Electricity pricing algorithm based on resource type and nodal approach

Year 2018, Volume: 2 Issue: 3, 292 - 298, 15.12.2018

Hayri Oğurlu , Nurettin Çetinkaya

Abstract

The aim of the electrical system operators is to ensure that energy is delivered to the consumer in good quality and without interruption. The main purpose of the electricity market operators is to provide the electricity to the end user as adequate, continuous and low cost. Demand for energy in the world is constantly increasing due to technological developments, increasing world population and welfare of people. The lower cost of electricity will lead to a higher quality of life and a more competitive condition in the industry [1-3]. For this reason, the cost of electricity is very important for everyone. While revealing the price of electricity, many different data are taken into account. These are generation, transmission and distribution costs. Generation costs include such as initial investment, operation, and supply costs. Depending on the source used, electric energy can be generated at very different costs. Transmission costs include investment and operation costs of substation centers and transmission lines used in the transmission system. Distribution costs are the operation and investment of the distribution system and the expenditures of some ancillary services delivered to the end user. Electricity prices are offered to end users with specific tariffs. However, these tariffs are disadvantageous for some users. Because, in the calculations made, the type of production source or the geographical location of the plant are not considered [4]. Therefore; for both producers and consumers, it is thought that these calculations can be made in a more acceptable way, taking into account the location of the source of production in the system and the interconnected system.

Keywords

Electricity pricing algorithms, Nodal pricing, Regional pricing, Source types, Multi stage dynamic, Programming

References

• 1. Ahmadi, H. and Akbari Foroud, A., A stochastic framework for reactive power procurement market, based on nodal price model, International Journal of Electrical Power & Energy Systems, 2013. 49: 104-113.
• 2. Azad-Farsani, E., Agah, S. M. M., Askarian-Abyaneh, H., Abedi, M. and Hosseinian, S. H., Stochastic LMP (Locational marginal price) calculation method in distribution systems to minimize loss and emission based on Shapley value and two-point estimate method, Energy, 2016. 107: 396-408.
• 3. Azad-Farsani, E., Loss minimization in distribution systems based on LMP calculation using honey bee mating optimization and point estimate method, Energy, 2017. 140: 1-9.
• 4. Baghayipour, M., Akbari Foroud, A. and Soofiabadi, A., A comprehensive fair nodal pricing scheme, considering participants’ efficiencies and their rational shares of total cost of transmission losses, International Journal of Electrical Power & Energy Systems, 2014. 63: 30-43.
• 5. Bjørndal, E., Bjørndal, M., Cai, H. and Panos, E., Hybrid pricing in a coupled European power market with more wind power, European Journal of Operational Research, 2018. 264(3): 919-931.
• 6. Dourbois, G. A. and Biskas, P. N., A nodal-based security-constrained day-ahead market clearing model incorporating multi-period products, Electric Power Systems Research, 2016. 141: 124-136.
• 7. Egerer, J., Weibezahn, J. and Hermann, H., Two price zones for the German electricity market — Market implications and distributional effects, Energy Economics, 2016. 59: 365-381.
• 8. Ghasemi, A., Mortazavi, S. S. and Mashhour, E., Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response, Energy, 2015. 86: 649-660.
• 9. Gianfreda, A. and Grossi, L., Forecasting Italian electricity zonal prices with exogenous variables, Energy Economics, 2012. 34 (6): 2228-2239.
• 10. Goel, L., Wu, Q. and Wang, P., Nodal price volatility reduction and reliability enhancement of restructured power systems considering demand–price elasticity, Electric Power Systems Research, 2008. 78(10): 1655-1663.
• 11. Gökgöz, F. and Atmaca, M. E., Financial optimization in the Turkish electricity market: Markowitz's mean-variance approach, Renewable and Sustainable Energy Reviews, 2012. 16(1): 357-368.
• 12. Jokić, A., Lazar, M. and van den Bosch, P. P. J., Real-time control of power systems using nodal prices, International Journal of Electrical Power & Energy Systems, 2009. 31(9): 522-530.
• 13. Kaleta, M., 2016, A generalized class of locational pricing mechanisms for the electricity markets, Energy Economics.
• 14. Kang, C. Q., Chen, Q. X., Lin, W. M., Hong, Y. R., Xia, Q., Chen, Z. X., Wu, Y. and Xin, J. B., Zonal marginal pricing approach based on sequential network partition and congestion contribution identification, International Journal of Electrical Power & Energy Systems, 2013. 51: 321-328.
• 15. Kia, M., Setayesh Nazar, M., Sepasian, M. S., Heidari, A. and Sharaf, A. M., Coordination of heat and power scheduling in micro-grid considering inter-zonal power exchanges, Energy, 2017. 141: 519-536.
• 16. Kristiansen, T., Comparison of transmission pricing models, International Journal of Electrical Power & Energy Systems, 2011. 33(4): 947-953.
• 17. Kumar, M., Kumar, A. and Sandhu, K. S., Impact of distributed generation on nodal prices in hybrid electricity market, Materials Today: Proceedings, 2018. 5(1): 830-840.
• 18. López-Lezama, J. M., Contreras, J. and Padilha-Feltrin, A., Location and contract pricing of distributed generation using a genetic algorithm, International Journal of Electrical Power & Energy Systems, 2012. 36(1): 117-126.
• 19. Lorca, Á. and Prina, J., Power portfolio optimization considering locational electricity prices and risk management, Electric Power Systems Research, 2014. 109: 80-89.
• 20. Murphy, F. H., Mudrageda, M., Soyster, A. L., Sarić, A. T. and Stanković, A. M., The effect of contingency analysis on the nodal prices in the day-ahead market, Energy Policy, 2010. 38(1): 141-150.
• 21. Polisetti, K. and Kumar, A., Distribution System Nodal Prices Determination for Realistic ZIP and Seasonal Loads: An Optimal Power Flow Approach, Procedia Technology, 2016. 25: 702-709.
• 22. Ruiyou Zhang, Dingwei Wang and Yun, W. Y., Power-Grid-Partitioning Model and its Tabu-Search-Embedded Algorithm for Zonal Pricing. Proceedings of the 17th World Congress The International Federation of Automatic Control. Seoul, 2018. Korea,: 15928-15932.
• 23. Sahraei-Ardakani, M., Blumsack, S. and Kleit, A., Estimating zonal electricity supply curves in transmission-constrained electricity markets, Energy, 2015. 80: 10-19.
• 24. Singh, R. K. and Goswami, S. K., Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue, International Journal of Electrical Power & Energy Systems, 2010. 32(6): 637-644.
• 25. Sleisz, Á. and Raisz, D., Integrated mathematical model for uniform purchase prices on multi-zonal power exchanges, Electric Power Systems Research, 2017. 147: 10-21.
• 26. Tranberg, B., Schwenk-Nebbe, L. J., Schäfer, M., Hörsch, J. and Greiner, M., Flow-based nodal cost allocation in a heterogeneous highly renewable European electricity network, Energy, 2018. 150: 122-133.
• 27. Weibelzahl, M. and Märtz, A., On the effects of storage facilities on optimal zonal pricing in electricity markets, Energy Policy, 2018. 113: 778-794.
• 28. Zenón, E. and Rosellón, J., Optimal transmission planning under the Mexican new electricity market, Energy Policy, 2017. 104: 349-360.