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Optimally Coordinated Energy Management Framework for Profit Maximization Considering Dispatchable and Non-Dispatchable Energy Resources (2307.00277v1)

Published 1 Jul 2023 in eess.SY and cs.SY

Abstract: Contemporary distribution network can be seen with diverse dispatchable and non-dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non-dispatchable resources can provide several techno-economic and social benefits. Since, battery energy storage systems (BESSs) and microturbine (MT) units are capital intensive, a thorough investigation of their coordinated scheduling on pure economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty handling of non-dispatchable resources and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and MT units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility by employing a recently explored modified African buffalo optimization (MABO) algorithm. The key attributes of the proposed methodology are comprised of mean price-based adaptive scheduling embedded within a decision mechanism system (DMS) to maximize arbitrage benefits. DMS keeps a track of system states as a-priori thus guides the artificial intelligence based solution technique for sequential optimization. This may also reduce the computational burden of complex real-life engineering optimization problems. Further, a novel concept of fictitious charges is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33-bus test distribution system highlights the importance of the proposed methodology.

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References (39)
  1. J. P. Barton and D. G. Infield, “Energy storage and its use with intermittent renewable energy,” IEEE Trans. on Energy Conv., vol. 19, no. 2, pp. 441–448, June 2004.
  2. H. Alharbi and K. Bhattacharya, “Stochastic optimal planning of battery energy storage systems for isolated microgrids,” IEEE Trans. on Sustain. Energy, vol. 9, no. 1, pp. 211–227, Jan 2018.
  3. P. Fortenbacher, J. L. Mathieu, and G. Andersson, “Modeling and optimal operation of distributed battery storage in low voltage grids,” IEEE Trans. on Power Syst., vol. 32, no. 6, pp. 4340–4350, Nov 2017.
  4. M. Farrokhifar, S. Grillo, and E. Tironi, “Loss minimization in medium voltage distribution grids by optimal management of energy storage devices,” in 2013 IEEE Grenoble Conf., June 2013, pp. 1–5.
  5. ——, “Optimal placement of energy storage devices for loss reduction in distribution networks,” in IEEE PES-ISGT Europe, 2013, pp. 1–5.
  6. L. A. Hannah and D. B. Dunson, “Approximate dynamic programming for storage problems,” in Proc. of the 28th Int. Conf. on Machine Learning, ser. ICML’11.   USA: Omnipress, 2011, pp. 337–344.
  7. S. Grillo, A. Pievatolo, and E. Tironi, “Optimal storage scheduling using markov decision processes,” IEEE Trans. on Sustain. Energy, vol. 7, no. 2, pp. 755–764, April 2016.
  8. F. D. Galiana, F. Bouffard, J. M. Arroyo, and J. F. Restrepo, “Scheduling and pricing of coupled energy and primary, secondary, and tertiary reserves,” Proc. of IEEE, vol. 93, no. 11, pp. 1970–1983, Nov 2005.
  9. Y. M. Atwa and E. F. El-Saadany, “Optimal allocation of ess in distribution systems with a high penetration of wind energy,” IEEE Trans. on Power Syst., vol. 25, no. 4, pp. 1815–1822, Nov 2010.
  10. N. Gast, D. Tomozei, and J. Le Boudec, “Optimal generation and storage scheduling in the presence of renewable forecast uncertainties,” IEEE Trans. on Smart Grid, vol. 5, no. 3, pp. 1328–1339, May 2014.
  11. A. I. Bejan, R. J. Gibbens, and F. P. Kelly, “Statistical aspects of storage systems modelling in energy networks,” in Proc. 46th Annual Conf. on Inform. Scie. and Syst. (CISS), March 2012, pp. 1–6.
  12. N. Jayasekara, M. A. S. Masoum, and P. J. Wolfs, “Optimal operation of distributed energy storage systems to improve distribution network load and generation hosting capability,” IEEE Trans. on Sustain. Energy, vol. 7, no. 1, pp. 250–261, Jan 2016.
  13. J. Xiao, Z. Zhang, L. Bai, and H. Liang, “Determination of the optimal installation site and capacity of battery energy storage system in distribution network integrated with distributed generation,” IET Gen., Trans. Distr., vol. 10, no. 3, pp. 601–607, 2016.
  14. J. Lei and Q. Gong, “Operating strategy and optimal allocation of large-scale vrb energy storage system in active distribution networks for solar/wind power applications,” IET Gen., Trans. Distr., vol. 11, no. 9, pp. 2403–2411, 2017.
  15. Chen Jian, Liu Yutian, and Bao Guannan, “Optimal operating strategy for distribution networks with pv and bess considering flexible energy storage,” in 2016 IEEE PESGM, July 2016, pp. 1–5.
  16. A. Gabash and P. Li, “Flexible optimal operation of battery storage systems for energy supply networks,” IEEE Trans. on Power Syst., vol. 28, no. 3, pp. 2788–2797, Aug 2013.
  17. J. Qin, R. Sevlian, D. Varodayan, and R. Rajagopal, “Optimal electric energy storage operation,” in IEEE PES GM, July 2012, pp. 1–6.
  18. E. Telaretti, M. Ippolito, and L. Dusonchet, “A simple operating strategy of small-scale battery energy storages for energy arbitrage under dynamic pricing tariffs,” Energies, vol. 9, no. 1, 2016.
  19. J. Qiu, J. Zhao, Y. Zheng, Z. Dong, and Z. Y. Dong, “Optimal allocation of bess and mt in a microgrid,” IET Gen., Trans. Distr., vol. 12, no. 9, pp. 1988–1997, 2018.
  20. M. R. Jannesar, A. Sedighi, M. Savaghebi, and J. M. Guerrero, “Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration,” Applied Energy, vol. 226, pp. 957 – 966, 2018.
  21. I. B. Sperstad and M. Korpås, “Energy storage scheduling in distribution systems considering wind and photovoltaic generation uncertainties,” Energies, vol. 12, no. 7, 2019.
  22. W. Su, J. Wang, and J. Roh, “Stochastic energy scheduling in microgrids with intermittent renewable energy resources,” IEEE Trans. on Smart Grid, vol. 5, no. 4, pp. 1876–1883, July 2014.
  23. J. Tant, F. Geth, D. Six, P. Tant, and J. Driesen, “Multiobjective battery storage to improve pv integration in residential distribution grids,” IEEE Trans. on Sustain. Energy, vol. 4, no. 1, pp. 182–191, Jan 2013.
  24. Y. M. Atwa and E. F. El-Saadany, “Probabilistic approach for optimal allocation of wind-based distributed generation in distribution systems,” IET Renewable Power Generation, vol. 5, no. 1, pp. 79–88, 2011.
  25. A. R. Malekpour, T. Niknam, A. Pahwa, and A. K. Fard, “Multi-objective stochastic distribution feeder reconfiguration in systems with wind power generators and fuel cells using the point estimate method,” IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1483–1492, 2012.
  26. E. Zio, M. Delfanti, L. Giorgi, V. Olivieri, and G. Sansavini, “Monte carlo simulation-based probabilistic assessment of dg penetration in medium voltage distribution networks,” International Journal of Electrical Power & Energy Systems, vol. 64, pp. 852–860, 2015.
  27. V. K. Jadoun, V. C. Pandey, N. Gupta, K. R. Niazi, and A. Swarnkar, “Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm,” IET Renewable Power Generation, vol. 12, no. 9, pp. 1004–1011, 2018.
  28. Z. Wang, B. Chen, J. Wang, J. Kim, and M. M. Begovic, “Robust optimization based optimal dg placement in microgrids,” IEEE Transactions on Smart Grid, vol. 5, no. 5, pp. 2173–2182, 2014.
  29. N. Kanwar, N. Gupta, K. R. Niazi, and A. Swarnkar, “Optimal distributed resource planning for microgrids under uncertain environment,” IET Renew. Power Gen., vol. 12, no. 2, pp. 244–251, 2018.
  30. B. L. Gorissen, İ. Yanıkoğlu, and D. den Hertog, “A practical guide to robust optimization,” Omega, vol. 53, pp. 124–137, 2015.
  31. “National Renewable Energy Laboratory (NREL),” https://www.nrel.gov, 2020, [Online; accessed 01-June-2020].
  32. “Power System Operation Corporation Limited (A GoI Enterprise),” https://posoco.in/, 2020, [Online; accessed 01-June-2020].
  33. N. K. Meena, A. Swarnkar, N. Gupta, and K. R. Niazi, “Optimal accommodation and management of high renewable penetration in distribution systems,” The Journal of Engineering, vol. 2017, no. 13, pp. 1890–1895, 2017.
  34. S. Singh and M. Fozdar, “Double-sided bidding strategy for power suppliers and large buyers with amalgamation of wind and solar based generation in a modern energy market,” IET Generation, Transmission & Distribution, vol. 14, no. 6, pp. 1031–1041, 2019.
  35. P. Singh, N. K. Meena, A. Slowik, and S. K. Bishnoi, “Modified african buffalo optimization for strategic integration of battery energy storage in distribution networks,” IEEE Access, vol. 8, pp. 14 289–14 301, 2020.
  36. J. B. Odili, M. N. M. Kahar, and S. Anwar, “African buffalo optimization: A swarm-intelligence technique,” Procedia Computer Science, vol. 76, pp. 443–448, 2015.
  37. D. S. Wilson, “Altruism and organism: Disentangling the themes of multilevel selection theory,” The American Naturalist, vol. 150, no. S1, pp. s122–S134, 1997.
  38. M. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Trans. on Power Del., vol. 4, no. 2, pp. 1401–1407, April 1989.
  39. “Indian Energy Exchange (IEX),” https://www.iexindia.com/, 2020, [Online; accessed 01-June-2020].
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