Flexibility-Based Congestion Management Considering Consumers' Inconvenience Cost of Demand Response Programs

Document Type : Research paper

Authors

1 Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

2 Department of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran

3 Department of Electrical and Computer Engineering, Queen's University, Kingston, Canada

4 Power System Planning and Operation Research Department, Niroo Research Institute, Tehran, Iran

Abstract

One of the most important challenges of smart grids is the congestion of transmission lines. A flexible smart grid with demand-side resources can be a suitable solution to manage transmission lines congestion. This paper proposes a multi-objective model with the aim of congestion management through generation rescheduling considering cost and emission purposes in a flexible smart grid. An inconvenience cost for consumers is defined to model the consumers’ unsatisfactory as a consequence of participating in demand response programs (DRPs). Furthermore, a smart grid flexibility index (SGFI) has been presented to show the available flexibility of smart grid as a result of DRPs and gas turbine generators as fast response resources. The DRPs could increase the flexibility of the smart grids due to their impact on flattening the load curve, but this may cause some inconveniences for consumers. On the other hand, participation of consumers in DRPs and the power output gas turbine  are associated with uncertainty. In this paper, the uncertainty of consumer's participation in the DRPs has been modeled by Fuzzy-Markov. The proposed multi-objective particle swarm optimization (MOPSO) has been implemented on the IEEE 30-bus system. The results show that the total operation cost including the generation cost, DRP cost, inconvenience cost of consumers, and pollution is reduced. In fact, the share of generation of expensive generators is reduced.

Keywords

References

  1. Pillay, S. P. Karthikeyan, and D. Kothari, “Congestion management in power systems–a review,” Int. J. Electr. Power Energy Syst., vol. 70, pp. 83–90, 2015.
  2. K. Tumuluru and D.H. Tsang, “A two-stage approach for network constrained unit commitment problem with demand response,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 1175–1183, 2016.
  3. -M. Chung, C.-L. Su, and C.-K. Wen, “Dispatch of generation and demand side response in regional grids,” in 2015 IEEE 15th Int. Conf. Environ. Elec. Eng. (EEEIC), pp. 482–486, IEEE, 2015.
  4. B. Nappu and A. Arief, “Network losses-based economic redispatch for optimal energy pricing in a congested power system,” Energy Procedia, vol. 100, pp. 311–314, 2016.
  5. Riyaz, R. Upputuri, and N. Kumar, “Congestion management in power system—a review,” Recent Advances in Power Systems: Select Proceedings of EPREC 2020, pp. 425–433, 2021.
  6. Singh and A. Kumar, “Congestion management using demand response program,” in 2017 International conference on power and embedded drive control (ICPEDC), pp. 83–88, IEEE, 2017.
  7. Narain, S. Srivastava, and S. Singh, “Congestion management approaches in restructured power system: Key issues and challenges,” Electr. J., vol. 33, no. 3, p. 106715, 2020.
  8. A. Antonopoulos, S. Vitiello, G. Fulli, and M. Masera, Nodal pricing in the European internal electricity market, vol. 30155. Publications Office of the European Union Luxembourg, 2020.
  9. Han and A. Papavasiliou, “Congestion management through topological corrections: A case study of central western europe,” Energy Policy, vol. 86, pp. 470–482, 2015.
  10. Zheng, Y. Wang, K. Liu, and Q. Chen, “Locational marginal price forecasting: A componential and ensemble approach,” IEEE Trans. Smart Grid, vol. 11, no. 5, pp. 4555– 4564, 2020.
  11. Hanif, K. Zhang, C. M. Hackl, M. Barati, H.B. Gooi, and T. Hamacher, “Decomposition and equilibrium achieving distribution locational marginal prices using trustregion method,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3269–3281, 2018.
  12. K. Jain, S.C. Srivastava, S.N. Singh, and L. Srivastava, “Bacteria foraging optimization based bidding strategy under transmission congestion,” IEEE Syst. J., vol. 9, no. 1, pp. 141–151, 2013.
  13. Amanbek, “Decentralized transactive energy management framework for distribution systems,” 2020.
  14. M. Esfahani, A. Sheikh, and O. Mohammed, “Adaptive real-time congestion management in smart power systems using a real-time hybrid optimization algorithm,” Electr. Power Syst. Res., vol. 150, pp. 118–128, 2017.
  15. Jabari, M. Nazari-Heris, B. Mohammadi-Ivatloo, S. Asadi, and M. Abapour, “Toward energy-efficient microgrids under summer peak electrical demand integrating solar dish stirling heat engine and diesel unit,” J. Eng. Technol. Manage., vol. 4, no. 3, pp. 23–29, 2020.
  16. Khani, F. Jabari, M. Mohammadpourfard, and Mohammadi-ivatloo, “Design, evaluation, and optimizationof an efficient solar-based multi-generation system with an energy storage option for iran’s summer peak demand,” Energy Convers. Manag., vol. 242, p. 114324, 2021.
  17. Verma and V. Mukherjee, “Firefly algorithm for congestion management in deregulated environment,” Eng. Sci. Technol. Int J., vol. 19, no. 3, pp. 1254–1265, 2016.
  18. Chellam and S. Kalyani, “Power flow tracing based transmission congestion pricing in deregulated power markets,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 570–584, 2016.
  19. -A. Hooshmand, M.J. Morshed, and M. Parastegari, “Congestion management by determining optimal location of series facts devices using hybrid bacterial foraging and nelder–mead algorithm,” Appl. Soft Comput., vol. 28, pp. 57–68, 2015.
  20. Peesapati, V.K. Yadav, and N. Kumar, “Flower pollination algorithm based multi-objective congestion management considering optimal capacities of distributed generations,” Energy, vol. 147, pp. 980–994, 2018.
  21. Hemmati, H. Saboori, and M.A. Jirdehi, “Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources,” Energy, vol. 133, pp. 380–387, 2017.
  22. Verma and V. Mukherjee, “Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser,” IET Gener. Transm. Distrib., vol. 10, no. 10, pp. 2548–2561, 2016.
  23. S. Reddy, “Multi-objective based congestion management using generation rescheduling and load shedding,” IEEE Trans. Power Syst., vol. 32, no. 2, pp. 852–863, 2016.
  24. Gope, A.K. Goswami, P.K. Tiwari, and S. Deb, “Rescheduling of real power for congestion management with integration of pumped storage hydro unit using firefly algorithm,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 434–442, 2016.
  25. Menos-Aikateriniadis, I. Lamprinos, and P.S. Georgilakis, “Particle swarm optimization in residential demand-side management: A review on scheduling and control algorithms for demand response provision,” Energies, vol. 15, no. 6, p. 2211, 2022.
  26. Liu, X. Yu, C. Wang, C. Li, L. Ma, and J. Lei, “Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 3569–3583, 2017.
  27. Yi, Y. Zhang, Z. Zhao, and Y. Huang, “Multiobjective robust scheduling for smart distribution grids: Considering renewable energy and demand response uncertainty,” IEEE Access, vol. 6, pp. 45715–45724, 2018.
  28. Jabari, M. Nazari-heris, and M. Abapour, “Implementation and investigation of demand-side management polices in iran’s industrial and commercial sectors,” J. Eng. Technol. Manage., vol. 7, no. 1, pp. 34–42, 2023.
  29. Talari, M. Shafie-Khah, G.J. Osório, J. Aghaei, and J.P. Catalão, “Stochastic modelling of renewable energy sources from operators’ point-of-view: A survey,” Renew. Sustain. Energy Rev., vol. 81, pp. 1953–1965, 2018.
  30. Lokeshgupta and S. Sivasubramani, “Multi-objective dynamic economic and emission dispatch with demand side management,” Int. J. Electr. Power Energy Syst., vol. 97, pp. 334–343, 2018.
  31. Dehnavi and H. Abdi, “Determining optimal buses for implementing demand response as an effective congestion management method,” IEEE Trans. Power Syst., vol. 32, no. 2, pp. 1537–1544, 2016.
  32. Haque, P. Nguyen, F. Bliek, and J. Slootweg, “Demand response for real-time congestion management incorporating dynamic thermal overloading cost,” Sustain. Energy, Grids Netw., vol. 10, pp. 65–74, 2017.
  33. K. Prajapati and V. Mahajan, “Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources,” Energy, vol. 215, p. 119134, 2021.
  34. Good and P. Mancarella, “Flexibility in multi-energy communities with electrical and thermal storage: A stochastic, robust approach for multi-service demand response,” IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 503–513, 2017.
  35. D’hulst, W. Labeeuw, B. Beusen, S. Claessens, G. Deconinck, and K. Vanthournout, “Demand response flexibility and flexibility potential of residential smart appliances: Experiences from large pilot test in belgium,” Appl. Energy, vol. 155, pp. 79–90, 2015.
  36. F. Hobbs, J.C. Honious, and J. Bluestein, “What’s flexibility worth? the enticing case of natural gas cofiring,” Electr. J., vol. 5, no. 2, pp. 37–47, 1992.
  37. K. Alexopoulos, A.G. Anastasiadis, G.A. Vokas, S.D. Kaminaris, and C.S. Psomopoulos, “A review of flexibility options for high res penetration in power systems—focusing the greek case,” Energy Rep., vol. 7, pp. 33–50, 2021.
  38. M. Kazemi-Razi, H.A. Abyaneh, H. Nafisi, Z. Ali, and M. Marzband, “Enhancement of flexibility in multi-energy microgrids considering voltage and congestion improvement: Robust thermal comfort against reserve calls,” Sustain. Cities Soc., vol. 74, p. 103160, 2021.
  39. Zaeim-Kohan, H. Razmi, and H. Doagou-Mojarrad, “Multiobjective transmission congestion management considering demand response programs and generation rescheduling,” Appl. Soft Comput., vol. 70, pp. 169–181, 2018.
  40. J. Pardo and D. de la Fuente, “Fuzzy markovian decision processes: Application to queueing systems,” Comput. Math. with Appl., vol. 60, no. 9, pp. 2526–2535, 2010.
  41. Liu, X. Yu, C. Wang, C. Li, L. Ma, and J. Lei, “Energy-sharing model with price-based demand response for microgrids of peer-to-peer prosumers,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 3569–3583, 2017.
  42. Nasouri, G.N. Bidhendi, H. Hoveidi, and M.J. Amiri, “Parametric study and performance-based multi-criteria optimization of the indirect-expansion solar-assisted heat pump through the integration of analytic network process (anp) decision-making with mopso algorithm,” Sol. Energy, vol. 225, pp. 814–830, 2021.
  43. Shayesteh, M.P. Moghaddam, A. Yousefi, M.-R. Haghifam, and M. Sheik-El-Eslami, “A demand side approach for congestion management in competitive environment,” Eur. Trans. Electr. Power, vol. 20, no. 4, pp. 470–490, 2010.
  44. Dini, A. Hassankashi, S. Pirouzi, M. Lehtonen, Arandian, and A.A. Baziar, “A flexible-reliable operationoptimization model of the networked energy hubs with distributed generations, energy storage systems and demand response,” Energy, vol. 239, p. 121923, 2022.
Journal of Operation and Automation in Power Engineering
Volume 12, Issue 4
December 2024
Pages 352-364

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History

  • Receive Date: 19 November 2022
  • Revise Date: 09 February 2023
  • Accept Date: 30 May 2023

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