A tri-level optimization model for utilizing the potential of IoT-based subscribers and electric vehicles in energy and ancillary services markets

Subject Areas : Power Engineering

Leyla Karami ¹ , Amir Ahmarinejad ² , Mahmood Hosseini Aliabadi ³ , Arash Dana ⁴

1 - Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2023-07-10 Accepted : 2023-09-26 Published : 2024-02-20

Keywords: Electrical transmission and distribution networks, Optimization of energy and ancillary services markets, smart homes, electric vehicles, renewable energy resources,

Abstract :

This paper presents a tri-level model for the simultaneous management of energy and ancillary services markets between transmission and distribution networks integrated with renewable energy sources, smart homes based on the Internet of Things, and electric vehicles. In the first level of the proposed model, smart homes plan their participation in the energy and regulation markets and send it to the distribution network operator. In the second level, the operators of the distribution networks plan their area according to the programs received from the smart homes and determine their strategy for participation in the energy, reservation and adjustment markets. In the third level, the strategy of distribution networks is sent to the operator of the transmission system so that the final planning of the energy, reservation and adjustment markets can be done according to them. The proposed model is formulated as a mixed integer linear programming problem and solved by GUROBI solver in GAMS. The implementation of the proposed model showed that this model was able to significantly use the potential of subscribers based on the Internet of Things, electric vehicles, storage systems and demand response programs to improve the technical aspects of transmission and distribution networks as well as to improve the economic aspects of energy markets and ancillary services.

References:

1. K. Sevdari, L. Calearo, P.B. Andersen, M. Marinelli, “Ancillary services and electric vehicles: An overview from charging clusters and chargers technology perspectives,” Renewable and Sustainable Energy Reviews, vol. 167, pp. 112666, Oct 2022, doi: 10.1016/j.rser.2022.112666.


2. S. Impram, S. Varbak Nese, B Oral, “Challenges of renewable energy penetration on power system flexibility: A survey,” Energy Strategy Reviews, vol. 31, pp. 100539, Sep 2020, doi: 10.1016/j.esr.2020.100539.


3. N. Martin, J. Rice, “Power outages, climate events and renewable energy: Reviewing energy storage policy and regulatory options for Australia,” Renewable and Sustainable Energy Reviews, vol. 137, pp. 110617, March 2021, doi: 10.1016/j.rser.2020.110617.


4. M. Hamwi, I. Lizarralde, J. Legardeur, “Demand response business model canvas: A tool for flexibility creation in the electricity markets,” Journal of Cleaner Production, vol. 282, pp. 124539, Feb 2021, doi: 10.1016/j.jclepro.2020.124539.


5. S. Rivera S, Kouro, S. Vazquez, S.M. Goetz, R. Lizana, E. Romero-Cadaval, “Electric Vehicle Charging Infrastructure: From Grid to Battery,” IEEE Industrial Electronics Magazine, vol. 15, pp. 37-51, June 2021, doi: 10.1109/MIE.2020.3039039.


6. T. Jiang, C. Wu, R. Zhang, X. Li, H. Chen, G. Li, “Flexibility Clearing in Joint Energy and Flexibility Markets Considering TSO-DSO Coordination,” IEEE Transactions on Smart Grid, vol. 14, pp. 1376-1387, Mar 2023, doi: 10.1109/TSG.2022.3153634.


7. M. Habibi, V. Vahidinasab, M.S. Sepasian, “A privacy-preserving approach to day-ahead TSO-DSO coordinated stochastic scheduling for energy and reserve,” IET Generation, Transmission & Distribution, vol. 16, pp. 163-180, Jan 2022, doi: 10.1049/gtd2.12286.


8. S.I. Vagropoulos, P.N. Biskas, A.G. Bakirtzis, “Market-based TSO-DSO coordination for enhanced flexibility services provision,” Electric Power Systems Research, vol. 208, pp. 10883, July 2022, doi: 10.1016/j.epsr.2022.107883.


9. H. Chen, D. Wang, R. Zhang, T. Jiang, X. Li, “Optimal participation of ADN in energy and reserve markets considering TSO-DSO interface and DERs uncertainties,” Applied Energy, vol. 308, pp. 118319, Feb 2022, doi: 10.1016/j.apenergy.2021.118319.


10. L. Wang, J. Kwon, N. Schulz, Z. Zhou, “Evaluation of Aggregated EV Flexibility With TSO-DSO Coordination,”  IEEE Transactions on Sustainable Energy, vol. 13, pp. 2304-2315, Oct 2022, doi: 10.1109/TSTE.2022.3190199.


11. Z. Shen, M. Liu, L. Xu, W. Lu, “Coordinated scheduling of integrated transmission and distribution systems using an improved Lipschitz dynamic programming approach,” International Journal of Electrical Power & Energy Systems, vol. 140, pp. 108076, Sep 2022, doi: 10.1016/j.ijepes.2022.108076.


12. L. Marques, A. Sanjab, Y. Mou, H.L. Cadre, K. Kessels, “Grid Impact Aware TSO-DSO Market Models for Flexibility Procurement: Coordination, Pricing Efficiency, and Information Sharing,” IEEE Transactions on Power Systems, vol. 38, pp. 1920-1933, June 2022, doi: 10.1109/TPWRS.2022.3185460.


13. Y. Ruwaida, J.P. Chaves-Avila, N. Etherden, I. Gomez-Arriola, G. Gürses-Tran, K. Kessels, C. Madina, A. Sanjab, M. Santos-Mugica, D.N. Trakas, M. Tronica, “TSO-DSO-Customer coordination for purchasing flexibility system services: Challenges and lessons learned from a demonstration in Sweden,” IEEE Transactions on Power Systems, vol. 38, pp. 1883-1895, July 2022, doi: 10.1109/TPWRS.2022.3188261.


14. V.Z. Gjorgievski, N. Markovska, A. Abazi, N. Duić, “The potential of power-to-heat demand response to improve the flexibility of the energy system: An empirical review,” Renewable and Sustainable Energy Reviews, vol. 138, pp. 110489, March 2021, doi: 10.1016/j.rser.2020.110489.


15. S.A. Mansouri, A. Ahmarinejad, M.S. Javadi, A.E. Nezhad, M. Shafie-Khah, J.P.S. Catalão, “Chapter 9 - Demand response role for enhancing the flexibility of local energy systems,” Distributed Energy Resources in Local Integrated Energy Systems, pp. 279-313, 2021, doi: 10.1016/B978-0-12-823899-8.00011-X.


16. M.J. Rana, K.H. Rahi, T. Ray, R. Sarker, “An efficient optimization approach for flexibility provisioning in community microgrids with an incentive-based demand response scheme,” Sustainable Cities and Society, vol. 74, pp. 103218, Nov 2021, doi: 10.1016/j.scs.2021.103218.


17. T. Cheng, Z. Tan, H. Zhong, “Exploiting Flexibility of Integrated Demand Response to Alleviate Power Flow Violation during Line Tripping Contingency,” Journal of Modern Power Systems and Clean Energy, pp. 1-11, Feb 2022, doi:10.35833/MPCE.2021.000535.


18. R. Ansaripour, H. Barati, A. Ghasemi, “A chance-constrained optimization framework for transmission congestion management and frequency regulation in the presence of wind farms and energy storage systems,” Electric Power Systems Research, vol. 213, pp. 108712, Dec 2022, doi:10.1016/j.epsr.2022.108712.


19. R. Ansaripour, H. Barati, A. Ghasemi, “Multi-objective chance-constrained transmission congestion management through optimal allocation of energy storage systems and TCSC devices,” Electrical Engineering, vol. 104, pp. 4049-4069, July 2022, doi: 10.1007/s00202-022-01599-0.


20. Y. Chen, P. Xu, J. Gu, F. Schmidt, W. Li, “Measures to improve energy demand flexibility in buildings for demand response (DR): A review,” Energy and Buildings, vol. 177, pp. 125-139, Oct 2018, doi: 10.1016/j.enbuild.2018.08.003.


21. S.A. Mansouri, A. Ahmarinejad, E. Nematbakhsh, M.S. Javadi, A.R. Jordehi, J.P.S. Catalão, “Energy Management in Microgrids including Smart Homes: A Multi-objective Approach,” Sustainable Cities and Society, vol. 69, pp. 102852, June 2021, doi: 10.1016/j.scs.2021.102852.


22. S.A. Mansouri, A. Rezaee Jordehi, M. Marzband, M. Tostado-Véliz, F. Jurado, J.A. Aguado, “An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster,” Applied Energy, vol. 333, pp. 120560, March 2023, doi: 10.1016/j.apenergy.2022.120560.


23. S.A. Mansouri, E. Nematbakhsh, A.R. Jordehi, M. Marzband, M. Tostado-Véliz, F. Jurado, “An interval-based nested optimization framework for deriving flexibility from smart buildings and electric vehicle fleets in the TSO-DSO coordination,” Applied Energy, vol. 341, pp. 121062, July 2023, doi: 10.1016/j.apenergy.2023.121062.


24. A. Maneesha, K.S. Swarup, “Stochastic Optimal Bidding Strategy for Energy and Ancillary Services in Microgrid,” IEEE Transactions on Industry Applications, vol. 57, pp. 5698-5705, Dec 2021, doi: 10.1109/TIA.2021.3112936.


25. S.A. Mansouri, A. Ahmarinejad, E. Nematbakhsh, M.S. Javadi, A. Esmaeel Nezhad, J.P.S. Catalão, “A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources,” Energy, vol. 245, pp. 123228, April 2022, doi: 10.1016/j.energy.2022.123228.


26. S.A. Mansouri, E. Nematbakhsh, A.R. Jordehi, M. Tostado-Véliz, F. Jurado, Z. Leonowicz, “A Risk-Based Bi-Level Bidding System to Manage Day-Ahead Electricity Market and Scheduling of Interconnected Microgrids in the presence of Smart Homes,” 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), pp. 1–6, July 2022, doi: 10.1109/EEEIC/ICPSEurope54979.2022.9854685.


27. X. Wang, T. Zhao, A. Parisio, “Frequency regulation and congestion management by Virtual Storage Plants,” Sustainable Energy, Grids and Networks, vol. 29, pp. 100586, March 2022, doi: 10.1016/j.segan.2021.100586.


28. M. Roustaei, A. Letafat, M. Sheikh, A. Chabok, R. Sadoughi, M. Ardeshiri, “A cost-effective voltage security constrained congestion management approach for transmission system operation improvement,” Electric Power Systems Research, vol. 203, pp. 107674, Feb 2022, doi: 10.1016/j.epsr.2021.107674.


29. N. Tarashandeh, A. Karimi, “Utilization of energy storage systems in congestion management of transmission networks with incentive-based approach for investors,” Journal of Energy Storage, vol. 33, pp. 102034, Jan 2021, doi: 10.1016/j.est.2020.102034.


30. K. Paul, “Modified grey wolf optimization approach for power system transmission line congestion management based on the influence of solar photovoltaic system,” International Journal of Energy and Environmental Engineering, vol. 13, pp. 751-767, Jan 2022, doi: 10.1007/s40095-021-00457-2.


31. A. Singh, A.K. Bohre, “Congestion Management of System with N-1 Contingency by Optimal Placement of TCSC Using PSO,” Advances in Energy Technology, vol. 766, pp. 409–22, July 2021, doi: 10.1007/978-981-16-1476-7_38.


32. X. Wang, T. Xu, Y. Mu, Z. Wang, Y. Deng, T. Zhang, Q. Jiang, Y. Zhang, H. Jia, “Congestion management under peer-to-peer energy trading scheme among microgrids through cooperative game,” Energy Reports, vol. 8, pp. 59-66, April 2022, doi: 10.1016/j.egyr.2021.11.130.


33. F. Samadi Gazijahani, A. Ghaemi, M. Farrokhifar, A. Safari, “Interactive FACTS and demand response program as an incremental welfare consensus for maximizing wind power penetration,” International transactions on electrical energy systems, vol. 30, pp. e12526, July 2020, doi: 10.1002/2050-7038.12526.


34. Á.S Xavier, F. Qiu, F. Wang, P.R. Thimmapuram, “Transmission Constraint Filtering in Large-Scale Security-Constrained Unit Commitment,” IEEE Transactions on Power Systems, vol 34, pp. 2457-2460, May 2019, doi: 10.1109/TPWRS.2019.2892620.


35. B. Javanmard, M. Tabrizian, M. Ansarian, A. Ahmarinejad, “Energy management of multi-microgrids based on game theory approach in the presence of demand response programs, energy storage systems and renewable energy resources,” Journal of Energy Storage, vol. 42, pp. 102971, Oct 2021, doi: 10.1016/j.est.2021.102971.


36. A. A. Safaie, M. Alizadeh Bidgoli, S. Javadi, “A multi-objective optimization framework for integrated electricity and natural gas networks considering smart homes in downward under uncertainties,” Energy, vol. 239, pp. 122214, Jan 2022, doi: 10.1016/j.energy.2021.122214.


37. Y Chen, A Casto, F Wang, Q Wang, X Wang, J Wan, “Improving Large Scale Day-Ahead Security Constrained Unit Commitment Performance,” IEEE Transactions on Power Systems, vol. 31, pp. 4732-4743, Nov 2016, doi: 10.1109/TPWRS.2016.2530811.