Power-system planningunder uncertainty—a new approachBecause of the long lead time between ordering equipmentand commissioning, power-system planning has alwayscatered explicitly for uncertainties. In the past, elaboratedeterministic techniques have been used, but there are nowdoubts about whether the out-turn from such sophisticationjustifies the efforts required. Perhaps a more flexible, informalapproach to both planning and uncertainty is the answer, e.g.to be able to alter course to take account of actual conditions;also, optimisation techniques might be changed. Unexpectedpower-system structures, reliability and loss levels, and alsogeneration plant mixes, might well result, which are better formeeting actual requirements than those at present

by T. W. Berrie

Review of present practices

Planning cyclePlanning1 is basically estimating and evaluating,whether it is for operational purposes (up tothree years ahead), short-term planning (five toseven years ahead) or long-term planning (20or more years ahead). It is seldom realised thatit is a circular process with uncertainties atevery step, whereby some estimates require tobe made before others can be made. Yet thelatter often, through further links in the chain,imply when deduced rather different values ofthe first estimates, which must then beadjusted.1

This planning circle with all its uncertaintiesmust be broken into with confidence at somepoint by regarding certain estimates as basic,from which other estimates are then derived. Inpower-system planning, demand estimates(kW and kWh) have come to be regarded asbasic and these estimates are presently givengreat prominence in the planning circle,perhaps unwarrantably so considering out-turns. Assuming an electricity pricing policy,including consumers' response to price,forecasts are made for demand (kW and kWh),for each year to five or ten years ahead andthen at five-yearly intervals to the planninghorizon, say 20-30 years ahead. In mostdeveloped and in many developing countries,power systems tend to be in a demand-driven'equilibrium of growth', taking one year with

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another with a small or medium annual growthrate (3% to 10%).

In developing countries which have notreached growth equilibrium, the growth issupply-driven and consequently variesconsiderably from one year to the next, e.g.from 20% to over 100% per annum. Systemplanners on such systems do not believe it'worthwhile to use elaborate techniques toallow for uncertainties; they use instead themore ad hoc, flexible, scenario-type approachwhich this article suggests should be used forall power systems. However, at present, mostpower systems cater for uncertainty in theirplanning by the sophisticated approaches nowdescribed and critically reviewed.

Basic forecastsForecasts for five to ten years ahead of

demand (kW for thermal systems, kW pluskWh for hydro-thermal systems) are of crucialimportance for system planning because this isthe lead time for constructing most majorgeneration and transmission projects. It wouldhave been more convenient 'uncertainty-wise'if plant with shorter lead times, say diesels, gasturbines and combined-cycle plant, made upmost of the installed capacity. Such indeed isso for the large number of small, isolatedpower systems that exist in the world, mostlyin developing countries, and it may becomethe universal position if the approach toplanning under uncertainty advocated in this

POWER ENGINEERING JOURNAL MARCH 1987

article is widely accepted. In the meantime,load forecasts for five to ten years ahead arelikely to remain the basic estimates forsophisticated power systems, with all theuncertainty of trying to estimate that far inadvance, and most system-planning techniquesused today require at least some forecasts tobe made as far ahead as 20-30 years,considerably adding to the uncertainty.

However, there are many other uncertaintiesto be catered for. For example, if the electricitydemand is sensitive to weather, as in mostparts of the world, demand forecasts (kW andkWh) must be expressed in standard weatherconditions, adding the uncertainty of how faractual weather conditions in the event willdiffer from standard weather conditions, andhow this difference will affect the demand. Forsome years ahead, besides annual forecastsunder standard weather, additional forecastsmust be made for: (i) specific periods orseasons; (ii) typical days in the year; and (iii)both of these on a local and a national basis.All of these are fed into standard statisticalpatterns for expressing the basic forecasts ofdemand under uncertainty in a form which canthen be used directly in the system-planningprocess (see Reference 1, pp. 175-178), intheory for all years up to the planning horizon,but with greater emphasis on the next tenyears.

For systems with a large hydroelectriccapacity, the basic estimates tend to be forkWh, especially under weather conditionsknown statistically as a 'dry year'; for thermalsystems, the kW forecasts tend topredominate.

Optimum reliabilityIn sophisticated planning, it is considered

necessary to cater explicitly for uncertaintyabout reliability of supplies by providing amargin of spare generating plant, i.e. byordering generating plant to a capacity (kWand kWh) over and above that required just to

meet the maximum kW or the annual andseasonal kWh demands of the basic forecastsunder standard weather conditions. There arestill many arguments concerning how muchspare capacity should be thus installed, butfew argue about its necessity. On thermalsystems, the lowest estimate for this margin isderived from the proportion of the totalinstalled capacity (kW) likely to be available atthe time(s) of system maximum demand (kW)(see Reference 1, p. 178):

Margin of spare generating capacity to beinstalled

rH-il x100%

where X is the percentage of the total installedgenerating plant capacity likely to be availableat time(s) of system maximum demand.

On hydro-thermal systems, a larger marginof spare generating capacity than that derivedfrom the above formula has always beenconsidered necessary for reasons not clear, andderived from the product of installed capacity(kW) times the likely availability of the plantneeded to cater for a statistical 'dry year1.

Attempts have been made to extend theabove methodology to obtain a basic marginof spare transmission and distribution capacityto be built in at the planning stage to ensurean acceptable reliability.2 Although useful, suchattempts have not proved helpful. Theamounts of spare transmission and distributionplant built in at the planning stage is presentlydetermined: (i) nationally, by finding anoptimum network development plan(s), tailor-made to fit the optimum national generationdevelopment plan; and (ii) locally, by comparingalternative individual transmission anddistribution development schemes which fulfilthe same function.

Above the basic margin of spare generationplant, in practice a further 'risk tableau' mustbe built up, as shown in Table 1.

Table 1: 'Risk tableau' for spare generating plant to ensure adequate reliability

basic forecasts uncertainties in making basicforecast

best type of treatment to cater foruncertainty in basic forecasts

system demand (kW and kWh) inaverage weather conditions

errors in forecasting variance of likely degree ofuncertainty, from experience of thepast and from judgment for thefuture, leading to an amount ofgenerating spare capacity

unstandard weather as above

total generating plant capacityinstalled, from already committeddevelopment programmes andpresent scrapping policies

early or late commissioning of newplant

as above

refurbishment of old plant as above

availability of generating plantcapacity at time of systemmaximum demand (thermal);available capacity times availabilityof plant and of water in a statisticaldry year (hydro)

errors in forecasting as above

POWER ENGINEERING JOURNAL MARCH 1987 93

1 International prices forcrude oil, aluminium andcopper in constant (1980)US dollars

From the basic forecasts over the planninghorizon shown in Table 1 for: (i) systemdemand (kW and kWh); (ii) total generatingplant capacity installed; and (iii) availability ofplant and water, and also using likelymathematical variances3 in these under variousscenarios taken from the past and judgmentsfor the future, system planners presently workout (see Reference 1, pp. 181 -187), and withapparent precision, a required total margin ofspare generating plant to be built in at theplanning stage to ensure a reliable system inthe event. The larger the margin of sparecapacity provided, the lower the risk of notbeing able to meet the total system demand inthe event, and vice versa. However, the largerthe plant margin the greater the capital costand again vice versa. The economic margin iswhen (a) incremental extra net investmentneeded to provide more spare generating plantbalances (b) consequent incremental net gainsin consumers' welfare by a decrease in the riskof electricity failure, i.e. a decrease in the so-called consumer 'outage costs'.

Although plant margins have been widelyused by system planners over the past 25years, some say with a fair degree of success,the reliability optimisation process is, in fact,but one of many such optimisations involvinguncertainty in system planning. Experience hasshown4 that there is a better actual out-turn ifall such optimisation processes are done jointly,rather than one at a time. The other two mainoptimisation processes involving uncertainty in

system planning are those of: (i) system losslevels; and (ii) generating plant mix.

Optimisation of power-system loss levelWorldwide scarcity of energy resources and

increasing costs of energy supply havehighlighted the importance today of energyconservation and the elimination of waste byproducers and consumers of energy alike.Reduction of system loss levels is one of theprincipal ways for achieving this in the powersector.

Losses are incurred in generation,transmission and distribution. Generation losslevels may be reduced by improving thethermal efficiency of generation plant andreducing station use; by using newtechnologies such as combined-cycle plant,combined heat and power plant (CHP) anddistrict heating (DH); by higher efficiencydesigns in new hydro installations; by replacingold turbines etc. For generation, acceptablenorms for losses vary according to the plantmix. Recent work indicates that average losslevels in transmission and distribution shouldnormally be below 10% of gross generation,while economically optimal loss levels shouldbe as low as 5%. Some loss levels in existingnetworks today approach 20%, even afterallowing for substantial theft.5 To illustrate,consider a country like India with an annualelectricity production of about 10 GWh in1978, and assuming a value of US$5 per kWh;reducing losses from 20% to 10% ofgeneration would yield an annual saving ofUS$0-5 billion.

What are presently deemed to be.acceptable levels for losses in power-systemplanning and operation have been greatlyinfluenced by the relative values of runningversus equipment costs (including capitalisedlosses, e.g. in transformers). As shown in Fig. 1,oil prices (in constant terms) increased aboutfive-fold between 1965 and 1981 whereas thecorresponding prices of aluminium and copperremained roughly constant or even declined.An engineer today should be willing to use alot more copper or aluminium to reducesystem loss levels than in 1965. This meansthat acceptable overall target loss levels for theentire power system should be much less thanwhat conventional wisdom has allowed for inthe past, and that reducing loss levels is likelyto be more economic than building moregeneration capacity which, in developingcountries, saves considerable domesticresources and foreign exchange. However,setting optimum loss levels at the planningstage involves uncertainty. In the past,optimising system loss levels has been done inisolation from other optimisation processes inplanning, rather than combined with these.

Optimum plant mixGeneration fuels used may be determined

more by indigenousness than by the availabilityand prices of world fuels (see Reference 2,Chapter 5). Countries which had investedheavily in their coal industry went on usingcoal for power stations even when oil prices

94 POWER ENGINEERING JOURNAL MARCH 1987

were low. Since the oil price rises, coal usagehas been further encouraged and several newcoal deposits have been developed, not only incountries where coal has been extensively usedin the past, but also in countries where coalresources have hardly been tapped. A worldcoal trade might indeed be developing and, ifthis is so, there will soon be a world market forcoal, or at least regional market coal prices, andthese may prove to be more reliable forpurposes of generation planning than previousestimates of coal prices.

Bearing in mind forecasts for fuel availabilityplus price, and water availability for hydroplant, together with the degrees of uncertaintyinvolved in each case, the size and kind ofgeneration on offer and the shape of theannual electricity loading curve, what presentlyseems to be the optimum plant mix can beestablished for some year(s) ahead. It isconvenient to start with a year about seven toten years ahead because of generation leadtime. A start can be made by ignoring today'sinstalled plant. A simple comparison of thetotal (capital plus running) costs, of each typeof generating plant over a range of load factorsenables operating regimes to be found forwhich various types of plant have minimal totalcost. For a large thermal system the resultwould be something like that shown in Table 2.

Capital costs, including interest duringconstruction, are transformed into specific (perkW) annuitised annual charges, using interestrates equal to the nationally prescribeddiscount rate and an annuity life equal to theeconomic life of each plant. To these annualcharges are added the annual specific (perkWh) running costs. The total costs of thevarious plants per kWh output at a particularload factor are then compared.

It is more difficult to use the above methodfor a mixed hydro-thermal system; someempirical rules are needed for times when thehydro plant will be used. Determining whenhydroelectric energy will be most effective canbe quite difficult. The following must beknown:

• total annual (a) average, (b) firm, (c)minimum, and (d) maximum kWh likely tobe available from each hydro plant

• periods of the annual hydro plant-durationcurve when the above kWh can be fittedinto the annual system load-duration curveto maximise the savings on otherwiseoperating the most expensive thermal plant.

Once, in the manner described above, therehas been found the optimum generation plantmix assuming a 'green-field' situation for, say,seven, 10,15 and 20 years ahead, it isnecessary to find the optimum way ofproceeding from the existing generationposition to these optimum future plant mixes.Compromises must be made and the plant mixwill never be quite optimal; it may at somepoints be far from optimal, e.g. if loadforecasts, capital costs, fuel costs, plant orwater availabilities, consumer response etc. areseriously in error. All throw great uncertaintyinto the optimisation process for plant mix and

Table 2: Composition of generating plantmix

plant type

gas turbinedieselpumped storagecombined cycleconventional steam

has minimum totalcost over range ofload factor

0-10%0-15%

10-15%15-50%70-85%

even more uncertainty is involved whenchoosing between individual generatingplants.6

To find an optimum path from the presentplant mix to the best future mix, it is necessaryto bear in mind four main factors ofuncertainty:

(i) The present plant mix will or will not beoptimal because of errors made in thepast.

(ii) Existing plant may or may not be able tobe used more economically than atpresent, rather than buying new plant fora particular system loading, for example,mid-load times.

(iii) Making investment will or will not haveadverse short-term effects on the financialcash flows of the utility.

(iv) In developing countries, foreign exchangeand local costs will or will not be neededin excess of those likely to be available, toimplement the recommendeddevelopment programme over the years.

System planners must today go beyondcatering just for points (i) and (ii).

It is appropriate now to critically examinethe two well-established statistical methods fordealing with uncertainty.

Dealing with uncertainty by sensitivity analysisThe most popular method of taking

uncertainty into account is by using 'sensitivityanalysis'. This type of analysis (see Reference 2,Appendix 1) has been used extensively insystem planning over the last 20 years.Regarded by mathematicians as crude, thismethod is simple and consequently it is usuallyconsidered to be adequate. Sensitivity analysisconsists of:

(i) Looking carefully at each majorassumption about forecasts of parametersused in the planning process.

(ii) Picking out the 'primary uncertainties',which are the most important of theassumptions mentioned in (i), i.e. wherethe greatest uncertainties lie and/or theworst effects from any uncertainties arelikely to be felt; then weighing up howmuch the main parameters are likely to beaffected by these uncertainties.

(iii) Picking out the 'secondary uncertainties',i.e. one or two other plausibly importantuncertainties, then weighing up howmuch the main parameters will beaffected by these uncertainties.

(iv) Adjusting in accordance with (ii) and (iii),

POWER ENGINEERING JOURNAL MARCH 1987 95

individually or via permutations andcombinations, the parameters being usedin the system-planning process, e.g. forcalculating the optimum reliability level,the optimum level of losses or theoptimum generation plant mix, or all three.

(v) Recalculating these optimalities with thevarious changes in assumptionsmentioned in (iv) above.

(vi) Weighing up mentally just how likely tooccur are the variations in parametersmentioned in (iv), so as to obtainperspective on how much notice shouldbe taken of the variations in theseoptimalities in (v), and thereby to arrive atan overall judgmental conclusion for therisk of these optimalities being eitherindividually or collectively different in theevent from their most likely out-turn.

Dealing with uncertainty by probability analysisAlthough sensitivity analysis is both simple

and useful, its principal shortcomings are thatit is: (a) incomplete, and [b) ambiguous. It isincomplete because it considers only a finite,and in practice only a very small, number ofpossible out-turns, other than that judged tobe the 'most likely' out-turn, even though theout-turns it does consider have the higherprobabilities of occurring. Also, most sensitivityanalysis considers uncertainties one at a time,whereas changes in parameters might occur ingroups, or they might depend on each other insome complicated sequence.

Sensitivity analysis is ambiguous because itdoes not specify how likely or unlikely are theoccurrences of alternative values of theparameters. At most an implicit guess is madefrom past experience about the probability ofeach considered out-turn.

Probability analysis may be regarded as anextension to sensitivity analysis, or sensitivityanalysis may be regarded as a special case ofprobability analysis. In probability analysis, theincompleteness and the ambiguities ofsensitivity analysis are both eliminated. Theprocess of a probability analysis is as follows:

(i) Estimate the probability that alternativevalues to the most likely will be given tothe main parameters.

(ii) Then calculate how the optimalities beingdetermined would change with eachcombination of parameter values derivedfrom (i).

(iii) Arrive at weighted values for the probablelikelihood that the optimalities will throwup as being best any particular out-turnfor any combination of parameter values.

(iv) Hence, construct a cumulative probabilitydistribution to show the probability thatany particular combination of parameterswill result in a particular optimum value forreliability level, level of losses, plant mix, orall three.

The primary practical difficulty of doingprobability analysis for system planning lies inthe difficulties of: (a) guessing probabilities;(b) providing for its large work effort; and (c) itstediousness. Just to do a probability analysis

with, say, alternative values for six parameters,it is necessary to carry out the relevantoptimisations for 64 combinations ofparameter values. If instead of only onealternative to the most likely outcome for eachof the six parameters, four alternatives to themost likely are included, then the number ofcombinations of parameter values to beconsidered increases to 15 625, a large numbereven for analysis by today's computers,especially as each probability has to beindividually assigned.

Suggested new approaches to power-system planning

Bearing in mind the above critique ofpresent techniques used in system planningunder uncertainty and a claim today by somepractitioners that these techniques have notproduced optimum development programmesin the event, this article suggests thattechniques can be improved in three importantrespects, by: (a) carrying out a jointoptimisation, rather than separateoptimisations; (b) extending the presently usedcriteria for the actual optimisation process; and(c) adopting a more flexible, scenario approachto planning under uncertainty, instead of thepresent mechanistic approach. Thesesuggestions will now be briefly looked at inturn.

Joint optimisationSufficiently reliable data should normally

exist today on sophisticated power systems tocarry out satisfactory optimisations of all theimportant criteria. However, to obtainmaximum overall savings for both electricityproducers and consumers, a joint optimisationof all of these is necessary. Joint optimisation isillustrated in Fig. 2 (taken from Reference 7) fortwo of the criteria, optimum reliability level(involving outage costs) and optimum plantmix (involving system costs). The overall jointoptimisation would be a multidimensional'version of Fig. 2.

Although it is difficult to quote actualnumbers, it has been suggested that 5% ormore of the annual total (capital plus running)system costs could be saved by carrying out ajoint optimisation rather than separateoptimisations (see Reference 4, p. 48).

Optimisation criteriaWhether optimised separately or jointly, the

optimisation technique used at present is 'costminimisation'. Such a technique is well coveredin the literature (see Reference 2, Chapter 8).Part of the criticism of present planning reallyamounts to a critique of the optimisationtechnique itself, i.e. that the present techniqueis supply-side oriented, full of supplyuncertainties, whereas important uncertaintieson the demand side are barely considered, e.g.those concerned wjth outage costs orconsumer behaviour.

This article suggests that the present cost-minimisation technique be at least extended tocover more demand-side data than at present,8

e.g. to include full quantification of outage

96 POWER ENGINEERING JOURNAL MARCH 1987

costs. The next step would then be to considerhow to include directly within the optimisationprocess other demand-side parameters, forexample: (a) consumer response to changes inreliability level; (b) consumer response tochanges in electricity price; (c) gains or lossesto the whole economy (in addition to gains orlosses to electricity consumers) made bychanges in the reliability level or the systemloss level or a better/worse plant mix; and(d) effect on all optimisations of therequirement for satisfactory annual cash flowswithin the accounting books of producers andconsumers alike.

It may well be that, once sufficient demand-side data can be included, it will be better tochange from cost minimisation, in whichdemand-side 'benefits' are represented asnegative costs, to net-benefit maximisation inwhich benefits are classed as positive and costsas negative. Benefits will include such things as:(a) increases in consumer welfare and nationalwelfare, due to increasing reliability levels,lowering system loss levels, obtaining moreoptimum plant mixes, together with(b) revenues from electricity under each level orplant mix considered. If the ultimate techniqueused is the economic return on investment,which is favoured by most economists,9 theneither cost minimisation or net-benefitmaximisation must be used at least to someextent. The economic return on anyinvestment, say on a development programme,is the discount rate which makes thesummated present worth, taken over the yearsof the planning horizon, of annual total (capitalplus running) costs incurred by thedevelopment programme equal to the totalpresent worth of annual benefits due to thatdevelopment programme.

Economists argue that the developmentprogramme with the highest economic returnshould always be favoured. When a powersystem is under fierce opposition from othersectors in the economy for the resources itrequires, e.g. in developing countries, theeconomic return may indeed be the mostimportant criterion and in practice it may wellbe much easier to determine just how theeconomic return of a particular developmentprogramme varies under uncertainty than howthe optimum development programme itselfchanges due to uncertainties in the parameters(see Reference 2, Appendix 1).

Scenario approach to planningIt is probable that the indicated 'best' out-

turn, as determined by the mechanisticmethods of optimisation used today, anddescribed above, can never be very near to theactual best end result, just because it isimpossible at the planning stage to consider allthe permutations and combinations ofuncertainty in the parameters, especially if a lotmore parameters are included in the demandside than at present.

The philosophy of power-system planningunder uncertainty is shown in Fig. 3, takenfrom Reference 7. Such a philosophy does notfavour a mechanistic, deterministic approach;

indeed far from it, especially if a large degreeof uncertainty is involved in a large number ofthe permutations and combinations ofparameters, as in many developing countries.Probably the best way to carry out planningexercises today is, therefore, not to do them inthe manner they are done at present, usingmodern computers (usually on 'batch'production) to carry out a large number ofdeterministic exercises, producing a largenumber of probabilistically determined optimafor development programmes under variousassumptions of parameters.

Rather than this, the best way to use today'scomputers is two-fold: (i) to determinescenarios of what would be the optimumsystem plant mix, or the optimum reliabilitylevel, or the optimum system loss level, or theoptimum of all three, at various points of timein the future, say seven, 10,15 and 20 yearsahead, under different basic assumptionsabout the world economy, the nationaleconomy, the national energy sector policy,energy pricing, consumer responses to loadmanagement, conservation pressures andelectricity pricing etc.; and (ii) to make allcomputer models interactive with user, so thatthey can quickly show to a planner or to apolicymaker the likely affects of changing thepresent policy on, for example, electricitypricing, interest (discount) rates, capitalavailability, fuel prices, national economicgrowth rate etc., all of which make up part ofthe uncertainty pattern.

Application to a complex, largeinterconnected thermal power system

Some of the points raised in the article arenow briefly illustrated by giving the author'sviews on how they may be applied to oneparticular large, complex interconnectedthermal power system, namely that of Englandand Wales.10

Planning cycleIt appears to the author that there has been

little, if any, systemisation of the planning cyclein England and Wales, especially with respect

2 Reliability against costanalysis: SC - systemcosts, OC - outage costs,TC - total costs

POWER ENGINEERING JOURNAL MARCH 1987 97

plonning objectiveto supply, in so far asconstraints on the economythe power utility and theconsumer will allow,electricity as required

planning means;to meet the planningobjective with aleast-cost power systemdevelopment programmeover time with goodeconomic return

monitoring and control:to make sure that weachitve our planningobjective

planning constraintsfinancing planfinancial targetspricing policiesinstitutions

3 Circular power-systemplanning process

to the selection, construction and operation ofprojects, and also groups of projects whichform a coherent time slice of a developmentprogramme.11 Such an examination meanssystematically going through ex post theimportant decision points in the planning cyclefor each major project and/or time slice of thedevelopment programme, to determinewhether events came up to expectations, forexample, with respect to:

(i) Project identification or selection: Withhindsight, was the right project, or trancheof the development programme, identifiedfor detailed study and feasibility withrespect to overall size, scope, fuel mix,location, timing etc.?

(ii) Project appraisal: With hindsight, was theidentified or selected project orprogramme time slice designed, studiedand tested sufficiently well, and withrespect to all aspects (e.g. technical,economic, fiscal, financial, environmental,institutional, organisational andmanagerial)? What was its actual impacton the annual finances of the utility,compared to expected? What was itsactual, versus expected, economic return,total construction cost, construction time,output efficiency, environmental effectsetc.?

(iii) Project supervision: Compared toexpected, what were the actualarrangements made for: project/programme specification; drawing up and

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letting of tenders; project management;equipment delivery schedules; hiring ofcontractors and consultants; on-sitedelivery; inspection and testing ofequipment; bringing equipment intocommercial operation etc.?

(iv) Project operation: Compared to expected,what were the actual standards of: theproject's or the programme's outputefficiency; level of required manpower; roleplayed in the power system; level of losses;level of reliability; running costs; start-updifficulties and times; shutdown difficultiesand times; flexibility of operation,environmental effects etc.? Did the projecthave any unexpected financialconsequences to the utility?

(v) Project scrapping: What was the actual,compared to the expected, costs ofscrapping, difficulties of scrapping,scrapping time, scope and timing ofscrapping etc.?

Some authorities dealing with a large numberof power-system projects, e.g. the World Bank,believe in carrying out an ex post examinationsuch as the one suggested above on everyproject, or time slice of a developmentprogramme with which they specifically deal.Other authorities similarly placed with respectto power-system projects believe in ex postevaluation of only a cross-section of projects.However much ex post evaluation isattempted, the objective is the same, i.e. not toidentify blame but very much rather to alwaystry to identify, and if possible to quantify, keyfactors to be taken into account in the futurein the power-system planning cycle, whetherthe latter uses deterministic, probabilistic, fullyquantified methods, or a scenario approach.

Basic forecastsIt seems to the author that, in England and

Wales, a good deal of attention has been, andis still being, given to determining themaximum demand in kW and the annualelectrical energy in kWh forecasts for the sixth,seventh and eighth winter/year ahead,respectively (see References 1 and 2, and alsoannual reports of the Electricity Council andthe CEGB), because these are the lead timesfor major projects. A good deal of work alsoseems to be carried out on how theseforecasts have proved to be reliable in theevent. Some sensitivity analyses on the mainparmeters for these forecasts have beenperformed, together with a little probabilityanalysis. However, overall reasons behind whysuch load forecasts may well not live up toexpectations12 have probably not beensufficiently examined and taken into account,i.e. given the large discrepancy between theactual and forecast maximum kW and annualkWh over a fair number of years in the1960s/1970s.

Optimum reliabilityOnly a cursory reading of material published

by the Central Electricity Generating Boardshows that a lot of thought has indeed beengiven as to what should be the size of the

POWER ENGINEERING JOURNAL MARCH 1987

generating plant margin, over and above theforecast level of maximum demand (kW); i.e.the margin that should be built in at theplanning stage for the sixth winter ahead toprevent power cuts. A good deal of sensitivityanalysis and probability analysis has been done,and is no doubt being done, mainly for the•sixth winter ahead, on the likely level ofgenerating plant availability at time ofmaximum demand, together with the likelyerrors in forecasting the latter, plus the likelyerrors in forecasting other things, e.g. themaximum demand (kW) itself in, say, average-cold-spell conditions, the likelihood of theweather being other than average cold spell.

However, it seems to the author that littleanalysis is being done for England and Waleson the demand side, i.e. compared to the largeamount of analytical work on the supply sidedescribed above. At present what seems to bebeing assumed on the demand side is but ablanket assumption of how many winters in100 that the demand can be expected not tobe met in full. The author points out thatimplied in such an assumption is a standard(s)of economic welfare perceived by electricityconsumers, and indeed the national economy,as being lost if the demand for electricity is cutoff or reduced in quality. Sufficient data existtoday to be able to determine the optimumstandard of reliability, rather than to makeassumptions about how many winterselectricity supplies can be expected to becurtailed; for example using the consumeroutage costs referred to earlier in this article,for which References 1 and 2 together withFig. 2 refer.

Today, with uncertainties concerning thelikelihood or otherwise of nuclear and gas fuelsbeing used for burning in power stations inEngland and Wales, it seems to the author thata further degree of sensitivity/probabilityanalysis is warranted, i.e. above the generalnotice taken of this problem by the power-system planners in England and Wales.

Optimum power-system lossesWith respect to electrical losses in

generation, transmission and distribution,power-system planners in England and Wales,by and large, do not seem to the author tohave done sufficient exercises concerning (forexample) the economics of increasing capitalcosts considerably by installing much largerconductors, in order to determine whether thisdoes not, in fact, make even greater savings inthe cost of losses, taken over the economic lifeof the equipment in question.

The time to make a break with the old rulesfor determining what were considered to beeconomic system losses is when generation,transmission or distribution equipment is to beeither revitalised, or changed topographically,or increased in load-carrying capacity, orreplaced. Also, it is important to rememberthat, in fact, what is not realised is that allthese four options exist whenever anydevelopment project or programme is beingconsidered for extending generation,transmission or distribution equipment.

Optimum plant mixCurrently, in England and Wales, when

planning any generation programme, it seemsthat many deterministic calculations are carriedout, basically making small variations in aprogramme of development and mainlydealing with the sixth winter ahead. Thesesmall variations are made around what isconsidered to be a previously found optimumdevelopment(s) plant mix with respect to size,fuel and possible location of plant, taken to theplanning horizon of 20 or more years ahead.13

The purpose of this exercise seems to theauthor to be to examine the relative economicsof: installing an extra 1 kW of alternative newgeneration projects; and/or indulging in 1 kWof energy conservation; and/or scrapping 1 kWof alternative existing plant. Probably a mixtureof all three alternatives will normally be wise.

The author believes that the end productfrom this type of exercise, which has beencarried out over the last 25 years, could begreatly improved on by either: considering, butin less detail, many more marginal alternativesaround what is/are considered to be theoptimum development(s) of plant mix; and/orhaving many, many more alternative optimumdevelopments of plant mix; and/or usingdifferent optimisation criteria (see later); and/orhaving a quite different approach altogether,e.g. use some overall simple scenario planningmethods developed for other sectors, such asthe commodity sectors, and then focus onmany other winters ahead than the sixth, thusallowing for easy and flexible adjustments tobe made to the actual plant mix, followinglessons learned during the ex post evaluationof the planning cycle mentioned above.

Within any determination of the optimumproject, or of the optimum plant mix, andwhether these are determined by mechanisticmeans or by a scenario approach, it seems tothe author necessary that there must beincluded uncertainty as to whethergovernments will allow a fully flexible fuelapproach with respect to generating plant, orwhether gas and/or oil and/or nuclear fuelswill be either rationed for use to generateelectricity or whether these fuels will becompletely ruled out for use under power-station boilers.

Joint optimisationAs far as the author can make out, at

present in England and Wales the reliabilitystandard, the level of electrical losses and thegeneration plant mix are all optimisedseparately. Because of the points made earlierin this article, the author believes that theseoptimisations should be done jointly, i.e. as onesingle optimisation process, probably using amixture of already described optimisationtechniques, to include some appropriatedeterministic methods plus some scenarioplanning.

Optimisation criteriaThe author understands that power-system

planning in England and Wales is carried outmainly using the type of economic

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optimisation known as cost minimisation. Inaccordance with the manner described in thisarticle, the author believes that more demand-side data should be gradually fed into theplanning cycle, first treating benefits (includingrevenues) as negative costs; then, whensufficient demand-side data are able to bequantified, giving costs a negative sign,benefits a positive sign, and using the type ofeconomic optimisation known as 'net benefitmaximisation' i.e. instead of 'cost minimisation'.It is believed that this will improve the qualityof power-system planning, especially in so faras consumers and the national economy areconcerned, i.e. by gradually changing overfrom: (i) least cost to the utility and focusingalmost entirely on the supply side, towards(ii) maximum net benefit to the nationaleconomy, including electricity producers,electricity consumers and others, and focusingequally on both the supply side and thedemand side. The economic return on theproject or development programme will thentake its rightful place in power-systemplanning.14

Using a scenario approachTaking account of lessons learned from the

past can only at present be done over themiddle to long term in England and Wales, i.e.while using the existing deterministic methodsof power system planning. What seems to theauthor as being required is: (a) a mixture of abroad-brush scenario approach, in which thetechnical, economic, financial institutional etc.effects of making changes in plant mix,security standard, loss level or project selectioncan be examined very quickly indeed; while(b) a more mechanistic approach examinesbroad development programmes to be used asbackground material; and (c) all such planninghas an economic optimisation criterion of netbenefit maximisation, using much morequantified demand side data than at present.

Further workThe author believes that, in England and

Wales, further work is required in determiningfurther demand-side data for inclusion in theplanning optimisation processes; a start can bemade by analysing the results of work alreadydone on consumer response to changes inprice and reliability standard.15 Work is alsorequired in developing interactive-with-usercomputer models, mainly for use in thescenario approach to planning mentionedabove.

Conclusions and further work requiredThe following brief conclusions may be

drawn from the article with respect toimproving the process of power-systemplanning under uncertainty:

(i) Always carry out interrelated optimisationprocesses jointly and not separately,

(ii) Include as many as possible of thedemand-side parameters in anyoptimisation,

(iii) Do not place too much store onmechanistic, deterministic optimisations

but rather use modern computersinteractively to help system and nationalplanners to examine scenarios for thefuture under various likely out-turns fordevelopment of the world economy, thenational economy, the energy sector andchanges in consumer behaviour.

Further work needs to be done with respect to(ii) and (iii) above. Many computer models arepresently interactive, but the necessary systemgraphics needs to be extended. With respectto (iii) small, independent power systems havealways had this approach, and it may well bethat some ex post evaluation is needed tocompare the actual out-turn from(a) development programmes determined byusing simple methodologies, and(b) development programmes determinedusing the present sophisticated deterministictechniques.

References1 BERRIE, T. W.: 'The economics of system

planning in bulk electricity supply' in TURVEY, R.(Ed.): 'Public enterprise' (Penguin ModernEconomics, 1968), Chapter 5

2 BERRIE, T. W: 'Power system economics' (PeterPeregrinus Ltd., 1983), Chapter 10

3 REUTLINGER, S.: 'Techniques for projectappraisal under uncertainty'. World BankOccasional Paper No. 10,1970, WashingtonDC, USA

4 MUNASINGHE, M.: The economics of powersystem reliability and planning'. The JohnsHopkins University Press, Baltimore, USA, 1979

5 'Energy efficiency: optimisation of electricpower system losses', July 1982, EnergyDepartment Paper No. 6, World Bank,Washington DC, USA

6 TURVEY, R. and ANDERSON, p.: 'Electricityeconomies'. World Bank Publication, The JohnsHopkins University Press, Baltimore, USA

7 BERRIE, T. W: 'Power system economies',Electronics & Power, 1982, 28, pp. 670-673

8 BERRIE, T. W, FARMER, E. D. and CORY, B. J.:'Homeostatic system control using newtechnologies', IEE Conf. Publ. 266,1986,pp. 183-187

9 As favoured by the World Bank and UKOverseas Development Administration; see thelatter's publication 'Planning developmentprojects'(HMSO, 1983)

10 See annual reports of the CEGB11 BAUM, W: The project cycle'. World Bank

Publication, 1980. Also BAUM, W: The projectcycle', Finance & Development, December 1978

12 ANDERSON, D.: 'Forecasting errors of electricitydemand'. World Bank Economics DepartmentWorking Paper 79, June 1970

13 See the net effective cost calculations describedin References 1 and 2; also in many of thepapers written by the CEGB for the SizewellInquiry, 1985-86

14 BRIDGER, G. A. and WINPENNY, T: 'Planningdevelopment projects', Overseas DevelopmentAdministration, HMSO, 1983

15 BERRIE, T. W and ANARI, M. E. M.: 'Joint supply-demand optimization in electricity supply',Energy Policy, December 1986

© IEE: 1987

Tom Berrie is a Research Fellow at Imperial College,London. He is an IEE Fellow

100 POWER ENGINEERING JOURNAL MARCH 1987