Determinants of Pricing Tariffs and Consumer-perceived Value of Energy-mix Electricity Marketing in Nigeria
CHAPTER ONE
OBJECTIVE OF THE STUDY
The objectives of the study are;
- To ascertain the relationship between determinants of pricing of tariffs and consumer perceived value of energy
- To ascertain the relationship between pricing tariff and energy mix electricity marketing
- To ascertain the impact of determinant of pricing tariffs on Nigeria economy
CHAPTER TWO
REVIEW OF RELATED LITERATURE
Nigeria’s Electricity Sector
With only 3800 MW against an estimated demand of 10,000 MW, Nigeria has considerable suppressed and unmet demand. About 40% of Nigeria’s population has access to electricity2 with the rest of around 90 million people living in the dark. The country faced a long bout of underinvestment and poor planning in electricity infrastructure from 1981-99. Only 19 out of 79 generation units were operational in 1999, and the average daily generation was only 1,750 MW. No new infrastructure was built in the country for over a decade (1989-99), and the youngest power plant built was in 1990. Less than 2% of the Transmission Development Plan (1995 – 2005) was implemented, with the last transmission line built in 19873 . As a result, the existing power infrastructure is mostly in a dysfunctional state. In its response to this grim situation, the administration, in 1999 embarked on an ambitious program to improve the generation, transmission and distribution capacity in the country. The salient features of this program were as follows:
(a) Increase in generation capacity, through the rehabilitation of existing plants and building of new plants (new PHCN4 or NIPP5 plant, or third-party licensed IPPs).
(b) Reinforcement of transmission network, through the rehabilitation of existing system and building of new grid stations and transmission lines.
(c) Rehabilitation and extension of the distribution system, initiation of pilot demonstration projects and expanding rural electrification schemes.
(d) Initiation of sector reforms, including inter alia enactment of enabling legislation, restructuring of the monolithic utility NEPA, establishment of the independent regulator, and solicitation of private-sector investments. Hence, investments in the power sector over the last three decades have followed an irregular pattern. While substantial investments were made in the years following the oil price shocks of the seventies, there was a period of neglect which resulted in a crisislike situation in the nineties. It has been only in the last five or six years that the power sector has received growing attention from FGN, even though the bulk of the results are yet to materialize Modest but steady improvements witnessed during 2000-2005 could not be sustained for a variety of reasons (Figure-2).
CHAPTER THREE
RESEARCH METHODOLOGY
Research design
The researcher used descriptive research survey design in building up this project work the choice of this research design was considered appropriate because of its advantages of identifying attributes of a large population from a group of individuals. The design was suitable for the study as the study sought to determinants of pricing tariff and consumer perceived valued of energy mix electricity marketing of Nigeria
Sources of data collection
Data were collected from two main sources namely:
Primary source and Secondary source
Primary source:
These are materials of statistical investigation which were collected by the research for a particular purpose. They can be obtained through a survey, observation questionnaire or as experiment; the researcher has adopted the questionnaire method for this study.
Secondary source:
These are data from textbook Journal handset etc. they arise as byproducts of the same other purposes. Example administration, various other unpublished works and write ups were also used.
CHAPTER FOUR
PRESENTATION ANALYSIS INTERPRETATION OF DATA
Introduction
Efforts will be made at this stage to present, analyze and interpret the data collected during the field survey. This presentation will be based on the responses from the completed questionnaires. The result of this exercise will be summarized in tabular forms for easy references and analysis. It will also show answers to questions relating to the research questions for this research study. The researcher employed simple percentage in the analysis.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
Introduction
It is important to ascertain that the objective of this study was to ascertain determinants of pricing tariff and consumer perceived valued of energy mix electricity marketing of Nigeria
In the preceding chapter, the relevant data collected for this study were presented, critically analyzed and appropriate interpretation given. In this chapter, certain recommendations made which in the opinion of the researcher will be of benefits in addressing the challenges of determinants of pricing tariff and consumer perceived valued of energy mix electricity marketing of Nigeria
Summary
This study was on determinants of pricing tariff and consumer perceived valued of energy mix electricity marketing of Nigeria. Three objectives were raised which included; To ascertain the relationship between determinants of pricing of tariffs and consumer perceived value of energy, to ascertain the relationship between pricing tariff and energy mix electricity marketing, to ascertain the impact of determinant of pricing tariffs on Nigeria economy. In line with these objectives, two research hypotheses were formulated and two null hypotheses were posited. The total population for the study is 200 staff of PHCN, Portharcourt. The researcher used questionnaires as the instrument for the data collection. Descriptive Survey research design was adopted for this study. A total of 133 respondents made maketers, human resource managers, senior staff and junior staff was used for the study. The data collected were presented in tables and analyzed using simple percentages and frequencies
Conclusion
Even though Nigeria is abundantly rich in energy resources, it is clear that unless appropriate pricing is adopted both for electricity and gas, its energy sector growth will not be sustainable. However, these pricing measures will not yield the desired results unless complementary governance measures are adopted to make them sustainable.
Recommendation
This study can help in drawing the attention of policy makers and electricity market players to the benefits of dynamic and customized pricing, demand mapping, segmentation for electricity markets and automation technologies.
REFERENCES
- Abreu, J., Pereiray, F., Vasconcelos, J. and Ferrao, P. (2010). An approach to discover the potential for demand response in the domestic sector. IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply, pp.240-245.
- Agnetis, A., de Pascale, G., Detti, P. and Vicino, A. (2013). Load Scheduling for Household Energy Consumption Optimization. IEEE Trans. Smart Grid, 4(4), pp.2364-2373.
- Akay, D. and Atak, M. (2007). Grey prediction with rolling mechanism for electricity demand forecasting of Turkey. Energy, 32(9), pp.1670-1675.
- An, L., Lupi, F., Liu, J., Linderman, M. and Huang, J. (2002). Modeling the choice to switch from fuelwood to electricity. Ecological Economics, 42(3), pp.445-457.
- Borenstein, S., Jaske, M., & Rosenfeld, A. (2002). Dynamic pricing, advanced metering, and demand response in electricity markets. Center for the Study of Energy Markets.
- Bose, R. and Shukla, M. (1999). Elasticities of electricity demand in India. Energy Policy, 27(3), pp.137-146.
- Braithwait, S. (2010). Behavior modification. Power and Energy Magazine, IEEE, 8(3), 36-45.
- Catalão, J. P. S., Mariano, S. J. P. S., Mendes, V. M. F., & Ferreira, L. A. F. M. (2006). Application of neural networks on next-day electricity prices forecasting. In Proceedings of the 41st International Universities Power Engineering Conference, Newcastle upon Tyne, UK.s
- Contreras, J., Espinola, R., Nogales, F. J., &Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. Power Systems, IEEE Transactions on, 18(3), 1014-1020.
- Cui, T., Goudarzi, H., Hatami, S., Nazarian, S. and Pedram, M. (2012). Concurrent optimization of consumer’s electrical energy bill and producer’s power generation cost under a dynamic pricing model. Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES, pp.1-6.
- Daoyuan Zhang, Yajun Wang, and Luh, P. (2000). Optimization based bidding strategies in the deregulated market. IEEE Transactions on Power Systems, 15(3), pp.981-986.
- David, A. and Wen, F. (2000). Strategic Bidding in Competitive Electricity Markets: a Literature Survey. In: Power Engineering Society Summer Meeting, 2000.. IEEE, pp.Vol. 4, pp. 2168-2173.
- David, A. K., & Li, Y. Z. (1993). Effect of inter-temporal factors on the real time pricing of electricity. IEEE transactions on power systems, 8(1), 44-52.
