“Evaluating the Operational and Market Risks for Complex Energy
Portfolios and New Energy Investments Optimisation in Europe
employing the Power of Hybrid Integrated Modelling”
Dr Christos Papadopoulos, Regional Director Europe
Introduction
Today, it is widely accepted that the Global Community is facing significant challenges in the
energy sector that stem from the combination of serious related environmental challenges
and the emergence of a big variety of new cleaner (Renewables) and more efficient energy
technologies during the last decade. The emergence of these new energy technologies
though, is also accompanied by significant uncertainties on how they should be best
integrated into existing energy systems, but also on how they should be best handled within
the context of liberalised Energy Markets and Free Competition in the energy sector.
Unfortunately, for the European energy Sector, these are not the only challenges ahead that
can describe the whole “story”. Recent Geopolitical risks (Russian/Ukraine crisis) have
brought in the scene another challenge, that of Security of Gas Supply (once Russia is the
main supplier of European gas). Gas power generation holds a significant portion in the
European power supply mix. Nevertheless, it is also equally important for the reliability of
power supply by providing the necessary flexibility needed by the power systems to cope
with renewables’ intermittent production nature. All these and the ever increasing
integration of renewable generation in the power mix add a significant level of complexity
and some new risks in European Energy Policy and in the efficient operation of European
Energy markets. It must be said here that a general tendency of the policy makers is to
address any Security of Energy Supply issues higher in the public policies’ agendas rather
than issues on the efficiency of markets’ operation, for profound reasons. However, it is also
true that one of the main goals of an efficient energy market it is also its ability to exploit
and weigh, through the forces of competition and the different risk appetite of market
participants those market opportunities and risks that may arise in such “constraint”
situations and so to effectively address any potential supply scarcity problem.
For the European Energy Sector, the potential “gas supply scarcity & power supply reliability
problems” came up along with the significant efforts for the establishment of a more
liberalised Pan European Electricity & Gas Market. Thus, it is now even more necessary for
any European policy & decision makers to address all these risks simultaneously and in a
really more coherent way. Eastern and South Eastern Europe are those European Market
areas that are mainly confronted with the most important constraints and problems in
markets’ integration, due not only to the lack of enough physical interconnection capacity,
Adelaide
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both in gas and electricity, but also due to the lack of other necessary infrastructure (e.g.
LNG terminals and storage capacity that could mitigate potential supply risks) and so due to
the lack of adequate energy supply flexibility in general.
Figure 1 - Significant lack of Gas pipeline interconnection capacity and Gas Supply Flexibility in
SE Europe.
Within such a dynamic risk environment, both small and large traditional and new market
players are called to operate and invest.
Market Efficiency, Liquidity and Predictability – Why they are important in Risk
Evaluation and Energy Portfolios’ Optimisation?
Definition of Market Efficiency can go back to the pioneering studies of Roberts (1967) who
first coined the term and Fama (1970). Based on them we can say that a Capital (or
Commodity) Market is said to be Efficient with respect to a certain “Information Set” if
commodity’s prices would be unaffected by revealing that information to ALL market
participants.
So, public availability of Information (hence Transparency) is the key to an Efficient Market.
In a Fully Competitive Market, information is dynamically reflected to asset prices through
real market transactions (Trading). Market Efficiency and transparent informational flow
loses its importance if the Frequency and Scale of effective market transactions (market
Liquidity) is not proportional to that information flow. On lack of liquidity, practically due to
the lack of proper Market Design (in the sense of markets’ correct products and associated
transactions’ timeframes in place) the information flow cannot be translated to correct
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prices and market price signals. On the other hand, sufficient market liquidity provides the
necessary basis for managing and hedging portfolios risks... So a Market can be really
efficient ONLY if it is also sufficiently liquid.
The most important though is that Market Efficiency is also strictly connected to “Perfect
Market Competition”, where a sufficient number of market participants, and not a single
one or a limited number of them, are impacting price dynamics by possessing “Market
Power”. So in effect, the very basic recipes of a Free Competitive Market can be summarised
in the presence of:
 Free Information Flow & Market Transparency
 Sufficient Market Liquidity that is facilitated by well-designed markets, in both market
products and associated timeframes and
 A significant number of Market Participants
In reality though many energy markets, some less and others more, are neither
(informational) Efficient nor fully transparent or Liquid, so not fully competitive. This
practically means that they are also highly unpredictable (on the medium to long term, cause
in the short term unpredictability is a positive indicator of market efficiency) with regards to
future energy costs, prices and market outcomes and thus a higher risk is involved. This
subsequently has a significant effect on the further development of these markets. In
particular any new Investment Planning process unavoidably falls under the full scope of
Investment Planning and Decision Making Process under Uncertainty and Risk.
Under such circumstances, evaluating and optimising the operational and market risks of
modern complex energy portfolios and of any new energy investments is a very difficult task
that calls for enhanced sophistication in the modelling methods and tools needed. A
combination of statistical/quantitative and fundamental methods along with a more
integrated energy modelling approach can really offer such an enhanced sophistication in
energy risk management.
The Energy Markets’ New Era and Associated Risks.
For all the above, it becomes clear that in the New era of uncertainty and complexity in
Energy markets, Power and Gas Utilities have been forced to operate into extremely
challenging operating environments characterised by increasingly “risky” market conditions.
What are the main characteristics of this new era? The decline of Europe’s utilities during
the last few years has certainly been astonishing. At their peak in 2008, the top 20 energy
utilities were worth roughly €1 trillion ($1.3 trillion). NOW nearly 1/3 of that...
Under the “old” system, electricity prices spiked during the middle of the day and early
evening, falling at night with lower demand. So, power producers made all their money
during peak periods. NOW, the middle of the day belongs to solar generation that has
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competed away the positive price spikes that are replaced more and more with negative
ones.
In Germany in 2008, peak-hour prices were on average €14 per MWh above baseload prices.
NOW, this premium is around €3 per MWh and less. So not only the average electricity prices
have fallen by half since 2008 but also the peak premium has fallen by almost 80%.
Renewables have not just put pressure on the power systems reliability and profits margins.
They have already transformed the established business model for utilities.
Figure 2 - Impact of Growing Generation from RES on Supply Stack & the Wholesale Power Price
So, fundamental changes in the European energy markets are already affecting prices
introducing various types of risks coming from:




Significant changes in governmental energy policies (EMR)
Changes in the established markets’ design (Integration & Coupling of Markets)
Renewable Integration and Subsidies’ Policies reforms
Drop in energy demand and growth due to economic crisis but also improved energy
efficiency.
 Falling CO2 price
 Spark spreads going to negative and falling (expensive Natural Gas for power
generation)
 Dark spreads going positive (Cheaper Coal imports)
Addressing these, new considerations are now given to:
 Smart Grids and Demand Side Management
 New Energy Storage technologies
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 Capacity markets
 Systems flexibility and new markets products, with emphasis in balancing and
reserves markets
 Increased electrification of rail networks
 New regulatory frameworks and policies
What has definitely become clear though is that understanding renewables’ profiles and
accounting for their potential variations appears to be critical in forecasting energy costs
and prices and in evaluating the risks for Optimum Investment Planning and Portfolio
Optimisation.
Integrated Energy Portfolios Investment Planning, Risk evaluation and Cooptimisation through Hybrid Energy Market Models.
In order to understand the enhanced capabilities of hybrid quantitative and fundamental
models in risk evaluation it is necessary firstly to answer the question what a modern
Integrated Energy Portfolio may be. Within the modern energy industry a portfolio of assets
can be divided into two distinct parts:
 Physical Portfolio: Consisting of Physical Assets - Power Plants, Gas Fields, power/gas
flow lines, power/gas and other types of energy storages etc.
 Financial Portfolio: Consisting of Contractual Assets. Financial and Physical contracts
such as Futures, Forwards, Swaps, Options and FTRs but also PTRs contracts for
electricity and FTS for gas.
While the optimisation of a contracts’ portfolio in the traditional financial markets has been
well debated, problems can arise when optimising the entire energy portfolio.
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Figure 3 - A Modern Energy Portfolio.
With the advent of power markets and the evolution of market mechanisms both financial
and physical positions have uncertainty that requires quantification to better plan for the
future. For example, in today’s power sector transmission, load and storage competes with
each other and against generation. How so?
 Active demand response and energy efficiency can reduce the need for generation
capacity as well as transmission requirements.
 A load pocket can be served with transmission expansion, local generation or local
storage.
Physical asset holders and operators must evaluate all the risks of physical markets’
competition to see how competitive their solutions can be and then shortlist the most
favourable ones while using limited corporate resources to focus on the more likely winners.
It is also useful the understanding of the specific advantages that fundamental models and
methods can add to the traditional pure quantitative modelling methods for risk modelling
and evaluation and in association with those underlying drivers that affect power prices and
the intrinsic value of physical energy assets.
Modelling the intrinsic value of an energy asset employing “Hybrid” (fundamental and
quantitative) stochastic mathematical programming methods, involves the stochastic
optimisation of an appropriate Value or Payoff Function. Physical operational constraints
and associated market dynamics and price forecasts are all taken into account dictating the
Expected Costs, Revenues and Cash Flows and the inherent operational and market risks.
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Fundamental (Equilibrium) Models
 Future Prices are determined by supply and
demand principles and market participants’
behaviour
 Replicates actual market design and intended
behaviour meeting economic and physical
operational constraints
 Can capture technical constraints on physical
assets operating within the market
 Allows any type of “what if” analysis into the
future
 Can handle negative prices and strategic
bidding outcomes
 Can allow co-optimisation of other
requirements such as ancillary services and/or
district heating load etc.
 Produce results that reflect future structural
changes e.g. carbon price impacts, changes to
market rules, renewable integration
Quantitative Models
 Future Prices are determined based on
historical and current prices’ dynamics and
random processes
 Usually
probabilistic,
explore
the
distribution properties of prices
 Can suffer from in-sample bias of historical
data
 Scenarios only with parameters and/or
explanatory variables
 Most models cannot handle negative
prices/spikes
 Result focuses on prices only
 Limited understanding of what particular
input could be causing the resulting price
Apart from the inter-competition among physical energy assets, as described above which
strongly supports the use of Integrated Energy Systems & Markets’ Models along with hybrid
methods, there is also a crucial difference of the Electricity markets, when compared to
other commodity markets, that supports even further such an approach. In electricity
markets physical assets like e.g. Power Generators, exercise their options of selling power
to “Cash Markets” (Day Ahead, Intraday and Reserves’ markets) that are not traded on a
known forward prices’ basis, but rather the dispatch of a unit happens before all the relevant
market prices are known (Under Prices’ uncertainty)...
Figure 4 - Integrated Energy Systems Topologies
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All these facts introduce significant problems to the proper evaluation of both risks and/or
associated returns by purely employing only the traditional quantitative methods. The Value
at Risk (VaR) is the most commonly used standard to measure the probability that an
investment portfolio will fall by a given amount in a defined horizon. The issue with VaR
though is that it does not explore scenarios that exceed VaR i.e. the magnitude of risk of a
system costs beyond a certain tolerance level a.
Conditional Value-at-Risk (CVaR) accounts for the losses which exceed VaR i.e. CVaR
considers scenarios up to 𝜆, most notably scenarios that have a lower associated risk and a
higher cost.
The main advantage and benefit of integrating CVaR in a portfolio optimization problem is
the fact that the CVaR (e.g. the Conditional Cost Value at Risk CaR of a portfolio is a
continuous and convex function with respect to positions in instruments, whereas the VaR
may be even a discontinuous function. Integrating CVaR in the optimization problem allows
to generate an Efficient Frontier of solutions that vary on whether the user tends towards
high risk–low cost or low risk–high cost scenarios. This approach allows the user to
investigate the trade-off between the investment technology types and/or the amount of
capacity versus risk and also what decisions yield the greatest impact.
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Risk, Budget & Physical Constrained Stochastic Investment Planning optimisation, employing
the power of Hybrid Integrated Energy Modelling methods, can provide much more risk
analysis information in evaluating the Operational and Market Risks for the more complex
modern Energy Portfolios and new energy Investments Optimisation, significantly extending
the capabilities of traditional methods. The approach allows the analyst to explore the
impact of variability in key decisions on the overall solution and get a deeper insight. With
this technique they can be explored, both technically and financially, feasible and optimum
solutions from low cost on a portfolio with comparatively high VaR through to solutions with
higher costs but lowest risk. They can be also identified key investment decisions; such as
for example, what is the type of technology, amount and location of generation built (or
retired) that will have the greatest impact on risk?
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