Assessment of Electricity Consumption Characteristic: Textile Factory Case Study

Year 2023, Volume: 6 Issue: 4, 308 - 316, 15.10.2023

Cihat Çağdaş Uydur

https://doi.org/10.34248/bsengineering.1292533

Abstract

Currently, electrical energy tariffs are a crucial factor in the electricity market, as they significantly impact the decisions made by end users. They play a vital role in the effectiveness of energy management systems (EMS). Tariffs are not solely considered a fixed component of expenditure calculations. Instead, they are indirectly linked to the costs of power generation, electricity transmission, and electricity distribution, as well as other determinants such as government taxation. In certain regions, improper tariff calculation methodologies have resulted in substantial power losses, superluous investments, increased operating costs, and environmental pollution because of the underutilization of available renewable energy sources. This study examined the electrical energy consumption values and characteristics of an integrated textile factory. Additionally, analyses were conducted on the electricity tariffs published by the Energy Market Regulatory Authority (EMRA) Electricity Energy Market management, in order to decrease the electrical energy consumption costs of the textile factory. Based on the findings of the analyses, suggestions were put forward for regulating the electrical energy consumption characteristics and reducing the electrical energy consumption costs.

Keywords

Electricity consumption, Electricity tariffs, Energy management, Energy markets

References

• Ansarin M, Ghiassi-Farrokhfal Y, Ketter W, Collins J. 2020. The economic consequences of electricity tariff design in a renewable energy era. Appl Energy, 275: 115317. DOI: 10.1016/j.apenergy.2020.115317.
• Batlle C, Mastropietro P, Rodilla P. 2020. Redesigning residual cost allocation in electricity tariffs: A proposal to balance efficiency, equity and cost recovery. Renew Energy, 155: 257-266. DOI: 10.1016/j.renene.2020.03.152.
• Borenstein S. 2016. The economics of fixed cost recovery by utilities. Elect J, 29(7): 5-12. DOI: 10.1016/j.tej.2016.07.013.
• Brown DP, Sappington DEM. 2018. On the role of maximum demand charges in the presence of distributed generation resources. Energy Econ, 69: 237-249. DOI: 10.1016/j.eneco.2017.11.023.
• EMRA. 2023. Electricity tariff tables valid as of 1/4/2023. URL: https://www.epdk.gov.tr/Detay/Icerik/3-1327/elektrik-faturalarina-esas-tarife-tablolari (accessed date: February 10, 2023).
• Felder FA, Athawale R. 2014. The life and death of the utility death spiral. Electricity J, 27(6): 9-16. DOI: 10.1016/j.tej.2014.06.008.
• Grimm V, Orlinskaya G, Schewe L, Schmidt M, Zöttl G. 2021. Optimal design of retailer-prosumer electricity tariffs using bilevel optimization. Omega, 102. DOI: 10.1016/j.omega.2020.102327.
• Hinz F, Schmidt M, Möst D. 2018. Regional distribution effects of different electricity network tariff designs with a distributed generation structure: The case of Germany. Energy Pol, 113: 97-111. DOI: 10.1016/j.enpol.2017.10.055.
• Iscan S, Arikan O. 2022. Energy management planning according to the electricity tariff models in Turkey: A case study. Turkish J Elect Power Energy Syst, 2(1): 46-57. DOI: 10.5152/tepes.2022.22010.
• Li S, Luo F, Yang J, Ranzi G, Wen J. 2019. A personalized electricity tariff recommender system based on advanced metering infrastructure and collaborative filtering. Int J Electr Power Energy Syst, 113: 403-410. DOI: 10.1016/j.ijepes.2019.05.042.
• Neuteleers S, Mulder M, Hindriks F. 2017. Assessing fairness of dynamic grid tariffs. Energy Pol, 108: 111-120. DOI: 10.1016/j.enpol.2017.05.028.
• Ouédraogo S, Faggianelli GA, Pigelet G, Notton G, Duchaud JL. 2021. Performances of energy management strategies for a Photovoltaic/Battery microgrid considering battery degradation. Solar Energy, 230: 654-665. DOI: 10.1016/j.solener.2021.10.067.
• Poongavanam E, Kasinathan P, Kanagasabai K. 2023. Optimal energy forecasting using hybrid recurrent neural networks. Intell Automat Soft Comput, 36(1): 249-265. DOI: 10.32604/iasc.2023.030101.
• Qayyum F, Jamil H, Jamil F, Kim D. 2022. Predictive optimization based energy cost minimization and energy sharing mechanism for peer-to-peer nanogrid network. IEEE Access, 10: 23593-23604. DOI: 10.1109/ACCESS.2022.3153837.
• Ren Z, Grozev G, Higgins A. 2016. Modelling impact of PV battery systems on energy consumption and bill savings of Australian houses under alternative tariff structures. Renew Energy, 89: 317-330. DOI: 10.1016/j.renene.2015.12.021.
• Sulaima MF, Dahlan NY, Yasin ZM, Rosli MM, Omar Z, Hassan MY. 2019. A review of electricity pricing in peninsular Malaysia: Empirical investigation about the appropriateness of Enhanced Time of Use (ETOU) electricity tariff. Renew Sustain Energy Rev, 110: 348-367.
• Wu Y, Liu Z, Li B, Liu J, Zhang L. 2022. Energy management strategy and optimal battery capacity for flexible PV-battery system under time-of-use tariff. Renew Energy, 200: 558-570. DOI: 10.1016/j.renene.2022.09.118.
• Zaki DA, Hamdy M. 2022. A review of electricity tariffs and enabling solutions for optimal energy management. Energies, 15(22): 8527. DOI: 10.3390/en15228527.
• Zhao J, Wang W, Guo C. 2023. Hierarchical optimal configuration of multi-energy microgrids system considering energy management in electricity market environment. Int J Elect Power Energy Syst, 144: 108572. DOI: 10.1016/j.ijepes.2022.108572.
• Zorita AL, Fernández-Temprano MA, García-Escudero LA, Duque-Perez O. 2016. A statistical modeling approach to detect anomalies in energetic efficiency of buildings. Energy Build, 110: 377-386. DOI: 10.1016/j.enbuild.2015.11.005.