Economic Dispatch of Electrical Power in South Africa: An Application to the Northern Cape Province
Abstract
Power utility companies rely on forecasting for the operation of electricity demand. This presents an applicationof linear quantile regression, non-linear quantile regression, and additive quantile regression models for forecasting extreme electricity demand at peak hours such as 18:00, 19:00, 20:00 and 21:00 using Northern Cape data for the period 01 January 2000 to 31 March 2014. The selection of variables was done using the least absolute shrinkage and selection operator. Additive quantile regression models were found to be the best fitting models for hours 18:00, and 19:00, whereas linear quantile regression models were found to be the best fitting models for hours 20:00, and 21:00. Out of sample forecasts for seven days (01 to 07 April 2014) were used to solve the unit commitment problem using mixed-integer programming. The unit commitment problem results showed that it is less costly to use all the generating units such as hydroelectric, wind power, concentrated solar power and solar photovoltaic. The main contribution of this study is in the development of models for forecasting hourly extreme peak electricity demand. These results could be useful to system operators in the energy sector who have to maintain the minimum cost by scheduling and dispatching electricity during peak hours when the grid is constrained due to peak load demand.References
E. Arriagada, E. Lopez, M. Lpez, R. Blasco-Gimenez, C. Roa, and M. Poloujadoff, A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties, Electric Power Systems Research, vol. 121, pp. 325–332, 2015.
F.N. Al Farsi, M.H. Albadi, N. Hosseinzadeh, and A.H. Al Badi, Economic dispatch in power systems, In 2015 IEEE 8th GCC Conference & Exhibition, pp. 1–6, 2015.
S. Fan, and R.J. Hyndman, Short term load forecasting based on a semiparametric additive model, IEEE Trans Power Syst 2012, vol. 27, no. 1, pp. 134–41, 2012.
A. Pierrot, and Y. Goude, Short-term electricity load forecasting with generalized additive models, Proc ISAP Power 2011, pp. 593–600, 2011.
F. Cavallaro, Electric load analysis using an artificial neural network, International journal of energy research, vol. 29, no. 5, pp. 377–392, 2005.
M. Mohandes, Support vector machines for short-term electrical load forecasting, International Journal of Energy Research, vol. 26, no. 4, pp. 335–345, 2002.
G. Singh, D.S. Chauhan, A. Chandel, D. Parashar, and G. Sharma, Factor affecting elements and short term load forecasting based on multiple linear, In International Journal of Engineering Research and Technology, vol. 3, 2014.
Y. Goude, R. Nedellec, and N. Kong, Local short and middle term electricity load forecasting with semi-parametric additive models, IEEE transactions on smart grid, vol. 5, no. 1, pp. 440–446, 2014.
N. Elamin, Quantile regression model for peak load demand forecasting with approximation by triangular distribution to avoid blackouts, Annals of Statistics, pp. 65–81, 2018.
T. Hong, and S. Fan, Probabilistic electric load forecasting: A tutorial review, International Journal of Forecasting, vol. 32, no. 3, pp. 914–938, 2016.
E.A. Feinberg, and D. Genethliou, Load forecasting, In Applied mathematics for restructured electric power systems, pp. 269–285, 2005.
R.J. Hyndman, and S. Fan, Density forecasting for long-term peak electricity demand, IEEE Transactions on Power Systems, vol. 25, no. 2, pp. 1142–1153, 2010.
Y. Kim, H.G. Son, and S. Kim, Short term electricity load forecasting for institutional buildings, Energy Reports, vol. 5, pp. 1270–1280, 2019.
N. Maswanganyi, C. Sigauke, and E. Ranganai, Peak electricity demand forecasting using partially linear additive quantile regression models, In Annual Proceedings of the South African Statistical Association Conference (Vol. 2017, No. Congress 1, pp. 25–32), 2017. South African Statistical Association (SASA).
C. Sigauke, and D. Chikobvu, Short-term peak electricity demand in South Africa, African Journal of Business Management, vol. 6, no. 32, pp. 9243–9249, 2012.
C. Sigauke, A. Verster, and D. Chikobvu, Extreme daily increases in peak electricity demand: Tail-quantile estimation, Energy Policy, vol. 59, pp. 90–96, 2013.
C. Sigauke, M.M. Nemukula, and D. Maposa, Probabilistic hourly load forecasting using additive quantile regression models, Energies, vol. 11, no. 9, 2208, 2018.
M.Q. Raza, M. Nadarajah, and C. Ekanayake, On recent advances in PV output power forecast, Solar Energy, vol. 136, pp. 125–144,2016.
B. Saravanan, S. Das, S. Sikri, and D.P. Kothari, A solution to the unit commitment problemła review, Frontiers in Energy, vol. 7, no. 2, pp. 223–236, 2013.
H. Dai, N. Zhang, and W. Su, A literature review of stochastic programming and unit commitment, Journal of Power and Energy Engineering, vol. 3, no. 04, pp. 206, 2015.
B. Wright, A review of unit commitment, ELENE4511, May, vol. 28, 2013.
T. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli, and R.J. Hyndman, Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond 2016.
M. Kurban, and U.B. Filik, Unit commitment scheduling by using the autoregressive and artificial neural network models based short-term load forecasting, Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems, pp. 1–5, 2008.
R. Koenker, and G. Bassett Jr, Regression quantiles, Econometrica: journal of the Econometric Society, pp. 33–50, 1978.
P. Lauret, M. David, and H.T. Pedro, Probabilistic solar forecasting using quantile regression models, energies, vol. 10, no. 10, 1591, 2017.
C. Hwang, and J. Shim, A simple quantile regression via support vector machine, International Conference on Natural Computation, Springer, Berlin, Heidelberg, pp. 512–520, 2005.
M. Conde Amboage, W. Gonzlez Manteiga, and C.A. Snchez Sellero, Quantile regression: estimation and lack-of-fit tests, BEIO, Bolet´ın de Estad´ıstica e Investigación Operativa, vol. 34, no. 2, pp. 97–116, 2018.
R. Koenker, Quantile regression: 40 years on, Annual Review of Economics, vol. 9, pp. 155–176, 2017.
P. Gaillard, Y. Goude, and R. Nedellec, Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting, International Journal of forecasting, vol. 32, no. 3, pp. 1038–1050, 2016.
M. Fasiolo, Y. Goude, R. Nedellec, and S.N. Wood, Fast calibrated additive quantile regression, https://arxiv.org/abs/1707.03307, 2017.
M. Fasiolo, S.N. Wood, M. Zaffran, R. Nedellec, and Y. Goude, Fast calibrated additive quantile regression, Journal of the American Statistical Associations, pp. 1–11, 2020.
M. Ghofrani, and A. Suherli, Time series and renewable energy forecasting, Time series analysis and applications, pp. 77–92, 2017.
A.P. Dawid, and P. Sebastiani, Coherent dispersion criteria for optimal experimental design, Annals of Statistics, pp. 65–81, 1999.
J. Aghaei, A. Ahmadi, H.A. Shayanfar, and A. Rabiee, Mixed integer programming of generalized hydro-thermal self scheduling of generating units, Electrical Engineering, vol. 95, no. 2, pp. 109–125, 2013.
M.E. Lebotsa, C. Sigauke, A. Bere, R. Fildes, and J.E. Boylan, Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem, Applied Energy, vol. 222, pp. 104–118, 2018.
Monitoring renewable energy performance of power plants, Tracking progress of 2017 (Issue 11) March 2018 by NERSA, Prepared by: Dr A Gxasheka, Mr H Mokhonoana, Ms R Mokoena.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
