Abstract
This data article presents comparisons of energy generation costs from gas-fired turbine and diesel-powered systems of distributed generation type of electrical energy in Covenant University, Ota, Nigeria, a smart university campus driven by Information and Communication Technologies (ICT). Cumulative monthly data of the energy generation costs, for consumption in the institution, from the two modes electric power, which was produced at locations closed to the community consuming the energy, were recorded for the period spanning January to December 2017. By these, energy generation costs from the turbine system proceed from the gas-firing whereas the generation cost data from the diesel-powered generator also include data on maintenance cost for this mode of electrical power generation. These energy generation cost data that were presented in tables and graphs employ descriptive probability distribution and goodness-of-fit tests of statistical significance as the methods for the data detailing and comparisons. Information details from this data of energy generation costs are useful for furthering research developments and aiding energy stakeholders and decision-makers in the formulation of policies on energy generation modes, economic valuation in terms of costing and management for attaining energy-efficient/smart educational environment.
Keywords: Smart campus, Energy consumption, Energy efficiency, Load forecasting, Energy management, Learning analytics, Nigerian university, Education data mining
Specifications Table
| Subject area | Engineering |
| More specific subject area | Electrical Engineering, Mechanical Engineering, Engineering Economics, Engineering Physics |
| Type of data | Tables, graphs, figures and spreadsheet files |
| How data was acquired | Monitoring, logging in records and cumulated for each month of the year |
| Data format | Raw, analyzed |
| Experimental factors | Data monitoring and logging were performed manually rather than being automated |
| Experimental features | Ordered statistics was employed in combination with cumulative distribution fitting analyses, Kolmogorov-Smirnov goodness-of-fit statistics (K-S GoF) was employed for test-of-significance of the data distribution fitting |
| Data source location | The dataset of energy generation cost provided in this article were collected at Covenant University, Canaanland, Ota, Nigeria (Latitude6.6718°N, Longitude3.1581°E) |
| Data accessibility | A comprehensive dataset of energy generation cost is provided in this article. |
Value of the data
-
•
Accessibility to datasets of energy generation cost of a distributed generation system of electrical energy production using gas-fired and diesel engine generators that could be used for fostering systems of practical data-driven research in the understanding of energy cost modeling valuations and how this can be improved towards efficient integration of energy generation for a smart university campus [1], [2], [3], [4], [5].
-
•
Costs of energy generation data that could be employed for energy generation planning in the development of new energy generation plants as well as in the decision making of how to combine energy generation systems for electricity consumers in a smart university campus [6], [7].
-
•
Availability of energy generation costs that could be used for estimations of energy generation cost parameters and cost concepts, such as levelised cost of electric energy, society cost of electric energy, returns on investment on energy generating plants, projection of future energy costs for budget purposes, for energy stakeholders and decision-makers of energy production in a smart university campus [8], [9], [10].
-
•
Applicability and/or developmental prospects of Smart Electrical Energy Network (SEEN) for a stronger, more sustainable controls of centralized distributed generation of electric energy system via systems of the electric energy generation costing for a smart university campus [3], [4], [11].
1. Data
Although rising energy demand remains a global issue, it is still a well-known fact that a large and increasing portion of the populace in a developing country like Nigeria has no access to the national grid generated electric power [12], [13], [14], [15], [16].Therefore, for many establishments and households in the country, resorting to off-grid self-generation of electric energy remains the only option for maintaining comfortable working and living environments. In a smart university campus, for instance, adequate electricity generation is required for ensuring comfortable indoor conditions for staffs and students in the office spaces, classrooms, as well as in the staffs and students accommodations [17]. Apart from these, sustainable educational processes necessitate that the electric load requirements of facilities, equipment and support services of the smart university community be satisfied to acceptable standards [5]. In meeting such a demand, the self-generation of electricity will attract an additional cost of educational service rendering which definitely requires innovative approach for keeping such cost of electric energy generation from being exorbitant or unaffordable. Therefore, data that is being made available to the public in the present paper is constituted of pertinent information on the electric energy generation cost from distributed energy requirement in Covenant University, Ota, Nigeria; an ICT-driven smart university campus. The data were monitored and compiled for 12 consecutive months spanning January-December, 2017, from gas-fired turbine system and diesel-fueled electric-generating units, used for backup applications that ensure continuous electric energy generation for meeting the energy needs of the university campus [3], [18], [19].
Table 1 contains data of electric energy generated for consumers in the university campus and the cost of generating the electric energy using the gas-fired turbine tower and the diesel-fuel generators on campus. Also included in the table are the running hours of employing the diesel fuel unit as backups, the volume, in liters (L), of the diesel fuel consumed for every month in the year 2017, as well as the summation of all the data presented in the table for the year.
Table 1.
Electric energy generation data at Covenant University for the Year 2017.
| S/N | Month | Days Monitored (day) | Time (h) | Electric energy (kW h) | Maintenance Cost (€) |
Gas-fired Turbine |
Diesel-fired Generator |
||
|---|---|---|---|---|---|---|---|---|---|
| Cost (€) | Diesel Cost (€) | Diesel (L) | Running time (h) | ||||||
| 1 | Jan | 31 | 744 | 906,296.00 | 0.00 | 64,953.98 | 30,241.78 | 84,754.80 | 96.52 |
| 2 | Feb | 28 | 672 | 726,964.74 | 0.00 | 57,159.76 | 74,213.23 | 207,988.00 | 234.35 |
| 3 | Mar | 31 | 744 | 1,225,867.80 | 0.00 | 131,607.46 | 39,406.64 | 110,440.00 | 125.83 |
| 4 | Apr | 30 | 720 | 1,374,884.00 | 0.00 | 162,524.20 | 9140.46 | 25,616.80 | 26.27 |
| 5 | May | 31 | 744 | 1,130,266.50 | 0.00 | 127,546.57 | 8791.92 | 24,640.00 | 26.05 |
| 6 | Jun | 30 | 720 | 652,749.00 | 6553.62 | 71,632.81 | 4433.64 | 12,425.60 | 14.20 |
| 7 | Jul | 31 | 744 | 675,810.20 | 0.00 | 75,077.52 | 3202.77 | 8976.00 | 10.33 |
| 8 | Aug | 31 | 744 | 565,981.50 | 1930.99 | 73,950.10 | 19,467.82 | 54,560.00 | 62.63 |
| 9 | Sep | 30 | 720 | 974,627.00 | 0.00 | 112,526.22 | 13,187.88 | 36,960.00 | 42.05 |
| 10 | Oct | 31 | 744 | 828,387.17 | 1090.90 | 87,229.96 | 3921.40 | 10,990.00 | 14.15 |
| 11 | Nov | 30 | 720 | 712,010.00 | 743.18 | 48,545.70 | 12,310.12 | 34,500.00 | 50.00 |
| 12 | Dec | 22a | 528 | 409,340.00 | 79,721.60 | 27,909.29 | 16,815.62 | 47,127.00 | 68.05 |
22 days were monitored in December 2017, after which the university proceeded to end-of-year break.
Directly proceeding from Table 1 includes further data of running time (h) of the gas-fired turbine and the electric energy generation cost per hour (€/h) for the period spanning January-December 2017 was estimated and these parameters are respectively presented in Fig. 1(a) and (b).
Fig. 1.
Plots for the monthly period of the year 2017 of estimated data in Covenant University, Ota, Nigeria, of (a) running time of gas-fired turbine generator, and (b) electric energy generation cost per hr.
2. Experimental design, materials and methods
As detailed, the electric energy generation cost data were monitored and cumulated for each month of the year 2017, at Covenant University, Ota, Nigeria, as a part of continuous data sourcing and management in the smart university campus. The generation of the distributed electric energy is required for the maintenance of conducive/comfortable education and working environments that promote the well-being of students and staffs in the fully residential university [5], [17]. For aiding understanding and reusability of the electricity generation cost data, analyses of each variable for the cost data, presented in Table 1 and in Fig. 1 are conducted using applications of ordered statistics and cumulative distribution function models of the Normal, Weibull and Gumbel distributions. For these cumulative distribution function analyses, which require estimations, especially, of the shape and scale parameters of the Weibull distribution and the location and scale parameters of the Gumbel distribution models [12], [20], [21], maximum likelihood estimation, as detailed in [22], [23], [24], [25], [26], were employed. These analyses gave the data of cumulative distribution fittings of the Normal, Weibull and Gumbel probability distributions which are presented in Fig. 2, for timeframes, Fig. 3 for operation and maintenance and Fig. 4 for effects and materials ensuing from the electric energy generation cost data.
Fig. 2.
Analyzed cumulative distribution functions of timeframes for electricity generation cost data.
Fig. 3.
Analyzed cumulative distribution functions of operations and maintenance requirements for electricity generation cost data.
Fig. 4.
Analyzed cumulative distribution functions of energy effects from the generation cost data.
The cumulative distribution function analyses of the energy generation cost data necessarily lead to the estimations of the descriptive statistics parameters to which was combined the testing of significance of the compatibility of the electric energy generation cost data to each of the statistical distributions [27], [28], [29], [30], [31]. For the distribution fitting compatibility testing, the Kolmogorov-Smirnov goodness-of-fit statistics [31], [32], [33], [34], [35] was used. Data from the descriptive statistics and the K-S p-value, i.e. the probability value obtained from the Kolmogorov-Smirnov goodness-of-fit statistical analyses, are presented in Table 2 for the Normal, Table 3 for the Weibull and Table 4 for the Gumbel distributions.
Table 2.
Descriptive statistics of electrical energy generation cost data using the Normal distribution.
| Energy generation cost parameter | Normal Mean (μN) | Normal Standard Deviation (σN) | Probability(µN) | K-S p-value |
|---|---|---|---|---|
| Days Monitored (day) | 29.67 | 2.57 | 0.5 | 0.0414 |
| Time (h) | 712.00 | 61.69 | 0.5 | 0.0414 |
| Electric energy (kW h) | 848,598.66 | 284,884.43 | 0.5 | 0.9362 |
| Maintenance cost (€) | 7,503.36 | 22,820.15 | 0.5 | 0.0145 |
| Cost (€) | 86,721.96 | 39,177.89 | 0.5 | 0.6755 |
| Gas-fired turbine running time (h) | 647.80 | 98.79 | 0.5 | 0.3535 |
| Diesel cost (€) | 19,594.44 | 20,362.86 | 0.5 | 0.3680 |
| Diesel (L) | 54,914.85 | 57,068.40 | 0.5 | 0.3680 |
| Diesel-fired generator running time (h) | 64.20 | 64.18 | 0.5 | 0.5081 |
| Energy cost/hr (€/h) | 161.91 | 54.49 | 0.5 | 0.4730 |
Table 3.
Descriptive statistics of electrical energy generation cost data using the Weibull distribution.
| Energy generation cost parameter | Weibull Mean (μW) | Weibull Standard Deviation (σW) | Probability(µW) | K-S p-value |
|---|---|---|---|---|
| Days Monitored (day) | 29.75 | 1.66 | 0.4413 | 0.1000 |
| Time (h) | 713.93 | 39.88 | 0.4413 | 0.1000 |
| Electric energy (kW h) | 844,736.28 | 308,508.43 | 0.5098 | 0.9616 |
| Maintenance cost (€) | 3,107,591,209.09 | 7,883,020,667,225.09 | 0.9960 | 0.0913 |
| Cost (€) | 86,813.00 | 41,644.57 | 0.5349 | 0.8857 |
| Gas-fired turbine running time (hr) | 648.82 | 82.44 | 0.4567 | 0.4919 |
| Diesel cost (€) | 20,635.11 | 20,925.01 | 0.6343 | 0.9207 |
| Diesel (L) | 57,831.41 | 58,643.89 | 0.6343 | 0.9207 |
| Diesel-fired generator running time (h) | 67.44 | 66.94 | 0.6310 | 0.9472 |
| Energy cost/hr (€/h) | 161.50 | 57.61 | 0.5079 | 0.6405 |
Table 4.
Descriptive statistics of electrical energy generation cost data using the Gumbel distribution.
| Energy generation cost parameter | Gumbel Mean (μG) | Gumbel Standard Deviation (σG) | Probability(µG) | K-S p-value |
|---|---|---|---|---|
| Days Monitored (day) | 30.50 | 5.10 | 0.5704 | 0.0371 |
| Time (h) | 731.91 | 122.47 | 0.5704 | 0.0371 |
| Electric energy (kW h) | 869,413.46 | 335,572.30 | 0.5704 | 0.9857 |
| Maintenance cost (€) | 5,870.67 | 10,077.30 | 0.5704 | 0.0131 |
| Cost (€) | 89,316.12 | 45203.28 | 0.5704 | 0.9055 |
| Gas-fired turbine running time (h) | 668.46 | 163.16 | 0.5704 | 0.2329 |
| Diesel cost (€) | 19,196.39 | 16,305.12 | 0.5704 | 0.8451 |
| Diesel (L) | 53,799.29 | 45,696.30 | 0.5704 | 0.8451 |
| Diesel-fired generator running time (h) | 63.43 | 53.52 | 0.5704 | 0.9622 |
| Energy cost/hr (€/h) | 165.23 | 63.69 | 0.5704 | 0.7182 |
Acknowledgements
This work is carried out under the IoT-Enabled Smart and Connected Communities (SmartCU). The research is fully sponsored by Covenant University Centre for Research, Innovation and Discovery (CUCRID), Covenant University, Ota, Nigeria. We also acknowledge the physical planning and development unit of Covenant University, Ota, Nigeria, for their assistance and supports in the course of this research.
Footnotes
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2018.02.022.
Transparency document. Supplementary material
Supplementary material.
.
References
- 1.Adefarati T., Papy N.B., Thopil M., Tazvinga H. In: Non-renewable Distributed Generation Technologies: A Review. Bansal R., editor. Springer International Publishing AG, Cham; Switzerland: 2017. [Google Scholar]
- 2.Greer M. Academic Press, Elsevier Inc.; Oxford, UK: 2011. Electricity Cost Modeling Calculations. [Google Scholar]
- 3.Chicco G., Mancarella P. Characterization and planning of distributed multi-generation plants. In: Lefebvre C.M., editor. Electric Power: Generation, Transmission and Efficiency. Nova Science Publishers, Inc; New York: 2007. pp. 17–73. [Google Scholar]
- 4.Farret F.A., Simoes M.G. John Wiley & Sons, Inc; Hoboken, New Jersey: 2006. Integration of Alternative Sources of Energy. [Google Scholar]
- 5.Popoola S.I., Atayero A.A., Okanlawon T.T., Omopariola B.I., Takpor O.A. Smart campus: data on energy consumption in an ICTdriven university. Data Brief. 2018;16:780–793. doi: 10.1016/j.dib.2017.11.091. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Greer M. Elsevier Inc; Oxford, UK: 2012. Electricity Marginal Cost Pricing: applications in Eliciting Demand Responses. [Google Scholar]
- 7.Nuclear Energy Agency (NEA), International Energy Agency (IEEA), Organisation for Economic Co-Operation and Development (OECD), Projected Costs of Generating Electricity . OECD; Paris, France: 2005. 2005 Update. [Google Scholar]
- 8.Roche M.Y., Ude N., Donald-Ofoegbu I. Nigerian Economic Summit Group and Heinrich Böll Stiftung Nigeria; Abuja: 2017. True Cost of Power Technical Report: Comparison of Costs of Electricity Generation in Nigeria. [Google Scholar]
- 9.Benes K.J., Augustin C. Beyond LCOE: a simplified framework for assessing the full cost of electricity. Electr. J. 2016;29:48–54. [Google Scholar]
- 10.Popoola S.I., Atayero A.A., Badejo J.A., John T.M., Odukoya J.A., Omole D.O. Learning analytics for smart campus: data on academic performances of engineering undergraduates in Nigerian private university. Data Brief. 2018;17:76–94. doi: 10.1016/j.dib.2017.12.059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Strzelecki R., Benysek G. Springer-Verlag London Limited; 2008. Power Electronics in Smart Electrical Energy Networks. [Google Scholar]
- 12.Okeniyi J.O., Ohunakin O.S., Okeniyi E.T. Assessments of wind-energy potential in selected sites from three geopolitical zones in Nigeria: implications for renewable/sustainable rural electrification. Sci. World J. 2015;2015:1–13. doi: 10.1155/2015/581679. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Okeniyi J.O., Anwan E.U., Okeniyi E.T. Waste characterisation and recoverable energy potential using waste generated in a model community in Nigeria. J. Environ. Sci. Technol. 2012;5:232–240. [Google Scholar]
- 14.Fagbenle R.O., Katende J., Ajayi O.O., Okeniyi J.O. Assessment of wind energy potential of two sites in North-East, Nigeria. Renew. Energy. 2011;36:1277–1283. [Google Scholar]
- 15.Mehrzad E., Masoud A., Mansour E. Socioeconomic factors affecting household energy consumption in Qom, Iran. J. Appl. Sci. 2007;7:2876–2880. [Google Scholar]
- 16.Elhadidy M.A., Shaahid S.M. Wind resource assessment of eastern coastal region of Saudi Arabia. Desalination. 2007;209:199–208. [Google Scholar]
- 17.Dimitrov A.V. CRC Press, Taylor & Francis Group; Boca Raton, FL: 2017. Introduction to Energy Technologies for Efficient Power Generation. [Google Scholar]
- 18.Breeze P. Academic Press, Elsevier Ltd; Oxford, UK: 2016. Gas-Turbine Fired Generation. [Google Scholar]
- 19.Brunet Y. Energy storage: applications to the electricity vector. In: Sabonnadière J.-C., editor. Low Emission Power Generation Technologiesand Energy Management. John Wiley & Sons, Inc; Hoboken, NJ: 2009. pp. 1–49. [Google Scholar]
- 20.Okeniyi J.O., Loto C.A., Popoola A.P.I. Corrosion inhibition performance of Rhizophora mangle L bark-extract on concrete steel-reinforcement in industrial/microbial simulating-environment. Int. J. Electrochem. Sci. 2014;9:4205–4216. [Google Scholar]
- 21.Bollen M.H., Hassan F. IEEE Press and John Wiley & Sons, Inc.; Hoboken, New Jersey: 2011. Integration of Distributed Generation in the Power System. [Google Scholar]
- 22.J.O. Okeniyi, A.O. Abioye, Z.C. Adikpewun, A.A. Otesanya, M.D. Eleshin, O.O. Gabriel, O. Adeoye, Effect of C5H11NO2S on reinforcing-steel corrosion in concrete immersed in industrial/microbial simulating-environment, in: Proceedings of the 3rd Pan American Materials Congress, Springer International Publishing, Cham, Switzerland, 2017, pp. 191–203.
- 23.Okeniyi J.O. C10H18N2Na2O10 inhibition and adsorption mechanism on concrete steel-reinforcement corrosion in corrosive environments. J. Assoc. Arab Univ. Basic Appl. Sci. 2016;20:39–48. [Google Scholar]
- 24.Montgomery D.C., Runger G.C. Applied Statistics and Probability for Engineers. 6 Edition. John Wiley & Sons, Inc; Hoboken, NJ: 2014. [Google Scholar]
- 25.Okeniyi J.O., Ambrose I.J., Okpala S.O., Omoniyi O.M., Oladele I.O., Loto C.A., Popoola P.A.I. Probability density fittings of corrosion test-data: implications on C6H15NO3 effectiveness on concrete steel-rebar corrosion. Sadhana. 2014;39:731–764. [Google Scholar]
- 26.Okeniyi J.O., Loto C.A., Popoola A.P.I. Electrochemical performance of Anthocleista djalonensis on steel-reinforcement corrosion in concrete immersed in saline/marine simulating-environment. Trans. Indian Inst. Met. 2014;67:959–969. [Google Scholar]
- 27.Okeniyi J.O., Moses I.F., Okeniyi E.T. Wind characteristics and energy potential assessment in Akure, South West Nigeria: econometrics and policy implications. Int. J. Ambient Energy. 2015;36:282–300. [Google Scholar]
- 28.Ajayi O.O., Fagbenle R.O., Katende J., Aasa S.A., Okeniyi J.O. Wind profile characteristics and turbine performance analysis in Kano, north-western Nigeria. Int. J. Energy Environ. Eng. 2013;4:27. [Google Scholar]
- 29.Ajayi O.O., Fagbenle R.O., Katende J., Okeniyi J.O. Availability of wind energy resource potential for power generation at Jos, Nigeria. Front. Energy. 2011;5:376–385. [Google Scholar]
- 30.Ajayi O.O., Fagbenle R.O., Katende J., Okeniyi J.O., Omotosho O.A. Wind energy potential for power generation of a local site in Gusau, Nigeria. Int. J. Energy Clean. Environ. 2010;11:99–116. [Google Scholar]
- 31.Ogunnaike B.A. CRC Press, Taylor & Francis Group; Boca Raton, FL: 2010. Random Phenomena: Fundamentals of Probability and Statistics for Engineers. [Google Scholar]
- 32.Okeniyi J.O., Okeniyi E.T., Atayero A.A. Programming development of Kolmogorov-Smirnovgoodness-of-fittesting of data normality as a Microsoft Excel® Library Function. J. Softw. Syst. Dev. 2015;2015:238409. [Google Scholar]
- 33.J.O. Okeniyi, E.T. Okeniyi, A.A. Atayero, Implementation of data normality testing as a Microsoft Excel® library function by Kolmogorov-Smirnov goodness-of-fit statistics, in: Proceedings of the 23rd International Business Information Management Association Conference (Vision 2020: Sustainable Growth, Economic Development, and Global Competitiveness), IBIMA 2014, International Business Information Management Association, IBIMA, Vol. 1, 2014, pp. 2561–2578.
- 34.Okeniyi J.O., Okeniyi E.T. Implementation of Kolmogorov–Smirnov p-value computation in Visual Basic®: implication for Microsoft Excel® library function. J. Stat. Comput. Simul. 2012;82:1727–1741. [Google Scholar]
- 35.Soong T.T. John Wiley & Sons; Chichester, West Sussex, England: 2004. Fundamentals of Probability and Statistics for Engineers. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material.