The Value of Volatile Resources... Caltech, May 6 2010
Transcript of The Value of Volatile Resources... Caltech, May 6 2010
IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
The Value of Volatile Resourcesin Electricity Markets
Sean P. Meyn
Joint work with: Matias Negrete-Pincetic, Gui Wang, Anupama Kowli, EhsanShafieeporfaard, and In-Koo Cho
Coordinated Science Laboratoryand the Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign, USA
May 5, 2010
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IntroductionDAM/RTM Dynamic model
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Numerical ResultsConcluding Remarks
Outline
1 Introduction
2 DAM/RTM Dynamic model
3 Multi-settlement Electricity Market
4 Who Commands the Wind?
5 Numerical Results
6 Concluding Remarks
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Introduction
Increased deployment of renewable energy can lead tosignificant environmental benefits
However, the characteristics of renewable resources are verydifferent from those of conventional resources
It is imperative to understand their characteristics to fullyharness the benefits of renewable resources
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Numerical ResultsConcluding Remarks
Introduction
Increased deployment of renewable energy can lead tosignificant environmental benefits
However, the characteristics of renewable resources are verydifferent from those of conventional resources
It is imperative to understand their characteristics to fullyharness the benefits of renewable resources
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IntroductionDAM/RTM Dynamic model
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Numerical ResultsConcluding Remarks
Introduction
Increased deployment of renewable energy can lead tosignificant environmental benefits
However, the characteristics of renewable resources are verydifferent from those of conventional resources
It is imperative to understand their characteristics to fullyharness the benefits of renewable resources
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Numerical ResultsConcluding Remarks
Introduction
6
Renewable Portfolio Standards
State renewable portfolio standard
State renewable portfolio goal
www.dsireusa.org / February 2010
Solar water heating eligible * † Extra credit for solar or customer-sited renewables
Includes non-renewable alternative resources
WA: 15% x 2020*
CA: 33% x 2020
NV: 25% x 2025*
AZ: 15% x 2025
NM: 20% x 2020 (IOUs) 10% x 2020 (co-ops)
HI: 40% x 2030
Minimum solar or customer-sited requirement
TX: 5,880 MW x 2015
UT: 20% by 2025*
CO: 20% by 2020 (IOUs) 10% by 2020 (co-ops & large munis)*
MT: 15% x 2015
ND: 10% x 2015
SD: 10% x 2015
IA: 105 MW
MN: 25% x 2025 (Xcel: 30% x 2020)
MO: 15% x 2021
WI: Varies by utility; laog 5102 x %01
MI: 10% + 1,100 MW x 2015*
OH: 25% x 2025†
ME: 30% x 2000 New RE: 10% x 2017
NH: 23.8% x 2025
MA: 15% x 2020 + 1% annual increase
(Class I RE)
RI: 16% x 2020
CT: 23% x 2020
NY: 29% x 2015
NJ: 22.5% x 2021
PA: 18% x 2020†
MD: 20% x 2022
DE: 20% x 2019*
DC: 20% x 2020
VA: 15% x 2025*
NC: 12.5% x 2021 (IOUs) 10% x 2018 (co-ops & munis)
VT: (1) RE meets any increase in retail sales x 2012;
(2) 20% RE & CHP x 2017
KS: 20% x 2020
OR: 25% x 2025 (large utilities)* 5% - 10% x 2025 (smaller utilities)
IL: 25% x 2025 WV: 25% x 2025*†
29 states + DC have an RPS
(6 states have goals)
DC 0
OW
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Wind power
Wind power is currently favored over all renewables
Its deployment presents major challenges in system operationsdue to its:
limited control capabilitiesforecasting uncertaintyuncertainty and intermittency
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Wind power
Wind power is currently favored over all renewables
Its deployment presents major challenges in system operationsdue to its:
limited control capabilitiesforecasting uncertaintyuncertainty and intermittency
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Mon Tues Weds Thurs Fri SatSun0
1000
5000
6000
7000
Wind generation (MW)
Balancing Authority Load (MW) January 16-22, 2010
Figure: Example of load and wind generation data
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Different opinions...
“The Value of Wind - why more renewable energy means lower electricity bills,” AdamBruce, Chairman, British Wind Energy Association
“Wind is a free source of fuel. When the wind blows the UK’s electricity systemhas access to this free source and the power generated is automatically acceptedonto the system”
“Wind energy means lower bills; it’s as simple as that”
“Wind power’s dirty secret? Its carbon footprint”
“Wind power is touted as the cleanest and greenest renewable energy resource”
“You’ve got a non-carbon-emitting source of energy that’s free,” said DougJohnson of the Bonneville Power Administration
“However, Todd Wynn of the Cascade Policy Institute says it’s not as clean asadvocates claim. He says it’s simply because the wind is volatile and doesn’tblow all the time”
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Different opinions...
“The Value of Wind - why more renewable energy means lower electricity bills,” AdamBruce, Chairman, British Wind Energy Association
“Wind is a free source of fuel. When the wind blows the UK’s electricity systemhas access to this free source and the power generated is automatically acceptedonto the system”
“Wind energy means lower bills; it’s as simple as that”
“Wind power’s dirty secret? Its carbon footprint”
“Wind power is touted as the cleanest and greenest renewable energy resource”
“You’ve got a non-carbon-emitting source of energy that’s free,” said DougJohnson of the Bonneville Power Administration
“However, Todd Wynn of the Cascade Policy Institute says it’s not as clean asadvocates claim. He says it’s simply because the wind is volatile and doesn’tblow all the time”
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Scope
Goal of this work
Understand how volatility impacts the value of wind generationFocus: Competitive market setting
Beyond mean energy...
We evaluate impacts of wind resources by focussing on twoparameters: penetration and volatility of wind generation.
Vehicle
Dynamic electricity market model,
Multi-settlement structure
Captures wind volatility
Ramping constraints
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Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Scope
Goal of this work
Understand how volatility impacts the value of wind generationFocus: Competitive market setting
Beyond mean energy...
We evaluate impacts of wind resources by focussing on twoparameters: penetration and volatility of wind generation.
Vehicle
Dynamic electricity market model,
Multi-settlement structure
Captures wind volatility
Ramping constraints
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Scope
Goal of this work
Understand how volatility impacts the value of wind generationFocus: Competitive market setting
Beyond mean energy...
We evaluate impacts of wind resources by focussing on twoparameters: penetration and volatility of wind generation.
Vehicle
Dynamic electricity market model,
Multi-settlement structure
Captures wind volatility
Ramping constraints8 / 50
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Numerical ResultsConcluding Remarks
Day-ahead market motivations
The day-ahead market (DAM) is cleared one day prior to theactual production and delivery of energy.
Economic world...
Forward markets for electricity which improve market efficiency andserve as a hedging mechanism
...Physical world
Facilitate the scheduling of generating units
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Day-ahead market motivations
The day-ahead market (DAM) is cleared one day prior to theactual production and delivery of energy.
Economic world...
Forward markets for electricity which improve market efficiency andserve as a hedging mechanism
...Physical world
Facilitate the scheduling of generating units
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Real-time market (RTM)
At close of the DAM: The ISO generates a schedule of generatorsto supply specific levels of power for each hour over the next 24hour period.
RTM = Balancing Market
As supply and demand are not perfectly predictable, the RTMplays the role of fine-tuning this resource allocation process
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DAM/RTM
32000
30000
28000
26000
24000
22000
20000
00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Meg
awat
ts
Hour Beginning
Actual Demand
Day Ahead Demand Forecast
Available Resources Forecast
Figure: March 4, 2010 CAISO forecast and real-time supply and demand
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DAM/RTM
Notation
Total demand at time t, Dttl(t) = dda(t) +D(t),
Total capacity at time t, Gttl(t) = gda(t) +G(t)Total reserve at time t,Rttl(t) = Gttl(t)−Dttl(t) = G(t)−D(t) + rda(t)
DAM Reserve policy
rda(t) ≡ rda0 is constant.
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RTM Model
RTM model of Cho & Meyn consists of the following components:
Volatility
Deviation in demand D is modeled as a driftless Brownian motionwith instantaneous variance σ2.
Friction
Generation cannot increase instantaneously: There existsζ ∈ (0,∞) such that,
G(t′)−G(t)t′ − t
≤ ζ , for all t ≥ 0, and t′ > t.
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RTM Model
Write dG(t) = ζdt− dI(t), with I non-decreasing, to model theupper bound on the rate of increase in generation.
Reserve process regarded as a controlled stochastic system,
Reserve process
dR(t) = ζdt− dI(t)− dD(t)
with control I.
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RTM Model
Write dG(t) = ζdt− dI(t), with I non-decreasing, to model theupper bound on the rate of increase in generation.
Reserve process regarded as a controlled stochastic system,
Reserve process
dR(t) = ζdt− dI(t)− dD(t)
with control I.
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Numerical ResultsConcluding Remarks
Market Analysis
Market analysis assumptions:
Cost
The production cost is a linear function of G(t),of the form cG(t) for some constant c > 0.
Value of power
Consumer obtains v units of utility per unit of power consumed:Utility to the consumer = vmin(D(t), G(t)).
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Market Analysis
Market analysis assumptions:
Cost
The production cost is a linear function of G(t),of the form cG(t) for some constant c > 0.
Value of power
Consumer obtains v units of utility per unit of power consumed:Utility to the consumer = vmin(D(t), G(t)).
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Market Analysis
Disutility from power loss
The consumer suffers utility loss if demand is not met:Disutility to the consumer = cbo|R(t)| whenever R(t) < 0.
Perfect competition
The price of power P (t) in the RTM is assumed to be exogenous(it does not depend on the decisions of the market agents).
“price-taking assumption”
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Market Analysis
Disutility from power loss
The consumer suffers utility loss if demand is not met:Disutility to the consumer = cbo|R(t)| whenever R(t) < 0.
Perfect competition
The price of power P (t) in the RTM is assumed to be exogenous(it does not depend on the decisions of the market agents).
“price-taking assumption”
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Market Analysis
Disutility from power loss
The consumer suffers utility loss if demand is not met:Disutility to the consumer = cbo|R(t)| whenever R(t) < 0.
Perfect competition
The price of power P (t) in the RTM is assumed to be exogenous(it does not depend on the decisions of the market agents).
“price-taking assumption”
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Market Analysis
The objectives of the consumer and the supplier are specified bythe respective welfare functions,
Welfare functions
WS(t) := P (t)G(t)− cG(t)
WD(t) := vmin(D(t), G(t))− cbo max(0,−R(t))− P (t)G(t)
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Market Analysis
The consumer and supplier each optimize the discountedmean-welfare
Discounted mean-welfare expressions
For given initial values of generation and demand g = G(0),d = D(0), the respective discounted rewards are denoted
KS(g, d) := E[∫
e−γtWS(t) dt],
KD(g, d) := E[∫
e−γtWD(t) dt],
γ > 0 is the discount rate.
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Market Analysis
The social planner’s problem is defined as the maximization of thetotal discounted mean welfare,
Social planner’s objective
K(g, d) = E[∫
e−γt(WS(t) +WD(t)
)dt].
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Market Analysis
The optimal reserve process is a reflected Brownian motion (RBM)on the half-line (−∞, r̄∗], with
Reserve threshold
r̄∗ =1θ+
log(cbo + v
c
).
where θ+ is the positive solution to the quadratic equation12σ
2θ2 − ζθ − γ = 0
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Market Analysis
The equilibrium price functional is a piecewise constant function ofthe equilibrium reserve process,
Equilibrium price functional
pe(re) = (v + cbo)I{re < 0}
The sum cbo + v is in fact the maximum price the consumer iswilling to pay, often called the choke-up price.
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A little bit of technical details...
On substituting the price into the respective welfare functions:
Expected welfare values
E[W∗S (t)] = (v + cbo)(E[D(t)I{Re(t) ≤ 0}]
+ E[Re(t)I{Re(t) ≤ 0}])− cE[Re(t)]
E[W∗D(t)] = −(v + cbo)E[D(t)I{Re(t) ≤ 0}]
where R = Re is the equilibrium reserve process.
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How does this look?...
v + cbo
0
r∗
PricesReserves
Normalized demand
Figure: Model price dynamics
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Familiar, right?
Purchase Price $/MWh
Previous week
Spinning reserve prices PX prices $/MWh
100
150
0
50
200
250
10
20
30
40
50
60
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APX Power NL, April 23, 2007
California, July 2000Illinois, July 1998
Ontario, November 2005
0
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5000
Mon Tues Weds Thurs Fri Mon Tues WedsWeds Thurs Fri Sat Sun
Tues Weds ThursTime3 6 9 12 15 18 213 6 9 12 15 18 213 6 9 12 15 18 21
Demand in MW Last Updated 11:00 AM Predispatch 1975.11 Dispatch 19683.5
Hourly Ontario Energy Price $/MWh Last Updated 11:00 AM Predispatch 72.79 Dispatch 90.822000
21000
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0Fore
cast
Pri
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cast
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uro/
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h)
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h)
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Fri Sat Sun MonTues Wed Thus
Prev. Week Volume
Current Week Volume
Prev. Week Price
Current Week Price
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Numerical ResultsConcluding Remarks
Coupling DAM/RTM
Multi-settlement market model: A coupling of the DAM and RTM.
Consumers’ welfare in the multi-settlement market
W ttlD (t) := vmin(Dttl(t), Gttl(t))− cbo max(0,−R(t))
− P e(t)G(t)− pda(t)gda(t)
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DAM/RTM Consumer’s welfare
Principle of optimality =⇒ Formulae for total discounted meanwelfare,
Consumers’ mean welfare in the multi-settlement market
KttlD = K∗D(r) + γ−1(r −G(0))(pe(r)− pda)
+∫ ∞
0(v − p(t))dda(t) e−γt dt .
in which K∗D(r) corresponds to the RTM welfare.
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Coupling DAM/RTM
Total supplier welfare: Through similar and simpler arguments,
Suppliers’ welfare in the multi-settlement market
W ttlS (t) =WS(t) +
(pda(t)− cda
)gda(t) (1)
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DAM/RTM Supplier’s Welfare
Principle of optimality =⇒ Formulae for total discounted meanwelfare,
Supplier’s mean welfare in the multi-settlement market
KttlS = K∗S (r) + γ−1(r −G(0))(pda − cda)
+∫ ∞
0
(p(t)− cda
)dda(t) e−γt dt .
in which K∗S (r) corresponds to the RTM welfare.
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Integrating Wind...
Figure: Who Commands the Wind?
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Numerical ResultsConcluding Remarks
Extending the dynamic model
Goal
Extend the DA/RT market model to differentiate power generatedby wind and by conventional generation units
Assumption
All the wind power available is injected into the system
Key issue
Conventional generators serve residual demand.
Volatility of the residual demand will result in higher reserves inthe dynamic market equilibrium
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Numerical ResultsConcluding Remarks
Extending the dynamic model
Goal
Extend the DA/RT market model to differentiate power generatedby wind and by conventional generation units
Assumption
All the wind power available is injected into the system
Key issue
Conventional generators serve residual demand.
Volatility of the residual demand will result in higher reserves inthe dynamic market equilibrium
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Extending the dynamic model
Goal
Extend the DA/RT market model to differentiate power generatedby wind and by conventional generation units
Assumption
All the wind power available is injected into the system
Key issue
Conventional generators serve residual demand.
Volatility of the residual demand will result in higher reserves inthe dynamic market equilibrium
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IntroductionDAM/RTM Dynamic model
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Numerical ResultsConcluding Remarks
Extending the dynamic model
Goal
Extend the DA/RT market model to differentiate power generatedby wind and by conventional generation units
Assumption
All the wind power available is injected into the system
Key issue
Conventional generators serve residual demand.
Volatility of the residual demand will result in higher reserves inthe dynamic market equilibrium
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Numerical ResultsConcluding Remarks
Emerging issues
Not only mean energy
The details depend on both volatility of wind and its proportion ofthe overall generation
What about now?
If the penetration of wind resources is low, then the increase inload volatility will be negligible, and hence there will be negligibleimpact on the market outcomes
What about in 2020?
Potential negative market outcomes are possible with acombination of high wind generation penetration and highvolatility of wind
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Numerical ResultsConcluding Remarks
Emerging issues
Not only mean energy
The details depend on both volatility of wind and its proportion ofthe overall generation
What about now?
If the penetration of wind resources is low, then the increase inload volatility will be negligible, and hence there will be negligibleimpact on the market outcomes
What about in 2020?
Potential negative market outcomes are possible with acombination of high wind generation penetration and highvolatility of wind
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Numerical ResultsConcluding Remarks
Who Commands the Wind?
Approach
To quantify the impact of volatility we obtain expressions for thetotal welfare in two market models, differentiated by whocommands the wind generating units
Main finding
Volatility can have tremendous impact on the market outcomes
Asymmetric and surprising result
The supplier can achieve significant gains,even when the consumer commands the wind
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Numerical ResultsConcluding Remarks
Who Commands the Wind?
Approach
To quantify the impact of volatility we obtain expressions for thetotal welfare in two market models, differentiated by whocommands the wind generating units
Main finding
Volatility can have tremendous impact on the market outcomes
Asymmetric and surprising result
The supplier can achieve significant gains,even when the consumer commands the wind
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IntroductionDAM/RTM Dynamic model
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Numerical ResultsConcluding Remarks
Who Commands the Wind?
Approach
To quantify the impact of volatility we obtain expressions for thetotal welfare in two market models, differentiated by whocommands the wind generating units
Main finding
Volatility can have tremendous impact on the market outcomes
Asymmetric and surprising result
The supplier can achieve significant gains,even when the consumer commands the wind
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Numerical ResultsConcluding Remarks
Consumers command the wind
Wind generating units are commanded by the demand side:
Consumers’ and suppliers’ welfare expressions
W ttlD,W(t) = vmin(Dttl(t), Gttl(t) +Gttl
W(t))
− cbo max(0,−R(t))− P (t)G(t)− p(t)gda(t)
W ttlS,W(t) =
(P (t)− crt
)G(t) +
(p(t)− cda
)gda(t)
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Consumers command the wind
Welfare expressions: DAM/RTM decomposition
W ttlD,W(t) =W rt
D,W(t)
+{vdda(t)− p(t)(dda(t)− gda
W (t))}
+{
(P (t)− p(t))rda0 + vGW(t)
}W ttl
S,W(t) =W rtS,W(t) +
(p(t)− cda
)gda(t)
W rtD,W(t), W rt
S,W(t): Welfare obtained in the RTMwith residual demand Dnet(t).
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Suppliers command the wind
The other alternative is to consider wind as part of the supply side,
Consumers’ welfare
W ttlD,W(t) = vmin(Dttl(t), Gttl(t) +Gttl
W(t))
− cbo max(0,−R(t))− P (t)(G(t) +GW(t))− p(t)(gda(t) + gda
W (t))
=W rtD,W(t)
+{
(v − p(t))dda + (P (t)− p(t))rda0
}+ (v − P (t))GW(t)
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Suppliers command the wind
Suppliers’ welfare
W ttlS,W(t) =
(P (t)− crt
)G(t) + P (t)GW(t)
+(p(t)− cda
)gda(t) + p(t)gda
W (t)
=W rtS,W(t)
+(p(t)− cda
)gda(t) + P (t)GW(t) + p(t)gda
W (t)
The welfare gain p(t)gdaW (t) is now taken by the suppliers
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Numerical Results: Setting the stage I
Parameters used in all of our experiments:
Parameters [$/MWh]
Cost of blackout and value of consumption:
cbo = 200, 000 v = 50
Cost and prices of generation: crt = 30, and
cda = 0.75 ∗ crt, pda = 0.85 ∗ crt
Discount factor: γ = 1/12 (12 hour time horizon).
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Numerical Results: Setting the stage II
Parameters (physical)
Ramp limit: ζ = 200 MW/min
Mean demand: D̄ = 50, 000 MW
Standard deviation: σ = 500 MW.
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Numerical Results: Setting the stage III
Penetration and volatility
Coefficient of variation to capture the relative volatility of wind:
cv =σw
E[GttlW(t)]
Percentage of wind penetration:
k = 100 ∗ E[GttlW(t)]/D̄
.
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Numerical Results: Volatility Impacts
cv0 0.1 0.2 0.3 0.4
k = 0
k = 5
k = 10
k = 15
k = 20Optimal Reserve Level
0
20
40
60
80
100
120
140 x 103
Figure: Optimal reserves depend on penetration and coefficient ofvariation.
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Numerical Results: Volatility Impacts
x 106
0 1000200 400 600 80010
12
14
16
18
ζ = 200Total Welfare
σ
ζ =0.1
ζ = 500
Figure: Social planner’s welfare.
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Numerical Results: Consumers command the wind
0 0.1 0.2 0.3 0.40 0.1 0.2 0.3 0.4
x 106
Supplier Welfare
k = 0
k = 5
k = 10
k = 15
k = 20x 10
6
Consumer Welfare
10
0
5
15
k = 0
k = 5
k = 10
k = 15k = 20
3
4
5
cv
Figure: Consumer and supplier welfare when consumer owns wind fordifferent coefficient of variation cv and cda < pda < crt.
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Facing wind volatility
The results of this paper invite many questions:
C1
The impact of demand management and storage on the market
C2
The impact of supply management and storage on the market.e.g., modern wind turbines allow pitch angle control.
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Facing wind volatility
The results of this paper invite many questions:
C1
The impact of demand management and storage on the market
C2
The impact of supply management and storage on the market.e.g., modern wind turbines allow pitch angle control.
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Facing wind volatility
C3
Mechanisms to reduce consumers and suppliers exposure tosupply-side volatility.
C4
How to create policies to protect participants on both sides of themarket, while creating incentives for R&D on renewable energy?
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Facing wind volatility
C3
Mechanisms to reduce consumers and suppliers exposure tosupply-side volatility.
C4
How to create policies to protect participants on both sides of themarket, while creating incentives for R&D on renewable energy?
44 / 50
IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
The main messages...
It is not so easy...
The main message of this paper is that consumer welfare may falldramatically as more and more wind generation is dispatched
A beautiful world...
This is true even under the most ideal circumstances:
consumers own all wind generation resources and
price manipulation is excluded
No “ENRON games” – no “market power”
45 / 50
IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
The main messages...
It is not so easy...
The main message of this paper is that consumer welfare may falldramatically as more and more wind generation is dispatched
A beautiful world...
This is true even under the most ideal circumstances:
consumers own all wind generation resources and
price manipulation is excluded
No “ENRON games” – no “market power”
45 / 50
IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
The main messages...
It is not so easy...
The main message of this paper is that consumer welfare may falldramatically as more and more wind generation is dispatched
A beautiful world...
This is true even under the most ideal circumstances:
consumers own all wind generation resources and
price manipulation is excluded
No “ENRON games” – no “market power”
45 / 50
IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Concluding remarks
Volatility and mean energy are key parameters for new energysources. Our results show that resource volatility can havetremendous impacts on the market outcomes
Currently adopted wind integration schemes can be harmfulfor consumers with larger wind penetration
Suppliers can achieve significant gains even when theconsumer commands the wind
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Concluding remarks
Who faces the uncertainty and risk associated to volatileresources is a big challenge from an economic viewpoint
Under certain conditions consumers are better off notdispatching wind
Storage and demand management emerge as solutions for asmooth integration of wind power
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Concluding remarks
Who faces the uncertainty and risk associated to volatileresources is a big challenge from an economic viewpoint
Under certain conditions consumers are better off notdispatching wind
Storage and demand management emerge as solutions for asmooth integration of wind power
47 / 50
IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Concluding remarks
Market analysis requires dynamic rather than snapshotmodels
Greater wind penetration will require redesigns of bothpower system operations and electricity markets
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Thanks!
Figure: Celebrating with Dutch Babies after finishing part of this work
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IntroductionDAM/RTM Dynamic model
Multi-settlement Electricity MarketWho Commands the Wind?
Numerical ResultsConcluding Remarks
Bibliography
S. Meyn, M. Negrete-Pincetic, G. Wang, A. Kowli, and E. Shafieepoorfard. Thevalue of volatile resources in electricity markets. In Proc. of the 49th Conf. onDec. and Control, pages 4921–4926, Atlanta, GA, 2010.
I.-K. Cho and S. P. Meyn. Efficiency and marginal cost pricing in dynamiccompetitive markets. To appear in J. Theo. Economics, 2006.
M. Chen, I.-K. Cho, and S. Meyn. Reliability by design in a distributed powertransmission network. Automatica, 42:1267–1281, August 2006.
I.-K. Cho and S. P. Meyn. A dynamic newsboy model for optimal reservemanagement in electricity markets. Submitted for publication, SIAM J. Controland Optimization., 2009.
D. J. C. MacKay. Sustainable energy : without the hot air. UIT, Cambridge,England, 2009.
L. M. Wein. Dynamic scheduling of a multiclass make-to-stock queue.Operations Res., 40:724–735, 1992.
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