Comprehensive Day-Ahead Marketing Model Considering N-1 Security and Renewables
Publication: Journal of Energy Engineering
Volume 149, Issue 1
Abstract
Integrating renewable energies into the electricity grid is a challenge that requires an in-depth investigation. This paper presents a model of the day-ahead electricity market, including wind and photovoltaic (PV) generators, and contingency analysis. The comprehensive day-ahead marketing model is designed as a modular structure so that the individual modules can be easily modified in the future. First, the day-ahead electricity marketing of an electricity grid with and without renewable energies is studied. A market model solves the problem of unit commitment and economic dispatch. A grid model was used to calculate the power flow of the branches and the grid losses. Congestions in the branches were handled by redispatching. For this purpose, the generators were redispatched using mixed-integer linear programming and the power transfer distribution factor. A contingency analysis, investigating the outage of one branch, was applied by using the line outage distribution factor. The redispatch process was operated with these calculated increased power flows, which need to be handled by the grid to achieve N-1 security. The simulations were carried out using 30-bus 118-bus systems. Historic wind speed and solar radiation data of Germany were used as input data for renewable energy production. The load data of the 118-bus system were generated based on a standard load profile from Germany. The results show that both systems were able to meet the demand without violation of the grid capacity during normal conditions, with and without renewable energies. However, when applying the contingency analysis, an outrageous amount of load curtailment is needed.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request (all data). Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (code).
Acknowledgments
This work was supported by the Philipp Schwartz Initiative, which was launched by the Alexander von Humboldt Foundation and the German Federal Office. Tankut Yalcinoz would like to thank the Philipp Schwartz Initiative for their support.
References
Abdi-Khorsand, M., M. Sahraei-Ardakani, and Y. Al-Abdullah. 2017 “Corrective transmission switching for N-1-1 contingency analysis.” In Proc., IEEE Power & Energy Society General Meeting, 1–1. New york: IEEE. https://doi:10.1109/PESGM.2017.8274262.
Amprion GmbH, 50Hertz Transmission GmbH, TenneT TSO GmbH, and TransnetBW GmbH. 2018. “Grundsätze für die Ausbauplanung des deutschen Übertragungsnetzes” [Principles for the expansion planning of the German transmission grid]. [In German.] Accessed May 11, 2021. https://www.50hertz.com/Portals/1/Dokumente/Netz/%C3%9CNB-Planungsgrunds%C3%A4tze_Juli%202018.pdf?ver=2018-10-26-150403-247.
Bauer, L., and S. Matysik. 2021a. “Enercon E-70 E4 2.300.” Accessed May 29, 2021. https://www.wind-turbine-models.com/turbines/69-enercon-e-70-e4-2.300.
Bauer, L., and S. Matysik. 2021b. “Enercon E-82 E4 2.350.” Accessed May 29, 2021. https://www.wind-turbine-models.com/turbines/1487-enercon-e-82-e4-2.350.
Bauer, L., and S. Matysik. 2021c. “Vestas V90.” Accessed May 29, 2021. https://www.wind-turbine-models.com/turbines/16-vestas-v90.
Bergh, K., D. Couckuyt, E. Delarue, and W. D’haeseleer. 2015. “Redispatching in an interconnected electricity system with high renewables penetration.” Electr. Power Syst. Res. 127 (Oct): 64–72. https://doi.org/10.1016/j.epsr.2015.05.022.
Bundesnetzagentur. 2018. Leitfaden zum Einspeisemanagement, Version 3.0 [Feed-in Management Guide, Version 3.0]. [In German.] Bonn, Germany: Bundesnetzagentur.
Burger, B. 2021. “Energy charts: Klima.” Accessed May 29, 2021. https://www.energy-charts.de/climate_de.htm?source=windspeed&year=2019&month=9.
Daqaq, F., M. Ouassaid, R. Ellaia, and A. T. Zeine. 2018. “Optimal power flow solution including stochastic renewable resources.” In Proc., 2018 6th Int. Renewable and Sustainable Energy Conf. (IRSEC). New York: IEEE. https://doi.org/10.1109/IRSEC.2018.8702843.
Deutscher Bundestag. 2014. Gesetz für den Ausbau erneuerbarer Energien: EEG 2017, 21.07.2014 [Law for the expansion of renewable energies: EEG 2017, 21.07.2014]. [In German.] Berlin: Deutscher Bundestag.
ENTSO-E (The European Network of Transmission System Operators for Electricity). 2021. “Historical data (until December 2015).” Accessed May 26, 2021. https://www.entsoe.eu/data/data-portal/.
Eslahi, M., A. F. Nematollahi, and B. Vahidi. 2021. “Day-ahead scheduling of centralized energy storage system in electrical networks by proposed stochastic MILP-Based bi-objective optimization approach.” Electr. Power Syst. Res. 192 (Mar): 106915. https://doi.org/10.1016/j.epsr.2020.106915.
Fekete, P. 2020. “Redispatch in Deutschland, Auswertung der Transparenzdaten April 2013 bis einschließlich September 2020” [Redispatch in Germany, evaluation of the transparency data from April 2013 up to and including September 2020]. [In German.] Accessed January 22, 2022. https://www.bdew.de/media/documents/2020_Q3_Bericht_Redispatch_GOQPsvY.pdf.
Hirth, L., and I. Schlecht. 2019. Market-based redispatch in zonal electricity markets: Inc-Dec gaming as a consequence of inconsistent power market design (not market power). Kiel, Germany: ZBW—Leibniz Information Centre for Economics.
Hirth, L., I. Schlecht, C. Maurer, and B. Tersteegen. 2019. Cost- or market-based? Future redispatch procurement in Germany. Berlin: Neon Neue Energieökonomik.
Ignat-balaci, A., E. Szilagyi, and D. Petreus. 2021. “Day-ahead scheduling, simulation, and real-time control of an islanded microgrid.” Adv. Electr. Comput. Eng. 21 (4): 89–98. https://doi.org/10.4316/AECE.2021.04010.
Illinois Institute of Technology. 2021. “Index of data.” Accessed May 26, 2021. https://motor.ece.iit.edu/data/.
Jiang, Y., M. Chen, and B. Wen. 2018. “Interval optimization of the day-ahead clearing schedule considering the real-time imbalance power with wind power integration.” Int. Trans. Electr. Energy Syst. 28 (10): e2610. https://doi.org/10.1002/etep.2610.
Li, H., X. Wang, F. Li, Y. Wang, and X. Yu. 2018. “A robust day-ahead electricity market clearing model considering wind power penetration.” Energies 11 (7): 1772. https://doi.org/10.3390/en11071772.
Li, P., T. Yalcinoz, and K. Rudion. 2019. “A mixed integer linear programming for the day ahead electricity market with wind power generators.” In Proc., Power & Energy Student Summit (PESS 2019), 97–102. Magdeburg, Germany: Otto-von-Guericke-Universität. https://doi:10.24352/UB.OVGU-2019-086.
Ma, Y., K. Xie, H. M. Tai, and J. Dong. 2018. “Wind-thermal generating unit commitment considering short-term fluctuation of wind power.” J. Energy Eng. 144 (3): 04018032 https://doi.org/10.1061/(ASCE)EY.1943-7897.0000546.
Madani, M. 2017. “Revisiting European day-ahead electricity market auctions: MIP models and algorithms.” Ph.D. thesis, Louvain School of Management, Université catholique de Louvain.
Montero, L., A. Bello, and J. Reneses. 2022. “A review on the unit commitment problem: Approaches, techniques, and resolution methods.” Energies 15 (4): 1296. https://doi.org/10.3390/en15041296.
Nesamalar, J., P. Venkatesh, and C. Raja. 2016. “Energy management by generator rescheduling in congestive deregulated power system.” Appl. Energy 171 (Jun): 357–371. https://doi.org/10.1016/j.apenergy.2016.03.029.
Nourollahi, R., P. Salyani, K. Zare, and R. Razzaghi. 2022. “A two-stage hybrid robust-stochastic day-ahead scheduling of transactive microgrids considering the possibility of main grid disconnection.” Int. J. Electr. Power Energy Syst. 136 (Mar): 107701. https://doi.org/10.1016/j.ijepes.2021.107701.
Prajapati, V. K., and V. Mahajan. 2017. “Grey wolf optimization based energy management by generator rescheduling with renewable energy resources.” In Proc., 14th IEEE India Council Int. Conf. (INDICON), 1–6. New York: IEEE. https://doi:10.1109/INDICON.2017.8487960.
Pruckner, M., D. Eckhoff, and R. German. 2014. “Modeling country-scale electricity demand profiles.” In Proc., Winter Simulation Conf., 1084–1095. New York: IEEE. https://doi.org/10.1109/WSC.2014.7019967.
Sundar, K., H. Nagarajan, L. Roald, S. Misra, R. Bent, and D. Bienstock. 2019. “Chance-constrained unit commitment with N-1 security and wind uncertainty.” IEEE Trans. Control Network Syst. 6 (3): 1062–1074. https://doi.org/10.1109/TCNS.2019.2919210.
Tejada-Arango, D. A., P. Sánchez-Martin, and A. Ramos. 2018. “Security constrained unit commitment using line outage distribution factors.” IEEE Trans. Power Syst. 33 (1): 329–337. https://doi.org/10.1109/TPWRS.2017.2686701.
Van den Bergh, K., E. Delarue, and W. D’haeseleer. 2014. “DC power flow in unit commitment models.” TME Working Paper—Energy and Environment. Accessed September 11, 2022. https://www.mech.kuleuven.be/en/tme/research/energy_environment/Pdf/wpen2014-12.pdf.
Wiest, P. 2018. Probabilistische Verteilnetzplanung zur optimierten Integration flexibler dezentraler Erzeuger und Verbraucher [Probabilistic distribution network planning for optimized integration of flexible distributed generators and consumers]. 1st ed. [In German.] Göttingen, Germany: International Scientific Publisher.
Yang, N., Z. Dong, L. Wu, L. Zhang, X. Shen, D. Chen, B. Zhu, and Y. Liu. 2022. “A comprehensive review of security-constrained unit commitment.” J. Mod. Power Syst. Clean Energy 10 (3): 562–576. https://doi.org/10.35833/MPCE.2021.000255.
Zhou, H., K. Yuan, and C. Lei. 2022. “Security constrained unit commitment based on modified line outage distribution factors.” IEEE Access 10 (Mar): 25258–25266. https://doi.org/10.1109/ACCESS.2022.3156081.
Zhou, Y., W. Hu, Y. Min, X. Xu, and Y. Li. 2017. “Modeling and optimization of multitype power sources stochastic unit commitment using interval number programming.” J. Energy Eng. 143 (5): 04017036. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000465.
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© 2022 American Society of Civil Engineers.
History
Received: Apr 2, 2022
Accepted: Jul 22, 2022
Published online: Oct 17, 2022
Published in print: Feb 1, 2023
Discussion open until: Mar 17, 2023
ASCE Technical Topics:
- Business management
- Computer programming
- Computing in civil engineering
- Design (by type)
- Electric power
- Energy engineering
- Energy sources (by type)
- Engineering fundamentals
- Grid systems
- Load factors
- Marketing
- Practice and Profession
- Renewable energy
- Solar power
- Structural design
- Systems engineering
- Systems management
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