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Robust Transmission Planning under Uncertain Generation Investment - - PowerPoint PPT Presentation

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Robust Transmission Planning under Uncertain Generation Investment and Retirement Lizhi Wang Iowa State University PSERC Webinar April 19, 2016 Lizhi Wang Robust Transmission Planning under Generation Uncertainty 1 / 36 PSERC M-30

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Robust Transmission Planning under Uncertain Generation Investment and Retirement

Lizhi Wang Iowa State University PSERC Webinar April 19, 2016

Lizhi Wang Robust Transmission Planning under Generation Uncertainty 1 / 36

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PSERC M-30 Collaborators

Aftab Alam, CAISO Bryce Bowie, SPP Jay Caspary, SPP Juan Castaneda, SCE Bokan Chen, ISU Flora Flygt, ATC Anish Gaikwad, EPRI George Gross, UIUC Shih-Min Hsu, Southern Co. Anil Jampala, Alstom Murali Kumbale, Southern Co. Sakis Meliopoulos, Georgia Tech David Mindham, ITC Holdings Kip Morison, BC Hydro Aditya Jayam Prabhakar, MISO Jim Price, CAISO Curtis Roe, ATC Harvey Scribner, SPP Hussam Sehwail, ITC Holdings Robert Sherick, SCE Michael Swider, NYISO Mark Westendorf, MISO Lan Trinh, ABB Feng Zhao, ISO-NE

Lizhi Wang Robust Transmission Planning under Generation Uncertainty 2 / 36

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Outline

1

Background

2

Proposed approach

3

Case study

Lizhi Wang Robust Transmission Planning under Generation Uncertainty 3 / 36

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Introduction

Transmission planning is important for Serving increased demand Enhancing reliability Relieving congestion Facilitating renewable energy penetration Transmission planning is challenging because of Long planning horizon Multiple stakeholders Many sources of uncertainty Assessment criteria

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Literature review

Literature Objective GEP Uncertainty Model Buses Horizon AC/DC [Akbari12] I + O + L none load SP , multi-obj 24 12 yrs AC [Alguacil03] I + O none none MILP 46 1 period AC [Carrion07] I + O + L none line SP 48 2 periods DC [Chen15] I + O + L range load, GEP minimax 118 20 yrs DC [Choi05] I none line MILP 21 1 period DC [Escobar04] I + O none none MINP 93 1 period DC [Garces09] I + O bilevel load, line stochastic bilevel 24 10 yrs DC [Hemmati14] I + O none load, wind MINP 24 15 yrs AC [Khodaei13] I + O + L central line MINP 118 20 yrs DC [Maghouli11] I + O uncertainty GEP robust 51 15 yrs DC [Moeini12] I + O + L none load, wind MINP , multi-obj 51 10 yrs DC [Munoz14] I + O central policy, fuel SP 240 3 periods DC [Orfanos12] I + O + L none load, wind MINP 24 1 period DC [Pozo13] I + O bilevel load, wind trilevel 34 1 period DC [Sepasian09] I central none MINP 49 10 yrs DC [Shrestha04] I + O none none MINP 24 8 yrs DC [Torre08] I + O none load, fuel, GEP MINP 23 1 yr AC [Weijde12] I + O central load, policy SP 7 2 periods DC [Yu09] I none load, wind chance MINP 24 1 period DC [Zhang12] I + O none none MINP 118 10 yrs DC [Zhao09] I + O + L uncertainty load, fuel, GEP MINP 14 1 period DC This model I + O + L uncertainty GEP , policy, fuel min-max-min 240 20 yrs DC I: Investment cost. O: Operations cost. L: Load curtailment. GEP: Generation expansion planning. SP: Stochastic programming. Lizhi Wang Robust Transmission Planning under Generation Uncertainty 5 / 36

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Outline

1

Background

2

Proposed approach

3

Case study

Lizhi Wang Robust Transmission Planning under Generation Uncertainty 6 / 36

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Proposed model

Planning horizon: Multiple decision-making periods Decisions: Candidate transmission lines Uncertainty: Candidate generators investment and retirement, gas prices, and policies Objective: Minimize cost (investment, operations, and load-curtailment costs) under the worst case scenario

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Robust optimization illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

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Robust optimization illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

Lizhi Wang Robust Transmission Planning under Generation Uncertainty 9 / 36

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Robust optimization illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

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Robust optimization illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5 Decision space: 3 × 1012 Scenario space: 1 × 1049

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Trilevel modeling framework

Min Transmission plan proposition plan

❄

worst scenario

✻

Max Worst scenario identification min cost

❄

scenario

✻

Min OPF

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Trilevel formulation

min

x∈X

  • CI(x) + max

g∈G

min

z∈Z(x,g) CO(x, g, z)

  • x ∈ X: Transmission planning decisions, upper level

CI(x): Investment cost g ∈ G: Generation scenarios, middle level z ∈ Z(x, g): Operations decisions, lower level CO(x, g, z): Operations and load curtailment cost

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Algorithm – Motivation

min

x∈X

  • CI(x) + max

g∈G

min

z∈Z(x,g) CO(x, g, z)

  • min

x∈X,z(g)∈Z(x,g)

  • CI(x) + ζ : ζ ≥ CO(x, g, z(g)), ∀g ∈ G
  • For any ˆ

G ⊆ G, the following is a relaxation. min

x∈X,z(g)∈Z(x,g)

  • CI(x) + ζ : ζ ≥ CO(x, g, z(g)), ∀g ∈ ˆ

G

  • Lizhi Wang

Robust Transmission Planning under Generation Uncertainty 14 / 36

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Algorithm – Steps

Step 0: Initialize ˆ G ⊆ G and go to Step 1. Step 1: Solve the following, get optimal xR, and go to Step 2. min

x∈X,z(g)∈Z(x,g)

  • CI(x) + ζ : ζ ≥ CO(x, g, z(g)), ∀g ∈ ˆ

G

  • Step 2: Solve the following and get optimal gW.

max

g∈G

min

z∈Z(xR,g) CO(xR, g, z)

if gW ∈ ˆ G then

  • Stop. xR is optimal.

else Update ˆ G ← ˆ G ∪ {gW} and go to Step 1. end

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Algorithm – Illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

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Algorithm – Illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

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Algorithm – Illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

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Algorithm – Illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

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Algorithm – Illustration

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 d1 1 4 8 4 8 3 5 9 6 2 3 6 d2 9 9 6 1 4 4 3 7 4 3 9 3 d3 1 2 4 3 7 6 7 5 4 5 4 6 d4 7 3 5 9 4 1 2 4 5 3 2 7 d5 8 2 4 5 1 1 7 5 4 8 9 2 d6 8 2 1 5 2 2 2 3 9 2 9 2 d7 1 8 3 4 7 6 4 5 2 3 4 3 d8 4 6 2 9 9 7 6 5 5 2 2 3 d9 3 5 2 4 6 6 8 8 6 3 3 4 d10 8 2 3 2 1 5 1 8 6 4 4 5

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Outline

1

Background

2

Proposed approach

3

Case study

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WECC 240-bus test system [Price2011]

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WECC 240-bus test system [Munoz14]

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Assumptions

Planning horizon: Four 5-year periods. Solution space: 18 candidate lines. More than 3 × 1012 (three trillion) feasible solutions. Uncertainty space:

◮ GEP: 53 candidate generators for investment and 17 coal

generators for retirement. Almost 1049 scenarios.

◮ Policy: 20% or 40% mandate of new renewables ◮ Natural gas prices: Low or high ◮ Demand: Constant 0.1% annual load growth [EIA 2015]. Lizhi Wang Robust Transmission Planning under Generation Uncertainty 24 / 36

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240 buses

Southwest San Diego Los Angeles San Francisco Bay Area North Canada Celilo 1 60 120 180 240

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448 lines

Southwest San Diego Los Angeles San Francisco Bay Area North Canada Celilo

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Demand

Southwest San Diego Los Angeles San Francisco Bay Area North Canada Celilo

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Existing generators

biomass natural gas geothermal hydro nuclear renewables solar wind coal

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Candidate generators and transmission lines

biomass natural gas geothermal hydro nuclear renewables solar wind coal

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Four futures

Future 1: 20% new renewables and high gas prices Future 2: 20% new renewables and low gas prices Future 3: 40% new renewables and high gas prices Future 4: 40% new renewables and low gas prices

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Six transmission expansion plans

Plan 1: Optimal under future 1 Plan 2: Optimal under future 2 Plan 3: Optimal under future 3 Plan 4: Optimal under future 4 Plan 5: Too little and too late investment Plan 6: Too much and too early investment

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Six transmission plans

H plan 1 plan 2 plan 3 plan 4 plan 5 plan 6

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Twelve scenarios

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Investment, operations, and load curtailment costs

plan 1 plan 2 plan 3 plan 4 plan 5 plan 6

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Summary

Uncertainty in generator investment and retirement Robust optimization model for assessment of transmission planning Trilevel optimization model and algorithm New visualization techniques Bokan Chen and Lizhi Wang, “Robust transmission planning under uncertain generation investment and retirement,” to appear in IEEE Transactions on Power Systems.

Lizhi Wang Robust Transmission Planning under Generation Uncertainty 35 / 36

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Thank you

Lizhi Wang Associate Professor Iowa State University lzwang@iastate.edu lzwang.public.iastate.edu

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