An Op&miza&on and Monte Carlo Planning Approach for High Penetra&ons of Intermi;ent Renewables

Transcription

1 An Op&miza&on and Monte Caro Panning Approach for High Penetra&ons of Intermi;ent Renewabes Eaine K. Hart and Mark Z. Jacobson INFORMS Annua Mee>ng, Aus>n, TX November 8, 21 STANFORD UNIVERSITY AT M O SP H E R E / E N E R G Y Atmosphere/Energy Program Dept. of Civi and Environmenta Engineering Stanford University

2 The Chaenge How can the grid accommodate the intermittency of wind and soar to significanty reduce carbon emissions? 1/15

3 Low Carbon PorAoio Panning Mode 2/15

4 Probem Formua&ons Objective Functions Cost (incuding annuaized capita, fixed and variabe O&M, fue, and cost of carbon (in 25 scenario) Approximate Emissions inear function of natura gas generation and spinning capacity Linear Constraints System-wide power baance Generator governing equations (affine w.r.t. capacity, fue) Therma pant ramp rates Energy (integrated power) constraints 3/15

5 Generator Technoogy Modes Hart and Jacobson ABabcdfghiejk 4/15

6 Generator Technoogy Modes 5/15

7 Reaiza&on Modes System Load, Irradiance (GHI => DNI, DHI), Wind Speed Linear statistica modes of the form: f (t) = A(t)! x + b where A(t) = [ a 1 (t) a 2 (t) ] a i (t) = ˆ f (t) a i (t) = ˆ f (t "1) " f (t "1) a i (t) = #(t) a i (t) =1! x is comprised of func>ons ike: (a forecast) (prior forecast error) (diurna or seasona bias) (constant bias) Use inear regression to find and characterize distribu>on b 6/15

8 Reaiza&on Produc&on f (t) = A(t)! x + b and b ~ N (," mode ) is a random variabe To create each reaiza&on, at each &me step: - Appy inear mode - Sampe the mode error distribu>on - Impose site- site correa>ons where appropriate - Appy addi>ona modes where necessary - eg. GHI => DNI => DHI 7/15

9 Ensuring System Reiabiity Dispatch optimization incudes expensive deficit in case of extreme forecast error. Deficit signa is used with LOLE to determine necessary spinning reserve capacity and schedue Maximum required spinning reserve capacity Deficit Reaiza&on 1 Reaiza&on 2 Reaiza&on 3... Reaiza&on N 8/15

10 Case Studies Load scenarios: oad data, oad projection Renewabe Portfoios produced by minimizing: Cost, Emissions Data: Wind: Western Wind Integration Datasets (NREL, 3TIER) Irradiance: NSRDB (NREL) Load: Caifornia ISO OASIS Database Sove using CVX (Grant and Boyd, 21) [17,52 time steps with 2 reaizations each] 9/15

11 Soved Generator PorAoios Capacity Genera&on Capacity (GW) Min. Cost Soar Wind Natura Gas Geotherma Hydro Min. CO 2 Min. Cost Min. CO 2 Annua Generation (! 1 3 GWh) Soar Wind Natura Gas Geotherma Hydro Min. Cost Min. CO 2 Min. Cost Min. CO 2 25 Scenarios 25 Scenarios 25 Scenarios 25 Scenarios, 211. [In Review]! 1/15

12 Carbon Emissions Characteris&cs Carbon- Free Genera&on Carbon Emissions Carbon-Free Generation (%) Low-Cost Low-Carbon Carbon Emissions (1 6 tco 2 ) Low-Cost Low-Carbon 25 Scenario 25 Scenario 25 Scenario 25 Scenario, 211. [In Review]! 11/15

13 Exampe Reaiza&ons (25 Low- CO 2 ) Winter Power Generation (GW) 15 5 Geotherma Wind Photovotaic Soar Therma Hydroeectric Natura Gas NG Reserve Demand + T&D Losses 26!Feb!25 Power Generation (GW) !Jun!25 Spring Hour of Day Hour of Day Summer 15 26!Ju! !Nov!25 Autumn Power Generation (GW) 5 Power Generation (GW) Hour of Day Hour of Day, 211. [In Review]! 12/15

14 Stochas&c vs. Determinis&c Porboios produced by scaing 25 ow- carbon capaci>es uniformy Stochas&c simua&ons: Monte Caro simua>on with forecast error Determinis&c assump&on: Simuate singe reaiza>on where forecast = actua data Carbon Intensity (tco 2 /GWh) Stochastic Simuations Deterministic Assumption Penetration of Wind and Soar, 211. [In Review]! 13/15

15 Concusions We can provide >99% of generation from noncarbon based generators whie meeting an LOLE requirement of 1 day in 1 years With conservative assumptions regarding therma pant operation, can sti achieve significant reductions in carbon emissions from base case eves Stochastic anayses are needed in system panning Low capacity factors wi require enhanced capacity markets for therma pants 14/15

16 Next Steps Incude hour-ahead forecasts Improve forecast error characterization Incude phase errors Improve fexibiity of hydroeectric generators Buid participating oad and storage modes EV s, Batteries, Fue Ces, CAES 15/15

17 Ques&ons? Thanks to: Gi Masters, Nick Jenkins Precourt Ins>tute for Energy Efficiency, Stanford Graduate Feowship, NSF Graduate Research Feowship Bethany Corcoran, Mike Dvorak, Graeme Hoste, Eric Stoutenburg, John Ten Hoeve For more informa&on (and a copy of this presenta>on):