815,000 MWhr battery $989 billion!! Combining wind and solar 260,000 MWhr battery $130 billion!! Mixture of wind and solar to meet July load 100GW Solar energy The California “duck” Lecture outline 80 percent from wind and solar? The uncertainties of energy A spreadsheet model The PJM grid and planning process SMART-ISO – Modeling the PJM energy markets © 2010 Warren B. Powell Slide 45 © 2013 Warren B. Powell The timing of decisions Day-ahead planning (slow – predominantly steam) Near-term planning (fast – gas turbines) Real-time planning (economic dispatch) The timing of decisions The day-ahead unit commitment problem Noon Midnight Midnight Midnight Midnight Noon Noon Noon The timing of decisions The day-ahead unit commitment problem 12pm 1 2 3 12am Noon to midnight: Slow generators committed the day before Plan, but do not commit to fast generators The timing of decisions The day-ahead unit commitment problem Midnight to midnight: Plan and commit to slow generators Plan, but do not commit to fast generators The timing of decisions The day-ahead unit commitment problem Midnight to, say, 4am the next day Plan, but do not commit to any generator Solved only to minimize end-of-day truncation error. The timing of decisions Intermediate-term unit commitment problem 2:00 pm 1:00 pm 1:45 pm 1:15 pm 1:30 3:00 pm 2:15 pm 2:30 pm The timing of decisions Intermediate-term unit commitment problem 2:00 pm 1:00 pm 1:45 pm 1:15 pm 1:30 3:00 pm 2:15 pm 2:30 pm The timing of decisions Intermediate-term unit commitment problem 2 pm 4 pm 1:45pm – 2pm All generators committed the day before or the ½ hour before The timing of decisions Intermediate-term unit commitment problem 2:00pm – 2:30pm Plan and commit to fast generators whose notification+ startup times fall within this window The timing of decisions Intermediate-term unit commitment problem 2:30pm – 4:00pm Plan, but do not commit The timing of decisions Intermediate-term unit commitment problem t1 t3 2 Turbine 2 Turbine 3 Turbine 1 The timing of decisions Real-time economic dispatch problem 2pm 1pm 1:05 1:10 1:15 1:20 1:25 1:30 The timing of decisions Real-time economic dispatch problem 2pm 1pm 1:05 1:10 1:15 1:20 1:25 1:30 Slow generators committed thebefore day before SlowSlow generators Slow generators committed committed the day thebefore day generators committed thebefore day Fast generators committed ½ hour before Fast generators Fast generators committed committed the ½ hour before ½the hour before Fast generators committed the ½the hour before Optimize within andreserve spinning reserve mar Optimize within Optimize operational within operational andoperational spinning andreserve spinning margins reserve margins Optimize within operational and spinning margins Lecture outline 80 percent from wind and solar? The uncertainties of energy A spreadsheet model The PJM grid and planning process SMART-ISO – Modeling the PJM energy markets © 2010 Warren B. Powell Slide 60 Lecture outline SMART-ISO – Modeling the PJM energy markets Modeling and designing robust policies Calibration and LMPs Modeling offshore wind Mid-Atlantic Offshore Wind Integration Study © 2010 Warren B. Powell Slide 61 Designing a policy Dealing with uncertainty » We have to design policies to manage the different forms of uncertainty. » We do this by looking for robust policies, which are rules for making decisions. » We write our optimization problem in the form: T t min E C St , X ( St ) t 0 “policy” (rule for making a decision) Day-ahead, hour- “simulator” where ahead and real-time St 1 S M St , X ( St ),Wt 1 decisions Averaging over multiple samples Designing a policy The challenge » We need to design an implementable policy that is more robust to the uncertainty of rainfall, wind, solar, temperature, and market behavior. » Our approach has been to start with the process already in use by PJM, and make the simplest changes that produce a more robust policy. » We draw on our research on a wide range of applications where we have to make decisions under uncertainty. » We will do our best to make our policies look sophisticated and complicated, but don’t be fooled. We are building on industry-standard methods from the U.S. Designing a policy 1) Policy function approximations (PFAs) » Lookup tables, rules, parametric functions 2) Cost function approximation (CFAs) » X CFA ( St | ) arg min x X t C ( St , xt | ) t ( ) 3) Policies based on value function approximations (VFAs) » X tVFA ( St ) arg min x C ( St , xt ) Vt x Stx ( St , xt ) t 4) Lookahead policies » Deterministic lookahead: X tLA-D (St ) = arg minC(Stt , xtt ) + xtt , xt,t+1,..., xt,t+T T åg t '-t C(Stt ' , xtt ' ) t '=t+1 » Stochastic lookahead (e.g. stochastic trees) X tLA-S (St ) = arg minC(Stt , xtt ) + xtt , xt,t+1,..., xt,t+T T p(w ) å g å w ÎWt t '=t+1 t '-t C(Stt ' (w ), xtt ' (w )) Lookahead policies Lookahead policies peek into the future The lookahead model » Optimize over deterministic lookahead model . . . . t t 1 t2 t 3 The base model Lookahead policies Lookahead policies peek into the future The lookahead model » Optimize over deterministic lookahead model . . . . t t 1 t2 t 3 The base model Lookahead policies Lookahead policies peek into the future The lookahead model » Optimize over deterministic lookahead model . . . . t t 1 t2 t 3 The base model Lookahead policies Lookahead policies peek into the future The lookahead model » Optimize over deterministic lookahead model . . . . t t 1 t2 t 3 The base model Designing a policy 1) Policy function approximations (PFAs) » Lookup tables, rules, parametric functions 2) Cost function approximation (CFAs) » X CFA ( St | ) arg min x X t C ( St , xt | ) t ( ) 3) Policies based on value function approximations (VFAs) » X tVFA ( St ) arg min x C ( St , xt ) Vt x Stx ( St , xt ) t 4) Lookahead policies » Deterministic lookahead: X tLA-D (St ) = arg minC(Stt , xtt ) + xtt , xt,t+1,..., xt,t+T T åg t '-t C(Stt ' , xtt ' ) t '=t+1 » Stochastic lookahead (e.g. stochastic trees) X tLA-S (St ) = arg minC(Stt , xtt ) + xtt , xt,t+1,..., xt,t+T T p(w ) å g å w ÎWt t '=t+1 t '-t C(Stt ' (w ), xtt ' (w )) Popular with national labs pitching their super computers Lookahead policies Probabilistic lookahead » Here, we approximate the information model by using a Monte Carlo sample to create a scenario tree: » We can try to solve this as a single “deterministic” optimization problem. This is a direct lookahead policy. Lookahead policies We can then simulate this lookahead policy over time: The lookahead model . . . . t t 1 t2 t 3 The base model Lookahead policies We can then simulate this lookahead policy over time: The lookahead model . . . . t t 1 t2 t 3 The base model Lookahead policies We can then simulate this lookahead policy over time: The lookahead model . . . . t t 1 t2 t 3 The base model Lookahead policies We can then simulate this lookahead policy over time: The lookahead model . . . . t t 1 t2 t 3 The base model Designing a policy 1) Policy function approximations (PFAs) » Lookup tables, rules, parametric functions 2) Cost function approximation (CFAs) » X CFA ( St | ) arg min x X t C ( St , xt | ) t ( ) 3) Policies based on value function approximations (VFAs) » X tVFA ( St ) arg min x C ( St , xt ) Vt x Stx ( St , xt ) t 4) Lookahead policies » Deterministic lookahead: X tLA-D (St ) = arg minC(Stt , xtt ) + xtt , xt,t+1,..., xt,t+T T åg t '-t C(Stt ' , xtt ' ) t '=t+1 » Stochastic lookahead (e.g. stochastic trees) X tLA-S (St ) = arg minC(Stt , xtt ) + xtt , xt,t+1,..., xt,t+T T p(w ) å g å w ÎWt t '=t+1 t '-t C(Stt ' (w ), xtt ' (w )) SMART-ISO: Calibration Any dynamic model consists of two fundamental equations: » The decisions (determined by a policy) xt X ( St ) » The dynamics (determined by the physics of the problem) St 1 S M St , xt ,Wt 1 » We have initially focused on replicating the PJM policy xt X PJM ( St ) Once we calibrate our model, then we can start looking for a better policy. Designing a policy Some observations » These four classes of policies formalize what the ISOs are already doing… » … but it is likely that their policies will need to be retuned to handle higher levels of variability. » Below we report on our progress over the last four years developing a detailed model of PJM’s energy markets and power grid. » We will use this model to tune their policies to handle much higher levels of wind Lecture outline SMART-ISO – Modeling the PJM energy markets Modeling and designing robust policies Calibration and LMPs Modeling offshore wind Mid-Atlantic Offshore Wind Integration Study © 2010 Warren B. Powell Slide 78 SMART-ISO: Calibration Historical generation mix during 22-28 Jul 2010 Pumped hydro Comb. cycle+gas Steam Nuclear SMART-ISO: Calibration Simulated generation mix during 22-28 Jul 2010 Pumped hydro Comb. cycle+gas Steam Nuclear SMART-ISO: Calibration Real-time LMPs during 22-28 Jul 2010 Lecture outline SMART-ISO – Modeling the PJM energy markets Modeling and designing robust policies Calibration and LMPs Modeling offshore wind Mid-Atlantic Offshore Wind Integration Study © 2010 Warren B. Powell Slide 82 SMART-ISO: Offshore wind study Mid-Atlantic Offshore Wind Integration and Transmission Study (U. Delaware & partners, funded by DOE) 29 offshore sub-blocks in 5 build-out scenarios: » » » » » 1: 8 GW 2: 28 GW 3: 40 GW 4: 55 GW 5: 78 GW GW Modeling wind » Steadier than onshore? Where??? Modeling wind The power from wind: 1 P B Av3 2 v Wind speed (in m/sec) A Area of rotor blades in m3 Density of air ( 1.225kg/m3 ) B Power coefficient fraction of wind converted to mechanical energy .593 (the Betz limit) » The cubic relationship means small changes in speed translate to large changes in power. Onshore & offshore wind farms We were given access to data on the wind power generated by onshore wind farms within PJM Proposal: Use onshore data to calibrate a stochastic model of forecasting errors. Then use this model to create a simulated “actual” for offshore. Simulating onshore wind Actual (observed) time series (all farms in the Plains): Forecasted power from wind Actual Simulating onshore wind Histogram of the prediction error (observed/simulated – forecast) for all farms in the Plains. Observed Simulated Modeling wind Offshore wind – Buildout level 4 Modeling wind Offshore wind – Buildout level 4 Modeling wind Offshore wind – Buildout level 4 Lecture outline SMART-ISO – Modeling the PJM energy markets Modeling and designing robust policies Calibration and LMPs Modeling offshore wind Mid-Atlantic Offshore Wind Integration Study © 2010 Warren B. Powell Slide 94 SMART-ISO: Offshore wind study Additional grid capacity needed from ACPF (Jul10, w/ reserves): The grid limits the use of offshore wind to around 3 percent. All remaining analysis is done with an unconstrained grid. SMART-ISO: Offshore wind study Power shortages 140000 Simulated Power - 22-28 Jul 2010 - Buildout 4 - No reserves 120000 80000 60000 40000 Simulated Total Power Actual Total Demand 20000 0 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 1016 1051 1086 1121 1156 1191 1226 1261 1296 1331 1366 1401 1436 1471 1506 1541 1576 1611 1646 1681 1716 1751 1786 1821 1856 1891 1926 1961 1996 MW 100000 5-min Time Intervals Day Ahead Actual Hour-ahead SMART-ISO: Offshore wind study How do we get rid of these shortages? SMART-ISO: Offshore wind study Ramping Reserves (GW) 10 9 8 * 7 GW 6 5 4 3 2 1 0 Jan-10 Apr-10 Smallest reserve that would produce a run where all load is covered. Jul-10 Oct-10 * - Generators w/ any minimum operational capacity SMART-ISO: Offshore wind study Scheduling up- and down- ramping Need to schedule down-ramping to handle times when the wind unexpectedly rises. Need to schedule up-ramping to handle times when the wind unexpectedly drops. SMART-ISO: Offshore wind study 140000 SMART-ISO - Unconstrained Grid - 22-28 Jul 2010 Wind Buildout 4 - No ramping reserves 120000 80000 60000 Actual Demand (Exc) Simulated (Used) Wind Simulated Storage Power Simulated Fast Power Simulated Slow Power 40000 20000 0 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 1016 1051 1086 1121 1156 1191 1226 1261 1296 1331 1366 1401 1436 1471 1506 1541 1576 1611 1646 1681 1716 1751 1786 1821 1856 1891 1926 1961 1996 MW 100000 5-min Time Intervals SMART-ISO: Offshore wind study 140000 SMART-ISO - Unconstrained Grid - 22-28 Jul 2010 Wind WindBuildout Buildout44- -Ramping No ramping reserves reserves 9GW 120000 80000 60000 Actual Demand (Exc) Simulated (Used) Wind Simulated Storage Power Simulated Fast Power Simulated Slow Power 40000 20000 0 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 1016 1051 1086 1121 1156 1191 1226 1261 1296 1331 1366 1401 1436 1471 1506 1541 1576 1611 1646 1681 1716 1751 1786 1821 1856 1891 1926 1961 1996 MW 100000 5-min Time Intervals SMART-ISO: Offshore wind study SMART-ISO: Offshore wind study SMART-ISO: Offshore wind study SMART-ISO: Offshore wind study 22-28 Jul 2010 - Unconstrained Grid - Buildout 4 - Rsrv 9GW Avail Wind: 13.7%, Used Wind: 11.1% 60000 50000 Simulated (Used) Wind Simulated (Avail) Wind Available wind 30000 Used wind 20000 10000 0 1 36 71 106 141 176 211 246 281 316 351 386 421 456 491 526 561 596 631 666 701 736 771 806 841 876 911 946 981 1016 1051 1086 1121 1156 1191 1226 1261 1296 1331 1366 1401 1436 1471 1506 1541 1576 1611 1646 1681 1716 1751 1786 1821 1856 1891 1926 1961 1996 MW 40000 5-min Time Intervals SMART-ISO: Offshore wind study 40.0 Available energy from wind 35.0 Percent of total demand 30.0 25.0 20.0 15.0 10.0 5.0 0.0 Jan-10 Apr-10 Jul-10 Oct-10 SMART-ISO: Offshore wind study 90.00 Used wind as percent of available 80.00 No reserves With reserves 70.00 60.00 50.00 40.00 30.00 20.00 10.00 0.00 Jan-10 Apr-10 Jul-10 Oct-10 SMART-ISO: Offshore wind study Observations The integration of renewables will require moving through a series of plateaus: » Grid capacity - The current grid does not have the capacity to handle significant levels of off-shore wind. » Reserve capacity - Uncertainty in forecasts requires significant levels of reserves, and increased use of gas turbines. » Storage – Grid level battery storage can smooth both diurnal cycles as well as stochastic volatility. » Demand response – We can reduce the load on the network, but notification times are important. » New generation technologies – Fast ramping generators enable faster response. » New markets – More responsive markets are needed to take advantage of fast notification times. It does not help to reduce ramping times from 12 hours to 8 hours if we still plan generation a day in advance.