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Zero-Carbon Analytics
Andy Philpott
Electric Power Optimization Centrewww.epoc.org.nz
University of AucklandNew Zealand
(Joint work with Michael Ferris and Anthony Downward)
Supported by Marsden grant UOA1520
INFORMS, Seattle, October 21, 2019
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 1 / 55
Extinction Rebellion October 2019(http://rebellion.earth/international-rebellion.)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 2 / 55
Wellington Extinction Rebellion protest, October 8(Photo from Getty Images)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 3 / 55
Wellington student protest, September 27, 2019(Photo by Kevin Stent, Stuff.)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 4 / 55
Wellington student protest, September 27, 2019(Photo by Joe Lloyd, Stuff.)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 5 / 55
Jacinda’s 2017 election deal
Introduce a Zero Carbon Act and establish anindependent Climate Commission.Request the Climate Commission to plan the transitionto 100% renewable electricity by 2035 (which includesgeothermal) in a normal hydrological year.
Stimulate up to $1 billion of new investment in lowcarbon industries by 2020, kick-started by aGovernment-backed Green Investment fund of $100M.
(Confidence and Supply Agreement between the New Zealand LabourParty and the Green Party of Aoteoroa)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 6 / 55
Jacinda’s 2017 election deal
Introduce a Zero Carbon Act and establish anindependent Climate Commission.Request the Climate Commission to plan the transitionto 100% renewable electricity by 2035 (which includesgeothermal) in a normal hydrological year.
Stimulate up to $1 billion of new investment in lowcarbon industries by 2020, kick-started by aGovernment-backed Green Investment fund of $100M.
(Confidence and Supply Agreement between the New Zealand LabourParty and the Green Party of Aoteoroa)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 6 / 55
Jacinda’s 2017 election deal
Introduce a Zero Carbon Act and establish anindependent Climate Commission.Request the Climate Commission to plan the transitionto 100% renewable electricity by 2035 (which includesgeothermal) in a normal hydrological year.
Stimulate up to $1 billion of new investment in lowcarbon industries by 2020, kick-started by aGovernment-backed Green Investment fund of $100M.
(Confidence and Supply Agreement between the New Zealand LabourParty and the Green Party of Aoteoroa)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 6 / 55
This talk is about
OR: helping government policy . . .
I to distinguish between objectives and actions;
I to understand effects of uncertainty;
I to understand effects of incentives.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 7 / 55
This talk is about
OR: helping government policy . . .
I to distinguish between objectives and actions;
I to understand effects of uncertainty;
I to understand effects of incentives.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 7 / 55
This talk is about
OR: helping government policy . . .
I to distinguish between objectives and actions;
I to understand effects of uncertainty;
I to understand effects of incentives.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 7 / 55
This talk is about
OR: helping government policy . . .
I to distinguish between objectives and actions;
I to understand effects of uncertainty;
I to understand effects of incentives.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 7 / 55
Summary
1 Introduction
2 Understanding uncertainty
3 Getting to 100 percent renewable electricity
4 Results
5 Understanding incentives
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 8 / 55
Summary
1 Introduction
2 Understanding uncertainty
3 Getting to 100 percent renewable electricity
4 Results
5 Understanding incentives
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 9 / 55
Renewable electricity is intermittent and random
[Source: http://www.caiso.com]
Duck curve shows increasing need for ramping plant in evening.
Electricity systems also need backup capacity to ensure supplywith random renewable generation (e.g. wind).
Very large literature on these topics (see e.g. ENRE sessions)using stochastic optimization models of various sorts.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 10 / 55
Renewable electricity is intermittent and random
[Source: http://www.caiso.com]
Duck curve shows increasing need for ramping plant in evening.
Electricity systems also need backup capacity to ensure supplywith random renewable generation (e.g. wind).
Very large literature on these topics (see e.g. ENRE sessions)using stochastic optimization models of various sorts.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 10 / 55
Renewable electricity is intermittent and random
[Source: http://www.caiso.com]
Duck curve shows increasing need for ramping plant in evening.
Electricity systems also need backup capacity to ensure supplywith random renewable generation (e.g. wind).
Very large literature on these topics (see e.g. ENRE sessions)using stochastic optimization models of various sorts.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 10 / 55
Hydro reservoirs have uncertain inflows
Ohau A power station in New Zealand’s South Island(Photo by By Ulrich Lange, Bochum, Germany - Own work)
If inflows are too low in winter then there is an energy shortage.
Need sufficient backup energy to ensure supply.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 11 / 55
Hydro reservoirs have uncertain inflows
Ohau A power station in New Zealand’s South Island(Photo by By Ulrich Lange, Bochum, Germany - Own work)
If inflows are too low in winter then there is an energy shortage.
Need sufficient backup energy to ensure supply.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 11 / 55
Hydroelectric reservoir optimization
Stochastic dynamic programming applied to
P: min limT→∞ E [ 1T
∑Tt=1 C (Xt ,Ut)]
s.t. Xt+1 = ft(Xt ,Ut , ωt),Ut ∈ U ,Xt ∈ X .
Popular finite-horizon approach is Stochastic Dual DynamicProgramming (SDDP) [Pereira & Pinto, 1991].
Use infinite horizon SDDP with discounting [Shapiro & Ding,2019], or average cost per year (e.g. formulation P above)[Downward & P., 2019]. We implement P in sddp.jl package[Dowson & Kapelevich, 2016].
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 12 / 55
Hydroelectric reservoir optimization
Stochastic dynamic programming applied to
P: min limT→∞ E [ 1T
∑Tt=1 C (Xt ,Ut)]
s.t. Xt+1 = ft(Xt ,Ut , ωt),Ut ∈ U ,Xt ∈ X .
Popular finite-horizon approach is Stochastic Dual DynamicProgramming (SDDP) [Pereira & Pinto, 1991].
Use infinite horizon SDDP with discounting [Shapiro & Ding,2019], or average cost per year (e.g. formulation P above)[Downward & P., 2019]. We implement P in sddp.jl package[Dowson & Kapelevich, 2016].
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 12 / 55
Hydroelectric reservoir optimization
Stochastic dynamic programming applied to
P: min limT→∞ E [ 1T
∑Tt=1 C (Xt ,Ut)]
s.t. Xt+1 = ft(Xt ,Ut , ωt),Ut ∈ U ,Xt ∈ X .
Popular finite-horizon approach is Stochastic Dual DynamicProgramming (SDDP) [Pereira & Pinto, 1991].
Use infinite horizon SDDP with discounting [Shapiro & Ding,2019], or average cost per year (e.g. formulation P above)[Downward & P., 2019]. We implement P in sddp.jl package[Dowson & Kapelevich, 2016].
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 12 / 55
Jacinda’s 2017 election deal
Introduce a Zero Carbon Act and establish anindependent Climate Commission.Request the Climate Commission to plan the transitionto 100% renewable electricity by 2035 (which includesgeothermal) in a normal hydrological year.
Stimulate up to $1 billion of new investment in lowcarbon industries by 2020, kick-started by aGovernment-backed Green Investment fund of $100M.
(Confidence and Supply Agreement between the New Zealand LabourParty and the Green Party of Aoteoroa)
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 13 / 55
Optimized hydro with 500 MW coal capacity
Source: [Fulton, 2018]
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 14 / 55
Optimized hydro with 0 MW coal capacity
Source: [Fulton, 2018]
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 15 / 55
Optimized hydro with 0 MW coal capacity
Source: [Fulton, 2018]
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 16 / 55
CO2 emissions comparison
Source: [Fulton, 2018]
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 17 / 55
Summary
1 Introduction
2 Understanding uncertainty
3 Getting to 100 percent renewable electricity
4 Results
5 Understanding incentives
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 18 / 55
100 percent renewable electricity for New Zealand
New Zealand GHG emissions (80 MT CO2-e) by sector in 2017
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 19 / 55
Integrated assessment modelsClimate economy models and energy economy models [Anadon et al, 2017]
DICE [Nordhaus, 1992-2017]
MERGE [Manne et al, 1995]
MARKAL/TIMES [Loulou et al, 2008]
GCAM [Calvin et al, 2011]
PAGE09 [Hope, 2011]
AIM/GCE [Fujimori et al, 2012]
FUND [Anthoff & Tol, 2013]
IMAGE [Stehfest et al, 2014]
REMIND [Luderer, 2015]
WITCH [Emmerling et al, 2016]
EMPIRE [Skar et al, 2016]
MESSAGE [Huppman et al, 2019]
ReEDS [Cohen et al, 2019]
Texas Case Study [Boffino et al, 2019]
Gemstone [Ferris & P., 2019]
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 20 / 55
Simplified two-stage stochastic model
Capacity decisions are x at cost K (x).Operating decisions are: generation y at cost C (y)
loadshedding q at cost Vq.Random demand is d(ω).
Minimize annual capital cost plus expected operating cost.
P: min K (x) + Eω[C (y(ω))− V (d(ω)− q(ω))]s.t. y(ω) ≤ x ,
y(ω) + q(ω) ≥ d(ω),x , y(ω), q(ω) ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 21 / 55
Simplified two-stage risk-averse model
Capacity decisions are x at cost K (x).Operating decisions are: generation y at cost C (y)
loadshedding q at cost Vq.Random demand is d(ω), and F a coherent risk measure.
Minimize annual capital cost plus risk-adjusted operating cost.
P: min K (x) + Fω[C (y(ω))− V (d(ω)− q(ω))]s.t. y(ω) ≤ x ,
y(ω) + q(ω) ≥ d(ω),x , y(ω), q(ω) ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 22 / 55
Penalize load shedding at V (value of lost load)
Capacity decisions are x at cost K (x).Operating decisions are: generation y at cost C (y)
loadshedding q at cost Vq.Random demand is d(ω).
Minimize annual capital cost plus expected operating cost.
P: min K (x) + Eω[C (y(ω))− V (d(ω)− q(ω))]s.t. y(ω) ≤ x ,
y(ω) + q(ω) ≥ d(ω),x , y(ω), q(ω) ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 23 / 55
Gemstone: New Zealand capacity expansion model
[described in full in Ferris & P., 2019]b = load block, H(b) = hours in load block b.
Winter load duration curve for New Zealand
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 24 / 55
Load duration curve
[Ferris & P., 2019]b = load block, H(b) = hours in load block b.
Load duration curve as load blocks
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 25 / 55
Gemstone model detail
k = technology
Minimize fixed and expected variable costs.
P: min∑
k(Kkxk + Lkzk) +∑
t Eω[Z (t, ω)]s.t. Z (t, ω) =
∑b∈t H(b) (
∑k Ckyk(t, ω, b) + Vq(t, ω, b)) ,
xk ≤ uk ,zk ≤ xk + Uk ,
yk(t, ω, b) ≤ µk(t, ω, b)zk ,∑b∈t H(b)yk(t, ω, b) ≤ νk(t, ω)
∑b∈t H(b)zk + s(t − 1)− s(t),
q(t, ω, b) ≤ d(t, ω, b),d(t, ω, b) ≤
∑k yk(t, ω, b) + q(t, ω, b),
s(t) ≤ Szk ,x , z , y , q, s ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 26 / 55
Operating costs are random
ω = scenario, t = season, b = load block containing H(b) hours.
Minimize fixed and expected variable costs.
P: min∑
k(Kkxk + Lkzk) +∑
t Eω[Z (t, ω)]s.t. Z (t, ω) =
∑b∈t H(b) (
∑k Ckyk(t, ω, b) + Vq(t, ω, b)),
xk ≤ uk ,zk ≤ xk + Uk ,
yk(t, ω, b) ≤ µk(t, ω, b)zk ,∑b∈t H(b)yk(t, ω, b) ≤ νk(t, ω)
∑b∈t H(b)zk + s(t − 1)− s(t),
q(t, ω, b) ≤ d(t, ω, b),d(t, ω, b) ≤
∑k yk(t, ω, b) + q(t, ω, b),
s(t) ≤ Szk ,x , z , y , q, s ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 27 / 55
Capacity of wind and run-of-river is random in a season
ω = scenario, t = season, b = load block containing H(b) hours.
Minimize fixed and expected variable costs.
P: min∑
k(Kkxk + Lkzk) +∑
t Eω[Z (t, ω)]s.t. Z (t, ω) =
∑b∈t H(b) (
∑k Ckyk(t, ω, b) + Vq(t, ω, b)),
xk ≤ uk ,zk ≤ xk + Uk ,
yk(t, ω, b) ≤ µk(t, ω, b)zk ,∑b∈t H(b)yk(t, ω, b) ≤ νk(t, ω)
∑b∈t H(b)zk + s(t − 1)− s(t),
q(t, ω, b) ≤ d(t, ω, b),d(t, ω, b) ≤
∑k yk(t, ω, b) + q(t, ω, b),
s(t) ≤ Szk ,x , z , y , q, s ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 28 / 55
Modeling stored hydro
s(t) = stored water at end of season t.
Minimize fixed and expected variable costs.
P: min∑
k(Kkxk + Lkzk) +∑
t Eω[Z (t, ω)]s.t. Z (t, ω) =
∑b∈t H(b) (
∑k Ckyk(t, ω, b) + Vq(t, ω, b)) ,
xk ≤ uk ,zk ≤ xk + Uk ,
yk(t, ω, b) ≤ µk(t, ω, b)zk ,∑b∈t H(b)yk(t, ω, b) ≤ νk(t, ω)
∑b∈t H(b)zk + s(t − 1)− s(t),
q(t, ω, b) ≤ d(t, ω, b),d(t, ω, b) ≤
∑k yk(t, ω, b) + q(t, ω, b),
s(t) ≤ Szk ,x , z , y , q, s ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 29 / 55
Energy input from reservoir inflows is random in a season
ω = scenario, t = season, b = load block containing H(b) hours.s(t) = stored water at end of season t.
Minimize fixed and expected variable costs.
P: min∑
k(Kkxk + Lkzk) +∑
t Eω[Z (t, ω)]s.t. Z (t, ω) =
∑b∈t H(b) (
∑k Ckyk(t, ω, b) + Vq(t, ω, b)),
xk ≤ uk ,zk ≤ xk + Uk ,
yk(t, ω, b) ≤ µk(t, ω, b)zk ,∑b∈t H(b)yk(t, ω, b) ≤ νk(t, ω)
∑b∈t H(b)zk+s(t − 1)− s(t),
q(t, ω, b) ≤ d(t, ω, b),d(t, ω, b) ≤
∑k yk(t, ω, b) + q(t, ω, b),
s(t) ≤ Szk ,x , z , y , q, s ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 30 / 55
Accounting for CO2 emissions
Some capacity xk , k ∈ N , is deemed non-renewable.
Generation gk(ω) =∑
t
∑b∈t H(b)yk(t, ω, b), emits βkgk(ω) tonnes.
For a choice of θ ∈ [0, 1], constraint is either:∑k∈N xk ≤ (1− θ)
∑k∈N xk(T )
(reduce non-renewable capacity compared with year T ),
Eω
[∑k∈N gk(ω)
]≤ (1− θ)
∑k∈N gk(ωT )
(reduce expected non-renewable generation compared with year T ),
Eω [∑
k βkgk(ω)] ≤ (1− θ)∑
k βkgk(ωT )(reduce expected CO2 emissions compared with year T ).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 31 / 55
Emission KPIs
Some capacity xk , k ∈ N , is non-renewable.
Generation gk(ω) =∑
t
∑b∈t H(b)yk(t, ω, b), emits βkgk(ω) tonnes.
For a choice of θ ∈ [0, 1], constraint is either:∑k βkgk(ω) ≤ (1− θ)
∑k βkgk(ωT )
(constrain CO2 emissions almost surely ),
Pω [∑
k βkgk(ω) ≤ (1− θ)∑
k βkgk(ωT )] ≥ 1− α(constrain CO2 emissions with probability 1− α ),
Fω [∑
k βkgk(ω)] ≤ (1− θ)∑
k βkgk(ωT )(constrain risk-adjusted expected CO2 emissions ).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 32 / 55
Assumptions for New Zealand in 2035
Huntly 500MW coal plant decommissioned. No new coal.
0.5% p.a. demand growth in residental and commercial load.
Additional demand (5.7 TWh) from transport electrification(assumes 0.25% EVs in 2019 becomes 50% EVs in 2035).
Additional demand (4.0 TWh) from process heat electrification(33% switch from coal/gas).
Electricity demand 39.2 TWh in 2017 becomes 53.6 TWh in 2035
16 wind scenarios × 13 hydrology scenarios = 208.
Can build run-of-river hydro, onshore wind, solar PV, somegeothermal, batteries, gas plant (CCGT and OCGT), CCGT withcarbon capture and storage (CCS).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 33 / 55
Assumptions for New Zealand in 2035
Huntly 500MW coal plant decommissioned. No new coal.
0.5% p.a. demand growth in residental and commercial load.
Additional demand (5.7 TWh) from transport electrification(assumes 0.25% EVs in 2019 becomes 50% EVs in 2035).
Additional demand (4.0 TWh) from process heat electrification(33% switch from coal/gas).
Electricity demand 39.2 TWh in 2017 becomes 53.6 TWh in 2035
16 wind scenarios × 13 hydrology scenarios = 208.
Can build run-of-river hydro, onshore wind, solar PV, somegeothermal, batteries, gas plant (CCGT and OCGT), CCGT withcarbon capture and storage (CCS).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 33 / 55
Assumptions for New Zealand in 2035
Huntly 500MW coal plant decommissioned. No new coal.
0.5% p.a. demand growth in residental and commercial load.
Additional demand (5.7 TWh) from transport electrification(assumes 0.25% EVs in 2019 becomes 50% EVs in 2035).
Additional demand (4.0 TWh) from process heat electrification(33% switch from coal/gas).
Electricity demand 39.2 TWh in 2017 becomes 53.6 TWh in 2035
16 wind scenarios × 13 hydrology scenarios = 208.
Can build run-of-river hydro, onshore wind, solar PV, somegeothermal, batteries, gas plant (CCGT and OCGT), CCGT withcarbon capture and storage (CCS).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 33 / 55
Assumptions for New Zealand in 2035
Huntly 500MW coal plant decommissioned. No new coal.
0.5% p.a. demand growth in residental and commercial load.
Additional demand (5.7 TWh) from transport electrification(assumes 0.25% EVs in 2019 becomes 50% EVs in 2035).
Additional demand (4.0 TWh) from process heat electrification(33% switch from coal/gas).
Electricity demand 39.2 TWh in 2017 becomes 53.6 TWh in 2035
16 wind scenarios × 13 hydrology scenarios = 208.
Can build run-of-river hydro, onshore wind, solar PV, somegeothermal, batteries, gas plant (CCGT and OCGT), CCGT withcarbon capture and storage (CCS).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 33 / 55
Assumptions for New Zealand in 2035
Huntly 500MW coal plant decommissioned. No new coal.
0.5% p.a. demand growth in residental and commercial load.
Additional demand (5.7 TWh) from transport electrification(assumes 0.25% EVs in 2019 becomes 50% EVs in 2035).
Additional demand (4.0 TWh) from process heat electrification(33% switch from coal/gas).
Electricity demand 39.2 TWh in 2017 becomes 53.6 TWh in 2035
16 wind scenarios × 13 hydrology scenarios = 208.
Can build run-of-river hydro, onshore wind, solar PV, somegeothermal, batteries, gas plant (CCGT and OCGT), CCGT withcarbon capture and storage (CCS).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 33 / 55
Assumptions for New Zealand in 2035
Huntly 500MW coal plant decommissioned. No new coal.
0.5% p.a. demand growth in residental and commercial load.
Additional demand (5.7 TWh) from transport electrification(assumes 0.25% EVs in 2019 becomes 50% EVs in 2035).
Additional demand (4.0 TWh) from process heat electrification(33% switch from coal/gas).
Electricity demand 39.2 TWh in 2017 becomes 53.6 TWh in 2035
16 wind scenarios × 13 hydrology scenarios = 208.
Can build run-of-river hydro, onshore wind, solar PV, somegeothermal, batteries, gas plant (CCGT and OCGT), CCGT withcarbon capture and storage (CCS).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 33 / 55
Assumptions for New Zealand in 2035
Huntly 500MW coal plant decommissioned. No new coal.
0.5% p.a. demand growth in residental and commercial load.
Additional demand (5.7 TWh) from transport electrification(assumes 0.25% EVs in 2019 becomes 50% EVs in 2035).
Additional demand (4.0 TWh) from process heat electrification(33% switch from coal/gas).
Electricity demand 39.2 TWh in 2017 becomes 53.6 TWh in 2035
16 wind scenarios × 13 hydrology scenarios = 208.
Can build run-of-river hydro, onshore wind, solar PV, somegeothermal, batteries, gas plant (CCGT and OCGT), CCGT withcarbon capture and storage (CCS).
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 33 / 55
Summary
1 Introduction
2 Understanding uncertainty
3 Getting to 100 percent renewable electricity
4 Results
5 Understanding incentives
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 34 / 55
CO2 emissions as constraint binds
Gemstone: increasing θ on CO2 constraints
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 35 / 55
Cost increase (2017 NZD M p.a.) as CO2 constraint binds
Gemstone: increasing θ on CO2 constraints
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 36 / 55
Effect of risk aversion
Risk aversion uses (1− λ)E[Z ] + λAVaR0.90(Z ), λ = 0, 0.5, 0.8.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 37 / 55
Capacity mix: non-renewable capacity constraint
Gemstone: solutions as non-renewable capacity constrained
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 38 / 55
Capacity mix: non-renewable energy constraint
Gemstone: solutions as non-renewable energy constrained
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 39 / 55
Capacity mix: CO2 emission constraint
Gemstone: solutions as CO2 emissions constrained.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 40 / 55
Capacity mix: increasing carbon tax
Gemstone: solutions as carbon tax increases.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 41 / 55
Takeaways
Electricity systems have uncertainties at different timescales that we can approximate in a single model.Outcomes depend on definition of 100% renewableelectricity. Reduce nonrenewable capacity, expectednonrenewable energy, or expected CO2 emissions?Outcomes depend on CO2 emissions KPI. Constrainemissions on average, almost surely, with highprobability, risk-adjusted ?Getting to 100% is much harder than getting to 99%Risk averse social plan produces different technologymix.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 42 / 55
Takeaways
Electricity systems have uncertainties at different timescales that we can approximate in a single model.Outcomes depend on definition of 100% renewableelectricity. Reduce nonrenewable capacity, expectednonrenewable energy, or expected CO2 emissions?Outcomes depend on CO2 emissions KPI. Constrainemissions on average, almost surely, with highprobability, risk-adjusted ?Getting to 100% is much harder than getting to 99%Risk averse social plan produces different technologymix.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 42 / 55
Takeaways
Electricity systems have uncertainties at different timescales that we can approximate in a single model.Outcomes depend on definition of 100% renewableelectricity. Reduce nonrenewable capacity, expectednonrenewable energy, or expected CO2 emissions?Outcomes depend on CO2 emissions KPI. Constrainemissions on average, almost surely, with highprobability, risk-adjusted ?Getting to 100% is much harder than getting to 99%Risk averse social plan produces different technologymix.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 42 / 55
Takeaways
Electricity systems have uncertainties at different timescales that we can approximate in a single model.Outcomes depend on definition of 100% renewableelectricity. Reduce nonrenewable capacity, expectednonrenewable energy, or expected CO2 emissions?Outcomes depend on CO2 emissions KPI. Constrainemissions on average, almost surely, with highprobability, risk-adjusted ?Getting to 100% is much harder than getting to 99%Risk averse social plan produces different technologymix.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 42 / 55
Takeaways
Electricity systems have uncertainties at different timescales that we can approximate in a single model.Outcomes depend on definition of 100% renewableelectricity. Reduce nonrenewable capacity, expectednonrenewable energy, or expected CO2 emissions?Outcomes depend on CO2 emissions KPI. Constrainemissions on average, almost surely, with highprobability, risk-adjusted ?Getting to 100% is much harder than getting to 99%Risk averse social plan produces different technologymix.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 42 / 55
What’s missing?
What is the optimal path to destination? Largeuncertainty over long time scales (e.g. technologyadvances, future costs of climate change). Multistageproblem gives options to adapt, delay or abandon.Multi-horizon scenario trees [Skar et al, 2016 ] integratetime scales. See Emerald, a multistage stochasticversion of Gemstone solved in JuDGE.jl.
WB83 - Stochastic and Robust Optimization in Energy
Systems
11:40-12:00 Anthony Downward, Applying Multi-stage
Stochastic Programming to Investment Planning for
Energy Systems.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 43 / 55
What’s missing?
What is the optimal path to destination? Largeuncertainty over long time scales (e.g. technologyadvances, future costs of climate change). Multistageproblem gives options to adapt, delay or abandon.Multi-horizon scenario trees [Skar et al, 2016 ] integratetime scales. See Emerald, a multistage stochasticversion of Gemstone solved in JuDGE.jl.
WB83 - Stochastic and Robust Optimization in Energy
Systems
11:40-12:00 Anthony Downward, Applying Multi-stage
Stochastic Programming to Investment Planning for
Energy Systems.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 43 / 55
Postscript: what happened?
Climate Change Response (Zero Carbon) Amendment Bill, May 2019.[http://www.legislation.govt.nz]
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 44 / 55
Summary
1 Introduction
2 Understanding uncertainty
3 Getting to 100 percent renewable electricity
4 Results
5 Understanding incentives
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 45 / 55
Competitive models with risk averse agents
Previous models assume a social planner. How shouldthis change in competitive markets?Even with assumed perfect competition and convexity,competitive outcomes may differ from socially optimaloutcomes, e.g. when agents are risk averse.Competitive investment with risk trading when agentshave coherent risk measures [Ehrenmann & Smeers, 2011,Ralph & Smeers, 2015, De Maere dAertrycke et al, 2018, Kok et al,2019 ]
TC83 - Policy-Enabling Models for the Power Sector II
12:05 - 12:25 Golbon Zakeri, Investment Under
Uncertainty and Risk in Competitive Electricity
Markets.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 46 / 55
Competitive models with risk averse agents
Previous models assume a social planner. How shouldthis change in competitive markets?Even with assumed perfect competition and convexity,competitive outcomes may differ from socially optimaloutcomes, e.g. when agents are risk averse.Competitive investment with risk trading when agentshave coherent risk measures [Ehrenmann & Smeers, 2011,Ralph & Smeers, 2015, De Maere dAertrycke et al, 2018, Kok et al,2019 ]
TC83 - Policy-Enabling Models for the Power Sector II
12:05 - 12:25 Golbon Zakeri, Investment Under
Uncertainty and Risk in Competitive Electricity
Markets.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 46 / 55
Competitive models with risk averse agents
Previous models assume a social planner. How shouldthis change in competitive markets?Even with assumed perfect competition and convexity,competitive outcomes may differ from socially optimaloutcomes, e.g. when agents are risk averse.Competitive investment with risk trading when agentshave coherent risk measures [Ehrenmann & Smeers, 2011,Ralph & Smeers, 2015, De Maere dAertrycke et al, 2018, Kok et al,2019 ]
TC83 - Policy-Enabling Models for the Power Sector II
12:05 - 12:25 Golbon Zakeri, Investment Under
Uncertainty and Risk in Competitive Electricity
Markets.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 46 / 55
Competitive Risk Averse Model
Competing suppliers a ∈ A sell electricity at π(ω) and pay forexpected CO2 emissions at σ.
Pa(π,σ): min K a(xa) + Fa[Z a(ω)]
s.t. Z a(ω) =∑
k∈a(Ck − π(ω)yak (ω))
+σ∑
k∈a E[βkyk(ω)]
yak (ω) ≤ xak , k ∈ a,
yak (ω) ≥ 0, k ∈ a.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 47 / 55
Competitive Risk Averse Model
Electricity consumer c pays for electricity at π(ω).blank
Pc : min Fc [Z c(ω)]
s.t. Z c(ω) = −(V − π(ω))(d(ω)− qc(ω))
qc(ω) ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 48 / 55
Competitive Risk Averse MOPEC
Find electricity prices π(ω) and a CO2 price σ and actions (xa, y a),a ∈ A and qc so that
(xa, y a) solves Pa(π, σ);
qc solves Pc(π, σ);
0 ≤∑a∈A
∑k∈a
y ak (ω) + qc(ω)− d(ω) ⊥ π(ω) ≥ 0;
0 ≤ (1− θ)∑a∈A
∑k∈a
βkyak (ωT )− Eω
[∑a∈A
∑k∈a
βkyk(ω)
]⊥ σ ≥ 0.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 49 / 55
Equilibrium CO2 prices with increasing risk aversion
θ = 0.7, F(Z ) = (1− λ)E[Z ] + λAVaR0.90(Z ), λ = 0, . . . , 0.7.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 50 / 55
Trading risk recovers social optimum
Can recover risk averse social optimum if risk marketis complete.An Arrow-Debreu security (purchased in stage 1) forpays the holder $1 in scenario ω in stage 2.A contingent carbon credit (purchased in stage 1)entitles the holder to emit 1 tonne of CO2-e inscenario ω in stage 2.Arrow-Debreu securities complete the financial market.Expectation CO2 constraint can be satisfied in riskedequilibrium if agents trade contingent carbon credits.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 51 / 55
Trading risk recovers social optimum
Can recover risk averse social optimum if risk marketis complete.An Arrow-Debreu security (purchased in stage 1) forpays the holder $1 in scenario ω in stage 2.A contingent carbon credit (purchased in stage 1)entitles the holder to emit 1 tonne of CO2-e inscenario ω in stage 2.Arrow-Debreu securities complete the financial market.Expectation CO2 constraint can be satisfied in riskedequilibrium if agents trade contingent carbon credits.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 51 / 55
Trading risk recovers social optimum
Can recover risk averse social optimum if risk marketis complete.An Arrow-Debreu security (purchased in stage 1) forpays the holder $1 in scenario ω in stage 2.A contingent carbon credit (purchased in stage 1)entitles the holder to emit 1 tonne of CO2-e inscenario ω in stage 2.Arrow-Debreu securities complete the financial market.Expectation CO2 constraint can be satisfied in riskedequilibrium if agents trade contingent carbon credits.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 51 / 55
Trading risk recovers social optimum
Can recover risk averse social optimum if risk marketis complete.An Arrow-Debreu security (purchased in stage 1) forpays the holder $1 in scenario ω in stage 2.A contingent carbon credit (purchased in stage 1)entitles the holder to emit 1 tonne of CO2-e inscenario ω in stage 2.Arrow-Debreu securities complete the financial market.Expectation CO2 constraint can be satisfied in riskedequilibrium if agents trade contingent carbon credits.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 51 / 55
Trading risk recovers social optimum
Can recover risk averse social optimum if risk marketis complete.An Arrow-Debreu security (purchased in stage 1) forpays the holder $1 in scenario ω in stage 2.A contingent carbon credit (purchased in stage 1)entitles the holder to emit 1 tonne of CO2-e inscenario ω in stage 2.Arrow-Debreu securities complete the financial market.Expectation CO2 constraint can be satisfied in riskedequilibrium if agents trade contingent carbon credits.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 51 / 55
Challenges
Solving competitive equilibrium problems withincomplete risk markets is generally difficult.Competitive equilibrium problems with incomplete riskmarkets can have multiple equilibia [Gerard et al, 2018].
Risk aversion complicates competitive capacity choices[Mays et al, 2019].
Net-zero CO2 emissions targets typically excludeconsumers, which favours importers ofcarbon-intensive products [Economist, October 19, 2019].
International cooperation: a role for IFORS?
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 52 / 55
Challenges
Solving competitive equilibrium problems withincomplete risk markets is generally difficult.Competitive equilibrium problems with incomplete riskmarkets can have multiple equilibia [Gerard et al, 2018].
Risk aversion complicates competitive capacity choices[Mays et al, 2019].
Net-zero CO2 emissions targets typically excludeconsumers, which favours importers ofcarbon-intensive products [Economist, October 19, 2019].
International cooperation: a role for IFORS?
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 52 / 55
Challenges
Solving competitive equilibrium problems withincomplete risk markets is generally difficult.Competitive equilibrium problems with incomplete riskmarkets can have multiple equilibia [Gerard et al, 2018].
Risk aversion complicates competitive capacity choices[Mays et al, 2019].
Net-zero CO2 emissions targets typically excludeconsumers, which favours importers ofcarbon-intensive products [Economist, October 19, 2019].
International cooperation: a role for IFORS?
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 52 / 55
Challenges
Solving competitive equilibrium problems withincomplete risk markets is generally difficult.Competitive equilibrium problems with incomplete riskmarkets can have multiple equilibia [Gerard et al, 2018].
Risk aversion complicates competitive capacity choices[Mays et al, 2019].
Net-zero CO2 emissions targets typically excludeconsumers, which favours importers ofcarbon-intensive products [Economist, October 19, 2019].
International cooperation: a role for IFORS?
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 52 / 55
Challenges
Solving competitive equilibrium problems withincomplete risk markets is generally difficult.Competitive equilibrium problems with incomplete riskmarkets can have multiple equilibia [Gerard et al, 2018].
Risk aversion complicates competitive capacity choices[Mays et al, 2019].
Net-zero CO2 emissions targets typically excludeconsumers, which favours importers ofcarbon-intensive products [Economist, October 19, 2019].
International cooperation: a role for IFORS?
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 52 / 55
THE END
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 53 / 55
References
De Maere dAertrycke, G., Ehrenmann, A. and Smeers, Y. Investment with
incomplete markets for risk: The need for long-term contracts. Energy
Policy, 105, pp.571-583, 2017.
Dowson, O. and Kapelevich, L., SDDP. jl: a Julia package for Stochastic
Dual Dynamic Programming. Optimization Online, 2017
Downward, A. and Philpott,A.B., Infinite horizon SDDP. EPOC technical
report, 2019.
Ehrenmann, A. and Smeers, Y., Generation capacity expansion in a risky
environment: a stochastic equilibrium analysis. Operations Research, 59(6),
pp.1332-1346, 2011.
Ferris, M.C. and Philpott, A.B., 100% renewable electricity with storage,
EPOC technical report, 2019.
Fulton B. Security of Supply in the New Zealand Electricity Market,
University of Auckland Engineering Honours Thesis, 2018.
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 54 / 55
References
Kok, C., Philpott, A.B. and Zakeri, G., Value of transmission capacity in
electricity markets with risk averse agents, EPOC technical report, 2018.
Gerard, H., Leclere, V. and Philpott, A., On risk averse competitive
equilibrium. Operations Research Letters, 46(1), pp.19-26, 2018.
Mays, J., Morton, D. and O’Neill, R.P., Asymmetric Risk and Fuel
Neutrality in Capacity Markets, 2019.
Pereira, M.V. and Pinto, L.M., Multi-stage stochastic optimization applied
to energy planning. Mathematical programming, 52(1-3), pp.359-375, 1991.
Ralph, D. and Smeers, Y., Risk trading and endogenous probabilities in
investment equilibria. SIAM Journal on Optimization, 25(4), pp.2589-2611,
2015.
Skar, C., Doorman, G., Prez-Valds, G.A. and Tomasgard, A., A multi-horizon
stochastic programming model for the European power system, 2016.
All EPOC technical reports and this talk can be downloaded fromwww.epoc.org.nz
Philpott (www.epoc.org.nz) Zero-Carbon Analytics INFORMS October 21, 2019 55 / 55