Optimal Determination for Cost of Electric Power Generation and Plant Capacity of Utilities

Desmond Eseoghene IGHRAVWE; Sunday Ayoola OKE

Authors

Desmond Eseoghene IGHRAVWE Department of Mechanical Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomoso
Sunday Ayoola OKE Department of Mechanical Engineering, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos

Keywords:

ARIMA, robust regression, electricity, meta-heuristics, fuzzy logic

Abstract

Optimisation of power generation and plant capacity are of primary significance for the improvement of power supplies, cost decisions and economics. This paper develops a robust predictive model for electric power generation and capacity utilisation and integrates the output from predictive models into a multi-objective model. The optimal solution was determined after comparing the performance of a Real Coding Genetic Algorithm (RCGA), Particle Swarm Optimisation (PSO) and Big-Bang Big-Crunch Algorithm (BB-BC). Testing of the proposed model was carried out using data from a Nigerian electric power generation plant with a capacity of about 5 million Megawatt Hours (MWH) to test the presented methodology. Our findings indicate that the Auto-Regressive Integrated Moving average (ARIMA) model adequately predicts the power generation variables and compares favorably with literature results. The RCGA performed better than the BB-BC and PSO algorithms in terms of the quality of solutions for the proposed model. The outcome of this study suggests that computational complexity can be reduced in the evaluation of variables, yet producing a practical, simple and robust model.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Desmond Eseoghene IGHRAVWE, Department of Mechanical Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomoso

He is interested in optimisation

Sunday Ayoola OKE, Department of Mechanical Engineering, Faculty of Engineering, University of Lagos, Akoka-Yaba, Lagos

Oke lectures in the Department of mechanical Engineering, University of Lagos, Lagos, Nigeria

References

Q Zhang, BC McLellan, T Tezuka and KN Ishihara. An integrated model for long-term power generation planning toward smart electricity systems. Appl. Energ. 2013; 112, 1424-37.

B Greaber, R Spalding-Fecher and B Gonah. Optimising trans-national power generation and transmission investments: A Southern African example. Energ. Pol. 2005; 33, 2337-49.

A Papatopoulos and A Karaqiannidis. Application of the multi-criteria analysis method Electra III for the optimisation of decentralised energy systems. Omega 2008, 36; 766-76.

J Wurg, YS Lim, P Taylor and S Morris. Optimal utilisation of small-scale embedded generators in a developing country: A case study in Malaysia. Ren. Energ. 2011; 36, 2562-72.

AA Bazmi and G Zahedi. Sustainable energy system: Role of optimisation modeling techniques in power generation and supply. Ren. Sust. Energ. Rev. 2011; 15, 3480-500.

MGR Cannel. Carbon sequestration and biomass energy offset: Theoretical, potential and achievable capacities globally, in Europe and the UK. Biomass Bioenerg. 2003; 24, 97-116.

J Mathur, NK Bansal and HJ Wagner. Dynamic energy analysis to assess maximum growth rate in developing power generation capacity: Case study of India. Ener. Pol. 2004; 32, 281-7.

PJ Luickx, LM Helson and WD Dhaeseler. Influence of massive heat-pump introduction on the electricity-generation mix and the GHG effect: Comparison between Belgium, France, Germany and the Netherlands. Ren. Sust. Energ. Rev. 2008; 12, 2140-58.

M Berqqen, E Ljungqren and F Johnson. Biomass co-firing for electricity generation in Poland: Matching supply and co-firing opportunities. Bio. Bio-Energ. 2008; 32, 865-79.

Z Oktay. Investigation of coal-fired power plants in Turkey and a case study: Can plant. Appl. Therm. Eng. 2009; 29, 530-57.

M Shekarchian, M Maglavemmi, TMI Mahlia and Mazandarani. A review on the pattern of electricity and emission in Malaysia from 1976 to 2008. Ren. Sust. Energ. Rev. 2011; 15, 2629-42.

J Mullen, D Harries, T Braunl and S Whitely. Modeling the impacts of electric vehicle recharging on the Western Australian electricity supply system. Energ. Pol. 2011; 39, 4349-59.

A Keane, A Touhy, P Meibom, E Denny, D Flynn, A Mullane and MO Malley. Demand side resources operation on Irish power system with high wind power generation. Energ. Pol. 2011; 39, 2925-34.

AMA Mohammed, A Al-Habaibeli and H Abdo. An investigation into the current utilisation and prospective of renewable energy resources and technologies in Libya. Ren. Energ. 2013; 50, 732-40.

H Gujba, Y Malaqetta and A Azapagic. Power generation scenarios too Nigeria: An environmental and cost assessment. Energ. Pol. 2011; 39, 968-80.

R Ghajar and R Billinton. Utilisation of quantitative reliability concepts in evaluating the marginal outage costs of electric generating system. Relia. Eng. Sys. Saf. 1994; 46, 93-100.

S Augutis, I Zutautaite, U Radziukynas, R Krikstolaitis and S Kadisa. Application of Bayesian method for electrical power system transient stability assessment. 2012; 42, 465-72.

CA Amlabu, JU Agber, CO Onah and SY Mohammed. Electric load forecasting: A case study of the Nigerian power sector. Int. J. Eng. Innovat. Tech. 2013; 2, 23-7.

Y Wurg, F Ronilaya, X Chen and AP Roskilly. Reprint of modeling and simulation of a distributed power generation system with energy storage to meet dynamic household electricity demand. Appl. Therm. Eng. 2013; 53, 312-24.

A Pettinau, F Ferrara and C Amorino. Tecno-economic comparison between different technologies for a CSS power generation plant integrated with sub-bituminous coal mine in Italy. App. Energ. 2012; 99, 32-9.

H Tasdoven, BA Fiedler and V Garaye. Improving electricity efficient in Turkey by addressing illegal electricity efficient in Turkey by addressing illegal electricity consumption: A governance approach. Energ. Pol. 2012; 43, 226-34.

W Liu, H Lund and BV Mathiesen. Large scale integration of wind power into the existing Chinese energy system. Energ. Pol. 2011; 36, 4753-60.

AK Hossain and O Badr. Prospects of renewable energy utilisation for electricity generation in Bangladesh. Rene. Sust. Energ. Rev. 2007; 11, 1617-49.

D Hotza and SCD da Costa. Fuel cells development and hydrogen production from renewable resources in Brazil. Int. J. Hydro. Energ. 2008; 33, 4915-35.

IL Saver, H Tatizama and FAM Saloth. Power quality and energy efficiency assessment and the need for labelling and minimum performance standard of uninterruptible power systems (UPS) in Brazil. Energ. Pol. 2012; 41, 885-92.

J Paska, P Biczel and M Klos. Hybrid power systems: An effective way of utilising primary energy sources. Rene. Energ. 2009; 34, 2414-21.

DP Rao, SV Babu and VS Rao. Feasibility study of a large scale solar power generation system suitable for the arid and semi-arid zones. Sol. Energ. 1981; 27, 313-22.

XF Zheng, YY Yan and K Simpson. A potential candidate for the sustainable and reliable domestic energy generation: Thermoelectric cogeneration system. Appl. Therm. Eng. 2013; 53, 305-11.

C-C Cormos, K Vatopoulos and E Tzimas. Assessment of the consumption of water and construction materials in state of the art fossil fuel power generation technologies involving CO2 capture. Energy 2013; 51, 37-49.

SA Akdaq and O Guler. Evaluation of wind energy investment interest and electricity generation cost analysis for Turkey. Appl. Energ. 2010; 87, 2574-80.

A Meyler, G Kenny and T Quinn. Forecasting Irish Inflation-Technical Paper. Central Bank of Ireland and Financial Services Authority of Ireland, Ireland, 1988, p. 1-48.

R Nochai and T Nochai. ARIMA model for forecasting oil palm price. In: Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications. Universiti Sainis Malaysia, Penang, Malaysia, 2006.

J Fox and S Weisberg. Robust Regression. University of Minnesota, USA, 2013, p. 1-16.

R Andersen. Modern Methods for Robust Regression. SAGE Publications, Thousand Oaks, USA, 2008.

T Ofori, B Ackah and L Ephraim. Statistical models for forecasting road accident injuries in Ghana. Int. J. Res. Environ. Sci. Tech. 2012; 2, 143-9.

TJ Ross. Fuzzy Logic with Engineering Application. 2nd ed. John Wiley and Sons, USA, 2004, 90-119.

OK Erol and I Eksin. A new optimisation method: Big-bang big-crunch. Adv. Eng. Soft. 2006; 37, 106-11.

XS Yang. Firefly algorithms for multimodal optimization. Lecture Notes Comput. Sci. 2009; 5792, 169-78.

S Kirkpartrick, CD Gelett and MP Vecchi. Optimisation by simulated annealing. Science 1983; 220, 621-30.

M Dorigo and LM Gambardella. Ant colony system: A cooperative learning approach to the travelling salesman problem. IEEE Trans. Evol. Comp. 1997; 1, 53-66.

AP Engelbrencht. Computational Intelligence: An Introduction. John Wiley & Sons, USA, 2007.

G Cormier, R Boudreau and S Theriault. Real-coded genetic algorithm for Bragg grating parameter synthesis. J. Opt. Soc. Amer. B 2001; 18, 1771-6.

J Kennedy and R Eberhart. Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Piscataway, USA, 1995, p. 1942-8.

CV Rao and G Yesuratnam. Big-bang and big-crunch and firefly optimization: Application and comparison to optimal power flow with continuous and discrete control variables. Int. J. Elect. Eng. Inform. 2012; 4, 575-83.

S Sakthivel, SA Pandiyan, S Marikani and SK Selvi. Application of big bang big crunch algorithm for optimal power flow problems. Int. J. Eng. Sci. 2013; 2, 41-7.

M Mekidiche, M Belmokaddem and Z Djemma. Weighted additive fuzzy goal programming approach to aggregate production planning. Int. J. Syst. Appl. 2013; 4, 20-9.

Dominion, New 1329 MW Warren Country Power Station, Available at: www.energyonline.com, accessed January 2015.