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Offering strategy of a price-maker virtual power plant in the day-ahead market

Department of Electrical Engineering, School of Electrical and Electronics Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam

Received: 22 Mar 2023; Revised: 2 May 2023; Accepted: 19 May 2023; Available online: 27 May 2023; Published: 15 Jul 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
With the rapid increase of renewable energy sources (RESs), the virtual power plant model (VPP) has been developed to integrate RESs, energy storage systems (ESSs), and local customers to overcome the RESs’ disadvantages. When the VPP’s capacity is large enough, it can participate in the electricity market as a price-maker instead of a price-taker to obtain a higher profit. This study proposes a bi-level optimization model to determine the optimal trading strategies of a price-maker VPP in the day-ahead (DA) market. The operation schedule of the components in the VPP is also optimized to achieve the highest profit for the VPP. In the bi-level optimization problem, the upper-level model is maximizing the VPP’s profit while the lower-level model is the DA market-clearing problem. The bi-level optimization problem is formulated as a Mathematical Problem with Equilibrium Constraints (MPEC), reformulated to a Mixed Integer Linear Problem (MILP), then solved by GAMS and CPLEX. This study applies the bi-level optimization model to a test VPP system, including wind plants (WP), solar plants (PV), biogas energy plants (BG), ESSs, and several customers. The maximum power outputs of WP and PV are 100MW and 90MW, respectively. The total installed capacity of BG is 70MW, while the ESS’ rated capacity is 100MWh. The local customers have the highest total consumption of 100MW. In addition to the VPP, four GENCOs and three retailers participate in the DA market. The results show that the market-clearing price varies depending on the participants’ production/consumption quantity and offering/bidding price. However, based on the optimization model, the VPP can take full advantage of WP and PV available power output, choose the right time to operate BG, then obtain the highest profit. The results also show that with the ESS’ rated capacity of 100MWh, the ESS’ rated discharging/charging power increased from 10MW to 50MW will increase VPP’s profit from 45987$ to 49464$. The obtained results show that the proposed model has practical significance
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Keywords: Day-ahead market; mathematical problem with equilibrium constraints; mixed-integer linear programming; price-maker; renewable energy; virtual power plants
Funding: The Ministry of Education and Training, Vietnam under contract CT 2022.07.BKA.05

Article Metrics:

  1. AEMO. (2021). AEMO NEM Virtual Power Plant Demonstrations. https://aemo.com.au/-/media/files/initiatives/der/2021/vpp-demonstrations-knowledge-sharing-report-4
  2. ARENA. (2021). South Australia Virtual Power Plant Phase 3A. https://arena.gov.au/assets/2021/08/tesla-virtual-power-plant-lessons-learnt-1.pdf
  3. Baringo, A., & Baringo, L. (2017). A Stochastic Adaptive Robust Optimization Approach for the Offering Strategy of a Virtual Power Plant. IEEE Transactions on Power Systems, 32(5), 3492–3504. https://doi.org/10.1109/TPWRS.2016.2633546
  4. Baringo, A., Baringo, L., & Arroyo, J. M. (2019). Day-Ahead Self-Scheduling of a Virtual Power Plant in Energy and Reserve Electricity Markets under Uncertainty. IEEE Transactions on Power Systems, 34(3), 1881–1894. https://doi.org/10.1109/TPWRS.2018.2883753
  5. Baringo, L., Freire, M., García-Bertrand, R., & Rahimiyan, M. (2021). Offering strategy of a price-maker virtual power plant in energy and reserve markets. Sustainable Energy, Grids and Networks, 28, 100558. https://doi.org/10.1016/J.SEGAN.2021.100558
  6. British Chamber of Commerce Vietnam. (2022). Vietnam Renewable Energy Report. https://britchamvn.com/wp-content/uploads/2022/04/156e6152-729f-4225-89b9-ff1ad516b803.pdf
  7. Das, K. (2020). Renewables in Vietnam: Current Opportunities and Future Outlook. Vietnam Briefing. https://www.vietnam-briefing.com/news/vietnams-push-for-renewable-energy.html/
  8. Ding, H., Pinson, P., Hu, Z., Wang, J., & Song, Y. (2017). Optimal Offering and Operating Strategy for a Large Wind-Storage System as a Price Maker. IEEE Transactions on Power Systems, 32(6), 4904–4913. https://doi.org/10.1109/TPWRS.2017.2681720
  9. Electricity Regulatory Authority of Vietnam. (2018, September 27). Circular: Regulations on operation of the competitive electricity generation market. http://www.erav.vn/userfile/User/trungnla/files/2020/10/TT_28_2018_TT_BCT.pdf
  10. Fernández-Muñoz, D., & Pérez-Díaz, J. I. (2023). Optimisation models for the day-ahead energy and reserve self-scheduling of a hybrid wind–battery virtual power plant. Journal of Energy Storage, 57. https://doi.org/10.1016/J.EST.2022.106296
  11. Gazijahani, F. S., & Salehi, J. (2020). IGDT-Based Complementarity Approach for Dealing with Strategic Decision Making of Price-Maker VPP Considering Demand Flexibility. IEEE Transactions on Industrial Informatics, 16(4), 2212–2220. https://doi.org/10.1109/TII.2019.2932107
  12. Helman, U. (2019). Distributed energy resources in the US wholesale markets: Recent trends, new models, and forecasts. In Consumer, Prosumer, Prosumager: How Service Innovations will Disrupt the Utility Business Model (1st ed., pp. 431–469). Academic Press/Elsevier. https://doi.org/10.1016/B978-0-12-816835-6.00019-X
  13. Hu, J., Jiang, C., & Liu, Y. (2019). Short-Term Bidding Strategy for a Price-Maker Virtual Power Plant Based on Interval Optimization. Energies, 12(19), 3662. https://doi.org/10.3390/EN12193662
  14. IBM. (n.d.). ILOG CPLEX Optimization Studio | IBM. Retrieved March 31, 2022, from https://www.ibm.com/products/ilog-cplex-optimization-studio
  15. Internationale Klimaschutzinitiative. (2022, December 12). Vietnam: Biogas plants to reduce greenhouse gas emissions | Internationale Klimaschutzinitiative (IKI). https://www.international-climate-initiative.com/en/iki-media/news/vietnam-biogas-plants-to-reduce-greenhouse-gas-emissions/
  16. Kardakos, E. G., Simoglou, C. K., & Bakirtzis, A. G. (2016). Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach. IEEE Transactions on Smart Grid, 7(2), 794–806. https://doi.org/10.1109/TSG.2015.2419714
  17. Kieny, C., Berseneff, B., Hadjsaid, N., Besanger, Y., & Maire, J. (2009). On the concept and the interest of Virtual Power plant: Some results from the European project FENIX. 2009 IEEE Power and Energy Society General Meeting, PES ’09. https://doi.org/10.1109/PES.2009.5275526
  18. Kuiper, G. (2022). What Is the State of Virtual Power Plants in Australia ? From Thin Margins to a Future of VPP-tailers (Issue March). https://ieefa.org/wp-content/uploads/2022/03/What-Is-the-State-of-Virtual-Power-Plants-in-Australia_March-2022_2.pdf
  19. Lee, J., & Won, D. (2021). Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation. IEEE Access, 9, 56965–56983. https://doi.org/10.1109/ACCESS.2021.3072550
  20. Mashhour, E., & Moghaddas-Tafreshi, S. M. (2011). Bidding strategy of virtual power plant for participating in energy and spinning reserve markets-Part I: Problem formulation. IEEE Transactions on Power Systems, 26(2), 949–956. https://doi.org/10.1109/TPWRS.2010.2070884
  21. Mauky, E., Weinrich, S., Jacobi, H. F., Nägele, H. J., Liebetrau, J., & Nelles, M. (2017). Demand-driven biogas production by flexible feeding in full-scale – Process stability and flexibility potentials. Anaerobe, 46, 86–95. https://doi.org/10.1016/J.ANAEROBE.2017.03.010
  22. Neme, C., Energy Futures Group, Cowart, R., & Regulator Assistance Project. (2014). Energy Efficiency Participation in Electricity Capacity Markets – The US Experience. https://www.raponline.org/knowledge-center/energy-efficiency-participation-in-electricity-capacity-markets-the-us-experience/
  23. Next-Kraftwerke. (n.d.). Virtual Power Plant: The Power of Many. Retrieved April 19, 2023, from https://www.next-kraftwerke.com/vpp
  24. Ngo, C. (2021, October 27). Can solar power be curtailed by about 8 billion kWh in 2021? https://laodong.vn/kinh-doanh/dien-mat-troi-co-the-bi-cat-giam-khoang-8-ti-kwh-trong-nam-2021-961245.ldo
  25. Nguyen-Duc, H., & Nguyen-Hong, N. (2020). A study on the bidding strategy of the Virtual Power Plant in energy and reserve market. Energy Reports, 6, 622–626. https://doi.org/10.1016/J.EGYR.2019.11.129
  26. Nguyen, H. T., Le, L. B., & Wang, Z. (2018). A Bidding Strategy for Virtual Power Plants with the Intraday Demand Response Exchange Market Using the Stochastic Programming. IEEE Transactions on Industry Applications, 54(4), 3044–3055. https://doi.org/10.1109/TIA.2018.2828379
  27. Noi, S., Jelínek, M., & Roubík, H. (2022). Small-scale biogas plants in Vietnam: How are affected by policy issues? Ecological Questions, 33(4), 1–38. https://doi.org/10.12775/EQ.2022.037
  28. Oureilidis, K., Malamaki, K. N., Gallos, K., Tsitsimelis, A., Dikaiakos, C., Gkavanoudis, S., Cvetkovic, M., Mauricio, J. M., Ortega, J. M. M., Ramos, J. L. M., Papaioannou, G., & Demoulias, C. (2020). Ancillary services market design in distribution networks: Review and identification of barriers. Energies, 13(4). https://doi.org/10.3390/en13040917
  29. Pal, P., Krishnamoorthy, P. A., Rukmani, D. K., Antony, S. J., Ocheme, S., Subramanian, U., Elavarasan, R. M., Das, N., & Hasanien, H. M. (2021). Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework. Applied Sciences 11(9), 3814. https://doi.org/10.3390/APP11093814
  30. Pudjianto, D., Ramsay, C., & Strbac, G. (2007). Virtual power plant and system integration of distributed energy resources. IET Renewable Power Generation, 1(1), 10–16. https://doi.org/10.1049/iet-rpg:20060023
  31. Renewable Energy Agency, I. (2012). RENEWABLE ENERGY TECHNOLOGIES: COST ANALYSIS SERIES Biomass for Power Generation Acknowledgement. https://www.irena.org/publications/2012/Jun/Renewable-Energy-Cost-Analysis---Biomass-for-Power-Generation
  32. Richard, E. R. (2016). GAMS: A User’s Guide. GAMS Development Corporation
  33. Steven A. Gabriel, Antonio J. Conejo, J. David Fuller, Benjamin F. Hobbs, Carlos Ruiz, Gabriel, S. A., Conejo, A. J., Fuller, J. D., Hobbs, B. F., & Ruiz, C. (2013). Complementarity Modeling in Energy Markets. In Springer (Vol. 180). https://doi.org/10.1007/978-1-4419-6123-5
  34. Tavakoli, A., Karimi, A., & Shafie-Khah, M. (2021). Linearized Stochastic Optimization Framework for Day-Ahead Scheduling of a Biogas-Based Energy Hub under Uncertainty. IEEE Access, 9, 136045–136059. https://doi.org/10.1109/ACCESS.2021.3116028
  35. Vahedipour-Dahraie, M., Rashidizadeh-Kermani, H., Shafie-Khah, M., & Catalão, J. P. S. (2021). Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs. IEEE Transactions on Smart Grid, 12(2), 1405–1415. https://doi.org/10.1109/TSG.2020.3026971
  36. Yang, H., Li, C., Huang, R., Wang, F., Hao, L., Wu, Q., & Zhou, L. (2023). Bi-level Energy Trading Model Incorporating Large-scale Biogas Plant and Demand Response Aggregator. Journal of Modern Power Systems and Clean Energy, 11(2), 567–578. https://doi.org/10.35833/MPCE.2021.000632
  37. Yazdaninejad, M., Amjady, N., & Dehghan, S. (2020). VPP Self-Scheduling Strategy Using Multi-Horizon IGDT, Enhanced Normalized Normal Constraint, and Bi-Directional Decision-Making Approach. IEEE Transactions on Smart Grid, 11(4), 3632–3645. https://doi.org/10.1109/TSG.2019.2962968
  38. Yi, Z., Xu, Y., Wang, H., & Sang, L. (2021). Coordinated Operation Strategy for a Virtual Power Plant with Multiple DER Aggregators. IEEE Transactions on Sustainable Energy, 12(4), 2445–2458. https://doi.org/10.1109/TSTE.2021.3100088
  39. Zhao, Q., Shen, Y., & Li, M. (2016). Control and Bidding Strategy for Virtual Power Plants with Renewable Generation and Inelastic Demand in Electricity Markets. IEEE Transactions on Sustainable Energy, 7(2), 562–575. https://doi.org/10.1109/TSTE.2015.2504561
  40. Zhou, Y., Wei, Z., Sun, G., Cheung, K. W., Zang, H., & Chen, S. (2019). Four-level robust model for a virtual power plant in energy and reserve markets. IET Generation, Transmission and Distribution, 13(11), 2006–2014. https://doi.org/10.1049/iet-gtd.2018.5197

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