Transmission Planning Under Uncertainty: A Stochastic Two...

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Transmission Planning Under Uncertainty: A Stochastic Two Stage Modelling Approach A Stochastic TwoStage Modelling Approach H d W ijd Harry van der Weijde VU Amsterdam & EPRG, University of Cambridge | [email protected] Benjamin F Hobbs Benjamin F. Hobbs Johns Hopkins University, EPRG, & CAISO| [email protected] The Next Generation of Electricity Planning Models The Next Generation of Electricity Planning Models FERC, Nov. 4, 2010 Making networks fit for renewables www.eprg.group.cam.ac.uk Overview The problem The problem Existing studies O dl Our model How it works Data it needs Data sources + assumptions Some results Our conclusions Making networks fit for renewables … www.eprg.group.cam.ac.uk Our conclusions 2

Transcript of Transmission Planning Under Uncertainty: A Stochastic Two...

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Transmission Planning Under Uncertainty: A Stochastic Two Stage Modelling ApproachA Stochastic Two‐Stage Modelling Approach

H d W ijdHarry van der WeijdeVU Amsterdam & EPRG, University of Cambridge | [email protected]

Benjamin F HobbsBenjamin F. Hobbs Johns Hopkins University, EPRG, & CAISO| [email protected]

The Next Generation of Electricity Planning ModelsThe Next Generation of Electricity Planning ModelsFERC, Nov. 4, 2010

Making networks fit for renewables …www.eprg.group.cam.ac.uk

Overview

The problemThe problem

Existing studies

O d lOur model

– How it works

– Data it needs

– Data sources + assumptions

Some results

Our conclusions

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Our conclusions

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D ti h i !

The Problem: The Problem: HyperuncertaintyHyperuncertainty!!What’s a Poor Transmission Planner to do?What’s a Poor Transmission Planner to do?• Dramatic changes a‐coming!

• RenewablesH h?– How much?

– Where?

– What type?yp

• Other generation– Centralized? Do these uncertainties – Distributed?

• Demand

o ese u ce a eshave implications fortransmission investments now?

– New uses? (EVs)

– Controllability?

• PolicyMaking networks fit for renewables …

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• Policy

The problem, Cont.The problem, Cont.

Transmission planning– Generators respond: multi‐level – Decisions can be postponed: multi‐stage– Uncertainties & variability: stochastic

Important questions:– Optimal strategy under uncertainty?– Value of information? (EVPI)– Cost of ignoring uncertainty? (ECIU)– Option value of being able to postpone?p g p p

Deterministic planning can’t answer these!

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• Stochastic can! 

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Decision making uDecision making undernder uncertaintyuncertainty-----------Previous Work-----------

Real options analysis of single lines, usually

Single-stage trans-mission planning under uncertainty

Two-stage trans-mission planning

----------- Ours-----------

based on exogenous price processes (Hedman et

al. 2005; London Economics 2003; Fleten et al. 2009; Parail 2009)

under uncertainty with generator response (Awad et al. 2009;

Crousillat et al. 1993; De la Torre et al. 1999; Oolomi Buygi et al. 2004; Oliveira et al. 2007; Hyung Roh et al 2009; Sauma & Oren 2009)

mission planning under uncertainty with generator response

Hyung Roh et al. 2009; Sauma & Oren 2009)

Invest inline now?

Uncertainprices

(Some:Invest in

line later?)

Investtrans.now

Uncertainties(usually load)

Gen.operation

(& sometimes

Invest/ operatetrans. /

gen

Investtrans./gener.

Uncertainties(policy,

load,

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line later?) (&, sometimes, Investment)

gen.later

gnow

load,technology)

Our model: timelineStage 1 Stage 2

2010 2020 2030

3. Dispatch 6. Dispatch

1. Transmissioninvestment

4. Transmissioninvestment

2. Generationinvestment

5. Generationinvestment

Objective: min total costs (investment + generation) s.t. power flow constraints, wind availability, build limits,

bl t t

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renewables targets

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Mathematical Schematic– Math programming with recoursep g g

• scenarios s=1,2,..,S, each with probability PRs

– Simplest: Assume 2 decision stages:Simplest: Assume 2 decision stages:1. Choices made “here and now” before future is 

known  – E.g., investments in 2010g ,– These are x1

2. “Wait and see” choices, which are made after the future s is known.  

d h/– E.g., dispatch/operations, investments in 2020– These are x2s (one set defined for each scenario s)

– Model:– Model:MIN   C1(x1) + Σs PR

s C2s(x2s)s.t.     A1(x1) = B1

A2s(x1 x2s) = B2s ∀s

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A2s(x1, x2s) = B2s ∀s

Some assumptions

Alignment of generation and transmission objectivesAlignment of generation and transmission objectives

– e.g., nodal pricing + perfect competition

Generation

– Constant variable costs

– No start‐up costs, min run levels, ‘lumpy investment’

– No ramping constraints

Demand:

– No short‐term demand flexibility, demand‐side management

R bl t t t i t ffi i tMaking networks fit for renewables …

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Renewables targets met in most efficient way8

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Data necessary

regions+ transmission

scenarios(2020 2030) &

generator types + current capacities + maximum+ transmission

constraints+ losses

wind output and demand 

(2020, 2030) & probabilities:

generation costs (i l b i )

capacities + maximum build limits + costs

time series (1 year)+ interconnector flows

(incl. carbon price), transmission 

investment costs,demand,

renewable targets,nuclear feasibilitynuclear feasibility

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investment alternatives9

Data sources

Regional wind output: Neuhoff et al. (2007)

Hydro output: Duncan (2010)Hydro output: Duncan (2010)

Regional demand data: National Grid

BritNed Flows: Parail (2010)BritNed Flows: Parail (2010)

Maximum build limits: Various

Regions + trans. constraints: NG 7‐year statement (2009)g y ( )

Transmission losses: own calculations

Investment alternatives + costs: KEMA (2009)

Generation costs: NEA and IEA (2005), US DOE, own calculations

S i V i (Di LENS R d i t t )

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Scenarios: Various (Discovery, LENS, Redpoint, etc.)

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Alternatives (overnight construction cost)

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ScenariosGen. inv. cost Var. gen cost Trans. inv. 

cost 

Demand CO2

price

Others

Status Quo CCGT/OCGT/DG: + + +/‐ No RT

Low cost DG DG: ‐‐ CCGT/OCGT:  ‐

DG: ‐‐

+ ++ RT: +

Nuclear replacement onlyDG: ‐‐ Nuclear replacement only

Low Cost 

Large Scale 

G

Renewables : ‐‐ CCGT/OCGT/DG: ++ ‐‐ +++ RT: +++

Green

Low Cost 

Conventional

Conventional: ‐ CCGT/OCGT/DG: ‐ ++ + No RT

Paralysis All except 

offshore: +++

CCGT/OCGT/DG: + Onshore: +++

Others +

++ ++ RT: +

Nuclear replacement only

Techno+ All : ‐ CCGT/OCGT/DG: + ‐ ++ ++  RT: ++

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Some results

Disclaimer: the following results areDisclaimer: the following results are preliminary and based on restrictive 

assumptionsassumptions. 

They cannot be used to evaluate proposed transmission investmentstransmission investments.

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Optimal stochastic solution

Onshore  CCGTwind

Off h

CCGT

OCGTOffshore wind

OCGT

Nuclear

BiomassBiomass

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Cf. Traditional robustness analysis

2020 Installations by Scenario “Robust”

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Value of perfect information         

How much average savings if we knew which scenario would happen?scenario would happen?

1. Solve stochastic model

2 Solve deterministic model for each scenario2. Solve deterministic model for each scenario

3. Compare objectives (1) and (2)

• Results:

– For gen & transmission:    £3,729M  (3%)

– For trans alone: £101M  (0.1%)

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Cost of ignoring uncertainty

How much would costs go up if we naively plan for one scenario but other scenarios can happen?

1.Solve stochastic model

2.Solve naïve (deterministic) model for each2.Solve naïve (deterministic) model for each scenario

3 Solve stochastic model imposing first‐stage3.Solve stochastic model, imposing first‐stage transmission decisions from step 1

4 C bj ti (1) d (3)Making networks fit for renewables …

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4.Compare objectives (1) and (3)17

Cost of ignoring uncertainty

Scenario planned for ECIU (Transmission)(Present worth)(Present worth)

Status Quo £432MLow Cost DG £0 Low Cost Large Scale Green £29MLow Cost Conventional £196M

lParalysis £221M Techno+ £0        Average £146M = 0 12% ofAverage £146M = 0.12% of 

expected costs (stochastic solution)

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Option value of waiting

How much would costs go up if we had toHow much would costs go up if we had to make all decisions now?

1 Solve stochastic model1.Solve stochastic model

2.Solve stochastic model, imposing same i i i l f ll itransmission expansion plan for all scenarios

3.Compare objectives (1) and (2)

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Option value of waiting

2020 2020

Example: Paralysis

SCO

2020stoc

SCO

no option

UNO UNO

p

NOR

MID

NOR

MIDMID

CEN

SWE

ESTCEN

SWE

EST

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SWE

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Option value of waiting

Option value (transmission only):Option value (transmission only): 

= £102M present worth= 0.08% of total costs (stochastic)(stochastic) 

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Conclusions

For transmission planning:

I i i k h ifi bl i– Ignoring risk has quantifiable economic consequences

Option al es can be significant– Option values can be significant

– Approach useful for policy/planning questions

kFuture work

– Demand response

– Bi‐level formulation

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References E. O Crousillat, P. Dörfner, P. Alvarado, and H. M. Merrill, “Conflicting Objectives and Risk in Power System Planning,” IEEE Trans. Power Systems, vol. 8, pp. 887‐893, 1993.,N. Duncan, “”,2010.S. ‐E. Fleten, A. M. Heggedal, and A. Siddiqui, “Transmission Investment under Uncertainty: The Case of Germany‐Norway,” presented at the 1st International Ruhr Energy Conference, Essen, Germany.gy , , yK. W. Hedman, F. Gao, and G. B. Sheble, “Overview of Transmission Expansion Planning Using Real Options Analysis,” in Proc. IEEE North American Power Symposium, 2005.J. Hyung Roh, M. Shahidehpour, and L. Wu, “Market‐Based Generation and y g , p , ,Transmission Planning With Uncertainties,” IEEE Trans. Power Systems vol. 24, pp. 1587‐1598, 2009.KEMA “Assessment of overall robustness of the transmission investment proposed for additional funding by the three GB Electricity Transmission Owners”, 2009.London Economics, London, “Economic Evaluation of the Path 15 and Path 26 Transmission Expansion Projects in California”.National Grid, “Seven‐Year Statement”, 2009.

Making networks fit for renewables …

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References (cont’d)NEA and IEA, “Projected Costs of Generating Electricity – 2005 Update”, Nuclear Energy Agency and International Agency, OECD, Paris, France, 2005. K Neuhoff J Cust L Butler K Keats H Hoexter A Kreckzo G Sinden and AK. Neuhoff, J. Cust, L. Butler, K. Keats, H. Hoexter, A. Kreckzo, G. Sinden, and A. Ehrenmann, “Space and Time: Wind in an Investment Planning Model”. EPRG  Working Papers 0603, 2006.G. C. Oliveira, S. Binato, and M. W. Pereira, “Value‐Based Transmission Expansion Planning of Hydrothermal Systems Under Uncertainty,” IEEE Trans. Power Systems, g y y y, y ,vol. 22, pp. 1429‐1435, 2007.M. Oloomi Buygi, M. Shahidehpour, H. M. Shanechi, and G. Balzer, “Market Based Transmission Planning Under Uncertainties,” Proc. 2004 Int. Conf. on Probabilistic Methods Applied to Power Systems, pp. 563‐568.V. Parail, “Can Merchant Interconnectors Deliver Lower and More Stable Prices? The Case of NorNed,” EPRG Working Papers 0926, Nov. 2009.V. Parail, “Properties of Electricity Prices and the Drivers of Interconnector Revenue”, 2010.E. E. Sauma, and S. S. Oren, “Proactive Planning and Valuation of Transmission Investments in Restructured Electricity Markets,” Journal of Regulatory Economics 30, pp. 261‐290, 2006.

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