Implementation of Real Time Pricing Program on Industrial Consumers

Abstract

Demand Response(DR) programs are based on the logic of adjusting the consumption of consumers through incentives or penalties or by changing the price signal. The object of this study is to compare the price advantages by analyzing the invoice reduction rates as a result of the implementation of Real Time Pricing(RTP) program on different consumer types. For this purpose, three sectors with the highest energy consumption in Sanliurfa Organized Industrial Zone have been selected and sample load curves of these sectors have been prepared. Then four representative days are selected based on Turkey's annual energy consumption curve and three DR price signal (RTP, time of use and dynamic time of use) are generated for each representative day. In the last step, the graphs of normal usage and DR usage were plotted with MATLAB code which was designed for DR application. The price advantage obtained in RTP program was compared with the other two DR programs in terms of consumers and representative day. With the implementation of the RTP program, it was observed that the users reduced their electricity bills between 3.79% and 11.04%.

Keywords

• Demand Side Management,Demand Response,Real Time Pricing,Time of Use,Price Elasticity Matrix

Gerçek Zamanlı Fiyatlandırma Programının Sanayi Tüketicileri Üzerinde Uygulanması

Abstract

Talep Müdahalesi(DR) programları, tüketiciye verilecek teşvik veya cezalar yoluyla ya da fiyat sinyalinin değiştirilmesiyle tüketimlerinin ayarlanması mantığına dayanır. Bu çalışmanın amacı, gerçek zamanlı fiyatlandırma(RTP) programının değişik kullanıcı tipleri üzerinde uygulanması sonucu oluşacak fatura azaltım oranlarının analiz edilerek sağladıkları fiyat avantajlarının karşılaştırılmasıdır. Bunun için Şanlıurfa sanayisi özelinde enerji tüketimi en fazla olan 3 sektör belirlenmiş ve bu sektörlere ait örnek yük eğrileri çıkarılmıştır. Daha sonra, Türkiye’nin enerji tüketiminden hareketle yıl içerisinde 4 adet temsili gün belirlenerek her temsili gün için üç DR programına (RTP, Kullanım Zamanı ve Dinamik Kullanım Zamanı) ait fiyat sinyali grafiği belirlenmiştir. Son aşamada ise DR uygulaması için kurgulanan MATLAB kodu ile normal kullanım ve DR kullanımı grafikleri çıkarılmıştır. Gerçek zamanlı fiyatlandırma programında elde edilen fiyat avantajı tüketici ve temsili gün bazında diğer iki DR programı ile karşılaştırılmıştır. Seçilen temsili kullanıcıların RTP programı ile % 3,79-11,04 arasında fatura azaltımı sağladıkları görülmüştür.

Keywords

• Talep Tarafı Katılımı,Talep Müdahalesi,Gerçek Zamanlı Fiyatlandırma,Kullanım Zamanı,Fiyat Esnekliği Matrisi

References

1. [1]Borlase, S., Smart Grids: Advanced Technologies and Solutions Second Edition. 2nd Edition ed, ed. L.L. Grigsby. 2018: CRC Press. 828.
2. [2] Arturo, L., M. Pierluigi, and V. Antonio, Integration of Demand Response into the Electricity Chain. 2015, U.K.: Wiley Inc. 300.
3. [3] Salman, S.K., Introduction to the Smart Grid: Concepts, Technologies and Evolution. IET Energy Engineering Series. 2017, U.K.: The Institution of Engineering and Technology.
4. [4] EPDK, Türkiye Akıllı Şebekeler 2023 Vizyon ve Strateji Belirleme Projesi Sonuç Raporu. 2018, EPDK: Ankara. p. 96.
5. [5] Wang, Y. and L. Li, Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities. Applied Energy, 2015. 149: p. 89-103.
6. [6] Agha, A. and D. Jenkins. Energy analysis of a case-study textile mill by using real-time energy data. in ECEEE INDUSTRIAL SUMMER STUDY PROCEEDINGS. 2014.
7. [7] Azman, N.A.M., et al., Enhanced Time of Use Electricity Pricing for Industrial Customers in Malaysia. Indonesian Journal of Electrical Engineering and Computer Science, 2017. Vol. 6.
8. [8] Zethmayr, J. and D. Kolata, The costs and benefits of real-time pricing: An empirical investigation into consumer bills using hourly energy data and prices. The Electricity Journal, 2018. 31(2): p. 50-57.

9. [9] Wang, G., et al., The impact of social network on the adoption of real-time electricity pricing mechanism. Energy Procedia, 2017. 142: p. 3154-3159.
10. [10] Nezamoddini, N. and Y. Wang, Real-time electricity pricing for industrial customers: Survey and case studies in the United States. Applied Energy, 2017. 195: p. 1023-1037.
11. [11] Shoreh, M.H., et al., A survey of industrial applications of Demand Response. Electric Power Systems Research, 2016. 141: p. 31-49.
12. [12] Granell, R., et al., Power-use profile analysis of non-domestic consumers for electricity tariff switching. 2016.
13. [13] Deng, R., et al., A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches. IEEE Transactions on Industrial Informatics, 2015. 11(3): p. 570-582.
14. [14] Nikmehr, N., S. Najafi-Ravadanegh, and A. Khodaei, Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty. Applied Energy, 2017. 198: p. 267-279.
15. [15] Qu, X., et al., Price elasticity matrix of demand in power system considering demand response programs. IOP Conference Series: Earth and Environmental Science, 2018. 121: p. 052081.
16. [16] Mohajeryami, S., P. Schwarz, and P.T. Baboli. Including the behavioral aspects of customers in demand response model: Real time pricing versus peak time rebate. in 2015 North American Power Symposium (NAPS). 2015.
17. [17] Song, T., et al., A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer. Energies, 2018. 12(1).
18. [18] Woolf, T., et al., A Framework for Evaluating the Cost-Effectiveness of Demand Response. 2013.