DETERMINATION OF THE OPTIMUM LOAD PROFILE UNDER ENHANCED OF USE TARIFF (ETOU) SCHEME USING COMBINATION OF OPTIMIZATION ALGORITHMS AND SELF ORGANIZING MAPPING
DOI:
https://doi.org/10.11113/aej.v12.17324Keywords:
Demand Side Management, Optimization Algorithms, Demand Response, Electricity Cost, Time of Use TariffAbstract
Demand side management (DSM) has been conventionally adopted in many ways to efficiently managing the appropriate electricity loads. However, with the sophisticated design of the Time of Use (TOU) tariff to reflect electricity cost reduction, implementing proper Load Management (LM) strategies is challenging. To date, consumers still struggle to define a figure for the LM percentage to be involved in the demand response program. Due to that reason, this study proposes a method to find the best load profile reflecting the new tariff offered by using a combination of optimization algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Evolutionary PSO (EPSO), and Self-Organizing Mapping (SOM). The evaluation has been made to the manufacturing operation with the existing flat tariff to be transferred to the Enhanced Time of Use (ETOU). The test results show that the ability of the proposed combination method to define the optimal outputs such as energy consumption cost, maximum demand cost, load factor index, and building electricity economic responsive index. Meanwhile, the SOM algorithm has been used to classify the enormous numbers of those simulation results produced by algorithms while defining the best LM weightage. As the test results for the case study, it was found that the practical 6% LM weightage was able to reflect the optimal required load profile shifting to be applied by manufacturing operation. Thus, by determining the optimal load profile that suits the ETOU scheme, the consumers can enjoy cost benefits while supporting the demand response program concurrently.
References
Tenaga, S. 2017. Laporan Tahunan Suruhanjaya Tenaga 2016. Malaysia, ST (P), 15(08): 2017.
Azman, N.A.M., Abdullah, M.P., Hassan, M.Y., Said, D.M. and Hussin, F. 2017. Enhanced time of use electricity pricing for commercial customers in Malaysia. Pertanika Journal of Science & Technology, 25: 285-294.
Azman, N.A.M., Abdullah, M.P., Hassan, M.Y., Said, D.M. and Hussin, F. 2017. Enhanced time of use electricity pricing for industrial customers in Malaysia. Indonesian Journal of Electrical Engineering and Computer Science, 6(1): 155-160.
Sulaima, M.F., Dahlan, N.Y., Isa, M.H., Othman, M.N., Yasin, Z.M. and Kasdirin, H.A. 2019. ETOU electricity tariff for manufacturing load shifting strategy using ACO algorithm. Bulletin of Electrical Engineering and Informatics. 8(1): 21-29.
Sulaima, M.F., Dahlan, N.Y., Yasin, Z.M., Asari, N.A.M. and Bohari, Z.H., 2017. Optimum enhance time of use (ETOU) for demand side electricity pricing in regulated market: An implementation using evolutionary algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 8(1): 253-261.
Sulaima, M.F., Dahlan, N.Y., Yasin, Z.M., Rosli, M.M., Omar, Z. and Hassan, M.Y. 2019. A review of electricity pricing in peninsular Malaysia: Empirical investigation about the appropriateness of Enhanced Time of Use (ETOU) electricity tariff. Renewable and Sustainable Energy Reviews, 110: 348-367.
Ding, J.Y., Song, S., Zhang, R., Chiong, R. and Wu, C. 2015. Parallel machine scheduling under time-of-use electricity prices: New models and optimization approaches. IEEE Transactions on Automation Science and Engineering. 13(2): 1138-1154.
Ding, J.Y., Song, S., Zhang, R., Chiong, R. and Wu, C. 2015. Parallel machine scheduling under time-of-use electricity prices: New models and optimization approaches. IEEE Transactions on Automation Science and Engineering. 13(2): 1138-1154.
Wang, Y. and Li, L. 2013. Time-of-use based electricity demand response for sustainable manufacturing systems. Energy. 63: 233-244. DOI : https://doi.org/10.1016/j.energy.2013.10.011
Cheng, J., Chu, F., Liu, M., Wu, P. and Xia, W. 2017. Bi-criteria single-machine batch scheduling with machine on/off switching under time-of-use tariffs. Computers & Industrial Engineering, 112: 721-734. DOI: https://doi.org/10.1016/j.cie.2017.04.026
Ashok, S. 2006. Peak-load management in steel plants. Applied Energy, 83(5): 413-424. DOI : https://doi.org/10.1016/j.apenergy.2005.05.002
Ashok, S. and Banerjee, R. 2000. Load-management applications for the industrial sector. Applied Energy, 66(2): 105-111. DOI: https://doi.org/10.1016/S0306-2619(99)00125-7
Yang, P., Tang, G. and Nehorai, A. 2012. A game-theoretic approach for optimal time-of-use electricity pricing. IEEE Transactions on Power Systems, 28(2): 884-892.
Tan, Z.F., Wang, M.B., Qi, J.X., Hou, J.C. and Xue, L.I. 2008. Time-of-use price optimizing model and fuzzy solving method. Systems Engineering-Theory & Practice, 28(9): 145-151. DOI: https://doi.org/10.1016/S1874-8651(09)60039-9
Jota, P.R., Silva, V.R. and Jota, F.G. 2011. Building load management using cluster and statistical analyses. International Journal of Electrical Power & Energy Systems, 33(8): 1498-1505.
Lara, R.A., Pernigotto, G., Cappelletti, F., Romagnoni, P. and Gasparella, A. 2015. Energy audit of schools by means of cluster analysis. Energy and Buildings, 95: 160-171. DOI: https://doi.org/10.1016/j.enbuild.2015.03.036
Boudet, H.S., Flora, J.A. and Armel, K.C. 2016. Clustering household energy-saving behaviours by behavioural attribute. Energy Policy, 92: 444-454. DOI: https://doi.org/10.1016/j.enpol.2016.02.033
Schütz, T., Schraven, M.H., Fuchs, M., Remmen, P. and Müller, D. 2018. Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis. Renewable Energy, 129: 570-582. DOI: https://doi.org/10.1016/j.renene.2018.06.028
Aiad, M. and Lee, P.H. 2018. Energy disaggregation of overlapping home appliances consumptions using a cluster splitting approach. Sustainable cities and society, 43: 487-494. DOI: https://doi.org/10.1016/j.scs.2018.08.020
Kohonen, T. and Somervuo, P. 1998. Self-organizing maps of symbol strings. Neurocomputing, 21(1-3): 19-30.
Kohonen, T. 2013. Essentials of the self-organizing map. Neural networks, 37: 52-65. DOI: https://doi.org/10.1016/j.neunet.2012.09.018
Xu, X.S., Liang, X.L., Yang, G.Q., Wang, X.L., Guo, S. and Shi, Y. 2017. SOMH: A self-organizing map based topology preserving hashing method. Neurocomputing, 236: 56-64. DOI: https://doi.org/10.1016/j.neucom.2016.08.102
McLoughlin, F., Duffy, A. and Conlon, M. 2015. A clustering approach to domestic electricity load profile characterisation using smart metering data. Applied energy, 141: 190-199. DOI: https://doi.org/10.1016/j.apenergy.2014.12.039
Bohari, Z.H., Azemy, H.S., Nasir, M.N.M., Baharom, M.F., Sulaima, M.F. and Jali, M.H. 2015. Reliable short term load forecasting using self organizing map (SOM) in deregulated electricity market. Journal of Theoretical and Applied Information Technology, 79(3): 389.
Llanos, J., Morales, R., Núñez, A., Sáez, D., Lacalle, M., Marín, L.G., Hernández, R. and Lanas, F. 2017. Load estimation for microgrid planning based on a self-organizing map methodology. Applied Soft Computing, 53: 323-335. DOI: https://doi.org/10.1016/j.asoc.2016.12.054
Jamian, J.J., Mustafa, M.W., Mokhlis, H. and Abdullah, M.N. 2012, February. Comparative study on distributed generator sizing using three types of particle swarm optimization. In 2012 third international conference on intelligent systems modelling and simulation. 131-136. IEEE.
Jamian, J.J., Mustafa, M.W. and Mokhlis, H. 2015. Optimal multiple distributed generation output through rank evolutionary particle swarm optimization. Neurocomputing, 152: 190-198.
Lin, X., Tian, Z., Lu, Y., Zhang, H. and Niu, J. 2019. Short-term forecast model of cooling load using load component disaggregation. Applied Thermal Engineering, 157: 113630. DOI: https://doi.org/10.1016/j.applthermaleng.2019.04.040
Bohari, Z.H., Ab Ghani, S., Baharom, M.F., Nasir, M.N.M., Jali, M.H. and Thayoob, Y.M. 2014. Feature analysis of numerical calculated data from Sweep Frequency Analysis (SFRA) traces using self organizing maps. Jurnal Teknologi. 67(3): 37-42
Sulaima M.F., Jali M.H., Bohari Z.H., Baharom M.F., and Baharin N. 2017. Evaluation of power distribution network system by using Self-Organizing Map (SOM) as the best practice for buses characteristic classification,” Journal of Theoretical and Applied Information Technology, 95(20): 5415-5421
Bohari, Z.H., Azemy, H.S., Nasir, M.N.M., Baharom, M.F., Sulaima, M.F. and Jali, M.H. 2015. Reliable short term load forecasting using self organizing map (SOM) in deregulated electricity market. Journal of Theoretical and Applied Information Technology, 79(3): 389.
