managing a reservoir-based hydro-energy plant: building understanding in the buy and sell decisions...

Energy Policy 33 (2005) 939–947

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doi:10.1016/j.enp

Managing a reservoir-based hydro-energy plant: buildingunderstanding in the buy and sell decisions in a changing environment

Ann van Ackere*, Morten Ruud, Paal Davidsen

Management HEC Lausanne, BFSH 1, Universit!e de Lausanne, 1015 Lausanne-Dorigny, Switzerland

Abstract

This paper describes a modelling process at a Norwegian chemical producer, who owns 20% of a reservoir based hydro-energy

plant. While the initial objective was to increase the profitability of the energy plant (in particular by an improved understanding of

buying and selling decisions and a reconciliation of the managerial and engineering points of view in the context of a liberalised

energy market) the process resulted in the company’s decision to refocus on its core-business. The process illustrates how a

modelling process can lead to a fundamental reframing of the issue, resulting in major change for the company.

r 2003 Elsevier Ltd. All rights reserved.

Keywords: Modelling; Decision policies; System dynamics; Structural change; Hydro-energy

1. Introduction

As deregulation of energy markets progresses, itsconsequences are increasingly being felt by companies inall sectors. This impact operates both at the industrylevel and at the company level, and requires companiesto look at electricity in a different way. While manystudies have focussed on understanding this newcontext, they have tended to concentrate on the impactof deregulation at the industry level. References includeGrossman and Cole (2003), Hunt (2002), Midttun(1997) and Newbury (1999). On the other hand, verylittle work has been carried out at the company level.One exception is Dyner and Larsen (2001), who look atthe impact of deregulation on the planning process ofutilities, as they no longer face a monopoly situation.

Our work looks at the impact of deregulation on anindustrial concern which owns a share of a reservoirbased hydro-energy plant. We focus on how thiscompany has modified the way in which it insures itselfaccess to sufficient amounts of energy. In particular, weaim to illustrate how deregulation has had totallyunanticipated consequences for this company. Follow-ing deregulation energy became a tradable commodityin a competitive market. Rather than being just oneinput among others to the production process, the water

ng author. Tel.: +41-21-692-3454; fax: +41-21-692-

ss: [email protected] (A. van Ackere).

front matter r 2003 Elsevier Ltd. All rights reserved.

ol.2003.10.016

reservoir suddenly became a valuable asset. This had asignificant impact on how the company views electricity,and forced it to reassess its energy policy.

1.1. Historical context

Odda and Tyssedal, located in the western part ofNorway, were small agricultural communities until theend of the 19th century. For the western part of Norwayin general, and the community of Odda in particular, theinvention of the electrical generator and the hydroturbine became the foundation of a new era. Waterfallsthat for thousands of years had fallen from the highmountains, down to the fjords, without any value for theowners, suddenly became the objects of great interest forengineers. With dams in the mountains, and pipelinesdown to power plants, the potential energy of the watercould be transformed into electrical energy.

In those days, the technology for transmittingelectrical energy over long distances was still poorlydeveloped, and there was very little private consumptionof electricity. Therefore, electricity generation onlybecame valuable if one built energy intensive industrialplants near the energy production sites, ensuring a localdemand for the relatively large amounts of energy beingproduced.

In 1906 a company called Tyssefaldene was incorpo-rated. The company bought the rights to the waterfall inthe Tysso River from the local farmers. The autumn of1906 saw the start of construction of a dam in

ARTICLE IN PRESSA. van Ackere et al. / Energy Policy 33 (2005) 939–947940

Skjeggedal (Vetlevann), a tunnel and a pipeline down tothe fjord, and a power plant in Tyssedal. The firstconstruction period lasted for 2 years and the plant,Tysso 1, started power delivery in 1908. All the energywas used by local electrochemical plants that had beenconstructed at the same time. The main customer was afactory producing calcium carbide in Odda, just 6 kmaway.

The next 10 years saw further construction worksaimed at meeting the increasing demand for energy fromlocal industry. There were no transmission lines tooutside the community, so all the energy produced hadto be used locally. Similarly, shortfalls in energyproduction could not be met from other sources.

When the economic downturn of the early 1920sforced the local industry to stop production, the powercompany was unable to sell electricity. Very soon thecompany found itself in severe financial difficulty, andwas taken over by the banks. A new holding companyfor the power plant, AS Tyssefaldene (AST), wasestablished in 1924, jointly owned by three localfactories, which were the main energy consumers. Theseowners were the calcium carbide producer, OddaSmelteverk (OS), a producer of zinc, Det NorskeZinkcompaniet (which later became Norzink (NZ))and a producer of aluminium, DNN. Over the years,the need for energy increased, and additional powerplants were build (M(ageli in 1956, Tysso 2 in 1967,and Oksla in 1990 to replace Tysso 1). As a part ofthe agreement between AST and the Government, thenational power company Statkraft (SK) obtained theright to store a certain amount of energy (in the form ofwater) in the reservoirs of Tyssefaldene.

1.2. A changing environment

In 1967 the Tyssefaldene production system wasconnected to the national electricity network. This madeit possible both to sell energy when local consumption islow (e.g. when a company closes down for annualmaintenance), and to buy from the national networkwhen consumption exceeds local production.

The next major change occurred in 1991, when thenew Energy Act came into force. The act introducedcompetition to a business sector where the individualenergy utilities had enjoyed a supply monopoly in theirdistricts. This meant that customers could buy powerfrom the supplier offering the lowest price. Norway hasthe most liberal internal power market in the world. Thepurpose of the new Energy Act is a more efficient use ofresources in the energy sector, as well as a levelling ofprices for customers in different parts of the country.

This change in environment had a significant impacton the situation of OS. Suddenly its water reservoirswere no longer simply a guaranteed source of energy,but became a tradable asset. The price of power varies

according to precipitation, demand, the power price inneighbouring countries and the level of taxation andpublic charges. Consequently, it is difficult to predict,and participation in the power market will involveconsiderable financial risk both for the power sector andthe customers.

In 1997/1998 the OS management decided to managetheir reservoir more profitably. They sold significantamounts of energy in December 1997, and in spring andearly summer 1998, when prices were relatively high. Inlate summer they started buying energy from thenetwork at low prices, instead of producing locally, abehaviour not previously observed, thus accumulatingenergy (in the form of water) into their reservoir.

DNN and NZ did not sell in the spring. Conse-quently, they accumulated large amounts of energy intheir reservoirs. This, together with the unusually highlevel of the Statkraft inventory, led to a serious risk ofwater overflow. Any water overflow represents lostincome for the system, the cost of which would be bornby either DNN or NZ, depending on who had thelargest relative inventory at any point in time.

DNN and NZ were keen for OS to stop buying fromthe network, and instead produce energy locally, so as toavoid DNN and NZ having to choose between eitherwater overflows or producing and selling energy at a lowprice. OS clearly had an incentive to continue buying,thus keeping its energy stored in the form of water, untilprices increase. This created a tense situation.

1.3. The broader modelling project

The model presented in this paper is part of a largermodelling project at Tyssefaldene. The project includesa detailed operational model aimed at optimisingproduction from a technical point of view. This modelis used by management for policy design and by theoperators for daily production decisions: how toproduce a required amount of energy in the mostefficient way (Ruud et al., 1998). This operational modelhas been linked to a hydrological model. This enables itsuse for long-term system design, helping to answerquestions about the impact of operating under differentmeteorological conditions Ruud et al. (2000).

The model presented in this paper is of a moremanagerial nature, and focuses on the situation faced byOS. It aims to address the higher level question of howmuch and when to produce, as opposed to the moretechnical question of how to produce (i.e. which turbinesand which reservoirs).

1.4. Comparison to the literature

There is a broad literature on hydro-energy model-ling, covering a variety of aspects of this problem, usinga wide spectrum of analysis tools. For instance, Lamond

ARTICLE IN PRESSA. van Ackere et al. / Energy Policy 33 (2005) 939–947 941

and Boukhtouta (1996) provide a survey of the use ofMarkov decision processes to achieve long-term opti-misation of hydro-power production. Lyra et al (1996)address the issues of conflicting interests and provide asurvey of quantitative tools (multi-criteria optimisation)designed to assist in settling such disputes. Theyconsider both conflicts arising between owners ofdifferent plants sharing a water supply, and conflictsbetween different uses of a water resource (e.g. energyproduction versus navigation versus recreation).

Scott and Read (1996) consider the impact ofderegulation on the optimal behaviour of mixedhydro-thermal firms, using a dual dynamic program-ming approach. Bushnell (2003) uses a game-theoreticapproach to asses the advantage a reservoir based plantcan obtain in a regulated market through its ability to‘store’ electricity in the form of water in an oligopoly.

Sigvaldason (1976) considers optimisation as a sub-model nested in a simulation model. The underlyingconcept of the model is the mathematical representationof the chief operator’s perception of optimum operationand the derivation of this solution using a nestedoptimisation sub-model. Ahmad and Simonovic (2000)have applied system dynamics, a feedback-based object-oriented simulation approach.

The modelling work carried out for this project differsfrom the existing literature in several respects. First ofall, the company we consider is a small player, i.e. itcannot influence the market in any significant way as itonly represents about 2% of the total Norwegianproduction (2.5TWh/year out of a total of 120 TWh/year). Secondly, our aim is to understand how decisionsare being made, rather than try to identify an ‘optimal’policy according to some predefined criteria. Third, weconsider how a changing environment leads to amodification of behaviour over time.

1.5. Overview of the paper

In Section 2 we set the scene by explaining how theplant has historically been managed, and look at someof the existing problems, consequences of the changingenvironment described above. The model is described inSection 3. In Section 4 we discuss the impact this modelhas had on the company. Section 5 contains ourconcluding remarks.

2. A/S Tyssefaldene

2.1. Rights and priorities

Based on the agreement with the Government, ASTadministers the water resources of the total productionsystem. All the inflows, allocations and outflows arecalculated in energy equivalents (although the physical

unit is volume of water). AST administers this energystorage with the intention of supplying the local industryand community with sufficient energy. To do so, ASTputs aside some energy to cover long-term communitydeliveries and various contracts. The remaining energy isdistributed to AST’s owner companies on a weeklybasis, according to their respective shares. These parentcompanies manage their energy inventory position tocover their individual production needs, and sellsurpluses in the open energy market. Whenever acompany chooses to take out energy, this amount issubtracted from its inventory.

SK has no physical water inflow into its reservoir, butcan accumulate water through two different mechan-isms. First, SK receives 9.5% of the production at Okslaas inflow into its storage. Second, SK controls theproduction rate of AST’s plants. If SK sets a productionlevel below the local demand, it must deliver theshortfall from the national network. This energy isproduced somewhere else and transferred to AST tocover local demand. As ‘payment’, energy is moved outof the energy inventory of the different companies, andinto Statkraft’s inventory. Typically, SK will setproduction below consumption when it has excesscapacity at its river plants.

SK has the right to use the idle production capacity atAST, any time it wants, to produce locally and send theenergy back into the national network, thus cuttingdown its inventory. This specific set-up was the cause ofsome serious headaches during the initial modellingefforts, when data analysis revealed that energy con-sumption plus energy sales far exceeded energy produc-tion. The explanation: when SK provides energyproduced elsewhere to the companies, this is registeredas consumption by the companies, although this energyhas not been produced at AST.

2.2. Engineers versus managers

The deregulation of the electricity market providedOS with an incentive to manage their energy reservoirmore efficiently. This created a tension between the ASTengineers and OS management. From the engineers’point of view, optimal production means maximisingMWh per m3 of water. From the management’s point ofview, optimal production means maximising Norwegiancrowns (NOK) per m3. Given the price variations (bothwithin a day, and as part of the annual cycle) and thenon-linearities involved in energy production, these twodefinitions yield different decisions.

The average daily spot price lies mostly between 100and 600 NOK/MWh (Nord Pool ASA Arsrapport,2001), but within a single day, variations in hourly spotprice easily exceed 100%. The latest full year of detailedpublicly available data is 1999 (www.nordpool.no). Theaverage spot price that year was 172 NOK/MWh, and

http://www.nordpool.no


the hourly spot price varied between 32 and 620 NOK/MWh, with a maximal intra-day variation of 480 NOK/MWh (price ranging from 140 to 620 NOK/MWh).

In the days where AST was not linked into thenetwork (i.e. until 1967) the engineers’ point of view wasthe only option, as energy had no sales value.Additionally, as it was not possible to buy energy incase of water shortage, not running out of water was amain priority. As the water inflow takes place mainly inlate spring and summer, in the form of melting snow,while demand is highest in winter, it was crucial toinsure full reservoirs at the start of the winter.

From 1967 until 1991, the impact of being linked intothe network was limited, as local utilities continued toenjoy a monopoly position. AST thus operated underthe engineers’ mental model for most of its 90-yearexistence. Not surprisingly, any attempt at changing thismental model met with significant resistance. Specifi-cally, AST would allocate energy to the companies in aconservative, risk averse way, only allowing productionfor sale when there was, in its opinion, ‘too much’ waterin the reservoirs (typically during the summer, whenprices were relatively low).

2.3. Water versus energy

As mentioned previously, all allocations are expressedin terms of energy rather than volumes of water. Undertechnically efficient production there exists a one-to-onerelationship between water and energy. In other words,given the reservoir levels, AST could estimate withsatisfactory precision the amount of energy that couldbe produced. This situation resulted in a perception ofequivalence between water and energy allocations.Faced with AST’s risk-averse attitude, OS’s reactionwas ‘Give me my share of water and let me use it the way I

want’.The amount of energy produced from a given volume

of water and a given pressure depends on the productionrate. Under economically efficient production the totalenergy production is lower as the production rate isincreased above the technically optimal level whenprices are high and reduced below this level when pricesare low. Consequently, in such an environment the totalamount of energy that will be produced cannot bedetermined ex ante, making energy allocations impos-sible.

Switching to water allocations is not a solution as thiswould create new problems, specifically, whoever useshis share of water ‘first’ benefits from a higher pressure,and thus gets most energy from a given volume of water.

2.4. Objectives of the model and methodology

The initial objectives of this modelling effort were asfollows:

* understand the present production and sales deci-sions;

* enable discussion/understanding between engineersand managers;

* clarify implications and pitfalls of water versusenergy allocations;

* create a platform to experiment with various dis-tribution systems;

* understand the impact of changes in the behaviour ofthe different players.

As will be seen below, this initial rather ambitiousagenda was not realised due to an unexpected structuralchange, and only the first two issues were addressed in asatisfactory way. Still, the insights gained from thismodelling effort had unexpected and far reachingconsequences for OS.

As mentioned before, our aim is to understand howthe different actors view the changing environment andhow decisions are being made, rather than try to identifyan ‘optimal’ policy according to some predefinedcriteria. We have therefore selected to use a simula-tion-based approach rather an optimisation technique.Given that our focus is on building understanding, wehave chosen to ignore uncertainty and concentrate onthe accurate modelling of the underlying systemstructure and the decision-making policies. The uncer-tainty inherent to the environment (in particular priceuncertainty) is implicitly present in the model given thatwe model decisions based on price expectations ratherthan on future prices. These choices have led us to use asystem dynamics based simulation approach. A briefintroduction to this method can be found for instance inMorecroft and van Ackere (1997).

Several authors have used system dynamics-basedsimulation models to analyse energy markets. Examplesinclude Ahmad and Simonovic (2000), Bunn and Larsen(1992), Bunn and Dyner (1996), Neubauer et al (1997)and Yamamoto et al (1999).

3. Model description

The model was developed by the authors, with theassistance of several senior managers at OS. Lengthydiscussions and several iterations were required before aclear picture, acceptable to all parties, emerged. Broadlyspeaking, the model consists of three sectors: historicaldata inputs, a stock and flow structure representing theenergy reservoirs, and the decision policies concerningthe buying and selling of energy.

3.1. Exogenous factors: historical data

The purpose of the model is to evaluate OS’s decisionpolicies. Therefore the behaviour of DNN, NZ, SK and


AST is taken as given. We use historical data torepresent their respective production decisions. Otherexogenous factors include the spot price, productioncapacity and water inflows (expressed as energy). Theassumption of exogenous spot prices is justified by thefact that AST is a sufficiently small player, so that itsactions do not affect the market price in any significantway.

3.2. The stock and flow structure

The basic physical structure of the model is surpris-ingly simple, as shown in Fig. 1. The rectangle at thebottom (OS CumAllocEnergy) represents the amount ofenergy available to OS at the present time, and can bethought of as a reservoir. This is energy which has beenallocated to OS by AST, but which OS has not yet used.The three arrows below this reservoir represent OS’susage of energy, i.e. outflows from this reservoir. Theseusages are (i) the amount of energy produced by OS forits own consumption (H OS Consumption), (ii) theamount of energy wasted by OS by allowing wateroverruns from the reservoir (OS OverrunEnergy) and(iii) the amount of energy produced by OS for sale(OS EnergySales), the decision variable we are inter-ested in. The letter H at the start of a variable nameindicates that this variable represents historical ratherthan simulated data.

The two arrows pointing into OS’s energyreservoir represent OS’s energy sources. These are(i) energy allocated to OS to satisfy its needs forindustrial production, known as primary allocation(H OS PrimAllocation) and (ii) additional energy allo-cations which OS can use to produce electricity for sale,known as secondary allocations (H OS SecAllocation).Remember that the AST engineers are very risk-aversein allocating energy. Consequently, their tendency is tokeep significant amounts of energy in reserve (i.e. notallocated to the companies) for future needs. Therectangle at the top (OS EnergyClaimedUAEnergy)

Fig. 1. The stock and flow structure.

represents the amount of energy present in AST’sreservoirs to which OS is entitled, but which has notyet been allocated by AST. The primary and secondaryallocations are transfers of energy between these twoenergy reservoirs. The reservoir of unallocated energy isin fact an estimate of AST’s future cumulative energyallocations and plays an important role when decidinghow much energy to produce. The arrow pointing intothis reservoir, labelled OS ClaimedInflowEnergy, repre-sents OS’s share of the energy flowing into AST’sreservoirs (i.e. the energy equivalent of the water inflow).

3.3. Decisions: buying and selling

The model was developed using 2 years of data: 1997(more specifically from week 44 of 1996 to week 43 of1997), a period during which no energy purchases tookplace and 1998 (again from week 44 of 1997 until week43 of 1998), a period where for the first time significantenergy purchases took place during the summer.

From a decision-making point of view, we initiallydivided the year into three periods and developedpolicies on the assumption that energy purchases werenot an option. The three periods are:

(i)
winter (November to mid-March)This is a period with high prices and negligible
water inflow. OS is keen to sell energy at this timeof the year.

(ii)
spring/summer (mid-March to late June)In mid-March OS obtains information about the
expected future inflows, based on measurements ofthe snow-pack. Water starts flowing into thereservoirs around late May. The exact timingdepends on the weather. Prices drop during thisperiod, so OS is keen to sell excess energy as soon aspossible. Excess energy is defined as the differencebetween the amount of energy OS expects to haveavailable, and the amount it needs.

(iii)
summer/fall (July–October)Most of the water inflow occurs during this
period and prices are low, implying that OS wantsto keep energy sales to a minimum. It produces justenough energy for sale to avoid water overruns.

These policies were validated using the 1997 data. In asecond stage, the model was modified to allow forenergy purchases during the low-price summer period,and calibrated using the 1998 data.

3.3.1. Winter

Fig. 2 gives a simplified representation of the decisionpolicy for the winter, the period during which OS ismost keen to produce energy for sale, as prices are high.But, as no water inflows are expected for severalmonths, it must take care not to run out of water to


avoid having to purchase energy at high prices.Historically a lot of weight was given to this aspect,especially given that purchasing energy was not anoption until 1967.

First we determine the unconstrained desired salesdecision (OSDesWeeklyWinterSalesUnconstr, in bold).This quantity is determined by three elements: (i) howmuch OS wishes to sell during the remainder of thewinter (OS DesiredRemainingSalesWinter), itself afunction of the reservoir situation and the expectedenergy needs until the water inflow starts(OS CumEnergyConsTillWeek16), (ii) a subjective as-pect (SalesStartUpHesitation) which captures OS’shesitation to sell large amounts of energy at the startof the winter and (iii) the impact of price (EffPriceOn-OSSales), the higher the spot price is compared toexpectations for this time of year (PriceLimit), the moreOS will want to sell. The variable PriceLimit is asubjective assessment by management of what theywould expect price to be at this time of the year, giventhe reservoir situation, information about snowfall, etc.

To obtain the actual sales (OS RequestedWeeklyWinterSales, in bold italic) we need to take twoconstraints into account: the energy which has beenallocated to OS, which determines how much OS isallowed to sell (OS CurrentlyAllowedSales) and thespare capacity OS can dispose of (OSSpareCap). Sparecapacity is a function of how much capacity is availablefor the companies (OS, DNN and NZ) after all higherpriority needs are satisfied (CapacityRemainingFor-Companies). This is a function of the total capacity(Capacity), which in turn depends on the spot pricecompared to price expectations (PriceEffOnCap) as

Fig. 2. The winter policy.

AST will agree to a higher production level when pricesare relatively high.

In an initial modelling effort we had included theconstraint that the reservoirs should be full at the end ofthe summer. During our discussions with the manage-ment, significant emphasis had been laid on this point.When this constraint is included, energy sales remain atzero throughout the winter period. As it turned out, thisconstraint used to be crucial before AST was linked tothe national grid. While behaviour has evolved, thiselement was still very much present in the mental modelsof the management team.

3.3.2. Spring/summer

The spring/summer policy is shown in Fig. 3. As forthe winter policy, the actual sales (OS RequestedWeeklySpringSales, in bold italic) are determined bythe desired sales (OS RemDesSummerSales, bold) andthe constraints imposed by the current allocations(OS CurrentlyAllowedSales) and the spare productioncapacity (OSSpareCap). The desired sales are deter-mined by comparing the reservoir position that wouldresult at the end of the summer if no sales took place(OS ExpectedEndOfSummerPositionIfNoSales) with thedesired reservoir position at the end of summer(OS DesiredEndOfSummerPositionIfNoSales). The ex-pected reservoir position is determined by the currentlyallocated energy (OS CumAllocEnergy), the energypresent in the reservoirs but not yet allocated(OS EnergyClaimedUAEnergy), and the expectedremaining net inflow (OSRemNetSnowCorrSummerEnergyInflow). Note that this inflow is ‘snow corrected’,as it is estimated based on the measurements of thesnowpack which take place around mid-March. This netinflow is the difference between the remaining expectedinflow (OS TotRemainingCompanySnowCorrInflow)

Fig. 3. The spring/summer policy.


and the energy needs until the end of the summer(OS CumEnergyConsTillWeek43).

3.3.3. Summer/fall

This policy is shown in Fig. 4. The total desired salesfor the remaining of the summer (OSRemDesSummerSales, in italics) are calculated as inthe spring/summer model. The main difference com-pared to the previous policy is that at this time of theyear OS will delay sales as much as possible, waiting forprices to start moving back up. The company firstestimates how many weeks would be required to sell thesurplus energy (ReqWeeksToSellEx) at average capacityavailability (AvgSummerSaleCap). OS will only sellenergy (OS DesWeeklySales, bold) if the requirednumber of weeks exceeds the remaining number ofweeks. This amount will be transformed into actual sales(OS RequestedWeeklyFallSales, in bold italic) using thesame two constraints as before (spare capacity andallowed sales), as well as an additional pressure factor(EffectOfPressureToSell): the larger the required weeksto sell compared to the remaining weeks, the more OSwill want to sell.

3.3.4. Validation

Fig. 5 shows the simulation results for 1997 usingthese policies, comparing historical data to simulateddata for weekly sales and cumulative sales. Simulationtime 0 corresponds to week 44, i.e. the start of the winterseason. It is worth noting that on a couple of occasionssimulated sales lag the historical data. A strikingexample is around weeks 23–26. Discussion with themanagement revealed that there are instances whereAST announces beforehand when secondary waterallocations will take place. Therefore in reality OSmanagement has information about a future inflow intoits allocated reservoir (and thus higher allowed sales)which is not included in the model. This element cannotbe included due to the lack of historical records.

Fig. 4. The summer/fall policy.

3.3.5. Purchasing energy when the price is low

Next we modify the model to allow energy purchasesin the summer. A consequence of this possibility is thatthe spring/summer season disappears. There are onlytwo periods left: (i) winter/spring, where sales are afunction of price compared to price expectations, takinginto account that no inflows will take place before latespring, and (ii) summer/fall, where OS will sell or buyenergy depending on the price. The winter/spring periodruns from November to the middle of July, and uses thewinter policy described in Section 3.3.1.

The summer/fall policy is shown in Fig. 6. The desiredsales (OS RequestedWeeklyFallSales, in italics) arecalculated as before. The desired purchases are evalu-ated by a surprisingly simple policy. OS evaluates itsweekly energy needs for its industrial production(H OS ProductionNeeds) and decides what fraction ofthese needs will be covered by purchase rather than byenergy production (FractionPurchase). This fractionis modelled as a linear function of the spot price, as

Fig. 5. Simulation results for 1997.

Fig. 6. Energy purchase policy.


shown in Fig. 7. The requested purchases (OSRequestedWeeklyFallPurchases) are determined by theproduction needs and this fraction. The net sales (OSRequestedWeeklyFallSalesMinusPurchases) are thedifference between the requested sales and purchases.A positive amount indicates a sales decision, while anegative amount indicates a purchase decision.

The simulation results for 1998 are given in Fig. 8.Note that in simulation week 5 we again observe a slightlag in sales, caused by the same information problem asdiscussed in Section 3.3.4. The fit obtained for theenergy purchases is surprisingly good given the simpli-city of the proposed policy. We have been wonderingabout the possible causes of the difference in sophistica-tion between the buy and sell policies.

Fig. 7. Graphical relationship between spot price and fraction

purchased.

Fig. 8. Simulation results for 1998.

We propose two possible explanations:

(i)
1998 was the first year where OS actively sought topurchase energy during the summer. Being new tothis activity, they used very simple decision rules.This in contrast to energy selling, which they haddone actively since 1991.
(ii)
The second explanation is more subtle. While sellingexcess energy was a natural thing to do, becomingan active energy ‘trader’ by buying and sellingenergy for profit turns AST and OS into two actorsin a larger game. Given that they are small playerscompared to Statkraft and that Statkraft can exert alot of power due to its privilege to control the timingof production, it is not in their interest to irritateStatkraft by being too active. An element pointingin this direction is the fact that AST has done a lotof 24-h business. That is, buying and selling over a24-h period so the total daily volume ends up as zero(but of course with a profit). In aggregated reports,which SK can access, this operation could beinvisible.
4. Model impact

As stated in the introduction, the ambitious initialagenda (developing a model that would help withunderstanding the present production and sales deci-sions, enable discussion/understanding between engi-neers and managers, clarify implications and pitfalls ofwater versus energy allocations, create a platform toexperiment with various distribution systems and help inunderstanding the impact of changes in the behaviour ofthe different players) was not achieved. Only the firsttwo issues were really addressed, but this has had amajor impact on the company. It is important to recallthat this model was never intended as an operationaldecision tool (as opposed to the technical modelmentioned in Section 1.3.).

A key contribution of the model is that it made peoplethink. The questions we asked in our attempts toidentify the different decision policies, in particular withrespect to buying and selling energy, raised many issues.This enabled the differences in opinion between themanagerial and technical points of view to be clarifiedand discussed. The many iterations required to reconcilegeneral descriptions with the data illustrate that therewas no clear, unanimous view as to how the buy and selldecisions were reached.

More importantly, these discussions and the earlymodel presentations emphasised the idea of energyrights as an asset, as opposed to simply another(although highly valuable!) input to the productionprocess. Indeed, our model only showed energy-relatedstructures and decisions, and was not linked in any way


to the company’s production activities. This thinkingprocess resulted in energy being seen first as aproduction input, then as an asset that could yield someside-benefits (selling excess energy, buying if prices arereally low), and finally as a key asset in its own right. Inother words, the business was seen as consisting of twoclearly distinct entities with, on the one hand, aproduction process which required energy and, on theother hand, a business unit devoted to energy produc-tion and trading.

This approach forced the company to come to termswith a rather disturbing reality as they realised that invarious instances the original product line was lessprofitable than the energy production and tradingoperations. This realisation had several consequences.Firstly, it brought about a more pro-active approach tothe energy production and trading business. Secondly, itencouraged a review of the traditional activities with theobjective of identifying changes in operational proce-dures which could enhance the profitability of theenergy business. And thirdly, it resulted in managementlooking at the business in a different way, with a ratherunexpected outcome: a decision to refocus on the corebusiness.

5. Epilogue: structural change

Most papers end with conclusions and ideas forfurther work. This specific project ended in a moresurprising way. In June 1999, Odda Smelteverk andStatkraft reached a deal whereby Statkraft bought OddaSmelteverks’ shares in A/S Tyssefaldene. The price ofthese shares was determined so as to reflect the value offuture electricity production. This future value reflectsanticipated market price, anticipated inflow (statisticsshow a long run increase in precipitation in westernNorway) and expected operational gains resulting fromSK controlling a larger share of the electricity produc-tion. This deal can be seen as an extreme implementa-tion of the wish ‘give me my share of water and let me use

it the way I want’.Consequently, the energy production decision was

fundamentally changed, as Odda’s energy rights becameone (small) component of Statkraft’s hydro-energyproduction capacity. The operational model mentionedin Section 1.4 turned out to play a crucial role in thenegotiation process between Statkraft and Odda Smel-teverk, enabling both parties to start from a common setof explicit assumptions, from which they derived avaluation of Odda Smelteverk’s future water rightswhich was acceptable to both parties. The main losers inthis transaction may have been NZ and DNN, as

Statkraft’s power to choose the most desirable time-slotsfor production has increased due to its increased waterrights.

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