Modelling the Nigeria’s Electric Power System to Evaluate its Long-Term Performance
Momodu!, A. S., Oyebisi’, T. O. and Obilade’, T. O.
Abstract
The study presents a System Dynamic model of the Nigeria electric power system. The model
was developed with a view to using it to evaluate the long-term performance of the system. Both
primary and secondary sources were employed to collect the system’s baseline information. Results
from this formed input to develop a four-sector-model in Vensim software for the long-term
evaluation of the NEPS. Leverage points in the model were identified from the validated model
using data from 2005 to 2009 in the Base Run. The system behaviour, based on two other scenarios
(Scenario 1 representing improved basic level of consumption and Scenario 2 representing
industrialization target), was then evaluated on a timeframe of 30 years starting from 2009. The
study concluded that Compounded Annual Growth Rate (CAGR) and Economic Growth Rate
(EGR) were the most critical policy leverage intervention points for NEPS improvement within the
next 30 years.
Keywords: System Dynamics, Electric power system, VENSIM, Model, Nigeria
Background
The Nigeria Electric Power Sector Reform (EPSR) Act was enacted into law in March 2005
(FGN, 2005). This Act represents the legal and regulatory framework which guides the holistic
operations in the sector. With this Act as a legal and regulatory framework, it is hoped that Nigeria
electric power system (NEPS), would be moved away from state-dominated systems to that with a
greater role for market forces (Joskow, 2003) - resulting in more efficiency of operations to the
supplier and more reliable, adequate and efficient supply to the consumers. In summary, the Act
was supposed to have created (Biobaku & Co, 2010):
e an electric power sector that encourages investment and ultimately competition in
generation and distribution;
e allows for efficient and effective dispatch of generated electricity;
e an electric power market that meets current and future demand efficiently and
economically; and
e aneven playing field that will attract private investors.
These objectives were in anticipation that key stakeholders in the Electricity Supply Industry
(ESI) would be attracted into the sector for new investment decisions, particularly with
participation in the competitive segments of the industry. To achieve a level playing field for all
new entrants as well as those already operating in the market, the Act made provision for Nigeria
Electricity Regulatory Commission (NERC), an independent regulator. The goals of the objectives
as enunciated in EPRI (2003) are succinctly captured as follows:
e Lower the cost of reliable, safe, clean electric service
Attract capital for infrastructure development
Enable greater consumer choice
Greater economic efficiency
Level the competitive playing field
* Research Fellow with PhD in Systems Planning and Energy Management, Centre for Energy Research and
Development, Obafemi Awolowo University, lle-Ife, Nigeria. Email: abiodun.momodu@yahoo.com
? Professor and Lecturer, African Institute for Science, Policy and Innovation, Obafemi Awolowo University, Ile-lfe,
Nigeria. Email: tooyebisi@yahoo.com
a Professor and Lecturer, Department of Mathematics, Faculty of Science, Obafemi Awolowo University, lle-lfe,
Nigeria. Email: tobilade@ yahoo.com
1
To appreciate improvements made in the system through reforms, whether these goals are
achieved would be weighed against current realities of:
e Higher cost and lower reliability
e Reduced investment confidence and incentive
e Loss of accountability to consumers
e Greater financial risk
e Market volatility
However, unexpected anomalies in the global performance of liberalized electric power systems
necessitated a rethink towards the issue of reorganizing the electricity sector. The flagship of these
is the case of California power market, which suffered sustained shortage of generation capacity,
and this led to an energy and price crisis in the summer of 2000 and 2001 (Olsina, 2005, Besant-
Jones and Tenebaum, 2001). Other things noticed in some other markets are inefficiencies in
resource allocation as a consequence of excess capacity in their systems. United Kingdom and
Argentina power markets have registered low unprofitable prices due to massive entry of combined
cycle gas turbine (CCGT) based capacity. In the US markets, signal of overinvestment is what is
currently exhibited (Olsina, 2005). A combination of these anomalies had made some observers
argue in the one hand that deregulation should be scrapped/decommissioned, while others argue on
the other hand that deregulation is a noble endeavour and that these problems can be solved with
structural adjustments to the markets (EIA, 2000). The concept of this study agrees with the latter
school of thought that the problem of electric power system can be solved through structural
adjustment of the system. It is important however to state that dealing with the problems of electric
power system, (in fact all energy system as recent unfolding events in Nigeria shows), is
notoriously difficult, and the results of conventional solutions are often poor enough to create
discouragement about the prospects of ever addressing them (Aronson, 1998).
In order to gain significant insight into structure and long-term performance of Nigeria electric
power system, a simulation model based on Systems Thinking (Frasser and Brettner, 2002,
Forrester, 1968), making use of System Dynamics principles was employed for this study, to
evaluate the entire system from policy making to service delivery. This is with the aim of using the
computer to reproduce the structure of the NEPS and the relationships that exist among its
components in order to simulate its long-term performance. The approach included dynamic
framework to enhance its usefulness for decision makers (Smith and Ackere, 2002). Thus the
objective of the study is to present a SD model to forecast the long-term performance of the NEPS.
Significance of study
The importance of the electricity sector cannot be overemphasized in many economy. It is
over 25 years since the advocacy for reorganizing the electricity sector began worldwide. It is also
about a decade since the California electricity crisis and the collapse of Enron occurred (Joskow,
2008). Presently, there are two government documents related to improving Nigeria’s electricity
sector. These documents are namely, Vision 2020 Energy Report and Roadmap for power sector
reform. These two documents were taken into consideration in evaluating government targets of
achieving improvement in the electric power sector.
The electric power industry is a closed, feedback dominated, non-linear system featuring time
delays as in capacity approval, capacity construction, scrapping/decommissioning and response of
price to change in demand — (Olsina, 2005; Ventosa et al., 2005; Oladeji, 2005; Ford, 1997).
Mathematical representation of many situations in electric power system is difficult due to its
stochastic nature. Further, the electricity system is characterized by incomplete information,
uncertainty and distributed decision-making with the implication that it is imperative to incorporate
the bounded rationality of the actors into modelling. It is also essential that any evaluation of the
system should be able to take into consideration the soft but important variables of the system (e.g.
the effect of maintenance on extending plant reliability, labour issues/staff welfare) to enhance
validity of the analysis.
Electric power industry exhibit major dynamics with respect to management, technology
progress, consumer behaviour, industry configuration and government policy (Dyner et al., 2003).
In addition, ill-defined policy assessment or company mismanagement which impacts significantly
on performance or outcome of the system raises related questions. Thus, with the benefit of
hindsight, it can be argued that in all circumstances some evaluation tool with capacity to
incorporate feedback for analysis in the system would be helpful. Such is the System Dynamics
(SD) modelling tool. SD modelling has underlying bases for dynamic features that display
consistent high intuitive pattern of behaviour. Further, SD modelling has need of fewer details,
contains fewer equations, and cost less to develop and run than other detailed models. Accordingly,
this study focuses on the scope of SD principles for evaluation of the NEPS. This helped to
highlight how feedback-evaluated system can take an important role in “learning environments” as
well as form a support tool for decision-making and policy assessment.
The available data in the system were collated and described in compatible statistical database
to simulate the long-term performance of the NEPS. It also integrated conventional micro-
economic principles into the SD framework. This is with the aim of offering readily accessible and
analytical guidance to policy makers on the dynamic implications of policy as they affect decision
making. The key insight of this approach is to yield numerical estimates of the paths that could be
taken by key policy variables, as well as present any equilibrium to which they might converge
particularly as they relate to the Nigerian process of reforming and restructuring her electric power
industry.
Methodology
Conceptual framework: the Nigeria electric power system
The conceptual framework for this study was developed from the canonical form of feedback
system in control system (Dorf, 1976). Its application to model the Nigeria electric power system
(NEPS) is as presented in Figure 1. Each of the variables, namely, R(s), G(s), C(s), and H(s) in the
system is explained.
R(s) in the system is the input which is the desired national electricity consumption level. This
has usually been given by policy pronouncements as targets by the government as a level to be
attained for economic development and growth. These policy pronouncements become backed by
either laws or decrees (depending on the kind of government in power at that time). These policies
usually generate regulations and rules (bureaucracy) demanding organizational structures such as
regulators (NERC), investors (Independent Power Producers - IPPs) and government involvement
(Federal Ministry of Power - FMP and Energy Commission of Nigeria - ECN) for implementation.
G(s) had earlier been defined as the aggregation of the supply side of the system under study
which consists of generation, transmission, distribution system operation and retail trading. G(s) is
thus the aggregation of the supply side, which includes generation, transmission/system operations
and distribution as well as the human resource available in the NEPS. Also, the output is C(s) is
described as the performance of the utility as measured by the electric power demand met measured
in GWh through the generation capacity measured in MW. H(s) represents stakeholders’ response
to performance.
Input factors — Theaystem=
a G(s) Output —
Fomalation C(s)
NEPS Lavs and i i aa ae
Policy ™ |
Conte m |
Regitony
Environment |
Margera |
Know-How |
Technical Affordability
Know-How a “| Feedback -
H(s)
Desied
Performance r
c0ess
fi Se
|
\
Quali Relity
Inesos etsy Demand
Finamee
Interest nas A
Figure 1 Conceptual framework for applying system dynamics for decision making in
NEPS (Drawn with Vensim® PLE)
Study design and scope of work
The study covers the planning and management procedure of the NEPS running through the
generation, transmission, distribution, retail and marketing sub-sectors of the system. In evaluating
its long-term performance, access (influenced by availability and adequacy) to electricity in the
system, affordability of electricity prices (usually meaning economical and cost reflective tariffs)
and power quality (meaning security and reliability) of the system are examined against the
backdrop of the EPSR Act 2005 goals of providing reliable supply, secured service, economic
efficiency, attracting new capital investments and providing level playing field. The performance
indicators were also viewed with regards to energy sector of the Vision 2020 Report (2010) and the
Power Sector Reform Roadmap (2010). These were also bench-marked against some countries
(USA, Egypt, Libya and South A frica) power system operations.
System boundary and general research approach
The electric power system can be characterized as a structural model (Olsina, 2005), having a
bottom-up approach, since the long-run development of the power market is determined by
modelling the variables having direct influence on long-term movements of supply and demand.
Figure 2 presents a simple description of the Nigeria electric power market showing the
4
competitive and regulated segments. The competitive segment is made up of the generation and
retail sectors as depicted by the 6 generation companies, 4 independent power producers (2 of
which are for state governments) and 11 distribution companies to also include the customers while
the regulated segment is made up of the ‘wired’ sector namely the transmission and distribution
networks. Regulation in the ‘wired segment of the NEPS is achieved under the aegis of the
Market/System Operator that handles the power exchange (in Nigeria’s case, the National Control
Centre, Osogho). It must however be pointed out that the codes for operating this segment falls
under the purview of the NERC.
In order to demonstrate the simulation focus, the required inputs, and the system boundary of
the research approach, a bull’s eye diagram of the model as adapted from Kilanc and Or (2008) is
presented in Figure 3. In the bull’s eye diagram, endogenous variables are placed in the center of
the bull’s eye; exogenous variables are placed in the outer frame. Excluded variables are placed
outside the outer frame. The bull’s eye diagram is a convenient way to show the relative balance
between required inputs (exogenous variables) and endogenous variables. If endogenous variables
are more, this is a good sign indicating that ‘‘model generates interesting dynamic behaviour from
within the system’’ (Ford, 1999). If a variable appears somewhere in a feedback loop, or it is
influenced by another variable that it is in a feedback loop, then the related variable is said to be an
endogenous variable in the model.
6Gen
companies
ww
Transmission Company of Nigeria
(TCN)/Market/System Operator
11 Distribution
Companies
Large customers
Connected to the
Transmission
network
[EBB ccoursten scomenr a compere scoment => PHYSICAL FLOW OF POWER ==> MONEY FLOW
Figure 2 Simple flow chart of the competitive and regulated segments of the Nigeria Electric
Power System (NEPS) as described by the EPSR Act 2005
Figure 3
Bull’s eye diagram of the model (adapted from Kilanc and Or, 2008)
In addition to the bull’s eye diagram, to provide a snapshot picture of the extension of the
model, the major factors under consideration are grouped under eight categories namely, laws and
policy, regulation, labour, implementation, investments, technology, electricity demand and
finance. The categories are represented in loose form in the Table 1.
Table 1 Factors and measurements of scales for the Nigeria’s Electric Power System
No
Factors/variables
1
Examine laws/decrees/policy establishing NEPS
e Examine regulations for daily operations of NEPS
¢ Quantify assets in NEPS
e Plant/generation related
o Availability factor (as changes throughout the simulation run depending on aging)
o Remaining primary energy resources reserve
o Seasonal effects on availability of wind and hydropower plants
o Construction times
o Cost times
o Age of the already installed and operational power plants
o Capacity factors
o Length of forecast period
o Initial power plant portfolio (installed and under construction)
Transmission + Distribution facilities
e Benchmarking above (3) against internationally accepted standards
e Policy related
o Policy formulation
o Policy contents
o Government involvement
e Implementation related
o Investors related
o Financing
o Tariffs
o Revenue generation
o Specialized arbitration
o Arbitration time
o Labour issues
o Pool and demand related
o Demand (if prices elasticity of the demand is non zero)
o Market shares
o Amount of electricity supplied by each company
o Supply-demand balance (electricity reserve margin in the market)
o Pool/wholesale price
o Expected supply-demand gap and expected pool price, which is used in the investment decision
of the investors
o Loss of Load Probability (LOLP)
o Annual and monthly peak demand
o Annual demand growth rate
o Load duration curves
o Price elasticity of electricity demand
o Presence of long term contracts
Regulatory related
o Competence
o Interest
o Licensing rate
o Enforcement
o Mechanism
iii. ¢ Cost of service provision
e Affordability
Accessibility
¢ Quality
e Ability to attract capital for infrastructure development
e Enable greater consumer choice
e Greater economic efficiency
e Level the competitive playing field
iv. o Projection using Vensim tool
Source: NERC, ECN, PHCN, CBN, NBS, NERC, Internet
Tasks in the study objective
Having established the conceptual framework upon which the study is predicated, an
explanation of tasks to be accomplished in each of the specific objective of the study is given in this
section.
The sole objective of this study is the evaluation of future performance of NEPS through
improved supply-demand scenarios by developing a model based on its baseline information. The
first step to this evaluation is to generate a feedback from the results obtained in baseline
information of the system. This result serves as the input factors into developing the model for
evaluation of the performance of the NEPS using feedback compatible data in Vensim
environment. Thus the task of in the study objective would first be the incorporation of the relevant
inputs to the systems equation, and then followed by iteration process using V ensim software that is
developed based on System Dynamics principles. The essence of this task, which is sensitivity
analysis, is to ascertain variables/factors that would need to be changed to improve decision making
7
in NEPS that would in turn affect its technical and economic performance in the coming years. The
end result is to develop different supply — demand scenarios in the NEPS for the future. The
timeframe for the analysis is 30 years starting from 2009. This will also guide the design of
different market options for the scenarios.
Study Design and Modelling Overview
Study Design
In the design of the study, the following were taken into consideration. First step was to capture
data that are useful for electricity planning and management in Nigeria from secondary sources for
the period under review. These sources include the Annual Report of the PHCN, Energy
Commission of Nigeria, Nigeria Electricity regulatory Commission, Nigeria National Petroleum
Corporation, National Bureau of Statistics (formerly the Federal Office of Statistics (FOS)) annual
abstract of statistics, Bureau of Public Enterprises, Central Bank of Nigeria (CBN) statistical
bulletin and annual reports, World Bank Reports, and so on. These data were used in the first stage
of the NEPS long-term performance evaluation. Where data gaps are identified survey was
employed to fill the gap. The second step therefore is the application of partially structured
questionnaire (Johnson and Wichern, 1997) to capture these identified data gaps in the NEPS for its
long-term performance evaluation. Information was elicited from the various categories of
stakeholders in the NEPS. They are: the policy makers (FMP and ECN), players in the electricity
sector (PHCN -— generation, transmission, distribution and IPP operators), the NEPS regulator
(NERC) and all categories of customers in the NEPS, namely residential, commercial, industrial
and special purpose customers respectively. Based on the appropriate variable types identified, four
sets of questionnaires were designed. These variables were translated into a set of rough questions.
From these rough questions, a data set was developed for the questionnaire; these were
subsequently subjected to iterations by undergoing critical checks such as: relevance and wording
of the questions; appropriateness of the question sequencing; layout and appearance of
questionnaire to allow for easy data collection to ascertain its reliability. The sets of questionnaire
were subjected to further testing for clarity and understanding through pre-test amongst some
selected respondents to ascertain their validity, before being finally administered. The results from
the pre-test also formed the basis to test run the models in Vensim software for validity. Data
analysis was carried out using descriptive and inferential statistics. A combination of the outcome
of these exercises form key inputs to forecast the long-term performance of the NEPS using the
Vensim software (a simulation package) developed based on System Dynamics principles. The
timeframe for forecasting the long-term performance of the NEPS is 50 years starting from 2009.
Modelling Overview
Over 50% of future investments in electricity sector would be accounted for by the generation
sector over the next 25 to 30 years. Modelling of the sector must therefore capture important
variables relevant to describing the dynamics in the electric power system. These could be
conveniently grouped under generation, transmission and distribution, systems operations and retail
trading. This is essentially the G (s) of the entire system being studied.
Based on the EPSR Act 2005, the NEPS is being projected be operate as a merchant power
market when enabling environments for this kind of operations is achieved through the current on-
going reform process (FGN, 2005). G(s) component in the system can be represented in two ways
to explain the dynamics of the electric power markets and had well been described in Olsina
(2005). These two representations are its causal-loop, and the stock-and-flow diagramming. These
two representations thus take the present operational conditions of the NEPS into consideration to
develop the model for simulating internal behaviour dynamics. This dynamics is described by a set
of non-linear differential equations that account for existing system feedbacks, delays, stock-and-
flow structures and nonlinearities.
Basic feedback structure depicting a simplified causal-loop for electric power system as
adopted from Olsina (2005) is presented in Figures 4 and 5, respectively, to provide an overview of
the system’s dynamical structure and to guide further discussions when modelling the different
system components. The diagram shows the basic balancing feedback that governs the long-term
development of any power market. Market participants form expectation on the future electricity
prices is formed on the basis of current market conditions and expected fuel prices. These expected
prices play crucial role in determining the profitability of possible investment projects. This implies
that construction of new power plants is predicated on the assurance that there is enough certainty
of investment cost recovery. Therefore, the first delay in the feedback loop is in regard of
irreversibility of investment, that is, the investment decision delay, denoted with Ty. In addition to
this delay, new power plants are required to get permissions and they need a certain time to be
constructed and to be brought on-line. This forms the second delay on the feedback loop and is
denoted in Figure 5 as T2. The existing capacity plus the additions of new capacity, the
scrapping/decommissioning of old power plants and the current system demand will determine the
new reserve margin and the new prevailing price level. With this therefore, the market becomes
self-balancing and resembles the negative feedback loops commonly encountered in control
systems. This balancing mechanism is responsible for maintaining an adequate reserve margin to
ensure a reliable electricity supply.
However, this causal-loop-diagram (CLD) is useful to represent the causal relationships and the
market balancing feedbacks responsible for adjusting the production capacity, it is not capable to
show explicitly stock-and-flow structures embedded in the system. In Figure 5, the stock structure
underlying delay T2 is revealed. This stocks-and-flow-diagram (SFD) shows important variables
controlling rates of flow into stocks, making the issue of capacity adjustment mechanisms clearer.
Available Capacity
Energy and Demand for
Capacity Markets Energy
Capacity in
Development
Figure 4 Basic Feedback Structure of Electricity Markets
o
Electricity Demand
( Required reserve
margin
Reserve
4
Scrapping Wholesale prices
Investment long-term prices
decisions Expected fuel
+ OT; prices
Expected
ne
Ke Eipected costs
Figure 5 Causal-L oop Diagram of a Typical Electric Power System (adapted from Olsina, 2005)
5 Capacity t-) )
+ 1, —_— tet
4 Eoaaes
Model Development
Regulatory authority, NERC, is responsible for reviewing proposals submitted for building of
new power plants. Applications to construct new power plants by would-be investors accumulate to
NERC for approval or rejection. The time needed to process proposals depends on both capacity of
examining multiple projects and project complexities, such as proposed technologies (e.g. nuclear,
hydro, CCGT) and the specific siting of the new power station. This process could take some time
depending on the technology type and it usually ranges from six months for Gas Turbines (GTs) to
five years for nuclear and hydropower facilities. The normal assumption is that when the permits
are granted, would-be investor could hold to it in order to monitor market conditions and projected
profitability. If market conditions remain attractive after the time elapsed in reviewing of proposals
or within the license period, plant construction will be commenced. If it does not, permits will be
allowed to lapse.
Addition to this step, investors are permanently checking the stream of plants under
construction, since expected reserve margin and expected long-term prices are affected when new
plants come into operation. When expected long-term prices are lower, this will impact on expected
profitability, which in turn will lead to reduced rate of commencement and thus allowing more
permits to be discarded.
In order to achieve balancing feedback, time delay could be reduced to provide a higher
stability margin to the system. Nevertheless, when new efficient plants in pipeline are completed
and start to generate, electricity prices will be reduced, and likely leading to generators owning old
inefficient plants exiting business. The effect of this is increase in the rate of retirements, assuming
perfect competition exists.
Another factor that affects reserve margin aside from capacity scrapping/decommissioning rate
is the period electricity demand is expected to grow. When the growth leads to tight reserve margin,
it can cause a new wave of constructions due to both accumulated permits and a stream of new
proposals. For accumulated permits, commencements are immediate; stream of new proposals
would have to face delay in the time needed to obtain permits. This implies that even though
decision of investing in new plants may be simultaneous, it will however have a different time-
period for the market place.
The stock-and-flow structure must be further expanded in parallel stock chains in order to take
into account the different characteristics of the several available technologies. Since the stocks
10
represent different stages of the power plant operations such as the installed capacity, the capacity
under construction, etc., this has to be disaggregated to account for different lifetimes, construction
lead-times, permitting delays, amongst others. In addition to this disaggregation, for the same
generating technology, installed capacity has to be distinguished by age to keep track of thermal
efficiencies, and therefore, the spread in marginal cost of production.
With the foregoing, the first step to developing the SD model was to assemble and analyze an
array of data pertaining to the electric power sector in Nigeria (Oyebisi and Momodu, 2012). This
step helped to understand the interconnections in the system that affects its performance. From
these interconnections, was developed the causal-loop diagram presented in Figure 6. This
simplifies the development of stock and flow diagram that make up the model. With the stock and
flow diagram, the set of equations driven the model to run were derived.
Managerial
Know-How Investments
ra Interest rales
Implementation Finance
NEPS Laws and
Fomulation—” Policy
Performance
en Performance Power Quality
Ministries, °
Depennei nd Affordability
Figure 6 Causal-Loop Diagram of the NEPS
Models developed in Vensim are capable of using time frame of seconds to represent many
years, depending on the kind of system being evaluated in the model. The time frame used for
developing this model to evaluate the long-term performance of the Nigeria electric power system
is 50 years, 2005 to 2055. This can be changed within the model setting.
Evaluating the long-term performance of Nigeria electric power system requires developing a
model that takes the dynamics of the structure and behaviour of the system into consideration.
Having realized this need from the onset, SD technique was chosen using Vensim software
platform, which is one of the recognized software platforms with capability for developing such
model (http://www.vensim.com/sdmail/sdsoft.html; Wikipedia website). To develop this model, the
main ideas made use of Ford study (1999, 2001) as well that in Oyebisi and Momodu (2012). The
model assumes that the NEPS will act as one market, where price of electricity would be driven by
demand and supply, that is, levelised cost of energy as well as future retail tariff. Demand of
electricity is based on goverment forecasting data. However, due to suppressed energy demand
11
currently been witnessed within the system, which is targeted for elimination in the forecast, the
model also viewed demand from the perspective of the economy through the impact of electricity
on gross domestic product (GDP) and vice-versa.
Figure 6 shows the causal loop diagram showing the interconnections in the electric power
system in Nigeria. Following from knowing this structure and behaviour characteristics, the first
step to developing the model was to make use of historical data available for performance of the
system from 2005 to 2009. These include its installed capacity (taking different technologies in the
generation park into consideration), availability factor, capacity factor, peak load, energy generated,
transmission capacity, losses (transmission, distribution, non-technical and non billed energy sent
out), and energy sold. These formed the baseline data to develop the model. Other parameters that
were used for the baseline data include gross domestic product and population of the country. From
the baseline information other variables which contribute to the structure and behaviour of the
system were added. These include capacity addition, future capacity needed, and regulated tariff.
Having developed the model based on the baseline information, parameters such as energy intensity
(kWh/$) were calculated. This ensures both validation and robustness of the model to explain the
behaviour of the NEPS. In order to proceed into examining the model for future performance,
policy thrusts of the government concerning the electric power system were included in the model.
From this, such factors as performance (measured using reliability (outages), access and price in the
system) were calculated.
The four segments of the NEPS namely, generation, transmission, distribution and retail
trading were taken into consideration in developing this model. Therefore, the model is split into
four sectors to capture the dynamics of these sectors. So the model consists of generation capacity
sector, transmission and distribution sector, financial sector as well as the electricity demand sector.
A brief description of each of the sector is discussed next.
Generation Sector
In Vensim, state level variables simulate the systems dynamic behaviour using integration
technique that is capable of depicting continuous changes taking place within the system (Ventana
System, 2010). Figure 7 shows the Generation Sector of the model. The generation sector in the
model is made up of four state variables representing stock points, that is, point of accumulations in
the system. The first three variables are to depict changes that take place from initiating licensing to
build generation capacity up to the point of adding the completed project to installed capacity. Each
of the variables has flows into and out of it. Each of the flows has parameters controlling their flow
rates. It is this parameters or constants in the model that allows for sensitivity testing of the model
to changes in policy concerning the entire system.
The rates as regulated by the constants also depict leverage points through which system
adjustment can be made. For example, in the generation sector, CAGR (compounded annual
growth rate) represents the accumulated annual rate of capacity addition. This was estimated for the
NEPS from available data from 1965 to 2009. To change capacity addition in this sector, the
CAGR could just be changed. This change will have chain effects on the entire system, thus serving
as a leveraging point.
12
Nigeria Electric Power System: Generating Capacity Sector
Total Total N
6c; SapUnic total installed <>
GenCapUndRev GCapePenn —TotCapUndConst sions
Capwe
CapUnderRev - _ Uta copay we
yao
| " | TotScrappedGenCap
sen Ci ME Capacity
OttersNew _ of Ee be plcatl an coy oe
“iconsng ination (Review | Sono Lpentesion IGentap Proj lconsiurton! capac adation mere >
ay ‘ Installed capacity
Tee emission. {Others} J,
ia a cael SS | rapping rate
Hydro\ev
application rate TotalCAGR Installed capacity
(Themal]
approval rate
refusal rate yf
ode
Figure 7 Nigeria Electric Power System Model: Generation Sector
Transmission and Distribution Sector
Transmission and Distribution Sector of the model is shown in Figure 8. Unlike the Generator
Sector of the model, the Transmission and Distribution Sector has only one state variable, depicting
stock. This stock point could be seen easily as the system operation point (in NEPS the National
Control Centre) where electricity generated is sent for distribution to the 8 transmission stations in
the country. This sector in the model captured a lot of parameters that affect the operations in
transmission and distribution of the NEPS, as it relates to system performance in terms of quality
and the Financial Sector in terms of revenue generation and profit/losses. As in the Generation
Sector, a number of leverage points are identified in through many constants in the Sector.
Nigeria Electric Power System Model: Tx and Distribution Sector
gee heeling Capacity
<capacity factor>
<Wheeling
Total Electricity yutage capacity outage duration Capacity>
Generation ST ConvFac
QO * Hecty Effective ey MWhtokWh
F Distributed ii onsumption
Available capacity J
ro Own Use Tx Losse: “7
<Total Installed Distribution Losses
Capacity> ol capacity factor vow Losses
Availability factor
Figure 8 Nigeria Electric Power System Model: Tx and Distribution Sector
Financial Sector
The financial sector of the model shown in Figure 9 depicts the system’s retail trading. This
sector shows the meeting point between total cost stream and total revenue stream in the system.
Like the transmission and distribution sector, the financial sector only has one state level variable
which shows its stock point, profit in the system. So with the model it becomes easy to see the
profitability or otherwise in the system. As with other sector, the leverage points are the constants
such as the transmission cost, distribution cost, electricity tariff amongst others.
13
Fxd O&M Cost ,
cap specific
consumption
Fxd Cost var op unit cost
~~. fuelprice
Op cost
Systems
OSS | Financial
Total Revenue Profit Total Cost Stream
Subsidy Stream <Electricity
‘ \__Sgpertiom
Avg Tariff
NonBilled Losses Tx cost <C onvFac
<Effective Electricity MAW GIA ES
Bs nto K" n>
Paid Tariff Consumption>
Figure 9 Nigeria Electric Power System: Financial Sector
Electricity Demand Sector
The critical sector for long-term evaluation of the NEPS is the electricity demand sector. This
is because whether or not future demand is met will be determined the performance of supply end
of the system, namely the generation sector. Two important variables in the economy driving
electricity demand are population and gross domestic production (Inglesi, 2010). The sector is
therefore made up of population module, economic module depicted by gross domestic production
and electricity demand. As can be seen in the sector (Figure 10), there is only one state level
variable, namely, population, representing stock in the system. Others are flows, auxiliaries and
constants. Description of estimation of each of this module is given next. Also included in this sub
model is the module to estimate access level in the system.
a. Estimating future population growth
There are quite a number of variables for estimating future population depending on the level
of details needed. However, the most striking variables are those of fertility and mortality rates
(Wikipedia, 2011). In this study, a simple approach of birth and death was used to develop a future
population trend for Nigeria. Future birth per year of the existing population is a factor driven by
birth rate, while death is regulated by average life expectancy. Mathematically, this is represented
in the model as:
t=51
Population = J (birth — death)
tl
where
birth, , =birthrate,,, x Population, 44
death = averagelife exp ec tancy,,, x Population
(It is important to note that t=1 in the equation is equivalent to year 2006 and t=51 is equivalent to
year 2056.)
14
> Population
mortality rate cue
per capita
lation> ( sen
edt ieee
a ee PO
\ —__—_—_——— GDP
Future Generation aa —
Net Exports
ConvFact je
i aera: Final Consumption
{ * Expenditure Export of goods
our ina year CaPAUHY factor> mort of 0
Private Govt ort of goods
01 tion Ex] T services
ConvFackWlfoMWh Consumption Bry orsumpinn Exp
Gross Fixed Economic
Gi ational
beni Formation Growth Rate
Figure 10 Nigeria Electric Power System: Electricity Demand Sector
b. Estimating Gross Domestic Product and Per Capita Income
According to Xianchun (2002), there are three approaches to estimating GDP. They are
production approach, income approach, and expenditure approach. The specific formula of each
approach is shown as follows:
GDP by production approach = = value-added by production approach
= (output — intermediate input)
GDP by income approach = = value-added by income approach
= (compensation of employees + net production taxes + depreciation + operating surplus)
GDP by expenditure approach
= Final consumption expenditure + Gross capital formation + Net export of goods and
services
= (Household consumption expenditure + Government consumption expenditure) + (Gross
fixed capital formation + Changes in Inventories) + (Export of goods and services — Import
of goods and services)
This study estimated the future gross domestic product for Nigeria using by the expenditure
approach due to data availability. Calculation of per capita income in the model was done based on
connecting the gross domestic product module to that of the population module.
CG Estimating long-term electricity demand and future capacity addition in Nigeria
Various theoretical studies have been documented on the approaches for the estimation of
electricity demand (Babatunde and Shuaibu, 2009; Lin, 2003). Dating back to mid-20" century,
electricity demand forecasting got matured in the 1980s (Lin, 2003). For econometric approaches,
detail work using different combination of variables had been documented in Zhang (1987),
Narayan et al. (2007), Lin (2003), Holtedahl and Joutz (2005), Bose and Shukla (1999), Dincer and
Dost (1997), Al-Zayerand and Al-Ibrahim (1996), Houthakker, et al. (1974), Zachariadis and
Pashourtidou (2006), Ziramba (2008), and Chang and Martinez-Combo, (1997). These set of
15
studies examined the residential demand for electricity in the context of household production
theory. If unconstrained by data limitations, studies of the empirical model of the residential
demand for electricity are based on household production theory which can be expressed as a
function of own price, price of a substitute source of energy, real income, price of household
appliances and other factors that may influence household preferences, such as temperature
(Babatunde and Shuaibu, 2008).
For this study, the basic model for estimating future electricity demand was adopted from
Babatunde and Shuaibu (2008). The study made use of GDP, price of electricity, population
(specifically, the urbanization rate) and electricity intensity (as a proxy for industrialization rate) as
driving variables for determining demand for electricity in Nigeria. In an economy that is not
distorted (as in Nigeria), its GDP is affected by per capita income which in tum is affected by its
economic growth rate and its impact on living standards. It is the combination of these variables
that represent the main driving force of electricity consumption growth (see Lin, 2003). Therefore,
higher real per capita income will increase purchases of electrically powered equipment and hence
increase electricity demand. Nevertheless, an increase in the price of residential electricity will
cause the residential electricity demand to decrease. Population is another important factor to
determine electricity demand higher population level is expected to increase electricity
consumption. A positive correlation between population growth and electricity demand is therefore
expected. However, more significant is that urban household energy use accounts for a large
proportion of commercial fuel consumption in Nigeria. As population and principally urbanization
increase, consumption is expected to rise rapidly in the future (Adegbulugbe and Akinbami, 1995).
This study therefore sees the need to have information on the utilization pattern and factors driving
consumption of urban household energy. Such information is useful within the national energy
planning framework for deriving strategies for a more rational energy utilization and increased
reliability of energy supply to the urban household.
According to the definition of a demand function, electricity demand, in general, is determined
by some main factors including gross domestic product (GDP), prices, and population. To forecast
future electricity demand, because of data paucity in country like Nigeria, the approaches
mentioned above would be difficult in practice. So this paper adopted a simplistic approach. It first
examined the historic effective electricity consumption in the country from 1990 — 2009 from
which it derives the per capita electricity consumption and also the GDP for the corresponding
period. Next it used the combination of these values to forecast the future electricity demand using
the average electricity price (EP), per capita electricity consumption (PCE), population (Y) and
Gross Domestic Product (GDP. This is represented mathematically as:
ED = EP x PCE xY Eqn 3.2
GDP
Where:
e ED is Electricity demand (kWh)
EP is Average Electricity Tariff (US$/kWh)
PCE is per capita electricity consumption (kWh/Person)
Y is population (Persons)
GDP is the Gross Domestic Product (US$)
Once the electricity demand is determined it becomes easy to estimate future capacity addition
needed within the system. This is done simply by converting the energy to power using the
conversion factors of capacity factor and number of hours in a year. The capacity addition needed is
given in MW/Y ear.
16
Forecast of long-term performance of the NEPS: Some Results and Analysis
This section presents results and analysis of baseline information and other two different
scenarios (namely, capacity addition scenario and that of industrialization process through
increased electricity consumption) runs.
The long-term evaluation of the NEPS based on the outcome of performance analysis of this
study is summarized in Figure 6, depicting cause and effect in the system. The model developed for
the evaluation was validated using NEPS operations data from 2005 to 2009. Being ‘management
models’ (Garcia, 2006), the developed model simply present options to be chosen from for
managing the future of the electric power system in a more sustainable way. To forecast long-term
performance of the NEPS, the basic socio-economic assumptions and other baseline information for
2009 are presented in Tables 2 and 3 respectively.
Table 2 Basic Socio-Economic Assumptions to Evaluate the Long-Term Performance of NEPS
2009 2010-2016 2017-2022 2023-2028 2029-2034 2035-2040
GDP (USD 168.99
Billion)
GDP Growth 6.9 13.4 13.8 10.0 9 7
Rate (%)
Electricity 20,838
Demand (GWh)
Electricity 5.8 9.5 12.5 10.5 10.0 10.0
Demand Growth
Rate (%)
Population 149.3
(Millions)
Population 2.2 2.2 2.15 2.05 2.00 1.85
Growth Rate (%)
Electricity 140 500 1000 1500 2000 2500
Demand Per
Capita
(kWh/Cap)
Average 0.070933 0.0667 0.0667 0.0667 0.0667 0.0667
Levelised Tariff
($/kWh)
Subsidy ($/kWh) 0.024 0.00 0.00 0.00 0.00 0.00
Determined tariff
($kWh) (Res +
Comm + 0.0466 0.00 0.00 0.00 0.00 0.00
Industrial)
Wholesale
generation prices
Energy ($/MWh) 7.705 7.705 na na na na
Capacity
($/MW/month) 9533.77 10014.00 na na na na
Transmission 1654.7818
losses (GWh)
Transmission 8 5 na na na na
losses (%)
Transmission
Charges ($/kWh) 0.008 0.008 na na na na
Distribution
losses (GWh) 2049.05
17
Distribution
losses (%) 11 10 9 8 ih 5
Non-Technical
Losses (GWh) 3352.99
Non-Technical 18 15 12 8 5 4
losses (%)
Source: NERC, (2008); Vision 2020 (2009b, 2010); The Presidency (2010);
Table 3 2009 Baseline Data for Model Runs
Description Values Change rates
Population 149.3 million 2.2%
Per Capita Income (US$/person) 2300
Persons per household 7 -0.5%
Total number of households 21.328 million. 0.5%
households
Customer population in NEPS 10.5 million households 2.2%
Connectivity rate (%)
Access rate (%) 46, 60, 80, 96
Installed Capacity (MW) 8,764.4 2.34%
Available Capacity (MW) 4825.17
Peak Demand (MW) 3710.1
New Capacity under Construction (MW) 5000
Approved Licenses for New Power Plants by NERC 10 0
System Wheeling Capacity (MW) 3875.25
Actual Capacity Required in Distribution (MW) 9057
Energy Delivered for Distribution (GWh) 18627.73
Transmission Losses (MWh) 1654.78
Distribution Losses (technical + non-technical) 5402.04
(MWh)
Capacity decommissioned (MW) 0
Wholesale Price US$/kWh 0.02071
Capacity charge — US$/kWh 0.013
Energy Charge — US$/kWh 0.00771
Average levelised Tariff — US$/kWh 0.07093
NERC adopted tariff — US$/kWh 0.0466
Subsidy on tariff — US$/kWh 0.024
Source: NERC, (2008); The Presidency (2010); PHCN Annual Report 2005 — 2009
Following from knowing the system structure and behaviour characteristics, the first step after
the NEPS model was developed was to make use of historical data available for performance of the
system from 2005 to 2009 to ensure both its validation and robustness to explain the behaviour of
the NEPS. Data inputted include its installed capacity (taking different technology in the generation
park into consideration), availability factor, capacity factor, peak load, energy generated,
transmission capacity, losses (transmission, distribution, non-technical and non billed energy sent
out), and energy sold. Other parameters include gross domestic product, economic growth rate,
electricity demand and growth rate, future population projection, reserve margin and
decommissioning rate of generators within the NEPS. From the baseline information other
variables which contribute to the performance of the system were added. These include capacity
addition, future capacity needed, and regulated tariff.
Having developed the model based on the baseline information, parameters such as energy
intensity (kWh/$) were calculated. In order to proceed in examining the model for future
performance, policy thrusts of the government concerning the electric power system were included
to the model. From this, such factors as performance (measured using reliability (outages), access
18
and price in the system) were calculated. Significantly the model developed was also able to show
how electricity consumption is strongly connected to the economy in a non-linear relationship.
As mentioned earlier, the period of forecast was 30 years starting from 2010 and terminating in
2039. Since the bulk of financial investment required in the electricity sector usually goes to the
generation subsector, Table 4a present the generating plants model parameters and Table 4b shows
the medium- and long-term generation addition plans in the NEPS.
Baseline Information Runs
Results of the Base Run (NEPS without intervention) from each of the sectors (generation
capacity, transmission and distribution, financial and electricity demand) of the model are presented
in this section. From the generation capacity sector, Figure 11 shows generation capacity addition
trend from project licensing initiation, with feed-in from future generation capacity needed. Figure
12, the installed capacity split into thermal, hydro and other (renewable) capacity respectively. The
dynamics of installed capacity long-term evolution from 2005 to 2040 is easily displayed in the
figure, recollecting that the variable is affected by capacity addition, CAGR and scrapping rate in
the model. Licensing initiation, approved application, generation capacity startup project, and
capacity addition amongst others form the rates in the sector. Electricity derived from available
capacity, reflecting electricity generated and electricity distributed in the system as shown in Figure
13. The gap between electricity generation and electricity distributed in the figure depicts losses
experienced between these two ends of the supply continuum.
Table 4a Generating Plants M odel Parameters
Parameter Generation Technology
Hard CCGT OCGT ST/GT Hydro
Coal
Installed Capacity for to (GW) 0 1.10 5.186 1.020 2.351
Proportion (%) 0 11.39 53.70 10.56 924.35
Lifetime (years) 40 30 25 25 40
Average unit size (MW) 120 150 30 200 100
Forced outage rate 0.05 0.05 0.05 .05 0.05
Average Construction time 40 24 12 9
(Months)
Fuel costs ($/MWh) 5.20 35.00 35.00 35.00 0
Investment Costs ($/kW) 4855.00 1218.00 1195.00 1186.00
Discount rate (%/year) 22 22 22 22 22
Amortization period (Y ears) 25 20 20 20 25
Sources: PHCN Annual Report (Several); Kaplan, S. (2008) Power Plants: Characteristics and
Costs — Prepared for Members and Committees of Congress, Congressional Research Services
Order Code RL34746 accessed from
www.nei.org/.../The Cost_of New Generating Capacity_in_Perspective.pdf on April 22 2011
White Paper (2008) The Cost of New Generating Capacity in Perspective - accessed from
www.nei.org/.../The Cost_of_New Generating Capacity_in_ Perspective.pdf on April 22 2011
19
Table 4b Generation Addition Plans in the NEPS
Capacity and Medium Term - Long Term - Estimated
Technology 2010-2014 2015-2040 Overnight Costs
Classification ($/kW)
Thermal (MW) — 4879 10066
CCGT
Nuclear (MW) 1000 1200.00
Gas (MW) 7066 1218.00
Coal-fired (MW) 2000 4855.00
Renewable Energy 198 4900
(MW)
Hydro (MW) 187 3400
Wind and Solar 11 1500
Power (MW)
Total 5077 14966
Source: FMP, 2010
40,000
30,000
& 20,000
10,000
0 / -——
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
TotNCapGencPem|[Thermal] : Base Run
TotGenC apUndRev[Thermal] : Base Run
Figure 11 Capacity Addition Trend
20
10,000 SS
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
Available capacity[Thermal] : Base Run
Total Installed C apacity[Thermal] : Base Run
Figure 12 Installed and Available C apacity Trend
80M
0
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
Electricity Generation[Thermal] : Base Run
Electricity Distributed[Thermal] : Base Run
Figure 13 Electricity Generated and Distributed from Installed Capacity
The transmission and distribution sector includes wheeling capacity, electricity generation,
electricity distribution and effective electricity consumption aside from the parameters (constants).
Figure 14 depicts effective electricity consumption trend in the system. Leaving the trend at the
present situation, even though this value seems to increase over the years, it must be stated that it is
a far cry from what is desired when compared to electricity demand as shown in Figure 15. The gap
between the two curves represents what could be seen as suppressed demand, with a gaping
difference between the beginning and the end of the timeframe.
The financial sector includes total revenue and total cost streams to produce the system profit
margin aside from the parameters (constants). Figure 16 shows the combination of the results of
total cost stream, total revenue stream and systems financial profitability. It must quickly be added
that values derived from the model are just approximate as actual tariff and number of customers
21
within the system are disaggregated in the model. The tariff used to calculate revenue stream is
based on average tariff in the system. To get a better estimate, there is the need to break the tariff
into various categories of customers in the system.
The last sector in the model is the electricity demand sector which comprises the population, the
economy represented by GDP and the electricity demand module. Between these variables,
estimates of future capacity addition, per capita electricity consumption, per capita income were
calculated. The sector also is linked from the financial sector effective electricity consumption and
electricity generation while the generation capacity sector is linked to the electricity demand sector
with future capacity addition needed. Shown in Figure 17 is the effective per capita electricity
consumption, and shown in Figure 18 is the future capacity addition needed. This capacity needed
to meet system electricity demand could be taken as the amount of self-generation within the
country in this time period. The figure rose from just below 5500 MW in 2008 to over 27000 MW
in 2040.
Figure 19 shows gross domestic product (GDP), electricity demand and population trend from
the model base run. The figure shows increasing trend of both population and GDP with declining
trend for electricity demand. Clearly, the result from this figure supports the assertion that
electricity is not contributing meaningfully to GDP growth as well as not meeting the yearnings and
aspiration of the people. To buttress this further, Figure 20 shows per capita income (PCI in
$/person*year), per capita electricity consumption (PCE in kWh/person*year) and the electricity
intensity (in kWh/$). The PCI and PCE both exhibit s-shaped growth tendency with sharp points of
inflexion, depicting exponential growth pattern. The electricity intensity on the other hand exhibits
goal seeking behaviour.
40B
30B
&
g 20B
10B
0
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
Efctive Electricity Consumption{T hermal] : Base Run
Efctive Electricity Consumption Hydro] : Base Run
Efctive Electricity Consumption{Others] : Base Run
Figure 14 Effective Electricity Consumption Trend
22
200B
150B
kwivYer
ran
S
Ss
ow
50B —r | ar
0
2005 =—-2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
Electricity Demand : Base Run
fective Electricity Consumption hemal] ; Base Run
Figure 15 Electricity Demand versus Effective Electricity Consumption
200B $/Year
1M $
100B $/Year
-200B $
0 $Near
-400B $
2005 = =2010 «2015 =2020 »§= 2025S 2030» 2035 == 2040
Time (Y ear)
‘Total Revenue Stream{Thermal] : Base Run Year
Total Revenue Stream[Hydr] : Base Run ser
Total Revenue Stream[Others] : Base Run $iVear
Total Cost Stream[Themal] : Base Run $iVear
Total Cost Stream{Hydro] : Base Run $iear
Total Cost Stream|[Others] : Base Run $iVear
Systems Financial Proft{Phemal] : Base Run $
Systems Financial Proft{Hydro]: Base Run s
‘Systems Financial Profit[Others] : Base Run $
Figure 16 Retail Trading Trend in the System
200
170
i
& 140
c
z
110
80
2005 = «2010-015 2020S- 2025S «2030 »S-2085 = 2040
Time (Y ear)
fective per capita electricity consumption : Base Run
elective per capita electricity consumption : NigeriaElecticPowerSystem
Figure 17 Effective Per Capita Electricity Consumption
23
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
Fuhure Generation Capacity nested : Base Run
Figure 18 Future Capacity Needed to Meet Future Electricity Demand
400M
200 B
4e+013
200 M
100B
2e+013
Person
kW) ear
$N ear
Person
kW) ear
$V ear
\
Person
kWh/Y ear
$M ear
oco
2005 = =2010 =2015 §=©2020 §=2025 = 2030 =. 2035
Time (Y ear)
Population : Base Run Person
Mestre earings RN ee RW or
GDP : Base Run $/Y ear
Figure 19 GDP, Electricity Demand and Population Trend
80,000
200
0.08
$/(Y ear* Person)
kWH/(Y ear* Person)
kwhi$
$/(Y ear* Person)
kWH/(Y ear* Person)
kwhi$
$/(Y ear* Person)
kWH/(Y ear* Person)
0 kwh/$
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
GDP per capita: Base Run $/(¥ear*Person)
fective per capita dlectrcty consumption : Base Run, ———________— kw y(Vear*Person)
Electricity intensity : Base Run kWhys
Figure 20 Per capita income, Per capita electricity consumption and Electricity Intensity
24
Leverage Point Identification
A fundamental principle of system dynamics states that the structure of the system gives rise to
its behaviour (Sterman, 2000). So that situational factors, that is, the character of the system
determines its behaviour or performance. The implication of this statement is that, in complex
systems, different people placed in the same structure tend to behave in similar ways (Sterman,
2000). Redesigning the system or governing policy is then dependent on identifying high leverage
points in it, where significant, sustained, beneficial effects on performance can be achieved
(Forrester, 1969; Meadows, 1982). In order to conduct scenario runs, leverage points need to be
identified in the model using sensitivity analysis. Knowing that the high leverage points in the
system are mostly parameters with the model (Meadows, 1990), the sensitivity analysis was limited
to these constants in each of the sector as listed in Table 4.24. For scenario runs, the leverage points
were varied at a certain percentage from that of the value used in the base run of the model. The
values for the leverage points in base run represent the figures for system as it currently is.
Multivariate leverage point intervention was pursued in the scenarios to evaluate the system
behaviour principally to achieve the intended performance turn around in the system. Of particular
interest is to achieve sustained increased capacity addition, loss reductions and profitability. It is
also imperative that the goals of increasing PCI and PCE are also achieved to drive industrialization
and economic growth.
Table 5 Identified Leverage Points in NEPS
Parameters Base Run Scenario 1 Scenario 2
CAGR 0.0234 +100% +200%
Availability Factor 0.52 +20% +20%
Tx Loss 0.12 -50% -50%
Distribution Losses 0.1 -50% -50%
Non Technical Losses 0.2 -50% -50%
Non billed losses 0.23 -50% -50%
Average electricity price 0.0706 +50% +50%
Price of electricity substitute 0.0702 450% 450%
Economic growth rate 0.053 450% +120%
Access level 0.46 +50% +80%
The Scenarios
Two scenario runs based on government aspiration were examined to evaluate NEPS long term
performance. The first run, Scenario 1, reflects government policy of increasing capacity from
10,000 MW in 2010 to 35,000 MW by 2030 (Vision 2020, 2009b and 2010). In this however, it is
observed that total planned capacity addition for the future is less than the expected 35000 MW
generation capacity by 2030. According to FMP (2010), total capacity addition in the pipeline is
20043 MW including that of private sector plans. Added to the existing capacity of 9762 MW as at
2010, this will leave a balance of 5195 MW to be mopped up by private sector investment to bridge
the gap as anticipated by government. This will be influenced significantly by the prevailing
investment climate in the country as from 2020 after government and other planned additions are
completed.
The second run, Scenario 2, looks also at government desire to increase per capita income from
its present level of US$ 1310 in 2010 to US$4000 by 2020 and higher in subsequent years and also
to increase electricity consumption from the current level of 124 kWh/cap to 500 kWh/Cap in 2015
and over 1000 kWh/cap by 2020 (Vision 2020, 2009b and 2010). This is anticipated will lead to
desired industrialization for the country to bring it amongst the first 20 economies in the world.
25
For Scenario 1, the impact of adjusting the identified leverage points with the exception of
economic growth rate in the model is reflected in installed capacity, electricity demand, future
capacity addition and effective per capita electricity consumption as shown in Figures 21a, b, c and
d respectively. At 50% CAGR increment at the same time improving all other identified
parameters, the system is able to achieve 35000 MW mark by 2038, whereas at 100% CAGR
increment while keeping other parameters as that of 50% CAGR increment increase, the system is
able to achieve its 35000 MW mark by 2033.
The target in Scenario 2 is to achieve increased per capita electricity consumption (PCE) as well
improved per capita income (PCI). So the adjustment was done by 200% increment to the CAGR
compared to its Base Run value while also adjusting the economic growth. Impact of these
adjustments is reflected in installed capacity, electricity demand, future capacity addition, effective
per capita electricity consumption, GDP and the per capita GDP amongst variables in the model. At
200% CAGR increment (i.e. 0.0936), keeping all other identified parameters as in Scenario 1, the
system is able to achieve 35000 MW installed capacity mark by 2024, 9 years earlier than Scenario
1 at CAGR of 0.0468. This is keeping in mind that all other parameters that could be adjusted in the
model were kept as those of Scenario 1. Other important land marks in the model runs as shown in
Table 4.25 are that the system could achieve over 500 kWh/Cap consumption for improved living
by 2018 and over 1000 kWh/Cap target for beginning of industrialization by 2029 in Scenario 2. It
could also achieve over 500 kWh/Cap target in Scenario 1 by 2037 but not achieving the over 1000
kWh/Cap target in the timeframe of analysis. These targets were never achieved in the Base Run.
For per capita income, the government target of US$4000 was met in the three scenarios, but at
different timeframe respectively. In Scenario 2, this was met by 2016, Scenario 1 by 2018 and in
Base Run by 2017. Interestingly, the system shows marked improvement particularly in installed
and available capacity in the years that per capita income grew above the target value of US$4000,
even though the system in Base Run remains an extractive industry dependent economy.
40,000
30,000
& 20,000
10,000
0
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
Base Rua. ——_ Scenario 1
Figure 21a Total Installed Capacity — Base Run versus Scenario 1
26
400B
300 B
E
g 200 B
ci
100 B
0
2005 = 2010» 2015-2020». 2025-S 2030» 2035 += 2040
Time (Y ear)
Base Run. ———__— Scenario 1
Figure 21b Electricity Demand — Base Run versus Scenario 1
60,000
45,000
3
3 30,000
15,000
0
2005 2010-2015 2020» 2025 »= 2030» 2035 += 2040
Time (Y ear)
Baser Run. ————____ Scenario 1
Figure 21c Future Capacity Addition Needed — Base Run versus Scenario 1
600
450
par ft
2005 2010 2015 2020 2025 2030 2035 2040
Time (Y ear)
Base Run. ——______- Scenario 1
Figure 21d Effective Per Capita Electricity Consumption — Base Run versus
Scenario 1
27
Table 6 Comparing Some Scenario Results of the Model Run
Year Installed Capacity (MW) Effective Per Capita Per Capita Income ($/Person)
Electricity Consumption
(kWh/Cap)
Scenario2 Scenario BaseRun Scenario Scenar Base Scenario2 Scenario Base Run
@CAGR 1@ @ CAGR iol Run @EGR 1@EGR @EGR
0.0936 CAGR 0.0234 0795 0.053 0.053
0.0468
2005 B06000“BNGNINDG/NGNI00 Te9.11 10406 130327 1,303.27 1308.27 ~
2006 5895192 583068 579744 17738 9733 145600 456100 1456100
2007 6/226:59 5863185 + 5/674139 172.10 93.13 1,454.33 1,454.33 1,454.33
2008 701912 615307 5,688.74 (7278-9128 = 56068 )=— S608 «= 560168
2009 813304 663471 580910 177.68 91.12 1541.20 1,541.20 1,541.20
2010 9,450.85 7,251.33 6,006.75 185.35 92.11 2,184.02 2,162.85 2,176.41
2015 17,281.84 11,304.16 7,576.52 238.22 103.75 3,602.37 3,237.14 3,467.13
249.34 106.41 402024 3,509.05 3,829.28
260.39 109.04 4,501.04 3,803.78 {/4)238/01
271.37 111.62 5,055.53 4123.28) 4,700.08
282.31 114.16 5,696.56 4,469.61 5,223.31
2020 26,919.87 16,243.74 293.23 116.64 6,439.45 4,845.03 5,816.78
2024 37/288163) 21,052.08 11,291.64 337.65 126.13 10,855.91 6,689.67 9,132.33
2029 55,759.85 28,754.22 13,786.82 O10I76) 397.67 137.52 22,401.30 10,012.42 16,809.06
2030 60,429.61 30,585.90 14,335.87 1,070.89 410.59 139.80 26,141.04 10,853.40 19,107.66
2033 76,923.83 B6)794I799) 16,101.00 1,273.70 451.60 146.71 42,334.89 13,824.42 28,413.85
2034 83,369.26 39,129.51 16,732.10 1,349.53 466.10 149.04 50,029.62 14,985.58 32,564.58
2037 106,134.31 47,055.53 18,768.21 1,605.25 (/502/309) 156.21 84,139.75 19,087.74 49,625.67
2040 135,119.20 56,583.01 21,041.47 1,909.48 562.96 163.63 145,553.63 24,312.81 77,023.73
2016 19,017.21 12,222.79 7,948.44
2017 20,830.43 13,172.46 8,331.28
2018 22,739.21 14,156.22 8,723.98
2019 24,762.52 15,178.32 9,126.19
9,537.97
Source: This study
Conclusion
There are currently three major trends regarding electric power system modelling based on
Analytical Thinking. These are namely, optimization models, econometric models and simulation
models. In order to gain significant insight into structure and long-term performance of Nigeria
electric power system, a simulation model based on Systems Thinking, making use of System
Dynamics principles was employed for this study. This was used to evaluate the entire system from
policy making to service delivery. This is because, unlike equilibrium market models, this approach
focuses on replicating the system structure of NEPS and the logic of relationships among system
components in order to derive its long-term performance. The approach included dynamic
framework to enhance its usefulness for decision makers.
References
Adegbulughe, A. O. and Akinbami, J-F. K. (1995) Urban Household Energy Use Patterns in
Nigeria Natural Resource Forum . Vol. 19 No. 2, pp. 125-133
Al-Zayer, J. and Al-Ibrahim, A., (1996) Modelling the impact of temperature on electricity
consumption in the eastern provinces of Saudi Arabia. Journal of Forecasting 15 (2), 97—
106.
Aronson, D. (1998). Overview of Systems Thinking. Accessed from www.thinking.net on 24
September 2008
28
Babatunde, M. A. and M. I. Shuaibu (2009). The demand for electricity in Nigeria: a bound testing
approach. Presented on 14th Annual Conference on Econometric Modelling for A frica, 9
July 2009.
Besant-Jones, J. and Tenenbaum, B. (2001). The California Power Crisis: Lessons for Developing
Countries. Energy and Mining Sector Board Discussion Paper Series, Paper No 1, The
World Bank, Washington DC
Gbenga Biobaku & Co (2010) Newsletter — Nigeria Launches Roadmap for Power Sector Reform
accessed on January 13, 2012 from http://www.gbc-
law.com/ROADMAP%20FOR% 20POWER%20SECTOR%20REFORM. pdf.
Bose, R. K. and Shukla, M. (1999) Elasticities of electricity demand in India. Energy Policy 27,
137-146.
Chang, Y and E. Martinez-Combo (1997) Electricity Demand Analysis Using Cointegration and
Error-Correction Models with Time Varying Parameters: The Mexican Case. Department of
Economics, Rice University, United States.
Dincer, I. and Dost S. 1997. Energy and GDP. International Journal of Energy Research 21:153-
167.
Dorf, R.C. (1976). Modern Control Systems — Second Edition. Addison-Wesley Publishing
Company, Massachusetts, USA
Dyner, I. R , Lopez B. E. V. and Arango, S. (2003). Modelling uncertainty in electricity markets for
learning, policy and strategy. Accessed from
www.systemdynamics.org/conferences/2003/proceed/PAPERS/173.pdf on 10 October
2008
Energy Information Administration (2000). The Changing Structure of the Electric Power Industry:
An Update. Office of Coal, Nuclear, Electric and Alternate Fuels, US Department of
Energy, Washington DC, 20585. Accessed from Internet at
http://www.eia.doe.gov/cneaf/electricity/page/pubs.html on February 24, 2009
EPRI (Electric Power Research Institute) (2003). Electricity Sector Framework for the Future
Volume I: Achieving a 21“ Century Transformation. Accessed from
www.globalregulatorynetwork.org/Resources/ESFF_volumel.pdf on February 20 2011
Fasser, Y . and Brettner, D. (2002). Management for Quality in High-Technology Enterprises. In
Sage, A. P. (Ed) Wiley Series in Systems Engineering and Management. Published by A.
Wiley & Sons, Inc., New Y ork, USA. ISBN: 0—471-20958-9.
FGN (2005). Electric Power Sector Reform Act, 2005. Federal Republic of Nigeria Official
Gazzette Vol 92 No 77 Government Notice No. 150
Ford, A. (1997). System Dynamics and the Electric Power Industry. Jay Wright Forrester Prize
Lecture, 1996 - System Dynamics Review Vol. 13, No. 1, (Spring 1997): 57-85. Published
by John Wiley & Sons, Ltd. CCC 0883-7066/97/010057-29
Ford, A. (1999). Modelling the Environment: An Introduction to System Dynamic Modelling of
Environmental Systems, Island Press, Washington D. C., p. 401.
Forrester, J.W. (1968) Principles of Systems. Productivity Press, Portland Oregon, US
Holtedahl, P. and Joutz, F. (2004) Residential electricity demand in Taiwan. Energy Economics 26,
201-224.
Houthakker, H. S., Verleger, P. K. and Sheehan, D. P., 1974. Dynamic demand analysis for
gasoline and residential electricity. American Journal of Agricultural Economics 56, 412-
418.
Inglesi, R. (2010) Aggregate electricity demand in South A frica: conditional forecasts to 2030.
Appl Energ. 87:197-202.
Johnson, R.A. and Wichern, D.W. (1997). Business Statistics: Decision Making with Data. John
Wiley and Sons, Inc. New Y ork, USA.
29
Joskow P.L. (2003a). Electricity Sector Restructuring and Competition: Lessons Learned — 03-014
WP (MIT Centre for Energy and Environmental Policy Research (CEEPR), USA.
Joskow, P.L. (2008). Lessons Leamed from Electricity Market Liberalization. The Energy Journal,
Special Issue. The Future of Electricity: Papers in Honor of David Newbery.
Kilanc, P.G. and Or, I. (2008). A decision support tool for the analysis of pricing, investment and
regulatory processes in a decentralized electricity market. Energy Policy 36 (2008) 3036 —
3044. Elsevier Ltd.
Lin, B. Q. (2003) Electricity demand in the People’s Republic of China: investment requirement
and environmental impact. A publication of the Asian Development Bank, ERD Working
Paper Series No. 37. Accessed from http://www.adb.org/economics on September 14, 2011
Narayan, P.K., Smyth, R., and Prasad, A., (2007). Electricity consumption in G7 countries: a panel
cointegration analysis of residential demand elasticities. Energy Policy 35, 4485-4494.
Oladeji, S.O. (2005). Production and Distribution of a Refined Petroleum Product in Nigeria.
Submission of material for the 6" System Dynamics Ph.D. Colloquium 2005, System
Dynamics Society, Boston, USA accessed on 9" October 2008 from www.is.bwl.uni-
mannheim.de/phd_colloquium2005/
Olsina, F. (2005). Long-Term Dynamics of Liberalized Electricity Markets. Ph.D. thesis submitted
to Department of Postgraduate Studies, Faculty of Engineering, National University of San
Juan, San Juan, Argentina accessed on September 8, 2008 from
www2.ing.puc.cl/power/paperspdf/Olsina.pdf
Oyebisi, T.O. and Momodu, A.S. (2012). ‘Planning for high technology facility in Nigeria: a
perspective’, Int. J. Technology, Policy and Management, Vol. 12, No. 1, pp.48-57.
Smith P.C. and van Ackere, A. (2002). A note on the integration of system dynamics and economic
models. Journal of Economic Dynamics & Control 26 (2002) 1-10
The Presidency (Federal Republic of Nigeria) (2010). Roadmap for Power Sector Reform: a
customer-driven sector-wide plan to achieve stable power supply accessed from
www.nigeriaelectricityprivatisation.com/wp-content/plugins/download-
monitor/download.php?id=43 on October 20 2010
Ventana Systems, Inc (2010). Vensim Manuals for the Ventana Environment Simulation: V ensim®
PLE for Windows Version 5b
Ventosa, M., Balillo, A., Ramos, A. and Rivier, M. (2005). Electricity market modelling trends.
Energy Policy 33 (2005) 897-913
Vision 2020 (2010). The First National Implementation Plan (2010 — 2013) Volume II: Sectoral
Plans and Programmes. Publication of the National Planning Commission accessed from
http://www.npc.gov.ng/vault/files/NV 2020-NIP-V olume-II-Original-
document_edited__versioin3_10_06_2010.pdf on June 16 2011.
Wikipedia (2011). Federal Ministries of Nigeria. Accessed from
http://en.wikipedia.org/wiki/Federal_Ministries_of_Nigeria on February 22 2011
Wikipedia Website (2011) List of system dynamics software
http://en.wikipedia.org/wiki/List_of_system_dynamics_software accessed on September 8
2011
Xianchun, X. (2002) Study on Some Problems In Estimating China’s Gross Domestic Product.
Review of Income and Wealth, Volume 48, Issue 2, pages 205—215, Article first published
online: 7 JAN 2003 DOI: 10.1111/1475-4991.00048.
Zachariadis, T and N. Pashourtidou, 2006. An Empirical Analysis of Electricity Consumption in
Cyprus. Department Of Economics, University Of Cyprus
Zhang, C. (1987) Electric Power Economics and Management. Beijing: Hydorelectricity Press.
Ziramba, E., (2008) The demand for residential electricity in South Africa. Energy Policy 36,
3460- 3466
30
Acknowledgments
The authors gratefully acknowledge the inestimable contribution of Prof Brian C. Dangerfield of
the Salford School of Business, University of Salford, Greater Manchester, UK to the development
of the SD model. The authors also wish to acknowledge the funding received from the Science and
Technology Education Post Basic (STEP-B), Federal Ministry of Education through grant from the
World Bank. This work is the second in series of such publication from this funding. The first,
titled ‘Planning for High Technology Facility in Nigeria: A Perspective’, has been published in
International Journal of Technology, Policy and Management, Vol 12 No 1, 2012
31
Some Results of Model Run
Base Run
Year Annual Total = Annual = Annual Available = Annual = Annual Annual Annual
Installed Total Capacity(MW) _‘Total Total Total Effective
Capacity (MW) Scrapped @ 0.52 Wheeling Electricity Electricity _Electricity
@CAGRof Capacity Availability Capacity Generation Distributed Consumption
| = | : | | |
2010 5,975 298.73 3,107 3,834
2015 7,430 371.48 3,863 5,790 27.07 23.23 16.26
2020 9,317 465.85 4,845 8,798 33.95 28.08 19.66
2025 11,707 585.36 6,088 13,442 42.66 32.97 23.08
2030 15,017 750.84 7,809 20,626 54.72 38.07 26.65,
2035 19,902 995.09 10,349 31,748 72.53 43.58 30.51
2040 27,345 1,367 14,219 48,974 99.65 49.68 34.78
Base Run Results
Year Electricit. Annual Difference Generation Total Installed Effective per. + GDP (US$
y Effective between Capacity Capacity or Capita Billions)
Demand Electricity Electricity neededto Needed to Be Electricity
(TWH) Consumpt Demandand Meet Installed (MW) Consumption
ion Consumption Demand — @ CAGR of (kWh/Cap)
(TWH) (TWH) (MW) 0.0234
104.06 180.46
97.33 206.22
93.13 210.70
91.28, 231.29
91.12 233.63
2010 40.69 13.25 27.44 5,807 5,975 92.11 337.47
2011 42.41 13.75 28.66 6,051 6,209 93.85 377.32
2012 44.39 14.33 30.06 6,334 6,480 96.04 422.75
2013 46.57 14.95 31.62 6,645 6,778 98.50 474.64
2014 48.89 15.60 33.29 6,977 7,095 101.10 534.00
2015 51.33 16.26 35.07 7,324 7,430 103.75 602.03
2016 53.85, 16.94 36.91 7,683 1,178 106.41 680.13
2017 56.44 17.61 38.83 8,054 8,141 109.04 769.96
2018 59.10 18.29 40.81 8,433 8,517 111.62 873.46
2019 61.82 18.97 42.85 8,822 8,909 114.16 992.92
2020 64.61 19.66 44.95 9,220 9,317 116.64 1,131.04
2021 67.47 20.34 47.13 9,628 9,745 119.07 1,291.05
2022 70.40 21.02 49.38 10,046 10,195 121.46 1,476.73
2023 BAL 21.70 5.71 10,475 10,669 123.81 1,692.59
2024 76.49 22.39 54.1 10,915 11,172 126.13 1,944.01
2025 79.67 23.08 56.59 11,369 11,707 128.43 2,237.36
2026 82.94 23.78 59.16 11,836 12,278 130.71 2,580.28
2027 86.32 24.48 61.84 12,317 12,890 132.98 2,981.88
2028 89.80 25.19 64.61 12,814 13,546 135.25 3,453.06
2029 93.40 25.92 67.48 13,327 14,254 137.52 4,006.90
2030 97.12 26.65 70.47 13,858 15,017 139.80 4,659.10
2031 100.96 27.39 73.57 14,407 15,842 142.09 5,428.56
2032 104.95 28.15 76.8 14,976 16,736 144.39 6,338.05
2033 109.07 28.92 80.15 15,564 17,705 146.71 7,415.05
2034 113.35 29.71 83.64 16,174 18,757 149.04 8,692.80
2035 117.78 30.51 87.27 16,807 19,902 151.41 10,211.54
2036 122.38 31.32 91.06 17,462 21,147 153.79 12,020.10
2037 127.14 32.16 94.98 18,143 22,502 156.21 14,177.83
2038 132.09 33.01 99.08 18,848 23,979 158.65 16,756.98
2039 137.22 33.89 103.33 19,580 25,589 161.12 19,845.65
2040 142.54 34.78 107.76 20,340 27,345 163.63 23,551.44
Total
Installed
Capacity or
Needed to Be
10948.12
11671.04
12419.3
13195.23
14001.53
14841.25
15717.6
16633.91
17593.65
18600.31
19657.5
20768.89
21938.23
23169.38
24466.32
25833.16
27274.15
28793.74
30396.55
32087.42
33871.42
35753.86
37740.32
39836.68
Scenario 1 Run - Increased Generation Capacity
Annual
Total
Scrapped
Capacity
(MW)
332.5679
357.3856
385.0161
414.717
446.0162
478.6377
512.4462
547.4063
583.5518
620.9653
659.7613
700.0767
742.0626
785.8798
831.6957
879.6823
930.0156
982.8752
1038.445
1096.912
1158.469
1223.316
1291.658
1363.708
1439.687
1519.828
1604.371
1693.571
1787.693
1887.016
1991.834
Annual
Available
Capacity
(MW) @
Availability
Factor
31072.61
Annual
Total
Wheeling
Capacity
(MW)
24570.32
25935.84
27376.82
28897.52
Annual Annual Annual
Total Total Effective
Electricity Electricity Electricity
Generation Distributed Consumption
(TWH) (TWH) (TWH)
33.12541 —30.80658 6.18559
BL7815 9.55674 25.12323
3154178 29.3338 24.9373
5239075 80.12334 5.60484
34.07342 31.6882 26.93499
36.35792 —-33.81281 28.74089
39.0711 36.3607 30.88566
42.0918 39,14532 33.27352
45.3885 42.16508 35.84032
48.76063 45.34733 38.54523
52.32696 48.6402 41.36442
56.02308 5210141 44.28619
59.84507 55.65586 47.30749
63.79669 59.33086 50.43123
67.8869 63.13476 53.66455
72.12827 67.07924 57.01735
76.53574 7117818 60.50145
8112584 -75,44698 64.12993
85.91615 _79.90196 67.91667
90.92496 -84.56016 71.87613
96.17109 89.43906 76.02319
101.6738 94.55654 80.37306
107.4526 99.9309 84.94126
113.5277 105.5808 89.74364
119.9196 111.5252 94.79643
126.6494 117.7839 100.1163
133.7388 124.377 105.7205
141.2102 131.3255 111.6266
149.087 138.6509 117.8533
157.3935 146.3759 124.4195
166.1548 154.524 131.3454
175.3976 163.1197 138.6517
185.1493 172.1888 146.3605
195.4392 181.7584 154.4946
206.2977 191.8568 163.0783
217.7569 202.5138 172.1368
Year —_Electricit Annual
y Demand Effective
(TWH) Electricity
Consumpt
ion
(TWH)
Scenario 1 Run Continued
Difference Generatio
between —_n Capacity
Electricity needed to
Demand Meet
and Demand
Consumpti (MW)
on (TWH)
7,096.92
7,789.47
8,370.75
9,017.92
9,713.58
34.67 10,446.68
37.20 11,210.75
39.83 12,002.62
42.55 12,821.46
45.35 13,668.07
48.26 14,544.37
51.28 15,453.06
54.41 16,397.34
57.67 17,380.74
61.08 18,407.04
64.64 19,480.15
68.37 20,604.10
72.28 21,783.02
76.39 23,021.11
80.71 24,322.67
85.25 25,692.10
90.04 27,133.91
95.08 28,652.77
100.39 30,253.48
105.99 31,941.05
111.89 33,720.66
118.12 35,597.73
124.69 37,577.93
131.63 39,667.19
138.94 41,871.73
146.66 44,198.10
154.81 46,653.17
Total
Installed
Capacity
or Needed
to Be
Installed
(MW) @
CAGR of
0.0468
6,651.36
7,147.1
7,100.32
8,294.34
8,920.32
9,572.75
10,248.92
10,948.12
11,671.04
12,419.30
13,195.23
14,001.53
14,841.25
15,717.60
16,633.91
17,593.65
18,600.31
19,657.50
20,768.89
21,938.23
23,169.38
24,466.32
25,833.16
27,274.15
28,793.74
30,396.55
32,087.42
33,871.42
35,753.86
37,740.32
39,836.68
Effective
per Capita
Electricity
Consumpti
on
(kWh/Cap
185.3539
194.7281
205.0882
215.9653
227.0664
238.2206
249.3394,
260.389
271.3705
282.3064
293.2314
304.1861
315.2133
326.355
337.6516
349.1407
360.8569
372.8322
385.0956
397.6739
410.5916
423.8718
437.5357
451.6036
466.095
481.0286
496.4227
512.2953
528.6645
545.548
562.9641
GDP (US$
Billions)
335.37
371.86
412.32
457.19
506.94
562.09
623.26
691.07
766.27
849.64
942.09
1,044.60
1,158.26
1,284.29
1,424.03
1,578.98
1,750.79
1,941.29
2,152.52
2,386.73
2,646.43
2,934.39
3,253.68
3,607.70
4,000.26
4,435.52
4,918.14
5,453.28
6,046.65
6,704.58
7,434.10
Scenario 2 Run - Increased Electricity Consumption for Industrialization
Total Annual — Annual Annual — Annual Annual Annual
Installed ‘Total Available Total Total Total Effective
Capacity Scrapped Capacity Wheeling Electricity Electricity —_Electricity
or Needed Capacity (MW)@ Capacity Generation Distributed Consumption
to Be (MW) 0.78 (MW) (TWH) (TWH) (TWH)
Installed Availability
(Mw) @ Factor
CAGR of
0.0936
6,060.00 303.00 4726.80 4395.92 33.13, 30.81 26.19
5189592 294.80 4598.82 4276.89 32.23 29.97 25.48
622659 = 311.33 4856.74 4516.76 34.04 31.65 26.91
TOIN2 = 350.96 5474.91 5091.66 38.37 35.68 30.33
833104 = 400.65 6343.77 5899.70 44.46 41.35 35.14
9,450.85 472.54 7371.66 6855.64 51.66 48.04 40.84
10,893.29 544.66 8496.77 7901.99 59.55 55.38 47.07
12,413.30 620.66 9682.37 9004.60 67.85 63.10 53.64
13,987.49 699.37 10910.24 —-10146.52 16.46 TAL 60.44
15,608.97 780.45 12174.99 1322.74 85.32 79.35 67.45
17,281.84 864.09 13479.83 12536.24 94.47 87.85 74.68
19,017.21 950.86 14833.42 — 13795.08 103.95 96.68 82.17
20,830.43 1041.52 -'16247.73.15110.38 113.86 105.89 90.01
22,739.21 1136.96 1736.59 —-16495.02 124.30 115.60 98.26
24,762.52 1238.13. 1931477 :17962.72 135.36 125.88 107.00
26,919.87 1345.99 2099750 —:19527.66 147.15 136.85 116.32
29,231.04 1461.55 -22800.21 1204.19 159.78 148.60 126.31
31,716.00 1585.80 2473848 —-23006.78 173.37 161.23 137.05
34,394.98 1719.75 6828.08 ~—-24950.11 188.01 174.85 148.62
37,288.63 1864.43 29085.13 -27049.16 203.83 189.56 161.13
40,418.27 2020.91 -31526.25 -29319.40 220.94 205.47 174.65
43,806.11 2190.31 —-34168.76 3176.94 239.45 222.69 189.29
47,475.51 2373.78 + 37030.90 -34438.73. 259.51 241.35 205.14
51,451.27 2572.56 — 40131.99 37322.75 281.24 261.56 222.32
55,759.85 2787.99 43492.68 4044818 304.80 283.46 240.94
60,429.61 3021.48 4713509 —-43835.63 330.32 307.20 261.12
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
65,491.11
70,977.30
76,923.83
83,369.26
90,355.34
97,927.34
106,134.31
115,029.39
124,670.20
135,119.20
3274.56
3548.87
3846.19
4168.46
4517.77
4896.37
5306.72
5751.47
6233.51
6755.96
51083.06
55362.29
60000.59
65028.02
70477.16
76383.33
82784.76
89722.92
97242.75
105392.98
47507.24
51486.93
55800.54
60476.05
65543.76
71036.48,
76989.82
83442.30
90435.75
98015.45
357.99
387.98
420.48
455.72
493.90
535.29
580.16
628.78
681.48
738.59
332.93
360.82
391.05
423.82
459.33
497.82
539.54
584.76
633.77
686.89
282.99
306.70
332.39
360.24
390.43
423.15
458.61
497.05
538.71
583.86
Scenario 2 Run Continued
Year Electricity Annual Generation Total Installed Effective per GDP
Demand Effective Capacity Capacity or Capita (US$
(TWH) Electricity —_ needed to Needed to Be Electricity _ Billions)
Consumpti Meet Installed (MW) — Consumption
on (TWH) — Demand @ CAGR of (kWh/Cap)
(MW) 0.0936
2005 49.74 26.19 8,188.76 6,060.00 189.11
2006 48.39 25.48 7,967.43 5,895.92 179.87
2007 51.10 26.91 8,419.25 6,226.59 185.71
2008 57.61 30.33 9,506.31 7,019.12 204.66
2009 66.75 35.14 11,043.95 8,133.04 231.83
2010 71.56 40.84 12,877.34 9,450.85 263.37
338.65
2011 89.40 47.07 14,902.97 10,893.29 296.77
380.41
2012 101.88 53.64 17,060.87 12,413.30 330.61
428.69
2013 114.80 60.44 19,323.42 13,987.49 364.20
484.66
2014 128.10 67.45 21,685.67 15,608.97 397.33
549.72
2015 141.83 74.68 24,157.94 17,281.84 430.06
625.51
2016 156.08 82.17 26,760.44 19,017.21 462.66
714.05
2017 170.96 90.01 29,519.49 20,830.43 495.43
817.75
2018 186.62 98.26 32,465.16 22,739.21 528.72
939.51
2019 203.23 107.00 35,629.72 24,762.52 562.88
1,082.88
2020 220.94 116.32 39,046.86 26,919.87 598.23
1,252.12
2021 239.90 126.31 42,751.42 29,231.04 635.05
1,452.45
2022 260.30 137.05 46,779.37 31,716.00 673.62
1,690.22
2023 282.28 148.62 51,168.08 34,394.98 714.17
1,973.20
2024 306.03 161.13 55,956.70 37,288.63 756.92
2,310.91
2025 331.72 174.65 61,186.56 40,418.27 802.09
2,715.04
2026 359.52 189.29 66,901.73 43,806.11 849.86
3,200.00
2027 389.64 205.14 73,149.48 47,475.51 900.44
3,783.58
2028 422.27 222.32 79,980.81 51,451.27 954.01
4,487.79
2029 457.63 240.94 87,451.03 55,759.85 1,010.76
5,339.96
2030 495.95 261.12 95,620.26 60,429.61 1,070.89
6,374.08
2031 537.50 282.99 104,554.00 65,491.11 1,134.61
7,632.54
2032
2033
2034
2035
2036
2037
2038
2039
2040
582.52
631.33
684.22
741.56
803.70
871.06
944.06
1023.19
1108.94
306.70
332.39
360.24
390.43
423.15
458.61
497.05
538.71
583.86
114,323.74
125,007.57
136,690.84
149,466.83
163,437.59
178,714.70
195,420.16
213,687.41
233,662.41
70,977.30
76,923.83
83,369.26
90,355.34
97,927.34
106,134.31
115,029.39
124,670.20
135,119.20
1,202.14
1,273.70
1,349.53
1,429.88
1,515.03
1,605.25
1,700.85
1,802.15
1,909.48
9,168.36
11,047.97
13,354.92
16,194.42
19,699.44
24,038.35
29,425.00
36,131.61
44,505.73
NEPS Four Sector Model Equations
1 6 2 2 Ae 2 Ee 2 fe 2 2S 2 eS EE A aE
-Control
1 eR 2 Ae 2 ES Ee fe 2 RO ER A EE
Simulation Control Parameters
(01) FINAL TIME = 2040
Units: Year
(02) INITIAL TIME = 2005
Units: Year
(03) SAVEPER = 1
Units: Year [0,?]
(04) TIME STEP = 0.0625
Units: Year [0,?]
eee FF 2 2 ff ff EE
-nigeriaelectricpowersystem2005v3a
1 RR A 2 fe 2 ER Ee 2 fe 2 RR AR EO A
(05) Access Lvl=
0.46
Units: Fraction
(06) All Plant Types:
Thermal,Hydro,Others
(07) application rate=
0.1
Units: Fraction/Year* Year
(08) approval rate=
0.1
Units: Fraction/Year
(09) approved application[All Plant Types]=
TotGenCapUndRey[All Plant Types]*approval rate
Units: MW/Year
(10) — Availability factor=
0.9
Units: Fraction/Year
(11) — Available capacity[All Plant Types]=
Total Installed Capacity[All Plant Types]*Availability factor
Units: MW/Year
(12) Average mortality rate=
0.018
Units: Fraction/Year
(13) Avg Tariff=
Paid Tariff+Subsidy
Units: $/kWh
(14) _ birth rates=
0.04065
Units: Fraction/Year
(15) _ births=
birth rates*Population
Units: Persons/Year
(16) Cancellation rates=
0.0012
Units: Fraction/Year
(17) _ cap specific consumption=
3.4843
Units: MJ/kWh
(18) capacity addition[All Plant Types]=
Capacity under construction[All Plant Types]*Total CAGR[AII Plant Types]
Units: MW/Year
(19) capacity factor=
0.8
Units: Dmnl
(20) Capacity under construction[Thermal]= INTEG (
GenCap Proj Startup[Thermal]-capacity addition[Thermal],
0)
Capacity under construction{Hydro]= INTEG (
GenCap Proj Startup[Hydro]-capacity addition[Hydro],
0)
Capacity under construction[Others]= INTEG (
GenCap Proj Startup[Others]-capacity addition[Others],
0)
Units: MW
(21) CapUnderRev[All Plant Types]=
10000
Units: MW
(22) CapWPerm=
100
Units: MW
(23) Connectivity rate=
0.022
Units: Fraction/Year
(24) ConvFac MWhtokWh=
1000
Units: kWh/(MW* Hours)
(25) ConvFackWhtoMWh=
0.001
Units: MW*Hours/kWh
(26) ConvFact=
le-006
Units: MW*Hours/kWh
(27) deaths=
Average mortality rate*Population
Units: Persons/Year
(28) Distribution Losses=
0.05
(29)
(30)
(31)
(32)
(33)
(34)
(35)
(36)
(37)
Units: Fraction/Year
Economic Growth Rate= WITH LOOKUP (
Time,
([(2005,0)-(2040,0.2)],(2005,0.062),(2006,0.069),(2007,0.053),(2008,0.064
),(2009,0.053),(2040,0.0795) ))
Units: Fraction/Year* Year
Effective Electricity Consumption[All Plant Types]=
Wheeling Capacity[All Plant Types]*capacity factor*hours in a year*(1-Losses
)*ConvFac MWhtokWh
Units: kWh/Year
effective per capita electricity consumption=
Effective Electricity Consumption[Thermal]/(Population)
Units: kWh/(Person* Year)
Electricity Demand=
(Avg Tariff*Electricity intensity*GDP/price of electricity substitute)
Units: kWh/Year
Electricity Distributed[AIl Plant Types]=
Electricity Generation[All Plant Types]*(1-(Own Use[All Plant Types]+Tx Losses
))-(outage capacity*outage duration* capacity factor)
Units: MW*Hours
Electricity Generation[All Plant Types]=
Available capacity[All Plant Types]*capacity factor*hours in a year
Units: MW*Hours
Electricity intensity=
effective per capita electricity consumption/GDP per capita
Units: kWh/($*Year)* Year
Export of goods and services=
5.71287e+010
Units: $/Year
Final Consumption Expenditure=
IF THEN ELSE(Time>2009,((Govt Consumption Exp+Private Consumption Exp)*
Exp) )
(38)
(39)
(40)
(41)
(42)
(1+Economic Growth Rate)*T), (Govt Consumption Exp+Private Consumption
Units: $/Year
fuelprice=
0.029
Units: $/MJ
Future Generation Capacity needed=
Electricity Demand/(capacity factor*hours in a year)*ConvFackWhtoMWh
Units: MW/Year
Fxd Cost=
"Fxd O&M Cost"
Units: $/kWh
"Fxd O&M Cost"=
0.01
Units: $/kWh
GDP=
IF THEN ELSE(Time<2006, (Final Consumption Expenditure+Gross capital
formation
(43)
(44)
(45)
+Net Exports) , (Final Consumption Expenditure+Gross capital formation+Net Exports
)*(1+Economic Growth Rate)‘T )
Units: $/Year
GDP per capita=
GDP/Population
Units: $/(Person* Year)
Gen Cap under Review[All Plant Types]=INTEG (
licensing initiation[All Plant Types]-approved application[All Plant Types
]-refused application[All Plant Types],
CapUnderRey[All Plant Types])
Units: MW
GenCap Proj Startup[All Plant Types]=
TotNCapGencPerm[All Plant Types]*Startup Rate
Units: MW/Year
(46)
(47)
savings
(49)
(50)
(51)
(52)
(53)
Govt Consumption Exp=
1.22849e+010
Units: $/Year
Gross capital formation=
IF THEN ELSE(Time <2010, (Gross Fixed Capital Formation+Gross national
) , (Gross Fixed Capital Formation+Gross national savings)*(1+Economic Growth Rate)*T
Units: $/Year
Gross Fixed Capital Formation=
9.85139e+009
Units: $/Year
Gross national savings=
1.44709e+007
Units: $/Year
hours in a year=
8760
Units: Hours
HydroNew=
Future Generation Capacity needed*0.1
Units: MW/Year
Import of goods and services=
3.44526e+010
Units: $/Year
Installed capacity[Thermal]= INTEG (
capacity addition[Thermal]-scrapped gen capacity[Thermal],
4500)
Installed capacity[Hydro]= INTEG (
capacity addition{Hydro]-scrapped gen capacity[Hydro],
1560)
Installed capacity[Others]= INTEG (
capacity addition[Others]-scrapped gen capacity[Others],
(54)
(55)
(56)
0)
Units: MW
licensing initiation[All Plant Types]=
(HydroNew+OthersNew+ThermNew)*application rate
Units: MW/Year
Losses=
NonTech Losses+Distribution Losses
Units: Fraction/Year
Net Exports=
IF THEN ELSE(Time<2010, (Export of goods and services-Import of goods and
services
(57)
(58)
(59)
(60)
(61)
) , (Export of goods and services-Import of goods and services
)*(1+Economic Growth Rate)T )
Units: $/Year
New GenCap with permission[All Plant Types]= INTEG (
approved application[All Plant Types]-Permission cancelled[All Plant Types
]-GenCap Proj Startup[All Plant Types],
CapWPerm)
Units: MW
No of Pple with Access=
Access Lvl*Population*(1+Connectivity rate)
Units: Persons/Year
NonBilled Losses=
0.115
Units: Fraction/Year* Year
NonTech Losses=
0.1
Units: Fraction/Year
Op cost=
Fxd Cost+var op unit cost
Units: $/kWh
(62) OthersNew=
Future Generation Capacity needed*0.05
Units: MW/Year
(63) outage capacity=
23
Units: MW/Year
(64) outage duration=
5
Units: Hours/Year* Year
(65) Own Use[All Plant Types]=
0.01
Units: Fraction/Year* Year
(66) Paid Tariff=
0.07093
Units: $/kWh
(67) Permission cancelled[All Plant Types]=
New GenCap with permission[All Plant Types]*Cancellation rates
Units: MW/Year
(68) Population= INTEG (
births-deaths,
1.38468e+008)
Units: Person
(69) price of electricity substitute=
0.1053
Units: $/kWh
(70) Private Consumption Exp=
1.35635e+011
Units: $/Year
(71) _ refusal rate=
0.01
Units: Fraction/Year
(72) refused application[All Plant Types]=
TotGenCapUndRey[All Plant Types]*refusal rate
Units: MW/Year
(73) scrapped gen capacity[Thermal]=
Installed capacity[Thermal]*scrapping rate
scrapped gen capacity[Hydro]=
Installed capacity[Hydro]*scrapping rate
scrapped gen capacity[Others]=
Installed capacity[Others]*scrapping rate
Units: MW/Year
(74) — scrapping rate=
0.05
Units: Fraction/Year
(75) Startup Rate=
0.5
Units: Fraction/Year
(76) Subsidy=
0
Units: $/kWh
(77) Systems Financial Profit[All Plant Types]= INTEG (
Total Revenue Stream[All Plant Types]-Total Cost Stream[AIl Plant Types],
1e+006)
Units: $
(78) T= WITH LOOKUP (
Time,
([(2005,0)-(2040,40)],(2005, 1),(2040,36) ))
Units: Dmnl
(79) ThermNew=
Future Generation Capacity needed*0.85
Units: MW/Year
(80) Total CAGR[AII Plant Types]=
(81)
(82)
(83)
(84)
(85)
(86)
(87)
(88)
(89)
(90)
0.0468*2
Units: Fraction/Year
Total Cost Stream[All Plant Types]=
(Op cost+Tx cost)*Electricity Generation[All Plant Types]*ConvFac MWhtokWh
Units: $/Year
Total Electricity Generation[All Plant Types]=
SUM (Electricity Generation[AIl Plant Types!])
Units: MW*Hours/Year* Year
Total Installed Capacity[All Plant Types]=
SUM (Installed capacity[All Plant Types!])
Units: MW
Total Revenue Stream[All Plant Types]=
Avg Tariff*Effective Electricity Consumption[All Plant Types]-(Avg Tariff
*Effective Electricity Consumption[All Plant Types]*NonBilled Losses)
Units: $/Year
TotGenCapUndRev[All Plant Types]=
SUM (Gen Cap under Review[All Plant Types!])
Units: MW
TotNCapGencPerm[All Plant Types]=
SUM (New GenCap with permission[All Plant Types!])
Units: MW
TotScrappedCap[All Plant Types]=
SUM (scrapped gen capacity[All Plant Types!])
Units: MW/Year
Tx cost=
0.008
Units: $/kWh
Tx Losses=
0.06
Units: Fraction/Year
var op unit cost=
cap specific consumption*fuelprice
Units: $/kWh
(91) | Wheeling Capacity[All Plant Types]=
Electricity Distributed[ All Plant Types]/(capacity factor*hours in a year)
Units: MW/Year
