Mul$- scale Demand- Side Management for Con$nuous Power- intensive Processes. EWO mee(ng, March 13, 2013

Transcription

1 Mul$- scale Demand- Side Management for Con$nuous Power- intensive Processes EWO mee(ng, March 3, 203 Sumit Mitra, Carnegie Mellon University, PA Advisor: Prof. Ignacio E. Grossmann Collaborators: Jose M. Pinto, Larry Megan, Nikhil Arora

2 Facing the challenge of variability, the power grid is in transi(on to the smart grid Genera$on/Supply Transmission, Distribu$on Customers/Demand Conven$onal Electricity ((me- dependent) Balance variability in supply & demand Electricity ((me- dependent) Residen$al (incl. electric cars) Commercial Renewables Load Informa(on Load Informa(on Industrial (power- intensive) improve: greenhouse gas emissions, reliability, energy security, economics Smart grid Integra$on of Renewables Microgrids 2 Demand- Side Management (DSM) (Distributed) Co- genera$on of electricity and heat Storage Demand Response Energy Efficiency. Systema(c u(lity and government ac(vi(es designed to change the amount and/or (ming of the customer s use of electricity for the collec(ve benefit of the society, the u(lity and its customers. Charles River Associates, 2005 Primer on Demand- Side Management, with an emphasis on price responsive programs, CRA No. D06090, Technical report, The World Bank. 2. A microgrid refers to a local grid that can work autonomously from the central power grid. 2

3 DSM is part of a complex mul(- scale design and opera(ons problem for power- intensive processes Example: air separa$on plant Air (free!) Air Separa$on Plant Liquid Oxygen Liquid Nitrogen LOX storage LIN storage Electricity Liquid Argon Gas. Oxygen Gas. Nitrogen LAR storage Pipelines Opera$onal layer Variability and uncertainty in prices Timing and produc(on quan((es (DR)* Inventory management Operability and safety concerns Sa(sfy demand Product distribu(on Process Flexibility Strategic (design) layer Sourcing of electricity Process improvements, retrofits (Energy efficiency) Addi(onal storage Demand/economic uncertainty Process flexibility and product inventory facilitate performing Demand- side Management (DSM) at air separa$on plants. * Demand Side Management (DSM) consists of Demand Response (DR) and Energy Efficiency (EE)

4 At the interface of the chemical industry and the power grid, challenging mul(- scale problems arise Overview of the PhD work Data Stochas$c Uncertainty in electricity prices (FOCAPO 2) Demand uncertainty Stochas(c programming (Strategic paper, part I&II) Determinis$c Short- term scheduling (CACE 2, Energy 3) - Air separa(on, cement - Combined heat and power genera(on Integra$on of opera(onal Decomposi(on and strategic (design, retrofit) algorithm decision- making (ESCAPE 2) (Strategic paper, part I&II) Mul(ple plants, supply chain Single plant Opera$onal Strategic (design) modeling Decisions solu$on method 4

5 Problem statement (integra(on of opera(onal and strategic decisions) for an air separa(on plant Air (free!) Air Separa$on Plant Liquid Oxygen Liquid Nitrogen LOX storage LIN storage Electricity Liquid Argon Gas. Oxygen Gas. Nitrogen LAR storage Pipelines Given: Determine: - Power- intensive plant - Products g G (Storable and Nonstorable) - Product demands d g t for season t T - Seasonal electricity prices on an hourly basis e t,h, t T, h H - Upgrade op(ons u U of exis(ng equipment - New equipment op(ons n N - Addi(onal storage tanks st ST - Produc(on levels - Mode of opera(on - Sales - Inventory levels - Upgrades for equipment - Purchase of new equipm. - Purchase of new tanks With minimum investment and opera$ng costs t,h Pr g y t,h t,h m,o, y m S g t,h INV g t,h for each season on an hourly basis t VU m,u t VN n t VS st,g

6 Incorpora(ng design decisions: seasonal varia(ons drive the development of a seasonal model, spring: Investment decisions 2, spring: Investment decisions Spring' Summer& Fall$ Winter' # # # # # # # # 50.00# 00.00# Mo Tu We Th Fr Sa Su 50.00# 00.00# Mo Tu 50.00# 50.00# 00.00# Su Mo Tu 00.00# Su Mo Tu Su 50.00# 50.00# 50.00# 50.00# 0.00# 0.00# 0.00# 0.00# # 25# 49# 73# 97# 2# 45# # 25# 49# 73# 97# 2# 45# # 25# 49# 73# 97# 2# 45# # 25# 49# 73# 97# 2# 45# Spring Summer Fall Winter Horizon: 0 years, each year has 4 periods (spring, summer, fall, winter) Big challenge! Each period is represented by one week on an hourly basis Varying inputs: electricity prices, demand data, configura$on slates Each representa(ve week is repeated in a cyclic manner (3 weeks reduced to week) Connec(on between periods: Only through investment (design) decisions Design decisions are modeled by discrete equipment sizes 6

7 It is possible to upgrade the exis(ng liquefier, buy an addi(onal liquefier and add storage tanks Plant superstructure Feed air Air separa$on plant Poten(al plant modifica(ons Exis(ng liquefier Op(on A Op(on B (upgrade) Liquid products Exis(ng Tanks New liquefier Gaseous products New Tanks Each tank costs $500k Mode superstructure Off Minimum up(me: 48 hours Minimum Amer 4hrs Exis(ng liquefier down(me: TransOffOn 24 hours transi(on (me Op(on A Op(on B Upgrade liquefier ($2,000k) TransOnOn2 Addi(onal liquefier ($7,000k) TransOffOn2 Amer 4hrs transi(on (me Minimum up(me: 48 hours New liquefier 7

8 The investment horizon of 0 years is represented with 20 different seasons Timing of poten(al investments (T invest ) 5-0 represented with one year for modeling purposes 4 seasons per year Total: 4*5=20 seasons Time Investments (upgrade, new liquefier, new tanks) are allowed at the each beginning of the first 4 years ((ming of investments is a degree of freedom) 5-0 is represented in an aggregated way to manage the problem size Each period is represented by one week on an hourly basis All cost factors are discounted appropriately Objec$ve: minimize investment + opera$onal costs Large- scale MILP: up to,000k variables (200k binaries) and 2,000k constraints 8

9 We inves(gate the influence of different assump(ons for the product demand Parameters in demand modeling Product demand Demand distribu(on is approximated with 3 scenarios per season High Medium μ t + b σ t μ t μ t - b σ t Low Addi(onally: cost parameter for lost demand Time Determinis$c vs. stochas$c demand. Demand is modeled determinis$cally with one scenario per season, for which the expected value is assumed (= medium scenario). 2. Demand is modeled with 3 scenarios (low, medium, high) per season; probabili$es are assigned accordingly (example see above) 9

10 Depending on the case, either the second liquefier and one LN2 tank are purchased or no investments are made Case Average baseline demand (u$liza$on) Annual growth rate Penalty for lost demand (scaled) Demand distribu$on {low, med, high} and probabili$es [low, med, high ] low moderate.0 {} [25%, 50%, 25%] Solu$on (investments) of determinis$c and stochas$c model No investment (determinis$c and stochas$c) VSS (value of stochas$c solu$on) 0 2 high high.0 {} [25%, 50%, 25%] Buy second liquefier and one LN2 tank in year (determinis$c and stochas$c) 0 3 medium medium.0 {} [25%, 50%, 25%] Determinis$c: No investment Stochas$c: Buy second liquefier and one LN2 tank in year 0.5% cost reduc(on 4 medium medium.33 {} [30%, 50%, 20%] Determinis$c: No investment Stochas$c: Buy second liquefier and one LN2 tank in year 5.9% cost reduc(on The modeling of stochas$c demand generates value (VSS) in cases where the determinis$c and stochas$c solu$on are different in terms of investments. 0

11 For determinis(c demand, investments are made only in case 2 to increase opera(onal flexibility Total cost in $MM shows value of current and addi(onal flexibility Determinis(c demand $MM - 0.3% - 7.6% Only in case 2, investments are made to increase opera$onal flexibility (2 nd liquefier and LN2 tank in year ) % - 2.3% -.9% Total cost, constant opera(on, no investments Total cost, flexible opera(on, no investments Total cost, flexible opera(on, with investments Case Case 2 Case 3 Case 4

12 For stochas(c demand, investments are made in cases 2-4 to increase opera(onal flexibility Total cost in $MM shows value of current and addi(onal flexibility Stochas(c demand - 7.3% In cases 2-4, investments are made to increase opera$onal flexibility (2 nd liquefier and LN2 tank in year ). $MM - 0.4% - 3.7% - 0.5% - 3.9% - 5.9% Total cost, constant opera(on, no investments - 4.4% Total cost, flexible opera(on, no investments Total cost, flexible opera(on, with investments Case Case 2 Case 3 Case 4 2

13 The VSS can be significant due to avoided demand penalty and more flexible opera(on Value of stochas(c solu(on (VSS) based on total cost comparison in $MM for cases 3 and 4 $MM - 0.5% $MM - 5.9% CAPEX Demand penalty Electricity Flexible opera(ons, no investment Flexible opera(ons, with investment Flexible opera(ons, no investment Flexible opera(ons, with investment Case 3 Case 4 3

14 In high demand scenarios, addi(onal equipment helps exploi(ng price swings and avoiding lost demand penalty Spring Summer Power&consump3on& Price&in&$/MWh& 200" 50" 00" 50" Power&consump3on& Price&in&$/MWh& 200" 50" 00" 50" 0" " 25" 49" 73" 97" 2" 45" Hour&of&a&typical&week&in&the&spring&season& Power"consump5on"w/"investment:"spring" Power"consump5on"w/o"investment:"spring" Spring" 0" " 25" 49" 73" 97" 2" 45" Hour&of&a&typical&week&in&the&summer&season& Power"consump5on"w/"investment:"summer" Power"consump5on"w/o"investment:"summer" Summer" Fall Winter Power&consump3on& Price&in&$/MWh& 200" 50" 00" 50" Power&consump3on& Price&in&$/MWh& 200" 50" 00" 50" 0" " 25" 49" 73" 97" 2" 45" Hour&of&a&typical&week&in&the&fall&season& Power"consump5on"w/"investment:"fall" Power"consump5on"w/o"investment:"fall" Fall" 0" " 25" 49" 73" 97" 2" 45" Hour&of&a&typical&week&in&the&winter&season& Power"consump5on"w/"investment:"winter" Power"consump5on"w/o"investment:"winter" Winter" Note: The graphs resemble the behavior of the schedule in a scenario with a u(liza(on close to 00%. 4

15 Stochas(c programming helps analyzing mul(ple demand scenarios and generates value in unclear setups Stochas$c solu$on suggests investment yes no A Medium baseline demand Medium growth Skewed distribu(on towards high demand cases Distribu(on with high variance High penalty for lost demand VSS > 0 C B Case 3, 4 Case 2 Case Low baseline demand Moderate growth Distribu(on with low variance VSS = 0 High baseline demand High growth Distribu(on with low variance VSS = 0 D If the demand values in the determinis(c case are calculated with an unbiased es(mator for the mean of the distribu(on used in the stochas(c case (this case is unlikely to happen) no yes Determinis$c solu$on suggests investment 5