Transmission Planning Under Uncertainty: A Two ... - Semantic Scholar

Transmission Planning Under Uncertainty: A Two‐Stage Stochastic Modelling Approach g pp Harry van der Weijde EPRG University of Cambridge EPRG, University of Cambridge

& Benjamin F. Hobbs j EPRG, Johns Hopkins University, & California ISO YEEES Cambridge 09 04 10 YEEES, Cambridge, 09.04.10 Making networks fit for renewables … www.eprg.group.cam.ac.uk

Outline • • • •

Basic questions g Modeling framework Assumptions Results – Robust plans – Value of information – Cost of naiveté – Option value

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

Plan transmission, but consider market response! market response! Stakeholders

Regulator

• A “multilevel” (Stackelberg) game: – Upper Upper level: level: planners (& regulator, planners (& regulator, stakeholders), who anticipate reactions of … – Lower level: Lower level: market response of market response of consumers, generators

Transmission Planner

Gen 2 Gen 1

Gen 3

M k t Market

Emissions & Renewables Markets

Demand-Side Planning

Gen 4

System Operation

Consumers

• Account for responses: – P Price effects on gen type and siting i ff t t d iti decisions – Influence of policy on above

• Account for uncertainties: – Want robust plans that do well under a range of possible futures g p


Basic questions • Optimal strategy under multiple scenarios? Optimal strategy under multiple scenarios? • Solve stochastic two‐stage model: “OPT”

• Value of perfect information (EVPI)? • Optimize separately for each scenario s: “PIs” • Compare cost of PIs vs OPT, averaged over scenarios

• Cost of ignoring uncertainty (ECIU)? C t fi i t i t (ECIU)? • Solve naïve model (no uncertainty): “NAÏVE” • Compare cost of NAÏVE vs Compare cost of NAÏVE vs OPT averaged over scenarios averaged over scenarios

• Option value? • Solve model assuming no options later: “OPENLOOP” • Compare cost of OPENLOOP vs OPT

Deterministic planning can’t answer these! Making networks fit for renewables … www.eprg.group.cam.ac.uk

Modelling framework • Two‐stage Stackelberg game: g gg 1. TSOs decide on transmission investment 2. Generators decide on generation investment 3 Decisions are implemented; Market operation 3. Decisions are implemented; Market operation 4. Repeat 1‐3


Modelling framework • If objectives are aligned: 1. Decisions on transmission+generation investment 2. Decisions are implemented; Market operation 3. Repeat 1‐2 p

• Our approach: – two stage stochastic programming – Mixed integer program (0‐1 transmission variables) g p g Making networks fit for renewables … www.eprg.group.cam.ac.uk

Modelling framework Scenarios Period 1 Period 1

2010 {z, Δx}

z Δx y

Period 2 Period 2

2020 {z, Δx, y}

Period 3 Period 3

2030 {y}

transmission investment decisions decisions to build new generation capacity ggeneration dispatch p Making networks fit for renewables … www.eprg.group.cam.ac.uk

Modelling framework: Two stage optimization Two stage optimization • Objective: MIN InvestCost2010 + Σs Ps (InvestCost2020,s + O&MCost + Σ + O&MCost2020+2030,s) • Subject to constraints: – Build limits for 2010 and each s in 2020: Build limits for 2010 and each s in 2020: • Transmission • Regional gen capacity

– Operating constraints for each s in 2020 & 2030: O ti t i t f h i 2020 & 2030 • Capacity constraints upon gen & flows • Kirchhoff’s laws for power flow • Renewable target

• ~ 0.5M variables, constraints – Coefficients, constraints can vary by s Coefficients constraints can vary by s Making networks fit for renewables … www.eprg.group.cam.ac.uk

Assumptions I ‐ Structural • Alignment of transmission & gen objectives • Generation: G ti – linear cost functions – no start‐up costs, min run levels, no start up costs min run levels ‘lumpy’ lumpy investment investment

• Transmission: constant flow limits • Demand: – constant regional fractions – no demand response p

• Renewables targets: met in most efficient way g • Storage Making networks fit for renewables … www.eprg.group.cam.ac.uk

Assumptions II ‐ Numerical • Wind data from Neuhoff et al. (2006) ( ) – One year of wind output in each region

• Transmission Transmission constraints from National Grid constraints from National Grid • Existing capacity and demand data from DECC • Transmission investment alternatives from f ENSG • Sampling Making networks fit for renewables … www.eprg.group.cam.ac.uk

Assumptions II ‐ Numerical Regions and Transmission investment alternatives Various new// upgrades £260M

SCO

Subsea HVDC £575M UNO

Subsea HVDC £575M

NOR MID

Various new/ upgrades £410M

CEN SWE


Onshore HVDC £410M EST

Assumptions II – Numerical Scenarios Investment Fuel cost cost t

Trans. C t Cost

Demand

CO2 price i

Other

15/15

No Renewables target

30/50

RT: 10/20%

Status

CC/GT/DG:

+2.5%/

Quo

+30/+80%

+5%

CC/GT

+2.5%/

‐10/‐20%

+5%

Low cost DG

DG ‐50%

No new

DG: ‐50% Low Cost Renewables CC/GT/DG Large Large ‐40% +60/+160 Scale Green

nuclear ‐20%/ ‐ 30%


50/80

RT: 20/30%

Assumptions II – Numerical Scenarios Investment Fuel cost cost Low Cost Conven‐ CC/GT/DG: Conven‐ tional ‐10/‐20% 10/ 20% ti tional l ‐30% 30%

Trans. Cost

Paralysis

CO2 price

Other

+20%/

20/25

No RT

30/50

RT: 10/20%; No new nuclear

/ 30/50

20/30% /

+40%

All except

CC/GT/DG:

offshore ff h

+30/+80% /

Onshore +20%/ +100% Others +40% +20%

CC/GT/DG: / /

All ‐20% ll

wind +100% Techno+ h

Demand

All ‐30% ll

+30/+80%

+10%// +20%


Results 1. Optimal robust solution Optimal robust solution – Two‐stage stochastic model

2. Value of Information (EVPI) – Deterministic model (best trans & gen solution for each scenario) Æ EVPIT&G – Deterministic model with generation decisions fixed Æ Deterministic model with generation decisions fixed Æ EVPITrans (lower bound)

3. Cost of naïve decision (ECIU) – Stochastic model, imposing naïve 1st stage transmission decisions Æ ECIUTrans

4 Option value of transmission 4. Option value of transmission – Stochastic with same transmission decision in every scenario p Æ Option value Trans Making networks fit for renewables … www.eprg.group.cam.ac.uk

Disclaimer The following results are (very) preliminary and used for method illustration purposes only. They cannot be used to evaluate proposed transmission investments.


Stochastic model – stage 1 Onshore wind

CC

2010 SCO

Offshore Off h wind Nuclear

GT UNO NOR

DG

MID

Biomass

CEN SWE


EST

Stochastic model – stage 2 Scenarios: Low Cost DG; Paralysis

2020

2020 SCO

SCO

UNO NOR

NOR

MID

MID

CEN SWE

UNO

CEN

EST SWE


EST

What if you had perfect foresight? Low Cost Large Scale Green, stages 1 & 2

2020

2010 SCO

SCO

UNO

UNO

NOR

NOR

MID

MID

CEN

CEN

EST

SWE

SWE


EST

Value of Perfect Information • Total EVPI = TC stoch − ∑π TC s = £7,182M (7.37% of TCstoch ) • EVPI when generators do in the 1 EVPI when generators do in the 1st stage what stage what they did in the stochastic model = £10M (0.01%) £10M (0 01%) = lower bound on EVPITrans s

s determ


What if you think you have perfect foresight?

2010

SCO

E.g., Paralysis imposed stage 1 stage 1

UNO NOR MID CEN

EST

SWE


Cost of Naïve Decisions: ECIUTrans Impose p Status Quo

£261M

0.27%

Impose Low Cost DG

£261M

0.27%

Impose Low Cost Large Scale Green £ 79M

0 08% 0.08%

Impose Low Cost Conventional

£261M

0.27%

Impose Paralysis

£ 36M

0.04%

Impose Techno+ Average

£ 36M

0.04%

£156M

0.16%

Comparable to 1st stage Transmission Investments Making networks fit for renewables … www.eprg.group.cam.ac.uk

What if there is no optionality? Same Stage 2 Transmission Investments in all scenarios

2010 SCO

UNO NOR MID CEN

EST

SWE


Option Value (Transmission) = Increase in cost when eliminate optionality p y = £117M (0.12%)


Conclusions • Main insights g – Ignoring risk has quantifiable economic consequences q – Option values can be significant – Approach useful for policy/planning questions pp p y/p gq

• Future work – Revisit assumptions Revisit assumptions – Bi‐level formulation Making networks fit for renewables … www.eprg.group.cam.ac.uk