DOI: https://doi.org/10.14710/ijred.2022.44586

Operational Planning and Design of Market-Based Virtual Power Plant with High Penetration of Renewable Energy Sources

1Institute for Globally Distributed Open Research and Education (IGDORE), Cleveland, Middlesbrough, United Kingdom

2University of Bradford, Richmond Rd, Bradford, BD7 1DP, United Kingdom

Received: 5 Feb 2022; Revised: 20 Mar 2022; Accepted: 4 Apr 2022; Available online: 16 Apr 2022; Published: 4 Aug 2022.
Editor(s): Grigorios Kyriakopoulos
Open Access Copyright (c) 2022 The Authors. 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.

Citation Format:
Abstract

Renewable energy sources (RESs) are becoming more prevalent as a source of clean energy, and their integration into the power market is speeding up. The fundamental reason for this is the growing global concern about climate change. However, their weather-dependent and uncertain nature raise questions about grid reliability particularly, when photovoltaics (PVs) and wind turbines (WTs) technologies are used. As a result, rationally managing Energy Storage Systems (ESSs) under the virtual power plant (VPP) setting is being encouraged as a way of minimizing the impact of the uncertain nature of renewable energies. A VPP is comparatively a new concept that aggregates the capacities of dispatchable and non-dispatchable energy sources, electrical loads, and energy storage systems for the purpose of improving energy supply and demand imbalance. It enables individual consumers and producers to participate in the power markets. In this study, a new market-based (MB)-VPP operational planning model is designed and developed with the aim to evaluate the optimal active power dispatched by (WT, PV, and ESS) operating in the day-ahead power market to maximize the social welfare (SW) of the market. SW can be described as the maximization of the consumer’s benefit function minus the cost of energy generation. The optimization process was carried out by using a scenario-based approach to model the uncertainties of renewable energy sources (i.e, WTs & PVs) and load demand. The proposed model and method performance is validated by simulation studies on a 16-bus UK generic distribution system (UKGDS). The simulation results reveal that the proposed approach maximizes overall system social welfare. The capacity of total active power dispatched by (WT, PV, and ESS) has a positive impact on the VPP profit maximization. This empirical study could be used as a reference baseline model for other energy services providers interested in conducting similar research in the future.

Fulltext View|Download
Keywords: Climate change; Renewable energy sources; Electricity market; Economic mechanism; Uncertainty modeling; Virtual power plant

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Solar Tracking System with Photovoltaic Cells: Experimental Analysis at High Altitudes Optimization of Aeration for Accelerating Municipal Solid Waste Biodrying Operational Planning and Design of Market-Based Virtual Power Plant with High Penetration of Renewable Energy Sources More recent articles
Most cited articles
Kinetic and Enhancement of Biogas Production For The Purpose of Renewable Fuel Generation by Co-digestion of Cow Manure and Corn Straw in A Pilot Scale CSTR System Fractional Order Sliding Mode Control of PMSG-Wind Turbine Exploiting Clean Energy Resource Energetic and exergetic Improvement of geothermal single flash cycle Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation First Aspect of Conventional Power System Assessment for High Wind Power Plants Penetration More cited articles
  1. Ahmed, S. A., & Mahammed, H. O. (2012). A statistical analysis of wind power density based on the Weibull and Ralyeigh models of “Penjwen Region” Sulaimani/Iraq. Jordan Journal of Mechanical and Industrial Engineering, 6(2), 135-140
  2. Anagnostopoulos, T., Kyriakopoulos, G. L., Ntanos, S., Gkika, E., & Asonitou, S. (2020). Intelligent predictive analytics for sustainable business investment in renewable energy sources. Sustainability, 12(7), 2817. https://doi.org/10.3390/su12072817
  3. Arabatzis, G., Kyriakopoulos, G., & Tsialis, P. (2017). Typology of regional units based on RES plants: The case of Greece. Renewable and Sustainable Energy Reviews, 78, 1424-1434. https://doi.org/10.1016/j.rser.2017.04.043
  4. Badar, A. Q., Patil, P., & Sanjari, M. J. (2022). Introduction and history of virtual power plants with experimental examples. In Scheduling and Operation of Virtual Power Plants (pp. 1-26). https://doi.org/10.1016/B978-0-32-385267-8.00006-8
  5. Baringo, L., & Rahimiyan, M. (2020). Virtual Power Plants and Electricity Markets. In e-Book. Springer Nature
  6. Baseer, M., Mokryani, G., Zubo, R. H., & Cox, S. (2019). Planning of HMG with high penetration of renewable energy sources. IET Renewable Power Generation, 13(10), 1724-1730. http://dx.doi.org/10.1049/iet-rpg.2018.6024
  7. Cordero, E. C., Centeno, D., & Todd, A. M. (2020). The role of climate change education on individual lifetime carbon emissions. PloS one, 15(2), e0206266. https://doi.org/10.1371/journal.pone.0206266
  8. Drosos, D., Kyriakopoulos, G. L., Ntanos, S., & Parissi, A. (2021). School Managers Perceptions towards Energy Efficiency and Renewable Energy Sources. International Journal of Renewable Energy Development,10(3). https://doi.org/10.14710/ijred.2021.36704
  9. Fateh, D., Eldoromi, M., & Birjandi, A. A. M. (2022). Uncertainty modeling of renewable energy sources. I Scheduling and Operation of Virtual Power Plants (pp. 193-208). Elsevier. https://doi.org/10.1016/B978-0-32-385267-8.00014-7
  10. Fourer, R., Gay, D. M., & Kernighan, B. W. (1990). A modeling language for mathematical programming. Management Science, 36(5), 519-554. https://doi.org/10.1287/mnsc.36.5.519
  11. Guo, W., Liu, P., & Shu, X. (2021). Optimal dispatching of electric-thermal interconnected virtualpower plant considering market trading mechanism. Journal of Cleaner Production, 279, 123446. https://doi.org/10.1016/j.jclepro.2020.123446
  12. Hirsh, R. F., & Koomey, J. G. (2015). Electricity consumption and economic growth: a new relationship with significant consequences? The Electricity Journal, 28(9), 72-84. https://doi.org/10.1016/j.tej.2015.10.002
  13. Jordehi, A. R. (2022). A stochastic model for participation of virtual power plants in futures markets, pool markets and contracts with withdrawal penalty. Journal of Energy Storage, 50, 104334. https://doi.org/10.1016/j.est.2022.104334
  14. Kabbani, A., & Honnurvali, M. S. (2021). PV Cell Parameters Modeling and Temperature Effect Analysis. International Journal of Renewable Energy Development, 10(3). https://doi.org/10.14710/ijred.2021.33845
  15. Koza, E., & Öztürk, A. (2021). A Literature Review to Analyze the State of the Art of Virtual Power Plants in Context of Information Security. Environmental Informatics, 49-69. DOI: 10.1109/POWERCON.2016.7754037
  16. Li, Y., & Zio, E. (2012). Uncertainty analysis of the adequacy assessment model of a distributed generation system. Renewable Energy, 41, 235-244. https://doi.org/10.48550/arXiv.1206.1204
  17. Liu, J., Tang, H., Xiang, Y., Liu, J., & Zhang, L. (2017). Multi-stage market transaction method with participation of virtual power plants. Electric Power Construction, 38(3), 137-144
  18. Lu, X., & Cheng, L. (2021). Day-Ahead Scheduling for Renewable Energy Generation Systems considering Concentrating Solar Power Plants. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/9488222
  19. Mao, T., Guo, X., Xie, P., Zhou, J., Zhou, B., Han, S., ... & Sun, L. (2020, November). Virtual power plant platforms and their applications in practice: a brief review. In 2020 IEEE Sustainable Power and Energy Conference (iSPEC) (pp. 2071-2076). IEEE. DOI: 10.1109/iSPEC50848.2020.9351147
  20. Mokryani, G. (2017). Active distribution networks operation within a distribution market environment. In Sustainable Development in Energy Systems (pp. 107-118). Springer, Cham. http://hdl.handle.net/10454/12800
  21. Mokryani, G., Majumdar, A., & Pal, B. C. (2016). Probabilistic method for the operation of three-phase unbalanced active distribution networks. IET Renewable Power Generation, 10(7), 944-954. https://doi.org/10.1049/iet-rpg.2015.0334
  22. Montoya-Bueno, S., Muñoz-Hernández, J. I., & Contreras, J. (2016). Uncertainty management of renewable distributed generation. Journal of Cleaner Production, 138, 103-118. https://doi.org/10.1016/j.jclepro.2016.02.135
  23. Naval, N., & Yusta, J. M. (2021). Virtual power plant models and electricity markets-A review. Renewable and Sustainable Energy Reviews, 149, 111393. https://doi.org/10.1016/j.rser.2021.111393
  24. Ntanos, S., Kyriakopoulos, G. L., Anagnostopoulos, T., Xanthopoulos, T., Kitagawa’s, C., & Drosos, D. (2022). Investigating the Environmental and the Energy Saving Behavior among School Principals through Classification Algorithms. International Journal of Renewable Energy Development, 11(2), 449-461. https://doi.org/10.14710/ijred.2022.43007
  25. Ntanos, S., Skordoulis, M., Kyriakopoulos, G., Arabatzis, G., Chalikias, M., Galatsidas, S., ... & Katsarou, A. (2018). Renewable energy and economic growth: Evidence from European countries. Sustainability 10(8), 2626. https://doi.org/10.3390/su10082626
  26. Oladipupo, S. D., Rjoub, H., Kirikkaleli, D., & Adebayo, T. S. (2022). Impact of Globalization and Renewable Energy Consumption on Environmental Degradation: A Lesson for South Africa. International Journal of Renewable Energy Development, 11(1) https://doi.org/10.14710/ijred.2022.40452
  27. Reddy, S. S., Abhyankar, A. R., & Bijwe, P. R. (2012, July). Market clearing for a wind-thermal power system incorporating wind generation and load forecast uncertainties. In 2012 IEEE power and energy society general meeting (pp. 1-8). IEEE. DOI: 10.1109/PESGM.2012.6345335
  28. Reddy, S. S., Panigrahi, B. K., Kundu, R., Mukherjee, R., & Debchoudhury, S. (2013). Energy and spinning reserve scheduling for a wind-thermal power system using CMA-ES with mean learning technique. International Journal of Electrical Power & Energy Systems, 53, 113-122. https://doi.org/10.1016/j.ijepes.2013.03.032
  29. Reddy, S. S., Bijwe, P. R., & Abhyankar, A. R. (2013). Joint energy and spinning reserve mark clearing incorporating wind power and load forecast uncertainties. IEEE Systems Journal, 9(1), 152-164. DOI: 10.1109/JSYST.2013.2272236
  30. Reddy, S. S., Bijwe, P. R., & Abhyankar, A. R. (2013). Optimal posturing in day-ahead market clearing for uncertainties considering anticipated real-time adjustment costs. IEEE Systems Journal, 9(1),177-190. DOI: 10.1109/JSYST.2013.2265664
  31. Rogelj, J., Den Elzen, M., Höhne, N., Fransen, T., Fekete, H., Winkler, H., ... & Meinshausen, M. (2006) Paris Agreement climate proposals need a boost to keep warming well below 2 C. Nature, 534(7609), 631-639
  32. Rouzbahani, H. M., Karimipour, H., & Lei, L. (2021). A review on virtual power plant for energy management. Sustainable energy technologies and assessments, 47, 101370. https://doi.org/10.1016/j.seta.2021.101370
  33. Sadeghi, S., Jahangir, H., Vatandoust, B., Golkar, M. A., Ahmadian, A., & Elkamel, A. (2021). Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach. International Journal of Electrical Power & Energy Systems, 127, 106646. https://doi.org/10.1016/j.ijepes.2020.106646
  34. Salkuti, S. R. (2019). Day-ahead thermal and renewable power generation scheduling considering uncertainty. Renewable Energy, 131, 956-965. https://doi.org/10.1016/j.renene.2018.07.106
  35. Soroudi, A. (2017). Power system optimization modeling in GAMS (Vol. 78). Switzerland: Springer
  36. Ugwu, J., Odo, K. C., Oluka, L. O., & Salami, K. O. (2022). A Systematic Review on the Renewable Energy Development, Policies and Challenges in Nigeria with an International Perspective and Public Opinions. International Journal of Renewable Energy Development, 11(1). https://doi.org/10.14710/ijred.2022.40359
  37. Ullah, Z., Mokryani, G., Campean, F., & Hu, Y. F. (2019). Comprehensive review of VPPs planning, operation and scheduling considering the uncertainties related to renewable energy sources. IET Energy Systems Integration, 1(3), 147-157. https://doi.org/10.1049/iet-esi.2018.0041
  38. Ullah, Zahid., Mirjat, N,H. (2021). "Optimisation and management of virtual power plants energy mix trading model." International Journal of Renewable Energy Development. https://doi.org/10.14710/ijred.2022.39295
  39. Ullah, Z., & Mirjat, N. H. (2021). Modelling and analysis of virtual power plants interactive operational characteristics in distribution systems. Energy Conversion and Economics. https://doi.org/10.1049/enc2.12033
  40. Vahedipour-Dahraie, M., Rashidizadeh-Kermani, H., Shafie-Khah, M., & Catalão, J. P. (2020). Risk-Averse Optimal Energy and Reserve Scheduling for Virtual Power Plants Incorporating Demand Response Programs. IEEE Transactions on Smart Grid, 12(2), 1405-1415. DOI: 10.1109/TSG.2020.3026971
  41. Wang, X., Liu, Z., Zhang, H., Zhao, Y., Shi, J., & Ding, H. (2019). A review on virtual power plant concept, application and challenges. 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 4328- 4333. DOI: 10.1109/ISGT-Asia.2019.8881433
  42. Zhang, J. (2022). The Concept, Project and Current Status of Virtual Power Plant: A Review. In Journal of Physics: Conference Series (Vol. 2152, No. 1, p. 012059). IOP Publishing. DOI: 10.1088/1742-6596/2152/1/012059
  43. Zubo, R. H., & Mokryani, G. (2019). Active distribution network operation: a market-based approach. IEEE Systems Journal, 14(1), 1405-1416. DOI: 10.1109/JSYST.2019.2927442
  44. Zubo, R. H., Mokryani, G., & Abd-Alhameed, R. (2018). Optimal operation of distribution networks with high penetration of wind and solar power within a joint active and reactive distribution market environment. Applied energy, 220, 713-722. https://doi.org/10.1016/j.apenergy.2018.02.016

Last update:

  1. Demand Response Strategy of a Virtual Power Plant for Internal Electricity Market

    Zahid Ullah, Muhammad Baseer, Arshad. 2022 IEEE 10th International Conference on Smart Energy Grid Engineering (SEGE), 2022. doi: 10.1109/SEGE55279.2022.9889759

  2. Multi Objective Slime Mould Algorithm Based Energy Management in Hybrid Micro Grid System

    Suravi Singha, Parthasarathi Bera. Indian Science Cruiser, 2024. doi: 10.24906/isc/2023/v37/i4/43717

  3. Virtual Power Plant as a Demand Response Aggregator

    Alireza Zare, Mohammad Agha Shafiyi. 2024 28th International Electrical Power Distribution Conference (EPDC), 2024. doi: 10.1109/EPDC62178.2024.10571710

  4. Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources

    Zahid Ullah, Arshad, Hany Hassanin. Energies, 15 (14), 2022. doi: 10.3390/en15145296

  5. Investigation of the Impact of Large-Scale Wind Power and Solar Power Plants on a Vietnamese Transmission Network

    Ngo Minh Khoa, Nguyen Thi Hai Van, Le Kim Hung, Doan Anh Tuan. International Journal of Renewable Energy Development, 11 (3), 2022. doi: 10.14710/ijred.2022.43879

  6. The Development of a Cross-Border Energy Trade Cooperation Model of Interconnected Virtual Power Plants Using Bilateral Contracts

    Zahid Ullah, Arshad, Jawad Ahmad. Energies, 15 (21), 2022. doi: 10.3390/en15218171

  7. The impact of Energy Storage System on the flexible operation of the Renewable-Based Virtual Power Plant

    Alireza Zare, Mohammad Agha Shafiyi. 2024 11th Iranian Conference on Renewable Energy and Distribution Generation (ICREDG), 2024. doi: 10.1109/ICREDG61679.2024.10607790

  8. Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty

    Zahid Ullah, Arshad, Hany Hassanin, James Cugley, Mohammed Al Alawi. Energies, 15 (19), 2022. doi: 10.3390/en15197290

  9. Evolution and role of virtual power plants: Market strategy with integration of renewable based microgrids

    Ahmad Faiz Minai, Akhlaque Ahmad Khan, Kitmo, Mouhamadou Falilou Ndiaye, Tabish Alam, Rohit Khargotra, Tej Singh. Energy Strategy Reviews, 53 , 2024. doi: 10.1016/j.esr.2024.101390

  10. Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

    Thanh Tuan Le, Jayabal Chandra Priya, Huu Cuong Le, Nguyen Viet Linh Le, Minh Thai Duong, Dao Nam Cao. International Journal of Renewable Energy Development, 13 (2), 2024. doi: 10.61435/ijred.2024.60119

Last update: 2024-12-20 23:34:00

No citation recorded.