European Climate and Energy Policy and
Computable Economic Modeling
Yves Smeers1,2
CORE, Université catholique de Louvain, Louvain-la-Neuve, Belgium
GDF Suez Chair:
Louvain-La-Neuve; June 3rd 2014
1
These slides argue for a much more rigorous EU centralization (“More Europe” in now politically incorrect language). The
reasoning is technical and independent of political preferences.
2
The slides on risk directly rely on work done in GDF Suez (Brussels and Paris); those on subsidies are based on S. Martin’s
PhD thesis (University of Malaga). The views are entirely personal.
Ambition and cost effectiveness
A target and a complex mix of policies
The target is ambitious: decarbonization
The mix of policies is complex
The ETS
The cornerstone of EU Climate Change Policy : a cap and trade system
covering ∼ 40% of GHG emissions
The “20/20/20” package
A set of legislative documents requiring 20% reduction of overall GHG,
20% renewable penetration, 20% improvement of energy intensity
The roadmap 2050
An objective of 80% decarbonization of the EU economy by 2050
In background: the IEM
Must integrate the EU electricity and gas systems
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European Climate and Energy Policy and Computable Economic Modeling
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A market made of “market instruments”!
For correcting environment externalities
Normal market forces do not protect the environment
Economists provide correcting instruments but they need to be calibrated
For dealing with the public goods, externalities and natural monopolies of
electricity
Introducing competition is more difficult than expected
The need for a sophisticated (and sometimes technically complex) market
organization
With the usual EU problem: nationally differentiated policies
One cannot impose serious harmonization ex ante; one cannot remedy the
lack of serious harmonization ex post
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European Climate and Energy Policy and Computable Economic Modeling
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Interactions at different levels
Policies overlap
Energy and Climate Change packages through the power sector
The ETS and the 20/20/20 through conservation and renewable
Between constructed markets
IEM and the ETS
20/20/20 targets through policies distorting the IEM
Incomplete policies: “details” left to Member State
Little control of these interactions
No federal government: Recall the lack of “federal government” in
the euro crisis. This also applies to Climate and Energy Policy
And the surprising emergence of competition law as the instruments
of market design
“State Aid” in adequacy! Can a “federal” competition policy replace
a federal government” to design instruments?
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European Climate and Energy Policy and Computable Economic Modeling
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A verbal statement of the problem
The objective is ambitious:
Overhaul the whole energy infrastructure
The market is not natural; it is engineered with instruments designed
to correct market failures
Introduced in an poorly coordinated way
Do objectives and instruments match?
The Commission offers impact assessments
Relying on“cost effective paths” to the objective
Do cost effective assessments really make sense?
The Commission now realizes that there might be a problem
One should also look at real implementation
Impact Assessment accompanying the Communication ”A policy
framework for climate and energy in the period from 2020 up to 2030”
(2014)
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European Climate and Energy Policy and Computable Economic Modeling
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The same in a more abstract way
The assessed “cost effectiveness”
With its market implications
Politicies modify the economics
And sometimes also the physics

T

min c x
s.t. Ax ≥ b, (π T )


x ≥0
(
0 ≤ π ⊥ Ax − b ≥ 0
0 ≤ x ⊥ c − πT A ≥ 0
(
0 ≤ π ⊥ Ax − b ≥ 0
0 ≤ x ⊥ d − πT C ≥ 0
(
0 ≤ π ⊥ Dx − f ≥ 0
0 ≤ x ⊥ d − πT C ≥ 0
(1)
(2)
(3)
(4)
Is the assessment of a ”cost effective” policy and assessment of the
policy?
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European Climate and Energy Policy and Computable Economic Modeling
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A problem for discussion: overhauling capacities
Schematically
Think of two stages: invest “today” and operate “later”
“Later”: simulate the short term market with fixed capacities:
compute the cash flows accruing to the capacities
“Today” : simulate the investment process: calculate the net present
value of the cash flows and compare it to the investment cost
The question: can one still meaningfully calculate the net present
value of future cash flows?
With a focus on two questions
Risk and its impact on cost effective paths
Subsidies, market distortion and their impact on cost effective paths
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European Climate and Energy Policy and Computable Economic Modeling
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Part 1: Unsustainable Risk
1. The climate and energy policy “reforms of
reforms”
Recall: “a process, not an event!”
1996
2002
2005
2008
1 st package 2 nd package
IEM.
2009
2011
2013
2014
2020
2030
2050
3 rd package
1 st phase
2 nd phase
3 rd phase
4 th phase
ETS
20/20/20
Roadmap 2050
Proposal for a
2 nd package
1 st package
*
Figure : The policy packages through time
The longer process, the more risk and the less cost effectiveness
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European Climate and Energy Policy and Computable Economic Modeling
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2. The Problem
Intended:
Investment in clean generation in a stable legal
and regulatory environment
Unintended: Except for subsidized capacities, investment
have come to an halt. “Regulatory uncertainty”
is invoked
Theme:
“Incomplete” markets and contracts
Principles and questions
Markets do not function well when risks are high and not tradable
“Regulatory uncertainty” is a main source of those risks
An assessment requires a model a not “well functioning market”
First: start with a well functioning counterfactual (the “efficient path”)
Then: introduce market failures
And compare
Can one do that in computable way? If not, can one assess the impact of
risk?
And if not, what is the assessment of cost effectiveness worth?
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European Climate and Energy Policy and Computable Economic Modeling
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3. The investment market and non hedgeable risks
The standard scenario approach (and its usual verbal precautions)
Start from the “cost effective” scenario analysis. Skip the usual but useless
verbal “a scenario is not a forecast”: scenarios are technically used as
forecast in standard quantitative analysis
The usual stochastic programming argument: each scenario assumes
certainty; is a set of analysis under certainty equivalent to an analysis over
uncertainty? In general no!
Each cost effective scenario has an interpretation of perfect competition (in
the sense of price taking) in a risk free market
Using a set of scenarios for individual’s decisions may be difficult: super grid
vs. intelligent distribution grid; centralized vs. decentralized development...
Using a set of scenarios for interpreting a market’s decision may be
impossible
We seem to be in a methodological deadlock! Make one move: go to
stochastic programming to check whether something can be done
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European Climate and Energy Policy and Computable Economic Modeling
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4. Turn to a market interpretation of a stochastic
program: doable but not trivial
Stochastic programs come in two brands: one with risk neutral agents and
one with a risk averse agents
The risk neutral interpretation was adequate in the old days: state or
regulated/protected companies assimilated to risk neutral agents. Risk
trading is irrelevant for risk neutral agents. But those days are gone
The risk averse interpretation is less known: the single risk averse agent
model represents a market with perfect risk trading (a “complete” market in
the sense of finance). This is not true in reality but could be an interesting
counterfactual. But we also need to be able to depart from it
The challenge: understand the impact of incomplete or no risk trading:
represent risk aversion in a proper way (using risk functions coming
from finance and now well established)
introduce contracts (base load, reliability options...) in the stochastic
version of the “cost effective” model
and check the effect.
Can it be done?
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European Climate and Energy Policy and Computable Economic Modeling
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5. To some extent (Ehrenmann’s talk), but not
trivial
The method:
Introduce (endogenous) contracts (contracts, not feed in tariffs!) “today”
that change payoffs “later”
And let agent trade these contracts by being driven by risk functions
The result (complete vs. incomplete markets): contracts really help
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European Climate and Energy Policy and Computable Economic Modeling
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6. But illiquidity hurts.
Incompleteness and illiquidity
The good thing: a few contracts seem to help a lot. The market looks
robust to incompleteness
The bad thing: but it is not robust to illiquidity
Unfortunately nothing is guaranteed in incomplete markets:
Contracts can be much more complicated than the simple forward or
reliability option type
With the result that contracts intended to improve things can
unexpectedly make them worse (Ehrenmann’s talk)!
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European Climate and Energy Policy and Computable Economic Modeling
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7. Major issues still lie ahead
Contracts are the normal way to efficiently reallocate risk. What are
those contracts?
Financial contracts in this case: but the market for financial contracts
is neither deep nor liquid enough (300 MW over 20 years vs. 50 MW
for one year) for incentivizing investment
Who is then the counter-party in the contract? If the government,
then a State Aid problem and the delicate application of competition
law on contracts (de Hauteclocque, 2013)
Bilateral contracts have been and are likely to remain suspect of
violation of competition law
Then what?
We may not have the right remedies to risk; it remains to address
some of its causes: do the reforms really need to create all that risk?
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European Climate and Energy Policy and Computable Economic Modeling
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8. Conclusion on risk: limited but possibly useful
Summing up
Risk can drastically change an impact assessment
What is plausible without risk (on a scenario) can be unrealistic with
risk
A cost effective path without risk is at best “hopefully” (in
expectation) cost effective with risk
And can be become simply cost ineffective when the risk market is not
there
A “Forward guidance” to European Climate and Energy Policy?
“Forward guidance” consists of careful statements by monetary
authorities (Central Banks) to reduce market risk.
Read “generation adequacy in the internal electricity market:
guidance on public intervention” (SWD (2013) 438)
And ask yourself: is that all the “forward guidance” the Commission
can offer to reduce the risk created by its Climate and Energy Policy?
There should be room for something better
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European Climate and Energy Policy and Computable Economic Modeling
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Part 2: Unsustainable Market
Distortions
1. The unintended effects of incomplete EU policies
Intended:
The ambitious 20/20/20 policy would induce a strong wave of
“learning by doing” in renewable and make European firms leaders
in the technologies
Unintended: Learn by building (in wind?) or from global research(PV?)? In
any case subsidies remain necessary
Incomplete computable modeling of policies
Analysis in terms of targets: but targets must be transformed into
policies through instruments. MS turned to subsidies for
implementation.
Subsidies are difficult to control (recall the QF of PURPA in the US)
Subsidies can get out of hand in budget terms
Technology specific subsidies result (by nature) in a wide variety of
implicit CO2 prices in the market, which is not cost effective
Controversial (if not flawed) ideas such as levelized cost and grid parity
based on cost transfers (creation of externalities) developed
More specific to our discussion: subsidies in renewable had very serious
consequences in the power market.
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European Climate and Energy Policy and Computable Economic Modeling
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2. A reminder of what happened
Subsidies enhance the penetration of wind and solar
These have zero operating cost; they demand more reserve and
ramping and contribute relatively little or not at all to ancillary services.
The restructured electricity market is based on a PX that acts
separately from the TSO that deals with ancillary services
Wind and solar come first in the PX and decrease the wholesale price
Price of ancillary services are determined at TSO level and do not
compensate for this loss of revenue.
Conventional plants no longer recoup their fixed OM costs
Owner of conventional plants demand additional payment through a
capacity payment mechanism
And a certain capacity of conventional plants needs to remain in the
system for reasons of system services
Public authorities consider subsidizing conventional plants that the
market retires
DG ENER does not seriously distinguish (for good or bad reasons) capacity
payment and capacity markets and argues in terms of State Aid
With the result that we seem to be in a deadlock
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European Climate and Energy Policy and Computable Economic Modeling
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3. The Commission offers proposal for revising
current support to renewable
Start from “European Commission guidance for the design of renewable
support policies” (SWD(2013) 439) and distinguish (qualifiers from the
author)
The good but volatile subsidies: tradable green certificates
One imposes a certain fraction of green energy of demand or production
In principle cost effective (except if “improved” by policy tricks)
The hard to manage subsidies: CfD, Feed in tariff and premium
The CfD and the feed in tariff guarantee the price (sometimes and the
volume) of the green energy
The premium determines a payment on top of the electricity price.
This modifies the incentive to generate and invest
The hidden subsidies (“per se” cost ineffective?)
Do not charge for incurred costs. Worse pass those costs to others.
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European Climate and Energy Policy and Computable Economic Modeling
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4. Hidden subsidies: the hard stuff
Definition
Hidden subsidies occur when a technology is not charged in the market for
the services that it requires:
Socialized reserve costs are not charged to those that induce them
Priority of dispatch implies that a plant can cause congestion without
paying for it
Obligation of purchase means that excess energy is paid by others
This is a departure from standard perfect competition economic
principle. Modeling them suggests that one is introducing non
convexities, therefore departing from cost effectiveness
Where do hidden subsidies materialize?
Services (and subsidies in services) are provided through balancing; this may
require some additional explanation
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European Climate and Energy Policy and Computable Economic Modeling
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5. Balancing and the two settlement system
Recall
The two stages: invest “today” and operate “later”
“Later” is a day of the future that itself consists of two periods:
“Day ahead” which is a day before electricity is produced and
consumed. Most of the energy is traded in day ahead
“Real time” is when energy is produced and consumed. Grid services
are produced and consumed in real time
These services go through balancing, which also serves other duties
There is no market design paradigm for hidden subsidies and balancing
(which explains why balancing is so difficult to integrate)
But hidden subsidies and balancing are both based on socializing
principles that imply that agents do not pay for their cost
Technically, this may introduce “non convexities”, that are not imposed
by the physics of the system and do not occur in “cost effectiveness”
assessments
The question is whether they are important
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European Climate and Energy Policy and Computable Economic Modeling
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6. Illustration
The starting point (not mentioned later in this presentation)
An analysis of flexibility: “There is plenty of flexibility, but so far it has no
value” (Energiewende 2013)
Suggesting to explore:
Whether reserve price effectively increases energy price (as in an energy
only market under an ISO) or
Can compensate for low energy price (when energy and TSO are
separated as in EU)
A model of reserve pricing:
Inspired by the Spanish situation
But with assumptions adapted from “Generation adequacy in the
internal electricity market: guidance on public intervention” (SWD
(2013) 438)
feed in premium
wind pays for balancing
no priority of dispatch (allow for wind spill)
with nothing said on reserve pricing
For computing the value of reserve and its sensitivity
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European Climate and Energy Policy and Computable Economic Modeling
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7. The underpinning economic assumptions
Try to capture the micro structure of the market design
Dynamic request for reserve in day ahead:
Plants’ demand for reserve proportional to their day-ahead schedule
But the demand by wind plant is higher (forecast error/ramping)
Depending on considerations of unit commitment (time necessary to
commit) the demand for reserve depends on machine flexibility
Pricing and costing
Wind and conventional plants pay and are paid for balancing in real
time
Conventional plants that provide reserve are paid at market price for
that reserve
“Equality of access to the grid” implies a hidden subsidy: the cost of
reserve is socialized; plants that demand more reserve do not pay more
for reserve
The “equality of access to the grid” violates in the reserve market a principle
that is applied in balancing
This is in principle not good: and it is indeed not good
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European Climate and Energy Policy and Computable Economic Modeling
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8. A curiosity? The market has several equilibria!
High demand for reserve leads to tight reserve constraints that induce a
multiplicity of disjoint equilibria because of improper pricing. Results are
with high generation capacity (28.5 GW) and low ramping capability
µ (%) expected wind
ρ+ (e/MWh) premium
λ risk aversion (0 is risk neutral)
Demand day ahead (Mwh)
Schedu. win. gen. (Mwh)
Schedu. dispat. gen. (Mwh)
Equilibrium price (e/MWh)
Reserve requirement (MW)
Up. reserve commit. (MW)
Down. reser. commit. (MW)
Max. available reserve (MW)
PROFIT (e)
Total (wind + dispatch.)
Total dispatch.
High capacity (28.5 GW), Low ramping, my = 60%
Algor. 1
Algor. 2
Algor. 1
Algor. 2
23.83
23.83
23.83
23.83
0.00
0.00
80.00
80.00
0.40
0.40
0.40
0.40
22620.46
27717.02
26836.06
27717.02
9256.57
7012.83
6979.66
7012.83
13363.89
20704.18
19856.41
20704.18
82.85
54.24
59.18
54.24
5821.22
4621.78
4584.92
4621.78
6403.34
4159.60
4126.43
4159.60
2561.34
1664.78
4126.43
1663.91
6403.34
4159.60
4126.43
4159.60
2711129.55
2159419.19
972551.61
680619.90
1766091.85
1030837.04
1392235.88
686784.65
Prices of 82.85 and 54.24 are obtained in identical conditions: with high
generation capacity, high demand for reserve but marginal plant at 43.45.
But the equilibrium price is driven by the demand for flexible reserve in day
ahead (because of TSO demand for ramping capacity)
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European Climate and Energy Policy and Computable Economic Modeling
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9. The policy question
Intended:
An energy only market driving generation adequacy for decarbonization!!!! (Possibly with the help of “strategic reserve”). Capacity payments interpreted as State Aid
Challenge: But reserve plays a crucial role in energy only market and may be
badly priced in a market design where energy is separated from
reserve and costs are socialized in balancing mechanisms
Is this more than an academic curiosity?
A multiplicity of disjoint equilibria is a bad theoretical signal
In any case it indicates that one is not cost effective (one of the equilibrium
is not cost effective)
It creates an unnecessary volatility in the market (not driven by
fundamentals)
And will certainly induce claims of exercise of market power
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European Climate and Energy Policy and Computable Economic Modeling
26/34
10. Conclusion on subsidies: limited but
possibly useful
Summing up
Market instruments can be well or badly designed
They need to be based on sound principles
That are sometimes difficult to apply for technical reasons (e.g.
making these instruments flexible as in the ETS)
But sometimes deliberately violated for reasons of “ambition”
Leading to systems that are bound to be cost ineffective
The EU has strong views on State Aids
Seen as subsidies distorting competition in the IEM; applied to
capacity markets (wrongly assimilated to capacity payments)
Read “European Commission guidance for the design of renewable
support policies” (SWD(2013) 439) and ask yourself
Is there no room for a much stronger “guidance” on subsidies based
on sound and simple economic principles?
There should be!
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European Climate and Energy Policy and Computable Economic Modeling
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Conclusion
Numbers for understanding policy conundrum.
Think of computable policy modeling as theorems and counter
examples:
Theorems tell what happens under some assumptions
Counter examples tell what can happen when those assumptions are
violated
Apply to European Climate and Energy policy:
Standard Scenario analysis tells about costs and quantities under
some “cost effectiveness” assumptions
Observation of policy implementation suggests that one is far from
those assumptions
Focused computable policy modeling can inform on the impact of
these violations
And produce structured information that stakeholders might try get in
EU guidance though the consultation process
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European Climate and Energy Policy and Computable Economic Modeling
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Backslides
1. A particular contract problem: the ETS and the
price of carbon?
Intended:
The ETS should have sent a strong price signal for
investment in low carbon; but the carbon price is not
what it should be!
Unintended: For different reasons carbon price has been in a range
of 6 to 10 euros/ton most of the time
Theme:
Tntroduce flexibility in the ETS by a contract
What happened
Various features that have nothing/not much to do with modeling
A Black Swan in September 2008 (Lehman-Brother) and the recession
The usual EU governance problem: MS in charge of setting their
quota; recovery from overallocation difficult
A policy misstep: the 20/20/20, enacted in 2009, contributed to reduce
emissions and hence the price of carbon
And something that is related to modeling: the ETS was not flexible
Is there anything one can do?
Suppose the policy is to have ETS regaining its central role (which seems to
be the intent of the Commission)
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European Climate and Energy Policy and Computable Economic Modeling
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2. Recall the (old fashioned) long term gas
contracts
They have a bad reputation today but:
In contrast with the ETS they effectively supported investment: they
enabled the development of the European gas system
But like the ETS they were victims of the same demand crash, until they
were partially renegotiated
Apply the same idea; public authorities being the counterpart
Contracts involved quantities (TOP) and price indexation clauses
A flexible ETS could play on prices or quantities or both through cap and/or
floors. Suppose one considers rules for withdrawing or injecting allowances
in the market, as well as cap and floor on allowance prices
This can be inserted in models that encompass uncertainty
And simulation with development of gas contracts in risky markets with mix
of spot and contractual clauses strongly suggest that it is also possible to
test flexible ETS markets and their impact on investments
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European Climate and Energy Policy and Computable Economic Modeling
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3. Tradable green certificates
Are efficient and hence do not pose dramatic modeling problems
A certificate is a good in the model
The demand for certificates is specified by the regulation; production is
modeled as in any other sector
Equality between supply and demand determines the value of the certificate
Tradability problems arise when the same certificate is subject to
technology differentiated treatments
E.g. one certificate for a MWh of a well developed technology; two
certificates for a MWh of an undeveloped technology
Certificates remain tradable but the “underlying green energy” is not
Model properties are fine except
When one departs from the one certificate one MWh principle
What is then the problem? Price volatility and non homogeneity of
certificates
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European Climate and Energy Policy and Computable Economic Modeling
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4. CfD, Feed in Tariff and Feed in premium
This directly intervenes in the pricing mechanisms (and hence creates
inefficiencies)
The instrument modifies the incentive to generate and hence the payoff
collected by the plant and the investment criterion
The plant receives the feed in tariff for all its generation when there is
wind
It receives the difference between the strike price and the spot price in
the CfD
It receives a function of the spot price in the feed in premium
The modification of payoff is of the type encountered for contracts
The question is then to model the change of payoff
It accommodates risk (as seems reasonable when dealing with wind and
solar)
As contracts, these may create non convexities
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European Climate and Energy Policy and Computable Economic Modeling
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