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Optimisation and Management of Virtual Power Plants Energy Mix Trading Model

1Institute for Globally Distributed Open Research and Education (IGDORE), Cleveland, Middlesbrough, TS1 4JE, United Kingdom

2Mehran University of Science and Technology, Indus Hwy, Jamshoro Sindh, 76062, Pakistan

3University of Bradford, Richmond Rd, Bradford, BD7 1DP, United Kingdom

Received: 19 Jun 2021; Revised: 31 Aug 2021; Accepted: 30 Sep 2021; Available online: 5 Oct 2021; Published: 1 Feb 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.

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Abstract
. In this study, a robust optimisation method (ROM) is proposed with aim to achieve optimal scheduling of virtual power plants (VPPs) in the day-ahead electricity markets where electricity prices are highly uncertain. Our VPP is a collection of various distributed energy resources (DERs), flexible loads, and energy storage systems that are coordinated and operated as a single entity. In this study, an offer and bid-based energy trading mechanism is proposed where participating members in the VPP setting can sell or buy to/from the day-ahead electricity market to maximise social welfare (SW). SW is defined as the maximisation of end-users benefits and minimisation of energy costs. The optimisation problem is solved as a mixed-integer linear programming model taking the informed decisions at various levels of uncertainty of the market prices. The benefits of the proposed approach are consistency in solution accuracy and traceability due to less computational burden and this would be beneficial for the VPP operators. The robustness of the proposed mathematical model and method is confirmed in a case study approach using a distribution system with 18-buses. Simulation results illustrate that in the highest robustness scenario, profit is reduced marginally, however, the VPP showed robustness towards the day-ahead market (DAM) price uncertainty
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Keywords: Renewable energies, Distributed energy resources; Power systems integration; Electricity market

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  1. Baringo, A., & Baringo, L. (2016). A stochastic adaptive robust optimization approach for the offering strategy of a virtual power plant. IEEE Transactions on Power Systems, 32(5), 3492-3504. DOI: 10.1109/TPWRS.2017.2713486
  2. Bertsimas, D., & Sim, M. (2003). Robust discrete optimization and network flows. Mathematical programming, 98(1), 49-71. https://doi.org/10.1007/s10107-003-0396-4
  3. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53. https://doi.org/10.1287/opre.1030.0065
  4. Correa-Florez, C. A., Michiorri, A., & Kariniotakis, G. (2019). Comparative analysis of adjustable robust optimization alternatives for the participation of aggregated residential prosumers in electricity markets. Energies, 12(6), 1019. https://doi.org/10.3390/en12061019
  5. He, G., Chen, Q., Kang, C., & Xia, Q. (2016). Optimal offering strategy for concentrating solar power plants in joint energy, reserve and regulation markets. IEEE Transactions on sustainable Energy, 7(3), 1245-1254. DOI: 10.1109/TSTE.2016.2533637
  6. Ju, L., Li, P., Tan, Z., & Wang, W. (2019). A dynamic risk aversion model for virtual energy plant considering uncertainties and demand response. International Journal of Energy Research, 43(3), 1272-1293. https://doi.org/10.1002/er.4366
  7. Kang, Y. (2017). Optimal energy management for virtual power plant with renewable generation. Energy and Power Engineering, 9(04), 308. DOI: 10.4236/epe.2017.94B036
  8. Kardakos, E. G., Simoglou, C. K., & Bakirtzis, A. G. (2015). Optimal offering strategy of a virtual power plant: A stochastic bi-level approach. IEEE Transactions on Smart Grid, 7(2), 794-806. DOI: 10.1109/TSG.2015.2419714
  9. Lin, X., Chen, C., & Gauzily, S. (2020). Integrated bidding strategy of distributed energy resources based on novel prediction and market model. International Journal of Energy Research, 44(5), 4048-4062. https://doi.org/10.1002/er.5198
  10. Liu, Y., Xin, H., Wang, Z., & Gan, D. (2015). Control of virtual power plant in microgrids: a coordinated approach based on photovoltaic systems and controllable loads. IET Generation, Transmission & Distribution, 9(10), 921-928. DOI: 10.1049/iet-gtd.2015.0392
  11. Luo, Z., Gu, W., Wu, Z., Wang, Z., & Tang, Y. (2018). A robust optimization method for energy management of CCHP microgrid. Journal of Modern Power Systems and Clean Energy, 6(1), 132-144. https://doi.org/10.1007/s40565-017-0290-3
  12. Mohammadi, J., Rahimi-Kian, A., & Ghazizadeh, M. S. (2011). Aggregated wind power and flexible load offering strategy. IET renewable power generation, 5(6), 439-447. DOI: 10.1049/iet-rpg.2011.0066
  13. Pandžić, H., Morales, J. M., Conejo, A. J., & Kuzle, I. (2013). Offering model for a virtual power plant based on stochastic programming. Applied Energy, 105, 282-292. https://doi.org/10.1016/j.apenergy.2012.12.077
  14. Podder, A. K., Islam, S., Kumar, N. M., Chand, A. A., Rao, P. N., Prasad, K. A., ... & Mamun, K. A. (2020). Systematic categorization of optimization strategies for virtual power plants. Energies, 13(23), 6251. https://doi.org/10.3390/en13236251
  15. 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. DOI: 10.1049/iet-rpg:20060023
  16. Rahimiyan, M., & Baringo, L. (2015). Strategic bidding for a virtual power plant in the day-ahead and real-time markets: A price-taker robust optimization approach. IEEE Transactions on Power Systems, 31(4), 2676-2687. DOI: 10.1109/TPWRS.2015.2483781
  17. Rangu, S. K., Lolla, P. R., Dhenuvakonda, K. R., & Singh, A. R. (2020). Recent trends in power management strategies for optimal operation of distributed energy resources in microgrids: A comprehensive review. International Journal of Energy Research, 44(13), 9889-9911. https://doi.org/10.1002/er.5649
  18. Riveros, J. Z., Bruninx, K., Poncelet, K., & D’haeseleer, W. (2015). Bidding strategies for virtual power plants considering CHPs and intermittent renewables. Energy Conversion and Management, 103, 408-418. https://doi.org/10.1016/j.enconman.2015.06.075
  19. Saniei, M. (2013). Short term scheduling of a virtual power plant in a Day-Ahead market under uncertainties using point estimate method. Journal of Novel Researches on Electrical Power, 2(1), 41-50. https://doi.org/ 10.1109/TPWRS.2010.2070883
  20. Sarker, E., Halder, P., Seyedmahmoudian, M., Jamei, E., Horan, B., Mekhilef, S., & Stojcevski, A. (2021). Progress on the demand side management in smart grid and optimization approaches. International Journal of Energy Research, 45(1), 36-64. https://doi.org/10.1002/er.5631
  21. Shabanzadeh, M., Sheikh-El-Eslami, M. K., & Haghifam, M. R. (2017). Risk-based medium-term trading strategy for a virtual power plant with first-order stochastic dominance constraints. IET Generation, Transmission & Distribution, 11(2), 520-529. DOI: 10.1049/iet-gtd.2016.1072
  22. Shayegan Rad, A., Badri, A., Zangeneh, A., & Kaltschmitt, M. (2019). Risk based optimal energy management of virtual power plant with uncertainties considering responsive loads. International Journal of Energy Research, 43(6), 2135-2150. https://doi.org/10.1002/er.4418
  23. Soroudi, A., & Amraee, T. (2013). Decision making under uncertainty in energy systems: State of the art. Renewable and Sustainable Energy Reviews, 28, 376-384. DOI: 10.1016/j.rser.2013.08.039
  24. Soroudi, A., & Ehsan, M. (2012). IGDT based robust decision making tool for DNOs in load procurement under severe uncertainty. IEEE Transactions on Smart Grid, 4(2), 886-895. DOI: 10.1109/TSG.2012.2214071
  25. Sučić, S., Dragičević, T., Capuder, T., & Delimar, M. (2011). Economic dispatch of virtual power plants in an event-driven service-oriented framework using standards-based communications. Electric Power Systems Research, 81(12), 2108-2119. https://doi.org/10.1016/j.epsr.2011.08.008
  26. Sun, G., Qian, W., Huang, W., Xu, Z., Fu, Z., Wei, Z., & Chen, S. (2019). Stochastic Adaptive Robust Dispatch for Virtual Power Plants Using the Binding Scenario Identification Approach. Energies, 12(10), 1918. https://doi.org/10.3390/en12101918
  27. 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
  28. Ullah, Z. & Mirjat, N.H., (2021). Virtual power plant: state of the art providing energy flexibility to local distribution grids. In E3S Web of Conferences (Vol. 231, p. 01002). EDP Sciences. https://doi.org/10.1051/e3sconf/202123101002
  29. 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
  30. Zhao, Q., Shen, Y., & Li, M. (2015). 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. DOI: 10.1109/TSTE.2015.2504561

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