SavedAdd your journal

Journals / Bilgisayar Bilimleri / 2022 / Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium Özel Sayı

A Study on Dynamic Energy Pricing in Smart Grids

Akıllı Şebekelerde Dinamik Enerji Fiyatlandırılması Üzerine Bir Çalışma

Zehva Yalçınöz(İNÖNÜ ÜNİVERSİTESİ)0000-0003-1574-7556
Asım Kaygusuz(İNÖNÜ ÜNİVERSİTESİ)0000-0003-2905-1816
Pages
21–31
Publication Date
2022-10-10
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Studies, advantages, and disadvantages of smart grids are examined, and smart tariff plans are compared.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Demand-side load management (DSLM), load forecasting, and dynamic energy pricing are critical in smart grids (SG) where there is mutual communication between manufacturers and consumers today and in the future.
📊
Key Findings & Data:The transmission of signals obtained with dynamic energy pricing via servers is a very important topic for demand management and the energy market.
💡
Conclusion & Impact:Studies, advantages, and disadvantages of smart grids are examined, and smart tariff plans are compared.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Dinamik Enerji Fiyatlandırma · Dağıtık Üretim · Enerji Dengesi

Abstract

Demand-side load management (DSLM), load forecasting, and dynamic energy pricing are critical in smart grids (SG) where there is mutual communication between manufacturers and consumers today and in the future. The transmission of signals obtained with dynamic energy pricing via servers is a very important topic for demand management and the energy market. Despite the distributed generation conditions, the energy balance is maintained by the demand side management during dynamic studies based on energy prices. With the dynamic structure of energy price signals and load forecasting, uncertainties arising from distributed generation are responded to. This study details demand response (DR) methods and smart pricing plans such as time-of-use (ToU), critical peak price (CPP), and real-time price (RTP). Studies, advantages, and disadvantages of smart grids are examined, and smart tariff plans are compared.

Özet

Üreticiler ve tüketiciler arasında karşılıklı haberleşmenin sağlandığı akıllı şebekelerde (SG) talep tarafı yük yönetimi (DSLM), yük tahmini ve bunlarla bağlantılı olan dinamik enerji fiyatlandırması günümüzde ve gelecekte büyük önem arz etmektedir. Dinamik enerji fiyatlandırması ile elde edilen sinyallerin sunucular aracılığıyla yayınlanması talep tarafı yönetim ve enerji piyasası için çok önemli bir başlıktır. Dinamik enerji fiyatlandırması tabanlı çalışmaların genelinde dağıtık üretim koşullarına rağmen enerji dengesi talep tarafı yönetim ile korunmaktadır. Enerji fiyat sinyallerinin dinamik yapıda olmasıyla ve yük tahmini ile dağıtık üretimden kaynaklanan belirsizliklere yanıt verilmektedir. Bu çalışmada talep yanıtı (DR) yöntemleri ve akıllı fiyatlandırma planları olan kullanım süresi (ToU), kritik tepe fiyatlandırması (CPP) ve gerçek zamanlı fiyatlandırma (RTP) ayrıntılı olarak incelenmiştir. Akıllı şebekelerde yapılan çalışmalar, avantajları, dezavantajları incelenmiş ve akıllı fiyat planları karşılaştırılmıştır.

🔖

Cite This Article

Yalçınöz, Z., Kaygusuz, A. (2022). A Study on Dynamic Energy Pricing in Smart Grids. Bilgisayar Bilimleri, Cilt: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium Özel Sayı, 21–31. https://doi.org/10.53070/bbd.1174257

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (54)

  1. Akçin, M., Alagöz, B. B., Keleş, C., Karabiber, A., ve Kaygusuz, A. (2013). Dağıtık kontrol ile akıllı şebekelerde geniş-alan yönetimi ve geleceğe dönük projeksiyonlar Wide-area management of smart grid by distributed control and near future projections. Sakarya üniversitesi fen bilimleri dergisi, 17(3), 457–470.
  2. Alagoz, B. B., ve Kaygusuz, A. (2016). Dynamic energy pricing by closed-loop fractional-order PI control system and energy balancing in smart grid energy markets. Transactions of the Institute of Measurement and Control, 38(5), 565–578. https://doi.org/10.1177/0142331215579949
  3. Alagöz, B. B., ve Kaygusuz, A. (2014). Kapalı Çevrim Kesir Dereceli PI Kontrolör ile Dinamik Enerji Fiyatı Kontrolü ve Akıllı Şebekelerde Otomatik Enerji Arz- Talep Dengelemesi Uygulaması. TOK, Kocaeli, 535–539.
  4. Alagoz, B. B., Kaygusuz, A., Akcin, M., ve Alagoz, S. (2013). A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market. Energy, 59, 95–104. https://doi.org/10.1016/j.energy.2013.06.074
  5. Anand, H., ve Ramasubbu, R. (2018). A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources. Renewable Energy, 127, 779–789. https://doi.org/10.1016/j.renene.2018.05.016
  6. Anonim. (2022). Elektrik piyasası. Vikipedi. https://tr.wikipedia.org/wiki/Elektrik_piyasası#:~:text=Her elektrik piyasasının faaliyet alanı,pazarlar ulusal sınırların ötesine geçebilir
  7. Arslan, B., ve Ertuğrul, İ. (2022). Çoklu regresyon, arıma ve yapay sinir ağı yöntemleri ile türkiye elektrik piyasasında fiyat tahmin ve analizi. Yönetim ve Ekonomi Araştırmaları Dergisi, 20(1), 331–353. https://doi.org/10.11611/yead.988146
  8. Boom, A., ve Schwenen, S. (2021). Is real-time pricing smart for consumers ? Journal of Regulatory Economics, 60(2), 193–213. https://doi.org/10.1007/s11149-021-09440-5
  9. Boonchuay, K., Optimization, B., ve Peak, C. (2017). Optimal Critical Peak Pricing Scheme with Consideration of Marginal Generation Cost. 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 226–229.
  10. Çetintaş, H., ve Bicil, İ. M. (2015). Elektrik piyasalarında yeniden yapılanma ve türkiye elektrik piyasasında yapısal dönüşüm. Optimum Ekonomi ve Yönetim Bilimleri Dergisi, 2(2), 1–15.
  11. Chakraborty, S., Ito, T., ve Senjyu, T. (2014). Smart pricing scheme: A multi-layered scoring rule application. Expert Systems With Applications, 41(8), 3726–3735. https://doi.org/10.1016/j.eswa.2013.12.002
  12. Cui, Q., Wang, X., Liu, C., ve Hou, F. (2015). Decision model of critical peak pricing coordinating wind power generation. International Conference on Clean Electrical Power (ICCEP), 775–779.
  13. Danxi, L., Bo, Z., Yan, Q., ve Yu-jie, X. (2017). Optimal Control Model of Electric Vehicle Demand Response Based on Real - time Electricity Price. EEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 1815–1818.
  14. Datta, A. R., ve Datta, S. (2016). Electricity Market Price-spike Classification in the Smart Grid. IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 1–5.
  15. Duan, Q. (2016). A Price-Based Demand Response Scheduling Model in Day-Ahead Electricity Market. IEEE Power and Energy Society General Meeting (PESGM), 1–5.
  16. Dutta, G., ve Mitra, K. (2017). A literature review on dynamic pricing of electricity. Journal of the Operational Research Society, 68(10), 1131–1145. https://doi.org/10.1057/s41274-016-0149-4
  17. Gellings, C. W. (1985). The concept of demand-side management for electric utilities. Proceedings of the IEEE, 73(10), 1468–1470.
  18. Gyamfi, S., Krumdieck, S., ve Urmee, T. (2013). Residential peak electricity demand response—Highlights of some behavioural issues. Renewable and Sustainable Energy Reviews, 25, 71–77. https://doi.org/10.1016/j.rser.2013.04.006
  19. Herter, K., ve Wayland, S. (2010). Residential response to critical-peak pricing of electricity: California evidence. Energy, 35(4), 1561–1567. https://doi.org/10.1016/j.energy.2009.07.022
  20. Huang, W., Zhang, N., Kang, C., Li, M., ve Huo, M. (2019). From demand response to integrated demand response: review and prospect of research and application. Protection and Control of Modern Power Systems, 4(1), 1–13.
  21. Jose, A. A., ve Pahwa, A. (2010). Economic evaluation of small wind generation ownership under different electricity pricing scenarios. North American Power Symposium 2010, 1–4. https://doi.org/10.1109/NAPS.2010.5618946
  22. Kaygusuz, A. (2019). Closed loop elastic demand control by dynamic energy pricing in smart grids. Energy, 176, 596–603. https://doi.org/10.1016/j.energy.2019.04.036
  23. Keleş, C. (2017). Akıllı Şebekelerde Yenilenebilir Enerji Üretimine Sahip Akıllı Evlerin Enerji Ve Yük Yönetimi. (Doktora Tezi), İnönü Üniversitesi, Fen Bilimleri Enstitüsü, Malatya.
  24. Khan, A. R., Mahmood, A., Safdar, A., Khan, Z. A., ve Khan, N. A. (2016). Load forecasting, dynamic pricing and DSM in smart grid: A review. Renewable and Sustainable Energy Reviews, 54, 1311–1322. https://doi.org/10.1016/j.rser.2015.10.117
  25. Khattak, H. A., Tehreem, K., Almogren, A., Ameer, Z., Din, I. U., ve Adnan, M. (2020). Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities. Journal of Information Security and Applications, 55, 1–8. https://doi.org/10.1016/j.jisa.2020.102615
  26. Kii, M., Sakamoto, K., Hangai, Y., ve Doi, K. (2014). The effects of critical peak pricing for electricity demand management on home-based trip generation. IATSS Research, 37(2), 89–97. https://doi.org/10.1016/j.iatssr.2013.12.001
  27. Mehta, V. K., ve Mehta, R. (2005). Principles of Power System: Including Generation, Transmission, Distribution, Switchgear and Protection: for BE/B. Tech. S. Chand Publishing.
  28. Nazar, N. S. M., Abdullah, M. P., Hassan, M. Y., ve Hussin, F. (2012). Time-based electricity pricing for Demand Response implementation in monopolized electricity market. IEEE Student Conference on Research and Development, 178–181.
  29. Onaiwu, E. (2010). How Does Bilateral Trading Differ From Electricity Pooling? University of Dundee, 3–6.
  30. Pahle, M., Schill, W., Gambardella, C., ve Tietjen, O. (2016). Renewable Energy Support, Negative Prices, and Real-time. Energy, 37, 147–170.
  31. Palensky, P., ve Dietrich, D. (2011). Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Transactions on Industrial Informatics, 7(3), 381–388.
  32. Pallonetto, F., Oxizidis, S., Milano, F., ve Finn, D. (2016). The effect of time-of-use tariffs on the demand response flexibility of an all-electric smart-grid-ready dwelling. Energy & Buildings, 128, 56–67. https://doi.org/10.1016/j.enbuild.2016.06.041
  33. Park, S. C., Jin, Y. G., Song, H. Y., ve Yoon, Y. T. (2015). Designing a critical peak pricing scheme for the pro fi t maximization objective considering price responsiveness of customers. Energy, 83, 521–531. https://doi.org/10.1016/j.energy.2015.02.057
  34. Roozbehani, M., Dahleh, M. A., ve Mitter, S. K. (2012). Volatility of Power Grids Under Real-Time Pricing. IEEE Transactions on Power Systems, 27(4), 1926–1940.
  35. Shao, S., ve Zhang, T. (2010). Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration. IEEE PES T&D 2010, 1–6. https://doi.org/10.1109/TDC.2010.5484336
  36. Song, X., ve Qu, J. (2014). An improved real-time pricing algorithm based on utility maximization for smart grid. Proceeding of the 11th World Congress on Intelligent Control and Automation, 2509–2513.
  37. Sun, Q., Ge, X., Liu, L., Xu, X., Zhang, Y., Niu, R., ve Zeng, Y. (2011). Review of smart grid comprehensive assessment systems. Energy Procedia, 12, 219–229. https://doi.org/10.1016/j.egypro.2011.10.031
  38. Tanrıöven, K., Yararbaş, S., ve Cengiz, H. (2011). Geleceğin Elektrik Dağıtım Şebekesi Smart Grid. Elektrik-Elektronik ve Bilgisayar Sempozyumu 2011, 52–55.
  39. Wang, J., Bloyd, C. N., Hu, Z., ve Tan, Z. (2010). Demand response in China. Energy, 35(4), 1592–1597. https://doi.org/10.1016/j.energy.2009.06.020
  40. Xu, F. Y., Zhang, T., Lai, L. L., ve Zhou, H. (2015). Shifting Boundary for price-based residential demand response and applications. Applied Energy, 146, 353–370. https://doi.org/10.1016/j.apenergy.2015.02.001
  41. Yalçınöz, Z., ve Kaygusuz, A. (2021). Dynamic price control using pole placement method in smart grids. Computer Science, 411–421. https://doi.org/10.53070/bbd.982884
  42. Yan, X., Ozturk, Y., Hu, Z., ve Song, Y. (2018). A review on price-driven residential demand response. Renewable and Sustainable Energy Reviews, 96, 411–419. https://doi.org/10.1016/j.rser.2018.08.003
  43. Yan, X., Wright, D., Kumar, S., Lee, G., ve Ozturk, Y. (2015). Real-Time Residential Time-of-Use Pricing: A Closed-Loop Consumers Feedback Approach. Seventh Annual IEEE Green Technologies Conference, 132–138. https://doi.org/10.1109/GREENTECH.2015.19
  44. Zehir, M. A., ve Bağrıyanık, M. (2013). Akıllı şebekelerde gelişmiş yerel talep yönetimi. V. Enerji verimliliği ve kalitesi sempozyumu, 14–18.
  45. Zeng, S., Li, J., ve Ren, Y. (2008). Research of Time-of-Use Electricity Pricing Models in China: A Survey. IEEE International Conference on Industrial Engineering and Engineering Management, 2191–2195. https://doi.org/10.1109/IEEM.2008.4738260
  46. Zhang, Q., Wang, X., ve Fu, M. (2009). Optimal Implementation Strategies for Critical Peak Pricing. International Conference on the European Energy Market, 1–6.
  47. Herter, K., ve Wayland, S. (2010). Residential response to critical-peak pricing of electricity: California evidence. Energy, 35(4), 1561–1567. https://doi.org/10.1016/j.energy.2009.07.022
  48. Huang, W., Zhang, N., Kang, C., Li, M., ve Huo, M. (2019). From demand response to integrated demand response: review and prospect of research and application. Protection and Control of Modern Power Systems, 4(1), 1–13.
  49. Keleş, C. (2017). Akıllı Şebekelerde Yenilenebilir Enerji Üretimine Sahip Akıllı Evlerin Enerji Ve Yük Yönetimi. (Doktora Tezi), İnönü Üniversitesi, Fen Bilimleri Enstitüsü, Malatya.
  50. Khattak, H. A., Tehreem, K., Almogren, A., Ameer, Z., Din, I. U., ve Adnan, M. (2020). Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities. Journal of Information Security and Applications, 55, 1–8. https://doi.org/10.1016/j.jisa.2020.102615
  51. Nazar, N. S. M., Abdullah, M. P., Hassan, M. Y., ve Hussin, F. (2012). Time-based electricity pricing for Demand Response implementation in monopolized electricity market. IEEE Student Conference on Research and Development, 178–181.
  52. Pahle, M., Schill, W., Gambardella, C., ve Tietjen, O. (2016). Renewable Energy Support, Negative Prices, and Real-time. Energy, 37, 147–170.
  53. Palensky, P., ve Dietrich, D. (2011). Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Transactions on Industrial Informatics, 7(3), 381–388.
  54. Zeng, S., Li, J., ve Ren, Y. (2008). Research of Time-of-Use Electricity Pricing Models in China: A Survey. IEEE International Conference on Industrial Engineering and Engineering Management, 2191–2195. https://doi.org/10.1109/IEEM.2008.4738260