Optimising trans-national power generation and transmission investments: a Southern African example

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Energy Policy 33 (2005) 2337–2349

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Optimising trans-national power generation and transmissioninvestments: a Southern African example

Bernhard Graebera, Randall Spalding-Fecherb,�, Brian Gonahc

aEnBW Gesellschaft fur Stromhandel mbH, Karlsruhe, GermanybECON South Africa, PO Box 26441, Hout Bay 7872, South Africa

cZimbabwe Electricity Supply Authority, Harare, Zimbabwe

Available online 13 August 2004

Abstract

Increased integration and co-operation within the Southern African power sector has opened up significant opportunities for

reducing the economic and environmental costs of meeting increasing electricity demand in Southern Africa. This paper applies a

linear programming model to investigate the economic and environmental benefits of regional integrated planning for electricity,

and the impact of including environmental costs in the decision-making process. We find that, from a financial perspective,

optimising generation and transmission investments in the region would result in savings of $2–4 billion over 20 years, or 5% of total

system costs. Introducing a tax based on the external damage costs of carbon dioxide as part of the decision-making process would

result in moderate increases in financial costs (15–20%), but would reduce regional carbon emissions by up to 55% at a mitigation

cost of $11 per tonne of carbon dioxide. This raises the possibility of financing regional power projects with Clean Development

Mechanism funding, which we explore with an example.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Southern Africa; Integrated resource planning; Clean development mechanism

1. Introduction

The end of civil wars in Mozambique and Angola andof apartheid in South Africa opened up the possibilityfor greater electricity co-operation in Southern Africa.The enlargement of the Southern African DevelopmentCommunity (SADC) to include South Africa in 1994increased the opportunities for energy resources fromthroughout the region to be developed to the benefit ofall countries. The scope for such regional development isvast, obvious examples being the Pande and Kudu gasfields in Mozambique and Namibia, respectively, thehydroelectric potential of Mozambique, Zambia, Zim-babwe and the Democratic Republic of Congo (DRC),and the oil and hydroelectric potential of Angola. For

e front matter r 2004 Elsevier Ltd. All rights reserved.

pol.2004.04.025

ng author. Tel.: +27-82-857-9486; fax: +27-21-790-

ss: [email protected]

cher).

the electricity sector, particularly in South Africa, thereis an opportunity to source electricity from lessexpensive and more environmentally benign sourcesinstead of utilising local generation. Further benefits area reduction in total reserve capacity required for supplysecurity because of different peak demand times indifferent countries.

Numerous studies have shown that greater regionaltrading and cooperation within SADC on powerdevelopment could substantially reduce investmentand operational costs, as well as carbon emissions(Rowlands, 1998; SADC, 1993; Sparrow et al., 1999).The need for a regional power trading pool, however,grew out of the power utilities’ recognition of thevulnerability of individual countries if each continued topursue a policy of self-sufficiency, rather than out of adesire to minimise the social or financial costs of theregion’s power. The 1995 SADC Protocol on Energy,1996 Energy Co-operation Strategy, and Energy ActionPlan all place a high priority on regional co-operation in

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ARTICLE IN PRESSB. Graeber et al. / Energy Policy 33 (2005) 2337–23492338

energy investment and trade, particularly in electricity.The creation of the Southern African Power Pool(SAPP) in 1995 from the region’s national utilitiesmarked the most important step toward realising thebenefits of regional electricity planning (SAPP, 1997).The SAPP Planning Sub-committee has completed aPool Plan for future co-ordinated expansion; while theOperations Sub-committee is managing the Co-ordina-tion Centre in Harare that administers short term tradesin surplus electricity and has finalised a ‘wheelingformula’ that will standardise the charges for electricitytrading across third parties (Mokgatle and Pabot, 2002;SAPP, 1999).

Despite this progress toward regional integration, oneof the major challenges is that, in view of the broadrange of alternative new generation projects and hugegeographical distances in the SADC region, long-termsystem expansion planning has to be based on a suitableplanning methodology, which includes generation and

transmission investments. In addition, other non-tech-nical aspects like national security requirements andexternal costs of electricity production also must beintegrated into the planning approach. This paperdescribes the application of a planning approach basedon mixed integer linear programming, called theregional integrated electricity planning (RIEP) model,which integrates all these different aspects of regionalelectricity planning to quantify the benefits of regionalintegrated planning for electricity. Section 2 introducesthe important concepts behind regional integrate plan-ning. Section 3 lays out the model structure andfunctionality. Section 4 presents the results of applyingthe model to current policy questions of regionalintegration, self-sufficiency requirements, including ex-ternal costs into the decision making process, and usingregional projects as potential Clean DevelopmentMechanism projects under the Kyoto Protocol to theUnited Nations Framework Convention on ClimateChange (UNFCCC). Conclusions follow in Section 5.

2. Regional integrated resource planning as a decision

making tool

2.1. Integrated resource planning and its relevance in a

trans-national context

Integrated resource planning (IRP) is an electricityplanning methodology that integrates supply- anddemand-side options for providing energy services at acost that appropriately balances the interests of allstakeholders (Swisher et al., 1997). In contrast totraditional supply-planning that took demand growthas a given, IRP incorporates the potential for reducingor shaping electricity demand—that is, demand-sidemanagement (DSM)—and the social and environmental

aspects of electricity production. With the liberalisationof energy markets, IRP is no longer a priority for mostutilities in the United States, where it had formerly beenthe strongest (Bakken and Lucas, 1996). But the basicconcept of IRP—to optimise the electricity system froma social perspective and to provide energy services atleast social costs—remains valid. From a policyperspective, government and utilities still want toachieve the best outcome for society through integratingdemand and supply options for energy services. Indeveloping regions such as Southern Africa, this broaderconcept of IRP is an appropriate tool to guide politicaland financial decisions in the electricity sector. Rapidlygrowing electricity demand and a great potential forefficiency improvements and DSM mean that a rationalbasis for decision-making can facilitate economic devel-opment in these and other developing countries.

IRP was generally implemented on a national orutility level, but there are several reasons for expandingthe fundamentals of IRP to a trans-national or regionallevel:

�
taking advantage of different resources in differentparts of the region;
�
taking advantage of different peak demand times indifferent parts of the region;
�
sharing generation reserve margins among severalutilities or countries;
�
increasing supply-security; � decreasing electricity prices; and � reducing environmental degradation.
For planning in Southern Africa, the key question iswhere and when to build a new large-scale power stationor high voltage transmission lines, considering anintegrated generation expansion plan and the associatednew transmission lines. Regional planning is not anattempt to replace national or local electricity planning.The planning process concentrates on elements of thesystem for which rational decisions are best made on aregional level and it builds upon national and localplanning (Graeber and Spalding-Fecher, 2000). Toimplement a regional, social planning approach withinthe SADC, appropriate institutional and regulatoryframeworks would have to be developed, which isalready starting with the regional electricity regulatorsassociation.

2.2. Multi-criteria decision-making and regional IRP

modelling

The use of multi-criteria analysis (also known asmulti-attribute analysis) for decision-making is one wayin which analysts are able to address the limitations ofappraising projects exclusively on the basis of costcriteria (Martinez-Aliers et al., 1999). Multi-criteria

ARTICLE IN PRESSB. Graeber et al. / Energy Policy 33 (2005) 2337–2349 2339

analysis uses a process of weighting different criteria toexplicitly reflect their relative importance to recognisethat there are different values and perspectives. Once thedifferent criteria have been weighted, projects areevaluated against them and an aggregated summaryindicator or single value is presented for each project.Those projects with the highest value have the greatestoverall benefit, given the weighting choices made.

Ideally, the evaluation of energy supply and demandoptions should embrace a range of values and perspec-tives, and be the result of a negotiated process of socio-political compromise. In the case of the SouthernAfrican power sector this means that, in the long run,it is important for not only the private utilities and thegovernments of the region, but also for a wider range ofcivil society actors, to be involved in the decision makingprocess—and putting forward the criteria by which theybelieve investments should be measured. Currently,most decisions are made almost entirely by the utilities,and only consider financial and technical criteria such asreliability.

The RIEP modelling approach is a first step toward ofbroadening the perspectives included in making deci-sions on power investments. The RIEP optimisationmodel includes a range of non-financial constraints—which may be related to political, social or environ-mental goals—when deciding the best set of options forregional development. For example, the RIEP modelincludes external costs in the objective function, whichwould not be considered in traditional utility planningmodels. Self-sufficiency requirements and the option ofsetting regional caps on emissions are additional modelfeatures that can reflect regional and even globalpolitical decisions (e.g. on future emissions limits forall countries). The RIEP model allows the integration ofthese various quantitative criteria within the modellingprocess itself, although it is less amenable to includingqualitative criteria. Future development of this researchwill also look at how political choices that are notquantitative could affect decisions about regionalinvestments.

3. Methodology and model structure

For quantifying the benefits of regional integratedplanning within SADC, an optimisation model devel-oped at the University of Stuttgart was applied to theSADC power sector (Graeber, 2002).1 The RIEP modelis a mixed integer linear programming model which usesthe general algebraic modelling system (GAMS) as aprogramming language (see Box 1 for an sample of keyequations and variables). This model has several major

1The RIEP model with an interface to Excel is available from B.

Graeber ([email protected]).

advantages over most generation expansion analysistools. First, it can be used to do both generation andtransmission planning simultaneously, which is criticalin regions with long transmission distances. Second, itexplicitly integrates external costs into the objectivefunction, and so can optimise for social costs. Third, ithas the flexibility to model a region with varyingregulatory and technical constraints within individualcountries.

The objective function (Eq. (8)) of the RIEP modelconsists of the total discounted system costs over thewhole time period studied. Costs include annualisedinvestment costs, variable operational costs, fixed annualoperation costs, and maintenance costs for every modelelement. Fig. 1 depicts the main elements that can beincluded in the model. First, there are nodes, which arethe main balancing points in the model. For all nodes, andfor every time section modelled, an energy balanceequation (Eq. (1)) is created. Nodes are linked tocountries. Because all other model elements are linkedto nodes, costs, external effects and other results can beallocated to countries. Transmission lines are the mainlinks between different nodes and are modelled as atransportation problem with upper capacity limits for thelines and a linear loss function. Transmission flows, whichcan occur in both directions, are variables in the modeland they are optimised for every time section. Existingtrade contracts can be included as restrictions on freetrade, as well as country-specific trade regulations.

Electricity demand is characterised by load curves fortypical days, peak demand, coincidence factors withnational and regional peak demand, annual demandgrowth rates, distribution losses between the node andthe customer, and outage costs. Demand at one nodecan be divided into several demand groups if required.Power delivered to demand groups is modelled usingdifferent demand variables. If the demand is not met,outage costs are incurred.

DSM can influence electricity demand. Most DSMprogrammes will be planned and implemented onnational or local level, so only a small number oflarge-scale DSM programmes will be considered withinregional planning. A DSM programme is characterisedby its temporal impact on electricity demand, an impactwhich can be either uniform (e.g. for energy efficiencyimprovement programmes) or time-specific (e.g. forradio-controlled interruptible water heaters). Detailsof programme start-up costs as well as variable costs(in $ per MWh), are required for each DSM programme.The decision whether to start or postpone a DSMprogramme and the scope of the programme arevariables in the model.

Power stations are characterised by their generationcapacity, scheduled and forced outage rates, variablegeneration costs, fixed operation and maintenance costs,and initial investment costs. The availability of a power

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Box 1Main equations and variables in RIEP model.

Energy balance equation for each node n forall time segments ðt ; yÞ:X

gn

PGgn ;t ;y þXlþn

Zlþn� PL�

lþn ;t ;y� PLþ

lþn ;t ;y

� �

þXl�n

Zl�n� PLþ

l�n ;t ;y� PL�

l�n ;t ;y

� ��Xdn

PDdn ;t ;y ¼ 0

(1)

Capacity limits for Generation units andtransmission lines:

PGg;t ;ypaGg;t ;y � CGg;y ð2Þ

PLþl;t ;ypaLl;t ;y � CLl;y ð3Þ

PL�l;t ;ypaLl;t ;y � CLl;y ð4Þ

External effect balance for every country c andexternal effect e (reduced to generation re-lated effects):

SEe;c;y ¼Xgc

durtt � fggc� PGgc ;t ;y ð5Þ

Annual regional cost balances:

Kf y ¼X

g

Xt

durtt � kvgg;y � PGgc ;t ;y þ kf gg � CGg;y

!

þX

l

kf ll � CLl;y (6)

Key ¼Xe;c

kee;c;y � SEe;c;y ð7Þ

Objective function:

Kt ¼X

y

duryy df y � Kf y þ dey � Key

� �¼! min

ð8ÞIndices

c countrydn demand group d which is connected

to node ne external effect (e.g. CO2-emissions)gn, gc generation unit g which is connected

to node n, which is in country c,respectively.

lþn transmission line l which has noden asstart node

l�n transmission line l which has noden asend node

n nodet time section as part of a year

y modelled year

Variables

CLl net transmission capacity of line l(discrete or continuous variable)

CGg net capacity of generation unit g (dis-crete or continuous variable)

Key total regional external system costs inyear y

Kf y total regional financial system costs inyear y

Kt total discounted system costsSEe;c;y annual quantity of external effect e in

country cPDd Power demand of demand group d

after DSM impactPGg Power output of generation unit gPLþ

l positive flow on transmission line l indirection start node to end node

PL�l positive flow on transmission line l in

direction end node to start node

Parameters

aGg, aLl time specific availability factor forgeneration unit g or transmission linel, respectively

dey discount factor for external costsdf y discount factor for financial costsdurtt duration of time segment tduryy number of years represented by mod-

elled year yfgg generation output related external ef-

fect factor (e.g. emission factor)kf gg, kf ll specific fixed annual costs (O&M

costs and annuity of investment costs)for generation unit g or transmissionline l, respectively

kvgg specific variable generation costsZl average transfer efficiency of line l (1�

loss factor)

B. Graeber et al. / Energy Policy 33 (2005) 2337–23492340

plant is modelled deterministically (Eq. (2)). Hydropower plants can be modelled as power plants withdaily, weekly or annual generation limits, whichare adequate for most plants. If required, water flowrates, reservoir limits and hydrological connectionsbetween plants can also be modelled. Pumped storageplants are mainly characterised by the reservoir capacityand the average cycle efficiency. For the reservoir,a weekly cycle with the same reservoir levels at

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D

H

G

S

D

D

D

D

H

G

G

G

Nodes

Transmission

Demand

Generation

Hydro

Storage

DSM

G

S

DSM

DSM

DSM

Fig. 1. Elements of RIEP model (example).

Fig. 2. Example of time structure.

B. Graeber et al. / Energy Policy 33 (2005) 2337–2349 2341

the beginning and end of every week is assumed.Power plants can be existing or new, and capacityexpansion can be modelled either in blocks or ascontinuously expandable. Furthermore, closing of aninefficient plant can be included as a decision variable inthe model.

Only key years that represent a block of years in themodelling horizon are modelled. A year is dividedinto seasons and a season is represented by typical daysof a week. Finally, instead of modelling 24 time sectionsper day, several hours with similar electricity demandcan be combined into one time section. An example ofthe time structure is shown in Fig. 2. As typical daysgenerally do not reflect peak demand conditions, andbecause actual plant availability might be much lowerthan expected availability, the regional peak demandtime is included as an additional time segment in themodel. The demand for this time section lies at specifiedpercentage points above annual peak demand. Addi-tional reliability criteria can be modelled as additionaltime sections. They are characterised by the demand atevery demand group and the availability of power plantsand lines.

To cater for the different legislative requirements thatcould govern the regional electricity sector, the follow-ing aspects are accommodated by the model:

�
national reserve margin requirements, which limitdependence on imports for meeting national peakdemand;
�
electricity production taxes; � import and export restrictions (limits for annual net
imports or exports);
� taxes on net electricity imports or exports.
Quantitative external effects can be included in themodel if they are important from a social perspective;examples could include air pollution emissions, jobcreation, and land usage (Eq. (5)).

Using existing data and some estimates, the modelwas applied to all 12 mainland SADC states. Most ofthe input data was obtained from the different regionalutilities (e.g. operational data, expansion plans, electri-city master plans, pre-feasibility studies, and interviews)or from previous regional research (e.g. Rowlands, 1998;SCEE, 1998). Where this was not possible (for example,for the calculation of emission factors for small non-utility generating plants), default data were used fromthe Environmental Manual (GTZ, 1995, 1996). Costdata in local currency has been converted to US$ withan average exchange rate for the specific year. All costdata has been converted to US$1999 using US inflationrates. To develop a consistent data set, carefulcomparison of different data sources and generic datawas necessary. Consultation with utility planningexperts helped to clarify inconsistencies. Emissionsfactors, where not available from the previous studiesmentioned above or utilities, were taken from defaultfactors in the Environmental Manual.

The RIEP model for SADC includes 16 nodes, morethan a hundred power plants (both existing and new)and 25 transmission lines. Modelled key years are 2000,2003, 2006, 2010 and 2015. Demand is represented byone demand group at every node. Three demandscenarios (high, medium and low) were used, withaverage annual electricity demand growth rates for thewhole region assumed to be 4.6%, 3.0%, and 1.5%respectively. Predictable changes in the load profile dueto large-scale industrial projects (e.g. the Mozalaluminium smelter in southern Mozambique) have beenconsidered where appropriate. Due to lack of data, noregional DSM programmes have been included so far,but this will be a priority area for future research.All costs are divided into financial and external costs(Eqs. (6) and (7)), for which real discount rates of 10%and 5% have been used, respectively. For publicinfrastructure investments in western countries, realrates of approximately 5% are used in most cases(IAEA, 1984). South Africa’s state-owned utility,

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Table 1

Surplus capacity within the SADC

Country (Utility) Surplus capacity (MW)

South Africa (Eskom) 7450


Eskom, typically uses 6% (Spalding-Fecher et al., 2002).For most regional economies and utilities, however,capital is more scarce and expensive, and thus wehave used a higher discount rate of 10% for financialcosts.

Mozambique (EDM) 1900

Angola (ENE ) 450

DRC (SNEL) 1400

Zambia (ZESCO) 320

Less deficit in other countries (350)

Total 11 170

Source: (Lines, 1999)

4. Model analysis of regional policy issues

The RIEP model was used to quantify the impacts ofkey political strategies in Southern Africa. Out of manypossible policy issues which have been considered, thispaper highlights three topical ones:

�
the financial and environmental benefits of greaterregional co-operation in generation and transmissionexpansion;
�
the economic and environmental impact of includingthe external costs of greenhouse gas emissions or anemissions cap in a regional expansion strategy;
�
the economic and environmental impact of a multi-country Clean Development Mechanism (CDM) project.
4.1. Benefits of integrated planning for SADC

As already discussed, although there has beenelectricity trade in the region for over 40 years, thishas been on a limited scale because of conflict betweenSouth Africa and its neighbours (Graeber, 1999; Raskinand Lazarus, 1991). The situation has improved, withregional bodies like the SADC and SAPP having beenformed to enhance trade and facilitate power exchangesbetween countries. Even with all these regional bodies inplace, however, most countries are still reluctant todepend significantly on imports. The main reason forthis could be the fear of being too reliant on foreignsupplies from both a security and tariff-setting point ofview.

As shown in Table 1, the region has approximately11 000 MW of surplus generating capacity. Despite thisover-capacity some utilities are in the process ofplanning for costly generation investments instead ofmaking use of what is available: for example, plans byZambia to expand Kafue, proposals in Zimbabwe toconstruct another 1400 MW thermal power station atGokwe North, and a proposal to build a gas-fired powerstation in Cape Town fuelled by natural gas from theNamibian Kudu gas fields. In the majority of cases thesenational power projects are developed in isolation,without a proper understanding of the economicdynamics in a regional context. Such uncoordinatedplanning could easily result in more stranded assets ascompetition in the wholesale generation market opensup in the region, which would put an unnecessaryburden on regional economies (Graeber and Spalding-Fecher, 2000).

The current state of affairs has been reflected in our‘traditional planning’ scenario by using national securitymargins of 5% (above the annual peak load) forAngola, DRC, Mozambique, South Africa, Tanzaniaand Zambia. Other countries already rely on imports formeeting their peak demand (or will rely on them in thenear future); so for the rest of the SADC countries(Botswana, Lesotho, Malawi, Namibia, Swaziland andZimbabwe) reliance factors of 70%, 100%, 20%, 35%,100% and 20% were used, respectively. The limitedelectricity trade currently in Southern Africa is reflectedin the model by restricting the total annual imports formost countries to 10% of the total demand. Higherimports were permitted for Botswana (70%), Lesotho(100%), Malawi (20%), Namibia (70%), Swaziland(100%) and Zimbabwe (20%).

4.1.1. Financial and investment impacts

The results from the traditional planning scenario(in which national security requirements and traderestrictions have been included) have been comparedwith the regional integrated planning case (no nationalsecurity requirements and trade restrictions) for mediumload growth. The differences in total annual systemcosts highlight the financial benefits of regional inte-grated planning for electricity. As shown in Table 2, forthe total modelling period of 20 years, savings of at least5% of total system costs can be expected, or $2.2 billion(note that all financial data are 1999 US dollars).Approximately 40% of these savings are associated withlower investment costs, while the remainder can beattributed to lower operational costs. In the case of highelectricity demand growth, larger savings of over $4billion can be expected through regional integratedplanning. The total benefits for the medium growth caseand their distribution among the countries are shown inTable 2 and Fig. 3, under the assumption of electricityimport prices of $8–20 per MWh for 2000 and anincrease of 5% per year in real prices. The presentvalue includes levelised investment costs for new plantsbut not for existing plants, since these investments are‘sunk costs’. For some exporting countries, the variablegeneration costs are much lower than the assumed

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Table 2

Total system and investment costs, 2000–2020, medium demand

growth (US$1999 billion)

Modelling scenario System cost Investment cost

Traditional planning 47.4 19.8

Regional planning 45.2 18.9

Regional planning+$4 carbon cost 47.1 19.4



Regional planning+carbon cap 62.1 25.3

CDM project case 48.3 20.2


export revenues, which results in negative financialcosts.

The optimal expansion strategy for medium demandgrowth under the regional planning scenario is shown inFig. 4. First, existing unused surplus capacity in theregion, mainly in South Africa and the DRC, is utilisedby expanding transmission capacity (e.g. new lines with500 MW capacity from DRC to Namibia throughAngola in 2006 and an additional capacity of300–400 MW by 2015). New generation capacity wouldmainly be built in South Africa. This includes recom-missioning of the mothballed coal power plants with atotal capacity of 3500 MW in the years 2010–2015, onenew coal power plant with 1300 MW capacity, severalsmall nuclear pebble bed reactors2 with a total capacityof 2000 MW in 2015, a total of 3000 MW pumpedstorage capacity, and 600 MW of gas turbine capacity.The results also include several smaller additions ofhydro generation capacity in Angola, Tanzania, Malawiand Zambia (about 1000 MW in total) as well as anadditional 140 MW gas plant in Tanzania. Import andexport balances would change accordingly. For exam-ple, South Africa would develop from a net importer of3500 GWh per year (mainly from Mozambique) in 2000to a net exporter of 16 000 GWh per year in 2015.

4.1.2. Local emissions

Local emissions can have an important impact onliving conditions in areas close to fossil power stations.Therefore, SOx, NOx, as well as particulate emissionshave been included in the modelling. Fig. 5 shows as oneexample the SOx emissions for the medium growthscenario. The total regional amount of SOx emissions(expressed in kg per MWh electricity consumption) willbe reduced by regional integrated planning, as shown inTable 3, but the distribution of these benefits is quiteuneven. Note that, other than South Africa, Zimbabweand Botswana, most countries use primarily hydroelec-

2Eskom’s proposed pebble bed modular reactors are included in this

analysis because they are in Eskom’s current long-term expansion

plans. The likelihood of licensing of new nuclear facilities in South

Africa, however, remains uncertain.

tricity or gas, so sulphur emissions are minimal. BecauseZimbabwe and Botswana would not run their own coal-fired power plants under the regional integrated plan-ning scenario, their emissions are virtually eliminated.

4.1.3. Greenhouse gas emissions

For the medium growth scenario, there are only smalldifferences in the total CO2 emissions between thetraditional independent planning and the regionalintegrated planning approach if external costs are notconsidered, as shown in Fig. 6 and Table 4. In earlyyears, emissions are slightly reduced due to betterutilisation of existing hydro generation facilities. In thelong term, emissions will increase compared to thetraditional planning due to more intense utilisation ofcoal resources in South Africa. This is because, under amedium growth scenario, bringing mothballed and newcoal power stations into service in South Africa is stillless expensive than the capital investment required intransmission and generation to bring the vast hydro-electric resources of the North to the high demandcentres in the South.

4.2. Social costing and electricity planning: the carbon

problem

Over the last two decades the environmental impactsresulting from energy generation and utilisation havebecome one of the major concerns of environmentalpolicy and a central issue in environmental and energyeconomics. Ideally, planning would consider first thelocal environmental and social impacts of powergeneration and transmission, and then the globalimpacts. This is because part of the rationale ofmodelling regional integrated planning is to provide atool for regional governments to integrate domesticsocial or political goals (e.g. increased access toinexpensive electricity while protecting environmentalquality) into the regional investment process. Unfortu-nately, however, quantitative information on the da-mages from local pollutants in the region is very limited.

Greenhouse gas emissions from the power sector areboth a major issue world-wide and easier to quantify—including a range of possible damage costs. Becausetechnologies for removing CO2 after combustion are notyet commercially viable, the medium-term solutions inthe power supply sector are largely to shift to differentsources of energy—either cleaner fossil sources or non-fossil fuels. Within SADC, South Africa, Zimbabwe andBotswana are the countries that rely most on coal as asource of power generation. Partly for this reason,South Africa contributes about 47% of the continent’sCO2 emissions and could therefore play a big part inreducing greenhouse gas contributions in the region(IEA, 2001). SADC countries on their own, however,cannot significantly affect global anthropogenic carbon

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Fig. 4. Optimal regional planning expansion strategy for medium load growth for the years 2000–2015, based on integrated planning.

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

SA

DC

Region

South A

frica

Nam

ibia

Lesotho

Sw

aziland

Mozam

bique

Botsw

ana

Zim

babwe

Zam

bia

Malaw

i

Tanzania

DR

Congo

Angola

Ave

rage

Eec

tric

ity C

osts

(U

S$/

MW

h) Traditional Integrated

Fig. 3. Potential economic savings from regional integrated planning by country.


emissions, nor do they have commitments to do sounder the Kyoto Protocol to the United NationsFramework Convention on Climate Change(UNFCCC).

In our analysis we use the external costs of CO2

emissions as one example of how to include non-financial costs: firstly because CO2 emissions fromenergy use are the most important contributors to

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0

5

10

15

20

25

30

SADC Reg

ion

South

Afri

ca

Namibi

a

Leso

tho

Swazila

nd

Moz

ambiq

ue

Botsw

ana

Zimba

bwe

Zambia

Mala

wi

Tanza

nia

DR Con

go

Angola

SO

x E

mis

sio

ns

(kg

/MW

h)

TraditionalIntegrated

Fig. 5. Comparison of SOx emissions in traditional and integrated planning scenarios (emissions per MWh electricity consumption 2000–2020).

Table 3

Total SOx and NOx emissions, 2000–2020, medium demand growth

(million tonnes)

Modelling scenario SOx NOx

Traditional planning 41.4 29.1

Regional planning 39.8 29.9




Regional planning+carbon cap 25.6 21.3

CDM project case 40.2 28.5

160

180

200

220

240

260

280

2000 2002 2004 2006 2008 2010 2012 2014Year

CO

2 E

mis

sio

ns

(Mto

n p

.a.)

TraditionalIntegrated

Fig. 6. Total regional CO2 Emissions for traditional planning versus

integrated planning.

Table 4

Total CO2 emissions, 2000–2020, medium demand growth (million

tonnes)

Modelling scenario Low

Traditional planning 4576

Regional planning 4560

Regional planning+$4 carbon cost 4123



Regional planning+carbon cap 3473

CDM project case 4540


global environmental damage, and the damage costs ofgreenhouse gases are available in the literature.Secondly, the reduction of fossil fuel use that wouldoccur in a GHG-limiting scenario will have importantspill-over effects on local energy-related pollutants (e.g.SO2, NOx). Thirdly, countries may be able to receiveexternal funding to finance projects that reduce green-house gas emissions (see Section 4.3 on the CDM)—funding that is not available to finance projects that onlyreduce local pollutants (e.g. flue gas desulphurisationtechnologies).3 From a modelling perspective, there areseveral options of how global emissions can influencethe optimal planning strategy. Two are considered inthis paper: the introduction of external costs into theoptimisation, and the introduction of an emission cap tostabilise the total quantity of emissions.

4.2.1. Results and cost of emission reductions

From a social perspective, the best approach forconsidering CO2 emissions in electricity planning is to

3Note that the RIEP model is not limited to environmental aspects

related to CO2 emissions or other air pollutants. The model can handle

any quantifiable environmental or social impact related to power

generation, transmission or distribution.

associate external costs with them. Given the scientificuncertainty and challenges of economic valuation ofenvironmental and health impacts of climate change,the literature contains a wide range of damage values(e.g. Fankhauser et al., 1998; Pearce et al., 1996; Tol,1999). To reflect this uncertainty, we have used values of$4, $9 and $15 per tonne of CO2 (tCO2). For one furtherscenario, an emission cap which stabilises the totalregional CO2 emissions in the electricity sector at the

ARTICLE IN PRESS

0

500

1000

1500

2000

2500

3000

3500

2003 2006 2010 2015

Ave

rag

e an

nu

al in

vest

men

t co

sts

(US

$ M

illio

n)

RefCap

4$9$ 15$

Fig. 8. Comparison of annual investment costs for different carbon

policy scenario. Note: Ref=reference scenario, based on integrated

planning without carbon policy; cap=policy scenarios with cap on

regional carbon emissions; $4–$15=scenarios with respective carbon

taxes included in optimisation.

0

10000

20000

30000

40000

50000

60000

70000

80000

Ref Ref+Ext Ref Ref+Ext Ref Ref+Ext Ref Ref+CapNP

V o

f to

tal c

ost

s (U

S$

mill

ion

)

external costsfinancial costs

4$/t CO2 9$/t CO2 15$/t CO2 9$/t CO2

Fig. 9. NPV of external and financial costs for different carbon policy

scenarios. Note: Dollar value in each section refers to carbon tax level;

Ref=reference scenario, based on integrated planning without carbon

policy; Ref+Ext=scenarios optimised for total cost including carbon

tax’ Ref+Cap=scenario optimised for total cost with carbon cap as a

constraint.


level of the year 2000 emissions has been assumed. Forall the carbon policy scenarios optimal regional plan-ning has been assumed, and therefore the regionalintegrated planning scenario from Section 4.1 is thereference scenario.

Fig. 7 depicts the development of the region’s CO2

emissions from power generation for these differentscenarios of damage costs assuming medium demandgrowth, with Table 4 containing the actual values. In2015, reductions of 22%, 47% and 55% can be achievedif taxes to reflect external cost values of $4, $9 and$15/tCO2 are used, respectively, using full cost optimi-sation. This level of reductions can only be achievedthrough regional co-operation, because they are largelydue to hydro resources in the north of the regiondisplacing fossil resources in the south.

To achieve these emission reductions, significantadditional investment costs would have to be incurred,as shown in Fig. 8. These costs are mainly theconstruction of new hydro generation facilities in thenorthern SADC region, together with high capacitytransmission lines to the main demand centres. Notethat while investment costs would increase, the impacton total financial costs of the power system would be farlower, as shown in Table 2 and Fig. 9. Financial costs ofsupply would increase by about 17% for the mid-rangecarbon abatement scenario, for example—if no outsidefunding were used to support this lower carbonelectricity system. Moreover, since the model is optimis-ing for full economic costs (i.e. financial cost plusexternal costs), the total economic costs of the carbonpolicy scenarios would be lower than the total economiccost of the purely financial regional integrated planningscenario.

Even including the additional investment costs, theaverage abatement costs for the scenarios with external

100

120

140

160

180

200

220

240

260

280

2000 2005 2010 2015Year

CO

2-E

mis

sio

ns

[M t

]

Ref

Cap

4$

9$15$

Fig. 7. Total regional CO2 emissions for different carbon policy

scenario. Note: Ref=reference scenario, based on integrated planning

without carbon policy; cap=policy scenarios with cap on regional

carbon emissions; $4–$15=scenarios with respective carbon taxes

included in optimisation.

costs are relatively low at $2–$11/tCO2 (present value)4,as these additional investment costs are partly out-weighed by reductions in operational costs. This isshown in Fig. 10. Fig. 11 shows what the optimalexpansion strategy would be if a tax of $15/tCO2 is used.

4.3. Opportunities for financing regional integration

through the CDM

The Kyoto Protocol to the UNFCCC created a newpossibility for North-South co-operation in mitigatingclimate change through joint projects. Under the KyotoProtocol, industrialised countries agreed to cut theirgreenhouse gas emissions by an average of 5.2% by2008–2012, relative to 1990. Instead of having to makeall those cuts at home, however, they have the option of

4Calculated as difference in 20 year financial NPV of each

mitigation/social costing scenario less NPV of reference scenario

divided by 20 year discounted emissions savings.


using the CDM and several other ‘flexible mechanisms’,to invest in projects in other countries and receive creditfor those emissions reductions against their ownemissions targets. The CDM allows industrialised

0

2

4

6

8

10

12

Cap 4$ 9$ 15$

Ab

atem

ent

cost

(N

PV

$/t

CO

2)

Fig. 10. Mitigation cost for different carbon policy scenarios.

Fig. 11. Least cost regional expansion stra

countries to purchase ‘certified emissions reductions’(CERs) from projects in developing countries thatmitigate climate change (Grubb et al., 1999; Spalding-Fecher, 2002).

Most of the focus so far has been on bilateral modelsfor the CDM—where an industrialised country govern-ment or company would invest in a project in adeveloping country and receive carbon credits from thatproject (e.g. Halsnaes, 2002; Jotzo and Michaelowa,2002; Woerdman and van der Gaast, 2001). Becauseemissions reductions from CDM projects must provide‘real, measurable, and long-term benefits related to themitigation of climate change’, projects in one countrythat are clearly bounded and defined are the easiest tomonitor and justify. What the analysis here has shown,however, is that some of the most important powersector mitigation options in Southern Africa involve co-operation among several countries. That co-operationcould simply be trading, but it could also include jointly

tegy using carbon tax of $15/tCO2.

ARTICLE IN PRESSB. Graeber et al. / Energy Policy 33 (2005) 2337–23492348

financed transmission and generation projects. To bemore specific, a transmission line upgrade between twocountries that effectively allows the substitution of gasor non-fossil fuels for coal-based electricity could be aspecific mitigation option that could be ‘packaged’ as aCDM project. The difficulty is that, because most of themethodological work on mitigation costing has con-sidered single projects in single countries (e.g. Halsnaes,et al., 1998; Sathaye and Meyers, 1995; World Bank,1998), the ownership of the credits is not clear (seeRowlands, 1998, for a regional example). An additionalissue is that many developing countries want theopportunity to do unilateral CDM projects—in otherwords to finance their own mitigation projects and theneither sell the credits on the international carbon marketor even ‘bank’ the credits against future commitments(Baumert et al., 2000). This means that, in theory,Southern African countries could consider a combina-tion of investments within the region as a CDMproject—even without international investors upfront. The challenge, however, would be quantifyingand monitoring the emissions impact of particularinvestments.

As a starting point for understanding multi-countryCDM projects, we examine how an investment thataffects more than one country could change the futureof the SADC electricity sector and resulting emissions.We have chosen the construction of a 750 MW gas-firedpower station in Northern Namibia using natural gasfrom the Kudu gas-fields. While this power stationlooked fairly likely several years ago, both Eskom andShell have since pulled out of the venture. This powerstation is not included in the reference case. The Kudugas power station would displace coal-fired powergeneration in South Africa. According to IPCC green-house gas inventory guidelines (IPCC, 1996), however,the reduction in emissions would all be reflected inSouth Africa’s inventory, even though it was aninvestment in Namibia that allowed this to happen.Namibia’s emissions, meanwhile, would increase eventhough most of the electricity would not be used to meetdomestic demand. The power station would run as baseload—80% load factor—and cost approximately $430million ($570/kW) with variable operating and main-tenance costs of $20/MWh. Existing lines would be usedto bring the electricity to South Africa. The purpose ofthe analysis is, then, to see how this project affects thetotal system costs and carbon emissions.

4.3.1. Results and cost of emission reductions

Two model runs have been conducted to evaluate theKudu gas power station as a potential CDM project:

�
Baseline/reference case: Our traditional planningscenario, which reflects the current trading regimeand utility plans within the region.
�
Project case: Our traditional planning scenario, withthe Kudu gas power station built in 2005 anddisplacing coal-fired power station development inSouth Africa.
Keeping all other parameters unchanged in these twoscenarios, we find that the total emissions savings overthe period 2005–2018 would be 36 mtCO2, as shown inTable 4. Emissions reductions would be fairly constantover the period, with reductions of 2.3, 2.8 and2.5 mtCO2 in 2006, 2010 and 2015, respectively. Thechange in the system costs is the net of the additionalcapital, operational & maintenance, and fuel costs of thegas-fired power station versus the coal-fired powerstation that it would replace. There will also be smallchange in distribution costs, so we use the model tocapture the total change in system costs. Over the period2006–2018, the present value of the change in systemcosts is $337 million, as shown in Table 2. This meansthe average abatement cost is $9.4/tCO2 if we do notdiscount future emissions savings. Using a 5% discountrate for future emissions, the cost per tonne of emissionsreduction would be $17.2/tCO2. As we mentioned in theprevious section, most of these costs are incurred inNamibia, while the emissions reductions occur in SouthAfrica. In fact, emissions in Namibia would increase.The policy and research challenge will therefore be tounderstand how these costs and benefits can be sharedequitably.

5. Conclusion

For Southern Africa, the potential impacts of aregional integrated planning approach have beenevaluated with the RIEP optimisation model, whichshows savings of $2–4 billion per year from optimisationof regional generation and transmission expansionplans. Regional integrated planning, however, is morethan just a financial optimisation approach, as it canincorporate environmental and social aspects of elec-tricity supply as well. Our application of the RIEPmodel to identify least-cost strategies for CO2 emissionsreduction in the power sector for Southern Africa showsthe potential to cut emissions by up to 50% versusbusiness as usual over the next 20 years, with electricitycosts rising by only 15–20%. This is not to suggest thatSADC countries should implement these higher costoptions on their own, given that they do not haveemissions limitation targets under the Kyoto Protocol,but rather to highlight the opportunities for climatechange-linked investment in the SADC power sector.The example regional CDM project analysed—a gas-fired power station in Namibia that would exportelectricity to South Africa—would be a moderate costCDM investment, and could be even less expensive


given more recent gas finds off the west coast ofNamibia and South Africa.

As electricity planning is a continuous process, theRIEP model has been designed to be flexible, to handleadditional constraints and new options (e.g. regionalDSM programmes). The modelling approach provides auseful tool for SADC energy ministries, electricityregulators and the respective utilities, as they look atwhich long-term projects to license or implement. Theregional integrated planning approach is not restrictedto Southern Africa, however. Because it combinesgeneration and transmission optimisation, with thepossibility of including a wide range of non-financialparameters, it could also be used in other regions of theworld where rational decisions for the expansion of anelectricity system covering a large geographical area arerequired.

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Documents

Optimising trans-national power generation and transmission investments: a Southern African example