Optimal Operation of a Virtual Power Plant in a Day Ahead Market Considering Uncertainties of Renewable Generation and Risk Evaluation

Öz

The air pollution and global warming because of the increasing usage of fossil fuels with the rapid growth of technology are one of the major problems for many countries. To cope with these problems, distributed energy resources (DERs) including renewable sources are adding into the modern power systems as an alternative to traditional generation. However, the uncertain nature of some sources such as wind power and photovoltaic power leads to variable output and instability of the power system. Virtual Power Plant (VPP) is a convenient solution to overcome these challenges in the power system. It aggregates various DERs including renewable-based and fueled-based generation, storage systems and dispatchable loads. In this study, optimum operating strategy of a VPP consisting of a Wind Power Plant (WPP), a Photovoltaic Power Plant (PVPP), a Conventional Power Plant (CPP) and a Pumped Hydro Storage Plant (PHSP) is determined to maximize the profit in a Day Ahead Market (DAM). The uncertainty analysis for the intermittent renewable power generation is made by modeling the uncertain parameters (wind speed and solar radiation) with scenarios based upon historical data. Moreover, the risk of low profit scenarios is evaluated by using Conditional Value at Risk (CVaR) as a risk measure.

Anahtar Kelimeler

• virtual power plant,day ahead market,profit,uncertainty,risk

Gün Öncesi Piyasasında Sanal Güç Santralinin Yenilenebilir Üretim Belirsizliklerini ve Risk Değerlendirmesini Göz Önünde Bulundurarak Optimum İşletilmesi

Öz

Teknolojinin hızla büyümesi ile artan fosil yakıt tüketimine bağlı olarak oluşan hava kirliliği ve küresel ısınma, bir çok ülke için büyük sorun oluşturan problemlerdendir. Bu sorunlarla başa çıkmak için, yenilenebilir kaynakları içeren dağıtık enerji kaynakları (DEK), modern güç sistemlerine geleneksel üretime alternatif olarak eklenmektedirler. Bununla birlikte, rüzgâr enerjisi ve fotovoltaik enerji gibi bazı kaynakların belirsiz doğası, güç sisteminin değişken çıkışına ve kararsızlığına yol açmaktadır. Sanal Güç Santrali (SGS), güç sistemindeki bu zorlukların üstesinden gelmek için uygun bir çözümdür. SGS, yenilenebilir ve yakıt bazlı üretim sistemleri, depolama sistemleri ve yönetilebilir yüklerden oluşan dağıtık enerji kaynaklarını bir araya toplamaktadır. Bu çalışmada, bir Rüzgâr Enerjisi Santrali (RES), bir Fotovoltaik Enerji Santrali (FVES), bir Konvansiyonel Enerji Santrali (KES) ve bir Pompaj Depolamalı Hidroelektrik Santral (PDHS) içeren bir SGS'nin optimum işletme stratejisi, Gün Öncesi Piyasasında (GÖP) maksimum kâr elde edecek şekilde belirlenmiştir. Kesintili yenilenebilir enerji üretimi için belirsizlik analizi, belirsiz parametrelerin (rüzgâr hızı ve güneş radyasyonu) geçmiş verilere dayanan senaryolarla modellenmesi ile yapılmıştır. Ayrıca, düşük kâr senaryoları riski, bir risk ölçütü olan Koşullu Riske Maruz Değer (CVaR) kullanılarak değerlendirilmiştir.

Anahtar Kelimeler

• sanal güç santrali,gün öncesi piyasası,kâr,belirsizlik,risk

Kaynakça

1. Liu, Y.; Li, Y.; Lian, H.; Tang, X.; Liu, C.; Jiang, C., Optimal dispatch of virtual power plant using interval and deterministic combined optimization, Electrical Power and Energy Systems, 2018, 102(2018): 235-244.
2. Shabanzadeh, M.; Sheikh-El-Eslami, M.; Haghifam, M., A medium-term coalition-forming model of heterogeneous DERs for a commercial virtual power plant, Applied Energy, 2016, 169(2016): 663-681.
3. Shara, H.; Mishra, S., Techno‐economic analysis of solar grid‐based virtual power plant in Indian power sector: A case study, Int Trans Electr Energ Syst., 2019;e12177.
4. Sadeghian, O.; Shotorbani, A. M.; Mohammadi-Ivatloo, B., Generation maintenance scheduling in virtual power plants, IET Generation, Transmission & Distribution., 2019, 13(12): 2584-2596.
5. Sun, G.; Qian, W.; Huang, W.; Xu, Z.; Fu, Z.; Wei, Z.; Chen, S., Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach, Energies, 2019, 12, 1918; doi:10.3390/en12101918.
6. Shafiekhani, M.; Badri, A.; Shafie-khah, M.; Catalao, J. P. S., Strategic bidding of virtual power plant in energy markets: A bi-level multiobjective approach, Electrical Power and Energy Systems, 2019, 113 (2019): 208–219.
7. Alahyari, A.; Ehsan, M.; Mousavizadehi M., A hybrid storage-wind virtual power plant (VPP) participation in the electricity markets: A self-scheduling optimization considering price, renewable generation, and electric vehicles uncertainties, Journal of Energy Storage, 2019, 25(2019), 100812.
8. Zhao, Y.; Lin, Z.; Wen, F.; Ding, Y.; Hou, J.; Yang, L., Risk-Constrained Day-Ahead Scheduling for Concentrating Solar Power Plants With Demand Response Using Info-Gap Theory, IEEE Transactions on Industrial Informatics, 2019, 15(10): 5475-5488.

9. Baringo, A; Baringo, L.; Arroyo, J. M., Day-Ahead Self-Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets Under Uncertainty, IEEE Transactions on Power Systems, 2019, 34(3): 1881-1894.
10. Kasaei, M. J., Energy and operational management of virtual power plant using imperialist competitive algorithm, Int Trans Electr Energ Syst., 2018;28:e2617. https://doi.org/10.1002/etep.2617.
11. Kardakos, E. G.; Simoglou, C. K., Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach, IEEE Transactions o Smart Grid., 2016, 7(2): 794-806.
12. Zhao, Q.; Shen, Y.; Li, M., Control and Bidding Strategy for Virtual Power Plants With Renewable Generation and Inelastic Demand in Electricity Markets, IEEE Transactions on Sustainable Energy, 2016, 7(2): 562-575.
13. Yang, D.; He, S.; Chen, Q.; Li, D.; Pandzic, H., Bidding Strategy of a Virtual Power Plant Considering Carbon-electricity Trading, CSEE Journal of Power and Energy Systems, 2019, 5(3): 306-314.
14. Nosratabadi, S. M.; Hooshmand, R-A.; Gholipour, E.; Prastegari, M., A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant, Int. Trans. Electr. Energ. Syst., 2016, 26: 1103–1120.
15. Zamani, A. G.; Zakariazadeh, A.; Jadid, S.; Kazemi, A., Stochastic operational scheduling of distributed energy resources in a large scale virtual power plant, Electrical Power and Energy Systems, 2016, 82(2016): 608-620.
16. Conejo, A. J.; Carrion, M.; Morales, J. M., Decision Making Under Uncertainty in Electricity Markets, Springer 2010.
17. Ozerdem, B.; Ozer, S.; Tosun, M., Feasibility study of wind farms: A case study for Izmir, Turkey, Journal of Wind Engineering and Industrial Aerodynamics, 2016, 94 (2006): 725–743.
18. Hadayeghparast, S.; Farsangi, A. S.; Shayanfar H., Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant, Energy, 2019, 172(2019): 630-646.
19. Shayegan, A.; Badri, A.; Zangeneh, A., Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties, Energy, 2017, 121(2017): 114-125.
20. Pandzic, H.; Kuzle, I.; Capuder, T., Virtual power plant mid-term dispatch optimization, Applied Energy, 2013, 101(2013): 134-141.
21. ‘Energy Exchange Istanbul (EXIST) webpage’, https://www.epias.com.tr/.
22. Gröwe-Kuska, N.; Heitsch, H.; Römisch, W., Scenario reduction and scenario tree construction for power management problems, 2003 IEEE Bologna PowerTech-Conference Proceedings, 2003, 3: 152–158.
23. 'GAMS Webpage', http://www.gams.com/ .