Multi-objective Economic Emission Dispatch Optimization Strategy Considering Battery Energy Storage System in Islanded Microgrid

Document Type : Research paper

Authors

1 Research Scholar, Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat, Haryana, India

2 Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology,Murthal, Sonipat, Haryana, India

3 National Power Training Institute, Faridabad, Haryana, India

Abstract

Economic dispatch (ED) is one of the key problem in power systems. ED tends to minimize the fuel/operating cost by optimal sizing of conventional generators (CG). Greenhouse/toxic gas emission is one of the major problem associated with the CG. Emission dispatch (EMD) deals with the reduction of greenhouse/toxic gas emissions by the optimal output of generators. The multi-objective economic emission dispatch (MOEED) problem has been formulated by considering both fuel cost and emission objectives. The main objective is optimization of fuel cost and environmental emissions from the CG in a compromised way. In this paper, CONOPT solver in General Algebraic modeling system (GAMS) has been proposed to find the the optimal solutions for ED, EMD, and MOEED problems of a microgrid. The microgrid consists of a wind turbine generator (WTG), a photovoltaic (PV) module, three CGs, and a battery energy storage system (BESS) option. The proposed algorithm has been implemented in four case studies, including all energy sources, without WTG, without PV module, and without renewable energy sources (RES). To establish the effectiveness of the proposed algorithm, it has been compared with various algorithms. The comparison result shows that proposed algorithm is more effective, novel, and powerful. Finally, result shows the effectiveness of proposed approach to optimize the objective function for all aforementioned case studies and the CONOPT solver in GAMS outperformed all the approaches in comparison. The impact of BESS on the operating/fuel cost of the microgrid has also been presented for ED. Paradigm is changing in terms of demand response in µG. Demand flexibility (DF) model has also been established with consumers demand variation in optimization process. Result with DF shows the reduction in cost and better management from demand side.

Keywords

References

  1. Soroudi, Power system optimization modeling in GAMS, vol. 78, Springer, 2017.
  2. Zhou, S. Yang, Z. Chen, and S. Ding, "Optimal load distribution model of microgrid in the smart grid environment," Renewable Sustainable Energy Rev., vol. 35, pp. 304–310, 2014.
  3. A. Khan, M. Naeem, M. Iqbal, S. Qaisar, and A. Anpalagan, "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable Sustainable Energy Rev., vol. 58, pp. 1664–1683, 2016.
  4. Nehrir, C. Wang, K. Strunz, H. Aki, R. Ramakumar, J. Bing, Z. Miao, and Z. Salameh, "A review of hybrid renewable/alternative energy systems for electric power generation: Con gurations, control, and applications," IEEE Trans. Sustainable Energy, vol. 2, no. 4, pp. 392–403, 2011.
  5. Rabiee, A. Soroudi, B. Mohammadi-Ivatloo, and M. Parniani, Corrective voltage control scheme considering demand response and stochastic wind power," IEEE Trans. Power Syst., vol. 29, no. 6, pp. 2965–2973, 2014.
  6. Khodaei, Provisional microgrids," IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1107–1115, 2014.
  7. D. Santillan-Lemus, H. Minor-Popocatl, O. Aguilar-Meja, and R. Tapia-Olvera, Optimal economic dispatch in microgrids with renewable energy sources," Energies, vol. 12, no. 1, p. 181, 2019.
  8. Soroudi, A. Rabiee, and A. Keane, Stochastic real-time scheduling of wind-thermal generation units in an electric utility," IEEE Syst. J., vol. 11, no. 3, pp. 1622–1631, 2015.
  9. P. Kothari, "Power system optimization," 2nd National conference on computational intelligence and signal processing (CISP), pp. 18–21, 2012.
  10. Kong, L. Bai, Q. Hu, F. Li, and C. Wang, "Day-ahead optimal scheduling method for grid-connected microgrid based on energy storage control strategy," J. Mod. Power Syst. Clean Energy, vol. 4, no. 4, pp. 648–658, 2016.
  11. Khodaei, S. Bahramirad, and M. Shahidehpour, "Microgrid planning under uncertainty," IEEE Trans. Power Syst., vol. 30, no. 5, pp. 2417–2425, 2014.
  12. Aghaei and M.I. Alizadeh, "Multi-objective self-scheduling of chp (combined heat and power)-based microgrids considering demand response programs and esss (energy storage systems)," Energy, vol. 55, pp. 1044–1054, 2013.
  13. Liu, Y. Chen, R. Zhuo, and H. Jia, "Energy storage capacity optimization for autonomy microgrid considering chp and ev scheduling," Appl. Energy, vol. 210, pp. 1113–1125, 2018.
  14. Petrollese, "Optimal generation scheduling for renewable microgrids using hydrogen storage systems," Università di Cagliari, 2015.
  15. Yousif, Q. Ai, Y. Gao, W. A. Wattoo, Z. Jiang, and R. Hao, "An optimal dispatch strategy for distributed microgrids using pso," CSEE J. Power Energy Syst., vol. 6, no. 3, pp. 724–734, 2019.
  16. Yousif, Q. Ai, W. A. Wattoo, Z. Jiang, R. Hao, and Y. Gao, "Least cost combinations of solar power, wind power, and energy storage system for powering large-scale grid," J. Power Sources, vol. 412, pp. 710–716, 2019.
  17. Rastegar, M. Fotuhi-Firuzabad, H. Zareipour, and M. MoeiniAghtaieh, "A probabilistic energy management scheme for renewablebased residential energy hubs," IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2217–2227, 2016.
  18. T. D. Perera, V. M. Nik, D. Mauree, and J. Scartezzini, “Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid,”Appl. Energy, vol. 190, pp. 232–248, 2017.
  19. Qian and H. Ran, "Key technologies and challenges for multi-energy complementarity and optimization of integrated energy system," Autom. Electric Power Syst., vol. 42, no. 4, pp. 2–10, 2018.
  20. Ai, S. Fan, and L. Piao, "Optimal scheduling strategy for virtual power plants based on credibility theory," Prot. Control Mod. Power Sys., vol. 1, no. 1, pp. 1–8, 2016.
  21. M. Vallem and A. Kumar, "Retracted: Optimal energy dispatch in microgrids with renew- able energy sources and demand response," Int. Trans. Electr. Energy Syst., vol. 30, no. 5, p. e12328, 2020.
  22. Khatsu, A. Srivastava, and D. K. Das, "Solving combined economic emission dispatch for microgrid using time varying phasor particle swarm optimization," 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 411–415, 2020.
  23. Xia and A. Elaiw, "Optimal dynamic economic dispatch of generation: A review," Electr. Power Syst. Res., vol. 80, no. 8, pp. 975–986, 2010.
  24. Young et al., Technical writer’s handbook, Univ. Sci. Books, 2002.
  25. Zhang, N. Gatsis, and G. B. Giannakis, "Robust energy management for microgrids with high-penetration renewables," IEEE Trans. Sustainable Energy, vol. 4, no. 4, pp. 944–953, 2013.
  26. Mahor, V. Prasad, and S. Rangnekar, "Economic dispatch using particle swarm optimization: A review," Renewable Sustainable Energy Rev., vol. 13, no. 8, pp. 2134–2141, 2009.
  27. Aghaei, M. Karami, K. M. Muttaqi, H. A. Shayanfar, and A. Ahmadi, "Mip-based stochastic security-constrained daily hydrothermal generation scheduling," IEEE Syst. J., vol. 9, no. 2, pp. 615–628, 2013.
  28. Shuai, J. Fang, X. Ai, Y. Tang, J. Wen, and H. He, "Stochastic optimization of economic dispatch for microgrid based on approximate dynamic programming," IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 2440–2452, 2018.
  29. Garcia-Torres, C. Bordons, and M. A. Ridao, "Optimal economic schedule for a network of microgrids with hybrid energy storage system using distributed model predictive control," IEEE Trans. Ind. Electron., vol. 66, no. 3, pp. 1919–1929, 2018.
  30. Wang, Q. Li, R. Ding, M. Sun, and G. Wang, "Integrated scheduling of energy supply and demand in microgrids under uncertainty: A robust multi-objective optimization approach," Energy, vol. 130, pp. 1–14, 2017.
  31. Nikmehr and S. N. Ravadanegh, "Optimal power dispatch of multi-microgrids at future smart distribution grids," IEEE Trans. Smart Grid, vol. 6, no. 4, pp. 1648–1657, 2015.
  32. Kou, D. Liang, and L. Gao, "Stochastic energy scheduling in microgrids considering the uncer-tainties in both supply and demand," IEEE Syst. J., vol. 12, no. 3, pp. 2589–2600, 2016.
  33. Maulik and D. Das, "Optimal power dispatch considering load and renewable generation uncer-tainties in an ac-dc hybrid microgrid," IET Gener. Transm. Distrib., vol. 13, no. 7, pp. 1164–1176, 2019.
  34. Zhao, H. Qiu, R. Qin, X. Zhang, W. Gu, and C. Wang, "Robust optimal dispatch of ac/dc hybrid microgrids considering generation and load uncertainties and energy storage loss," IEEE Trans. Power Syst., vol. 33, no. 6, pp. 5945–5957, 2018.
  35. Li, P. Wang, H. B. Gooi, J. Ye, and L. Wu, "Multi-objective optimal dispatch of microgrid under uncertainties via interval optimization," IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 2046–2058, 2017.
  36. Xiao, M. K. AlAshery, and W. Qiao, "Optimal price-maker trading strategy of wind power producer using virtual bidding," J. Mod. Power Syst. Clean Energy, vol. 10, no. 3, pp. 766–778, 2021.
  37. P. Hilbers, D. J. Brayshaw, and A. Gandy, "Efficient quanti cation of the impact of demand and weather uncertainty in power system models," IEEE Trans. Power Syst., vol. 36, no. 3, pp. 1771–1779, 2020.
  38. Augustine, S. Suresh, P. Moghe, and K. Sheikh, "Economic dispatch for a microgrid considering renewable energy cost functions," IEEE PES Innovative Smart Grid Technol. (ISGT), pp. 1–7, 2012.
  39. Dey, S. K. Roy, and B. Bhattacharyya, "Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms," Eng. Sci. Technol. Int. J., vol. 22, no. 1, pp. 55–66, 2019.
  40. Dey, B. Bhattacharyya, S. Raj, and R. Babu, "Economic emission dispatch on unit commitment based microgrid system considering wind and load uncertainty using hybrid mgwoscacsa," J. Electr. Syst. Inf. Technol., vol. 7, no. 1, pp. 1–26, 2020.
  41. Farrokhifar, F. H. Aghdam, A. Alahyari, A. Monavari, and A. Safari, "Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic milp model," Electr. Power Syst. Res., vol. 187, p. 106483, 2020.
  42. H. Aghdam, N. T. Kalantari, and B. Mohammadi-Ivatloo, "A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements," J. Energy Storage, vol. 29, p. 101416, 2020.
  43. H. Aghdam, N. T. Kalantari, and B. Mohammadi-Ivatloo, "A stochastic optimal scheduling of multi-microgrid systems considering emissions: A chance constrained model," J. Cleaner Prod., vol. 275, p. 122965, 2020.
  44. H. Aghdam, N. T. Kalantari, and S. N. Ravadanegh, "Recon guration-based hierarchical energy management in multimicrogrid systems considering power losses, reliability index, and voltage enhancement," Turk. J. Electr. Eng. Comput. Sci., vol. 28, no. 5, pp. 2433–2447, 2020.
  45. H. Aghdam, S. Ghaemi, and N. T. Kalantari, "Evaluation of loss minimization on the energy management of multi-microgrid based smart distribution network in the presence of emission constraints and clean productions," J. Cleaner Prod., vol. 196, pp. 185–201, 2018.
  46. Masoudi and H. Abdi, "Multi-objective stochastic programming in microgrids considering environmental emissions," J. Oper. Autom. Power Eng., vol. 8, no. 2, pp. 141–151, 2020.
  47. Dehghani, M. Mardaneh, and O. Malik, "Foa:‘following’optimization algorithm for solving power engineering optimization problems," J. Oper. Autom. Power Eng., vol. 8, no. 1, pp. 57–64, 2020.
  48. Azimi and A. Salami, "Optimal operation of integrated energy systems considering demand response program," J. Oper. Autom. Power Eng., vol. 9, no. 1, pp. 60–67, 2021.
  49. Dey, B. Bhattacharyya, A. Srivastava, and K. Shivam, "Solving energy management of renewable integrated microgrid systems using crow search algorithm," Soft Comput., vol. 24, no. 14, pp. 10433–10454, 2020.
  50. Dey, B. Bhattacharyya, and F.P.G. Márquez, "A hybrid optimization-based approach to solve environment constrained economic dispatch problem on microgrid system," J. Cleaner Prod., vol. 307, p. 127196, 2021.
Journal of Operation and Automation in Power Engineering
Volume 12, Issue 4
December 2024
Pages 296-311

Files

History

  • Receive Date: 31 August 2022
  • Revise Date: 25 December 2022
  • Accept Date: 16 February 2023

Share

How to cite

Statistics