AKILLI ŞEBEKELER İÇİN BULANIK MANTIĞA DAYALI GERÇEK ZAMANLI ENERJİ FİYATI DÜZENLEME YAKLAŞIMI

Abstract

Akıllı teknolojiler esnek, dinamik, verimli enerji üretimi ve yönetimini sağlamada öncü bir role sahiptir. Bu nedenle bulanık mantık, yapay sinir ağı, makine öğrenmesi, yumuşak hesaplama teknikleri gibi akıllı algoritmalar çeşitli ve çok sayıdaki dağıtık üretimlerin daha karmaşık hale getirdiği güç sistemleri için tek çaredir. Gerçek zamanlı kapalı çevrim kontrolü, literatürde yer alan çeşitli dinamik modellerin ve tahmin yazılımının istikrarlı ve güvenilir güç sistemi işletiminin sağlaması için arz ve talep denge noktasının temininde öne çıkan bir değişken olarak enerji fiyatını kullanmaktadır. Bu çalışmada bulanık mantığa dayalı bir fiyat düzenleyicisi (BMD-FD) tasarlanıp Türkiye'nin 2018 yılına ait yıllık enerji raporundan alınan bir yaz gününün saatlik verisi ile MATLAB/Simulink ortamında tasarlanan sistemin benzetimi yapılmıştır. Önerilen model oluşturulan benzetim durumlarında Oransal İntegral Türev (PID) fiyat denetleyicisi ile performans kriterlerine göre karşılaştırılmıştır. BMD-FD anlık referans talep sinyali değişikliklerini PID denetleyiciye göre daha hızlı geçici yanıt tepkisi ve minimum sürekli durum hatasıyla takip etmektedir.

Keywords

• Enerji fiyatlandırması
• Bulanık mantık
• Performans kriterleri
• PID denetleyici

A FUZZY LOGIC REASONING BASED REAL TIME ENERGY PRICE REGULATION APPROACH FOR SMART GRIDS

Abstract

Intelligent technologies have become pioneer force to provide flexible, dynamic and efficient energy generation and management. Thus, smart algorithms such as fuzzy logic, artificial neural network, machine learning, soft computing techniques are sole remedy against growing diverse and numerous distributed generations that make more complicated power system. Real time closed loop controlling requires energy price as a featured variable to procure supply demand equilibrium point for stable and reliable power system operation, where several dynamic models and estimation software are introduced in the literature. In this study, a fuzzy logic reasoning based price regulator (FLR-PR) is designed and simulated on MATLAB/Simulink environment using 2018 hourly data of a summer day taken from annual energy report of Turkey. Proposed model has been compared based on performance indexes to Proportional Integral Derivative (PID) price controller in the constituted simulation cases. FLR-PR tracks instant reference demand signal changes with minimum steady state error and fast transient response with respect to PID controller.

Keywords

• Energy pricing
• Distributed generation
• Fuzzy logic
• Performance indexes
• PID controller
• Smart grids.

References

1. [1] Kakran, S. and Chanana, S., “Smart operations of smart grids integrated with distributed generation: A review”, Renewable and Sustainable Energy Reviews, 81, pp. 524-535, 2018.
2. [2] Kong, P. Y., “Effects of communication network performance on dynamic pricing in smart power grid”, IEEE Systems Journal, Vol. 8(2), pp. 533-541, 2014.
3. [3] Qian, L. P., Zhang, Y. J. A., Huang, J., and Wu, Y., “Demand response management via real-time electricity price control in smart grids”, IEEE Journal on Selected areas in Communications, 31 (7), pp. 1268-1280, 2013.
4. [4] Motamedi, A., Zareipour, H., and Rosehart, W. D., “Electricity Price and Demand Forecasting in Smart Grids”, IEEE Trans. Smart Grid, 3(2), pp. 664-674, 2012.
5. [5] Samadi, P., Mohsenian-Rad, A. H., Schober, R., Wong, V. W., and Jatskevich, J., “Optimal real-time pricing algorithm based on utility maximization for smart grid”, First IEEE International Conference on Smart Grid Communications (SmartGridComm), 2010, pp. 415-420.
6. [6] Pourbabak, H., Luo, J., Chen, T., and Su, W., “A novel consensus-based distributed algorithm for economic dispatch based on local estimation of power mismatch”, IEEE Transactions on Smart Grid, 9(6), pp. 5930-5942, 2018.
7. [7] Tao, L., and Gao, Y., “Real-Time Pricing for Smart Grid with Multiple Companies and Multiple Users Using Two-Stage Optimization” Journal of Systems Science and Information, 6(5), pp. 435-446, 2018.
8. [8] Republic of Turkey Energy Market Regulatory Authority (EMRA), Annual Report 2017, 2018.

9. [9] Fioravanti, A. R., Mareček, J., Shorten, R. N., Souza, M., and Wirth, F. R., "On classical control and smart cities," 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, pp. 1413-1420.
10. [10] Lopes, M., et al., "An automated energy management system in a smart grid context." 2012 IEEE International Symposium on Sustainable Systems and Technology (ISSST), 2012, pp. 1-1.
11. [11] Alagoz, B. B., Kaygusuz, A., Akcin, M., and Alagoz, S., “A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market”, Energy, 59, pp. 95-104, 2013.
12. [12] Singhal, A., and Saxena, R. P., "Software models for smart grid" 2012 First International Workshop on Software Engineering Challenges for the Smart Grid (SE-SmartGrids), 2012, pp. 42-45.
13. [13] Zhu, H., Gao, Y., and Hou, Y., “Real-time pricing for demand response in smart grid based on alternating direction method of multipliers”, Mathematical Problems in Engineering, 2018.
14. [14] Turkish Electricity Transmission Corporation (TEIAS), Load Dispatch Information System (YTBS), 2018.
15. [15] Zadeh, L. A., “Fuzzy sets”, Information and control, 8(3), pp. 338-353, 1965.
16. [16] Bellman, R. E., and Zadeh, L. A., “Decision-making in a fuzzy environment”, Management science, 17(4), B-141, 1970.
17. [17] Chein, C., “Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part 1-2”, IEEE, Trans. on Systems, Man and Cybernetics, 20(2), pp. 404-428,1990.
18. [18] Altaş, I.H., Fuzzy Logic Control in Energy Systems with Design Applications in MATLAB®/Simulink®. Vol. 91. IET, 2017.
19. [19] Cakmak, R., and Altas, I. H., “The effect of integration types on FLC based MPPT systems”, Innovations in Intelligent Systems and Applications (INISTA) 2013, 2013, pp. 1-4.
20. [20] Altas, I. H., and Sharaf, A. M., “A generalized direct approach for designing fuzzy logic controllers in Matlab/Simulink GUI environment”, International journal of information technology and intelligent computing, 1(4), pp. 1-27, 2007.
21. [21] Cakmak, R., Altas, I. H., and Sharaf, A. M., “Modeling of FLC-Incremental based MPPT using DC-DC boost converter for standalone PV system”, Innovations in Intelligent Systems and Applications (INISTA) 2012, 2012, pp. 1-5.
22. [22] O'Dwyer, A., Handbook of PI and PID controller tuning rules, World Scientific, 2006.