Forecasting power and gas prices on various time frames...

Forecasting power and gas prices on varioustime frames and resolutions with PLEXOS®

Dr Christos PapadopoulosRegional Director Europe

Energy Exemplar (Europe) Ltd

5th Annual Electricity Price Modelling and Forecasting Forum

Energy Exemplar & PLEXOS® Integrated Energy Model

11-Sept-14

About Energy Exemplar PLEXOS® Integrated Energy Model - Released in 1999

Continuously Developed to meet Challenges of a Dynamic Environment

A Global Leader in Energy Market Simulation Software.

Offices in Adelaide, AUSTRALIA; London, UK; California, USA-WC; Connecticut, USA-EC,Johannesburg, SOUTH AFRICA.

High Growth Rate in Customers and Installations

30% staff with Ph.D. level qualifications spanning Operations Research, Electrical Engineering, Economics, Mathematics and Statistics

European Office:

Software Sales

Customer Support

Training

Consulting

European Systems/Markets &

Countries Datasets3Energy Exemplar - 5th Annual EPM & FF

11-Sept-14 Energy Exemplar - 5th Annual EPM & FF 4

Portfolio of clients in all five continents

Energy ExemplarEnergy Exemplar Europe

As of the end of July 2014, worldwide installations of PLEXOS have exceeded 850at over 145 sites in 35 countries.

11-Sept-14

PLEXOS® Integrated Energy Model for Energy (Power & Gas)Systems & Markets Simulation, Optimisation & Analysis.

Proven power market simulation tool & Integrated Energy Model

Uses cutting-edge Mathematical Programming based Constrained Optimisation techniques (LP/MILP/DP/SP),

Robust analytical framework, used by:

Energy Producers, Traders and Retailers

Transmission System /Market Operators

Energy Regulators/Commissions

Consultants, Analysts and Research Institutions

Power Plant Manufacturers and Construction companies

Power systems’ models scalable to thousands of generators and transmission lines and nodes

5Energy Exemplar - 5th Annual EPM & FF


Recently it was released and was integrated within PLEXOS® theNEW Gas (modelling) Module. The new PLEXOS® Gas moduleprovides the capability to model the costs and constraints of gasdelivery from its source fields via a network of pipelines,through storages and on to meet demands, including thoseassociated with the Power production model.

More importantly though, it is now possible in PLEXOS®, theIntegrated Modelling of both Natural Gas and Power Systems &associated Markets.

PLEXOS® Integrated Energy Model



GAS ELECTRIC COOPTIMIZATION &PRICE FORECASTING


That practically means that, it can be now also used in:

Simulation of electricity and natural gas prices in short term to long term. Natural gas network price formation linked to Gas Powered Generation

fuel costs Pipeline congestion pricing from Well Heads to Natural Gas Hubs Gas market integrated with competitive electricity market production

cost models Market driven production outputs for both gas and electricity sources Fundamentals of Supply and Demand modelling for gas and electric Hourly and sub hourly price forecasting for Day Ahead, Intraday & Real

Time Markets Flexibility for assessments for gas electric systems



Coordination of both the Natural Gas and Electric sectors is critical for:

Least cost co-optimization of OPEX and CAPEX of gas and electric systemexpansion

Combined economic benefit analysis for gas and electric rate payers Strategic energy development for public policy and renewables integration Valuation of gas and electric storage opportunities and dual fuel

optimizations Evaluation of gas and or electric contingencies that can impact reliability Derating of gas powered generators due to gas network constraints Assessing emerging gas constraints with generation retirements Interregional market and asset development planning for gas and electric

Natural Gas and Electric System Coordination

Integrating quantitative and fundamental price forecasting with PLEXOS®


Specific vs GenericEstimation vs Principal Laws

Numerical vs AnalyticalStochastic vs DeterministicMicroscopic vs Macroscopic

Discrete vs ContinuousQualitative vs Quantitative

A General Classification of Models


Statistical (or technical analysis) Models,Statistical approaches aim at finding the optimal model for electricity pricesin terms of its forecasting capabilities. They are either direct applications ofthe statistical techniques of load forecasting or power marketimplementations of econometric models. Most popular methods includemultivariate regression, time series models and smoothing techniques.While the efficiency and usefulness of such “technical analysis” tools infinancial markets is often questioned, in power markets these methods dostand a better chance. The main reason is the seasonality prevailing inelectricity price processes during normal (non-spiky) periods. This makesthe electricity prices more predictable than those of “very randomly”fluctuating financial assets. In order to enhance their performance, theyoften incorporate fundamental factors, like loads or fuel prices.

Power Markets’ Models Classification – Statistical


Artificial intelligence-based (or non-parametric, ANN, Fuzzy Logic,Genetic Algorithms) Models:

Artificial intelligence-based (AI-based) models, are employ patternrecognition type of techniques, modelling price processes via non-parametric tools such as artificial neural networks (ANNs), expert systems,fuzzy logic and support vector machines. AI based models tend to beflexible and can handle complexity and non-linearity. This makes thempromising for short-term predictions

Power Markets’ Models Classification – Artificial Intelligence


Quantitative (Econometric, Reduced-form) models:

Quantitative models characterize the statistical properties and dynamics ofelectricity prices over time, with the ultimate objective of derivativesevaluation and risk management.They aim to recover the main characteristics of electricity prices, typicallyat the hourly/daily time scale and monthly time horizons.Although in this context the models’ simplicity and analytical tractabilityare an advantage, in accurately forecasting e.g. hourly prices is a seriouslimitation, while the recovery of their main underlying characteristics is anexcessive luxury.

Power Markets’ Models Classification – Quantitative


Fundamental Models:Fundamental methods are based on the most basic economic principles ofsupply and demand describing price dynamics and modelling the impact ofimportant physical and economic factors on the market equilibrium price ofelectricity. The fundamental inputs (loads, weather conditions, systemparameters) are independently modelled and predicted, often employingstatistical, econometric or non-parametric techniques.Because of the nature of fundamental data which is typically collected overrelatively long time intervals and the data availability issues, purefundamental models are mostly used for medium to long-term analysis andpredictions.

Power Markets’ Models Classification – Fundamental


Production Cost (or cost-based) models:

Pure production-cost models simulate the operation of generating unitsaiming to satisfy demand at minimum cost. They may have the capability toalso forecast prices on an hour-by-hour, bus-by-bus level, however, whenignore market’s operational principles and strategic bidding practices arenot well suited for today’s competitive markets.

Fundamental Models’ Classification – Production Cost


Equilibrium (Game Theoretic) approaches may be viewed as generalizations of cost-basedmodels amended with strategic bidding considerations. They may give good insight into whetherprices will be above marginal costs and how this might influence the players’ outcomes.Various types of equilibrium approaches have been proposed:Perfect Equilibrium – Firms are price-takers, biding in their SRMC and possess no market powerCournot-Nash Game – Quantity is the strategic variable, and firms choose quantitiessimultaneously, under the assumption that other firms’ quantities are fixedBertrand Game – Price is the strategic variable, and firms choose prices simultaneously, assumingthat other firms’ prices are fixedSupply Function Equilibrium (SFE) – entire bid functions are the strategic variables, and firmschoose their supply functions simultaneously, under the assumption that other firms’ supplyfunctions are fixed; a market mechanism, e.g. an ISO, then determines price and sets thequantity. Cournot-Nash framework tends to provide higher prices than those observed in realityand the supply function equilibrium framework requires considerable numerical computationsand consequently, has limited applicability in day to-day market operations.

Fundamental Models’ Classification – Market Equilibrium

Fundamentals vs Quantitative modelling

Fundamental Models Prices are determined by supply and

demand principles Replicates actual market design and

intended behaviour meeting economicand operational constraints

Can capture technical constraints onphysical assets operating within themarket

Allows any type of “what if” analysis intothe future

Can allow co-optimisation of otherrequirements such as ancillary servicesand/or district heating load etc.

Produce results that reflect futurestructural changes e.g. carbon priceimpacts, changes to market rules,renewable integration

Quantitative Models Prices depend mostly on historical prices

and random processes

Usually probabilistic, explore thedistribution properties of prices

Can suffer from in-sample bias of historicaldata

Scenarios only with parameters and/orexplanatory variables

Most models cannot handle negative prices

Result focuses on prices only

Limited understanding of what particularinput could be causing the resulting price


Common criticisms of Fundamental Market models in replicating short term prices (trading)

Emphasis on a deterministic outcome Failure to capture bidding strategies by players in the market

(pure production cost models) Assumptions of perfect market theory Failure to capture the peak price volatility (pure equilibrium

models) Run times not conducive of using a large market model within

a trading environment when regular updating of inputs isrequired.

However the recent advances in computing power have ledto their adoption for short-term predictions.



Fundamentals vs Quantitative modelling


With the advent of power markets and the evolution of marketmechanisms both financial and physical positions have uncertainty thatrequires quantification to better plan for the future.

In today’s power sector transmission competes with generation and loadcompetes with generation and transmission.

How so?

Active demand response and energy efficiency can reduce the need forgeneration capacity as well as transmission requirements.

A load pocket can be served with transmission or local generation.

Physical asset developers must evaluate all the risks of physical competitionto see how competitive their solutions are and then shortlist the mostcompetitive ones and use limited corporate resources to focus on the morelikely winners.

Uncertainties and Risks involved


PLEXOS provides a framework for testing various pricing options forassets valuations in markets where competition can emerge in theform of load, transmission or generation solutions. Likewise for naturalgas infrastructure development, PLEXOS® provides a comprehensivevaluation methodology that considers both electrical and gas sectorgas demands.

For financial risk evaluation PLEXOS® mixes both statistical riskmodels with fundamentals models.

Statistical risk methods depend on historical data and can suffer fromin-sample bias where fundamentals models can generate price pathsthat can reflect structural change such as carbon price impacts, changein market rules, retirements and new entries of power plants, changesin demand forecasts and fuel forecasts and price paths subject toother uncertainty.

PLEXOS® Modelling Framework


The combination of statistical methods andfundamentals is the preferred approach of today’s riskmanagers.

In addition, PLEXOS® also offers the power of stochasticoptimization which enables the risk manager to forecastrobust forecasts for generation assets, market prices,and other quantities.





2 Day Forecast ARMA (3,0,3)(1,0,1)



Two fundamental modelling approaches in PLEXOS®Generation Cost based Stack model:

Each cost element that makes up agenerators offer into the market can beseparately inputted

Each generator offer price/quantity iscalculated based on SRMC plus any mark-ups,so it is finally transformed to a Bid-stack model.

Allows more flexibility when modelling theoverall effect of changes of certain generatorvalues (fuel costs, heat rates, outage rates etc.)

Harder to gain accurate technical andcommercial characteristics on competitorsplant.

If inputs are realistic, a more useful model forprice “forecasting” when compared to a Bidstack model, at the cost of increased run time

GENERATION (SRMC) STACK

Input Bid based Stack model:

Each generator is represented with offerprice/quantity files which must be known inadvance (“backasting - calibration”) orinferred.

Easier to setup, no need to calculate eachelement that makes up a generators SRMC.

Can link price/quantity files to an externalsource to update regularly andautomatically

Unit commitment decisions will still beoptimised by PLEXOS such as MUT, MDT,ramp limits, start profiles etc. However minstable level and max capacity of units needto be defined.

BID STACK11-Sept-14 27


Impact of Growing Generation from RES on Supply Stack & the Wholesale Power Price

LigniteHard coal

RES

Nuclear

Gas

20 40 60 800

25

75

50

DemandSupported RES generation brings volatile and less predictable

supply

The spot prices decline (not the final price for the consumer!)

Negative impact also - Lower utilization of non-RES generation

Source: CEZ


Supply (bid) Curves of multiple generators

Daily development of the supply curves submitted to the California Power Exchange during a 24-hour period

Energy Laboratory Publication # MIT_EL 00-004


Plants’ Bid Stack vs Generation (SRMC) Stack

Bid Stack Generation Stack

Power markets run on “marginal pricing” thus it is the “cost” of themarginal (or last) unit of serviced load that sets the energy price.

NOTE: Under Perfect Competition price must be equal to the SRMC of themarginal generating unit. In reality, generators bid above their SRMC.

Every linear programming problem, referred to as a primal problem, canbe converted into a dual problem, which provides an upper bound to theoptimal value of the primal problem. The dual problem deals witheconomic values (Shadow Prices).

Solving a linear program usually provides more information about anoptimal solution than merely the values of the decision variables.

Fundamental Hybrid modelling for price formation in PLEXOS®- Marginal Pricing


The different timeframes and resolution phases of PLEXOS®


New EraThe decline of Europe’s utilities has certainly been startling. At their peak in 2008, the top20 energy utilities were worth roughly €1 trillion ($1.3 trillion).Now, less than half that.Under the “old” system, electricity prices spiked during the middle of the day and earlyevening, falling at night with lower demand. So, companies made all their money duringpeak periods.Now the middle of the day belongs to solar generation that has competed away the pricespike.In Germany in 2008, according to the Fraunhofer Institute for Solar Energy Systems, peak-hour prices were €14 per MWh above baseload prices.In the first six months of 2013, the premium was €3 per MWh.

So not only have average electricity prices fallen by half since 2008, but the peak premium has also fallen by almost four-fifths.

Europe’s electricity providers face an existential threat(The Economist 12/10/2013)

Historical price analysis slowly becoming less relevant

Fundamental changes in the energy markets are already effecting prices Changing government policies (EMR) Change in market design (coupling of markets) Renewable Integration/Subsidies Drop in energy demand and growth due to economic crisis Falling CO2 price Spark spreads going to negative and falling (expensive Gas) Dark spreads going positive (cheap imported coal)

What do we have to consider next? Demand Side Management Energy Storage technologies Capacity markets or more importance on reserves and balancing Increased electrification of rail networks Government legislation and policies

Understanding renewables profiles and potential variations is becoming more critical in forecasting daily prices

Strong decrease of the weight in the peak hours in a typical daily profile



Price Forecasting: What Price(s)?

When we normally talk about Electricity Price Modelling andForecasting we imply Electricity Price and particularly Spot (DA)Prices.

Are still the only important ones?

There is a whole list of electricity associated market productsthat their significance is continuously revealed day by day and theirpricing and associated price forecasting will be become even moreimportant in the years to come.

Reserves (AS) and Balancing Prices and related Price Forecasting.

Price Forecasting: What Timeframes and at what Resolution?

LT• Optimal Expansion Plan

MT

• LRMC Recovery Method

• RSI

• Nash-Cournot Game

ST

• Cost-based Efficiency

• Bertrand Game

• Nash-Cournot Game

• Uplift ex-post price

Energy pricesCapacity payments (prices)

•LT prices

Company (player) revenue targets

Adjust bids: Mark-ups

•MT prices

Hourly (period) energy price forecast

(RT) Energy & Ancillary Services prices

•ST prices

3611-Sept-14 Energy Exemplar - 5th Annual EPM & FF

PLEXOS®

The variable portion of generation cost is set by fuel prices,generator efficiencies and any opportunity costs implied by otherconstraints.

Generators trading in the market expect to recover their variablecosts of operation in every period – referred to as their short-runmarginal cost (SRMC).

In the medium term, however, they must also cover fixed operatingcosts, make contributions to debt servicing, and return a profit toshareholders. These fixed cost charges together can be expressedas a per kW capacity charge across some period of time, generallyone year. The combined charge (variable plus fixed) is oftenreferred to as long-run marginal cost (LRMC)

Generation: Fixed v Variable Costs


PLEXOS® Equilibrium Model Mechanism for Calculating Market Price

The market price of energy is the marginal cost, as represented bygenerators’ price/quantity offers (usually, somewhere between SRMC &LRMC) of serving consumption at each node or region.

The marginal cost is found by simulating the least-cost economic dispatchof the entire market, emulating the steps followed by a Market Operator,subject to all: Generation technical characteristics and constraints; Transmission technical characteristics and constraints; and Forecast of load/demand and renewable generation

The market price, at Nodal Level (LMP) is made up of the marginal cost of: Generation; Transmission losses, to that node; and Transmission congestion, to that node

PLEXOS therefore can fully replicate the Nodal or Locational Marginal Pricing (LMP) market rules.



LMP

Marginal Cost of Generation at reference

bus

Marginal Cost of Losses

Marginal Cost of

Transmission Congestion

= + +

Locational (Nodal) Marginal Pricing (LMP) in PLEXOS®

λ is the system “lambda”αι is the node’s congestion charge βι is the node’s marginal loss charge

αι : is the congestion charge at node iωj: is the shadow price on the thermal limit constraints for path j Xi,k: is the angle reference matrix elementωκ: is the shadow price on the node phase angle constraints for node k

βi: is the marginal loss charge at node irj: is the resistance on line j fj’: is the flow on the line j at the optimal solution

λ ι = λ + αι + βι

39


LMP prices based on marginal costs do not include:

Start Cost

No-load cost

Fixed Costs

Therefore, units do not collect all of their costs, and electric prices are artificially low

Baseload and intermediate units can collect some of these costs because they collect above marginal costs while peakers are running

Peakers do not collect these costs

Market Solutions

External (i.e. Resource Adequacy)

Price Uplift

Revenue Adequacy

Market Equilibrium

While short term solutionsfor start and no-load costsare typically included ingenerator bids, Long-termcost recovery (fixed costs)is seldom met in anenergy-only market model.

This phenomena has beendubbed “the MissingMoney Problem”.Absent a solution, themarket generatesinsufficient revenue tosustain operations

40

What about Reserves (AS) & Balancing Prices Forecasting?

Utilities and grid operators must be prepared to account for power plantsor transmission lines that unexpectedly go out of service, or for unforeseenincreases or decreases in electricity supply and/or demand.

In addition, as utilities and grid operators increase their reliance onintermittent renewable generation capacity like wind and solar power,additional balancing resources are required to address any inconsistencies ingeneration (e.g. when sufficient wind and sun are not available).

The existing’ products address these short-term imbalances inelectricitAncillary Servicesy markets by dispatching resources within secondsor minutes of an unacceptable imbalance, but the question is, will theseexisting AS products be enough in this challenging new environment?

Due to all these and the increased role of AS, a significant diversification between DA and RT (balancing prices) might be expected in the future.


System Reserves include among others, coordinated system operation,frequency regulation, energy balance, voltage support and generationreserves.

Ancillary Services features of PLEXOS® are used in order to:

Optimise the uptake of renewables given this additional burden

Ensure provision of reserves in dispatch and expansion planning

But more importantly, to calculate the cost to the system and theeffect on energy prices of the additional reserve requirements and to

Calculate and forecast expected ancillary service prices and test anynew ancillary services provisions.

This analysis takes advantage of PLEXOS® ability to set dynamic reserverequirements based on generators’, load or line contingencies.


What about Ancillary Services & Balancing Prices Forecasting?


Co-optimisation & Pricing in Integrated Markets/Systems

Co-optimization is necessary to minimize the total costs ofcoordinating generation, transmission and reserves to meetdemand and ensure reliability.

Electricity and Reserves Shadow prices derived from theconstrained optimization accurately reflect the system-wideopportunity costs of associated scarce resources, both inter-temporally and spatially.

Co-optimization–Towards an Integrated solution

Ancillary Services Pricing & Price Forecasting

When requirements for reserves are considered, the optimal trade-offbetween energy and reserve provision must be determined.

The AS marginal price for an AS in a region is the incremental (Marginal)cost for meeting an additional MW of the requirement for the AS in thisregion.

If no additional compensation were required to cover the cost of a plantoperating at lower efficiency to provide reserves, the requiredcompensation is given by the opportunity cost of backing off generationto provide reserves.

In PLEXOS® this compensation will be automatically embodied in thereserves price, which is equal to the dual variable associated with theconstraint defining the required quantity of reserves.


Opportunity Costs


Under the Co-optimisation of Energy and multiple AS, the market clearing prices for the multipleproducts have the following 3 characteristics:

LMP for energy gives a precise representation of the cause-effect relationship that isconsistent with grid reliability management

Higher Prices for higher quality (more Flexible) Ancillary Services.

Spinning Raise Prices = Shadow Price (Clearing Price) of Spinning Reserve requirement constraint +Shadow Price (Clearing Price) of Regulation Raise requirement constraint

There is Marginal Equity between Energy and Reserves Prices

Energy LMP - Shadow Price (Clearing Price) of Regulation Raise requirement constraint =Marginal Cost (Shadow Price) of combined Energy and Regulation Reserves provision atthe node, when SR=0 and RR>0.

Energy LMP - Shadow Price (Clearing Price) of Regulation Raise requirement constraint -Shadow Price (Clearing Price) of Spinning Reserve requirement constraint = Marginal Cost(Shadow Price) of combined Energy, Spinning and Regulation Reserves provision at thenode, when SR>0.


Energy Exemplar performs a few renewable generation integration studiesusing the 3-stage DA-HA-RT sequential simulation approach. This approachcan be illustrated in the following flow-chart.

Modelling & Forecasting DA/ID/RT Prices


Locational Marginal Pricing (Nodal Pricing) (value = 0)

Generators receive the locational marginal price (LMP) at the node(s) they are connected to. If a generator is connected

to multiple nodes it receives the generation-weighted average price at those nodes according to the defined generation

participation factors.

Regional (Reference Node Pricing) (value = 1)

Generators receive the regional reference price modified by the generators’marginal loss factor.

Regional Weighted Price (value = 2)

Generators receive the load-weighted price in the region(s) they belong to.

Pay-as-Bid (value = 3)

Generators receive the offer price for each megawatt of generation cleared.

Uniform Pricing (value = 4)

Generators receive the single market price (uniform price).

Most Expensive Dispatched (value = 7)

The price is set at the SRMC of the most expensive dispatched Generator regardless of whether or not that Generator is

truly marginal.

None (value = 5)

Generators receive no payment for generation. This option is useful where generators sell their output into an external

energy market and revenues accrue to the trading portfolio (company) rather than the individual generating units.

Custom (value = 6)PLEXOS® makes a call to Open PLEXOS® to calculate pricing. This method allows the user to implement custom pricing.

Available Pricing Methods in PLEXOS®


Bertrand Competition Modelling in short-term price Forecasting?

Bertrand Competition is a game theoretic model in which firmsmanipulate the price component of their generation offer and keepquantities fixed. It is generally accepted that Bertrand Competition does not yieldhigh enough average prices to recover generator investment costs,but that it is a useful method for modelling short-term pricingespecially in the way it can capture gaming behaviour in times oftight supply-demand balance and/or transmission congestion.


PLEXOS® simulator implements a heuristic shadow pricing scheme that mimicsBertrand Competition. In this game generators choose prices for their output inorder to maximize profit making opportunities in a one-round game.

The Bertrand game is simulated independently for each dispatch interval e.g.hour, half-hour, etc.

The advantage of this is that the Bertrand Game can be run for any horizonlength from a single interval up. The disadvantage is that the game makes no reference to the medium termeffect of the pricing results i.e. it ignores the price elasticity of demand.

Bertrand Competition in Modelling short-term pricing?


The core mechanism of the BertrandGame is 'Shadow Pricing' i.e. pricinggeneration up to the next generator'soffer price in the merit order. This isillustrated in Figure 1. The stack ofgeneration is shown for threeindependent generators (G1, G2, G3).Figure 2 shows the offer prices thatresult from a simple shadow pricingpolicy:"G1" bids up to "G2" price less epsilon"G2" bids up to "G3" price less epsilon"G3" bids up to shortage price lessepsilon

Bertrand Game -'Shadow Pricing'

Figure 1: Generation bid-stack

Figure 2: Generation bid-stack after Shadow Pricing


Daily Bertrand v Real Energy Prices - Germany


Hourly Bertrand v Real Energy Prices - Germany


Daily Bertand v Real Energy Prices – France


Hourly Bertrand v Real Energy Prices - France

Thank you for your time and the opportunity

For further Information, please do not hesitate to contact EE Europe:

Dr Christos PapadopoulosRegional Director Europe

[email protected]

www.energyexemplar.com

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