Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



Yejun He1This email address is being protected from spambots. You need JavaScript enabled to view it., Lei Shi2, and Zhongren Chen1

1School of mechanical and electrical engineering, Zhongshan Polytechnics, Zhongshan 528400, Guangdong, China

2Huangpu Wenchong Shipbuilding Co., Ltd.,CSSC, Guangzhou 510000, Guangdong, China


Received: June 26, 2023
Accepted: April 18, 2024
Publication Date: June 22, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||  

Amicrogrid is a smaller-scale power system that helps integrate distributed energy generation and maximize demand-side management utilization. This article analyzes the economic dispatch of a typical multi-carrier microgrid with price-responsive loads in an uncertain environment. Integrating multiple energy infrastructures under the multi-carrier microgrid is shown as an energy hub. This paper proposes a novel price-responsive load that integrates the final price of energy of demanded loads for multiple carriers with energy market price, site generations, and energy purchase. Also, the proposed price-responsive method is analyzed ontwodifferent DRP models to verify the model’s effectiveness. The proposed multi-carrier microgrid is investigated considering the uncertainties in thermal and electrical loads, solar generations, and the electricity market. Previous investigations have optimized energy consumption from an infrastructure perspective without considering interactions. However, this study takes into account the interaction between energy system infrastructures in the presence of distributed energy generation and responsive loads. A series of simulations are conducted using GAMStodevelop amodelfor a connected microgrid that incorporates electricity, district heat networks, and natural gas to supply multiple energy demands. Results show that the simultaneous operation of different energy carriers and utilization of price-responsive loads resulted in lower operating costs for smart distribution grids. Finally, the impact of uncertain parameters was assessed in the system, enhancing the optimal solution’s trustworthiness.

Keywords: Economic dispatch; Demand response; Small-scale energy resources, multi-carrier microgrid.

  1. [1] M.Azimian,V.Amir,R.Habibifar,andH.Golmo hamadi,(2021)“Probabilisticoptimizationofnetworked multi-carriermicrogridstoenhanceresilienceleveraging demandresponseprograms"Sustainability13:5792. DOI:10.3390/su13115792.
  2. [2] N.Saito,T.Niimura,K.Koyanagi,andR.Yokoyama. “Trade-offanalysisofautonomousmicrogridsizing withPV,diesel,andbatterystorage”.In:IEEE,2009, 1–6.DOI:10.1109/PES.2009.5275820.
  3. [3] A. JosephandM.Shahidehpour.“Batterystorage systemsinelectricpowersystems”.In:IEEE,2006, 8–pp.DOI:10.1109/pes.2006.1709235.
  4. [4] H. Lotfi and A. Khodaei. “An efficient preprocess ing approach for uncertainty consideration in micro grids”. In: IEEE, 2016, 1–5. DOI: 10.1109/TDC.2016.7520003.
  5. [5] S.Bourbour. “Development of a Self-Healing strategy for future smart microgrids". (phdthesis). Murdoch Uni versity, 2016.
  6. [6] T.Krause, G. Andersson, K. Fröhlich, and A. Vaccaro, (2010) “Multiple-energy carriers: modeling of production, delivery, and consumption" Proceedings of the IEEE 99: 15–27. DOI: 10.1109/JPROC.2010.2083610.
  7. [7] M. Geidl and G. Andersson, (2007) “Optimal power f low of multiple energy carriers" IEEE Transactions on power systems 22: 145–155. DOI: 10.1109/TPWRS.2006.888988.
  8. [8] C. A. Saldarriaga, R. A. Hincapié, and H. Salazar, (2013) “A holistic approach for planning natural gas and electricity distribution networks" IEEE transactions on power systems 28: 4052–4063. DOI: 10.1109/TPWRS.2013.2268859.
  9. [9] Q. P. Zheng, S. Rebennack, N. A. Iliadis, and P. M. Pardalos, (2010) “Optimization models in the natural gas industry" Handbook of Power Systems I: 121–148.
  10. [10] K. Ravi, (2016) “Optimal power flow considering in termittent wind power using particle swarm optimiza tion" International Journal of Renewable Energy Research (IJRER) 6: 504–509.
  11. [11] S. Sreejith, I. Gandhi, D. Samiappan, and M. Muru ganandam, (2016) “Security constraint unit commit ment on combined solar thermal generating units using ABCalgorithm" International Journal of Renewable Energy Research (IJRER) 6: 1361–1372.
  12. [12] Q. P. Zheng, J. Wang, and A. L. Liu, (2014) “Stochas tic optimization for unit commitment—A review" IEEE Transactions on Power Systems 30: 1913–1924.
  13. [13] F.Adamek,M.Arnold,andG.Andersson,(2013)“On decisive storage parameters for minimizing energy supply costs in multicarrier energy systems" IEEE Transactions on Sustainable Energy 5: 102–109.
  14. [14] S. D. Manshadi and M.E.Khodayar, (2015) “Resilient operation of multiple energy carrier microgrids" IEEE Transactions on Smart Grid 6: 2283–2292.
  15. [15] N.Cai,N.T.T.Nga,andJ.Mitra.“Economicdispatch in microgrids using multi-agent system”. In: IEEE, 2012, 1–5.
  16. [16] M. Motevasel and A. R. Seifi, (2014) “Expert energy management of a micro-grid considering wind energy uncertainty" Energy Conversion and Management 83: 58–72.
  17. [17] N.Nikmehr andS. N. Ravadanegh, (2015) “Optimal power dispatch of multi-microgrids at future smart dis tribution grids" IEEE transactions on smart grid 6: 1648–1657.
  18. [18] I. A. Sajjad, G. Chicco, and R. Napoli, (2015) “Prob abilistic generation of time-coupled aggregate residential demand patterns" IET Generation, Transmission & Distribution 9: 789–797.
  19. [19] L. Uy, P. Uy, J. Siy, A. S. F. Chiu, and C. Sy, (2018) “Target-oriented robust optimization of a microgrid system investment model" Frontiers in Energy 12: 440–455.
  20. [20] J. L. R. Duarte and N. Fan, (2019) “Operations of a mi crogrid with renewable energy integration and line switch ing" Energy Systems 10: 247–272.
  21. [21] R. Urooj and S. S. Ahmad, (2017) “Assessment of elec tricity demand at domestic level in Balochistan, Pakistan" Advances in Energy Research 5: 57.
  22. [22] B. Heymann, J. F. Bonnans, P. Martinon, F. J. Silva, F. Lanas, and G. Jiménez-Estévez, (2018) “Continuous optimal control approaches to microgrid energy manage ment" Energy Systems 9: 59–77.
  23. [23] S.S.Reddy,J.Y.Park,andC.M.Jung,(2016)“Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm" Frontiers in Energy 10: 355–362.
  24. [24] Q. Zhang, B. C. Mclellan, T. Tezuka, and K. N. Ishihara, (2012) “Economic and environmental analy sis of power generation expansion in Japan considering Fukushima nuclear accident using a multi-objective opti mization model" Energy 44: 986–995.
  25. [25] D.Zhang,L.Ma,P.Liu,L.Zhang,andZ.Li,(2012)“A multi-period superstructure optimisation model for the op timal planning of China’s power sector considering carbon dioxide mitigation: discussion on China’s carbon mitiga tion policy based on the model" Energy policy 41: 173–183.
  26. [26] D. Zhang, P. Liu, L. Ma, Z. Li, and W. Ni, (2012) “A multi-period modelling and optimization approach to the planning of China’s power sector with consideration of carbon dioxide mitigation" Computers & Chemical Engineering 37: 227–247.
  27. [27] A.Zakariazadeh, S. Jadid, and P. Siano, (2014) “Multi objective scheduling of electric vehicles in smart distribu tion system" Energy Conversion and Management 79: 43–53.
  28. [28] B. Zhao, X. Zhang, J. Chen, C. Wang, and L. Guo, (2013) “Operation optimization of standalone microgrids considering lifetime characteristics of battery energy stor age system" IEEE transactions on sustainable energy 4: 934–943.
  29. [29] I. Koutsopoulos and L. Tassiulas, (2011) “Challenges in demand load control for the smart grid" Ieee Network 25: 16–21.
  30. [30] C. Chen, S. Duan, T. Cai, B. Liu, and G. Hu, (2011) “Smart energy management system for optimal microgrid economic operation" IET renewable power generation 5: 258–267.
  31. [31] S. L. Arun and M. P. Selvan, (2019) “Smart residential energy management system for demand response in build ings with energy storage devices" Frontiers in Energy 13: 715–730.
  32. [32] D.Neves,A.Pina,andC.A.Silva,(2018)“Comparison of different demand response optimization goals on an isolated microgrid" Sustainable Energy Technologies and Assessments 30: 209–215.
  33. [33] V. K. Prajapati and V. Mahajan, (2022) “Demand re sponse based congestion management of power system with uncertain renewable resources" International Jour nal of Ambient Energy 43: 103–116.
  34. [34] M. Jin, W. Feng, P. Liu, C. Marnay, and C. Spanos, (2017) “MOD-DR: Microgrid optimal dispatch with de mand response" Applied energy 187: 758–776. DOI: 10.1016/j.apenergy.2016.11.093.
  35. [35] M. H. Albadi and E. F. El-Saadany, (2008) “A sum mary of demand response in electricity markets" Electric power systems research 78: 1989–1996. DOI: 10.1016/j.epsr.2008.04.002.
  36. [36] S.PazoukiandM.-R.Haghifam,(2016) “Optimal plan ning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty" Interna tional Journal ofElectrical Power&EnergySystems 80: 219–239. DOI: 10.1007/s12667-017-0236-x.
  37. [37] M.Fisher, J. Apt, and F. Sowell, (2018) “The economics of commercial demand response for spinning reserve" En ergy Systems 9: 3–23. DOI: 10.1007/s12667-017-0236 x.
  38. [38] A. Mirzapour-Kamanaj, A. Talebi, K. Zare, and B. Mohammadi-Ivatloo. Optimal energy management of residential buildings to supply controllable and uncontrol lable loads under uncertainty. Springer, 2022, 77–101. DOI: 10.1007/978-3-031-08732-5_5.
  39. [39] B. M. Azar, A. Mirzapour-Kamanaj, R. Kazemzadeh, B. Mohammadi-Ivatloo,andK.Zare.Optimalcoalition operation of interconnected hybrid energy systems con taining local energy conversion technologies, renewable energy resources, and energy storage systems. Springer, 2022, 169–198. DOI: 10.1007/978-3-030-87653-1_7.
  40. [40] M. Agabalaye-Rahvar, A. Mirzapour-Kamanaj, K. Zare, and A. Anvari-Moghaddam. Hybrid Interval Stochastic Optimal Operation Framework of a Multi carrier Microgrid in the Presence of Hybrid Electric and Hydrogen-Based Vehicles Intelligent Parking Lot. Springer, 2023, 209–236. DOI: 10.1007/978-3-031-22186-6_8.
  41. [41] A. Mansour-Saatloo, M. A. Mirzaei, B. Mohammadi Ivatloo, and K. Zare, (2020) “A risk-averse hybrid ap proach for optimal participation of power-to-hydrogen technology-based multi-energy microgrid in multi-energy markets" Sustainable Cities and Society 63: 102421. DOI: 10.1016/j.scs.2020.102421.
  42. [42] A. Mansour-Saatloo, M. Agabalaye-Rahvar, M. A. Mirzaei, B. Mohammadi-Ivatloo, M. Abapour, and K. Zare, (2020) “Robust scheduling of hydrogen based smart micro energy hub with integrated demand response" Journal of Cleaner Production 267: 122041. DOI: 10.1016/j.jclepro.2020.122041.
  43. [43] A. Najafi, M. Marzband, B. Mohamadi-Ivatloo, J. Contreras, M. Pourakbari-Kasmaei, M. Lehtonen, andR.Godina,(2019) “Uncertainty-based models for op timal management of energy hubs considering demand re sponse" Energies 12: 1413. DOI: 10.3390/en12081413.
  44. [44] S. Bahramirad, W. Reder, and A. Khodaei, (2012) “Reliability-constrained optimal sizing of energy storage system in a microgrid" IEEE Transactions on Smart Grid 3: 2056–2062.
  45. [45] A.Sheikhi, M. Rayati, S. Bahrami, and A. M. Ranjbar, (2015) “Integrated demand side management game in smart energy hubs" IEEE Transactions on Smart Grid 6: 675–683. DOI: 10.1109/TSG.2014.2377020.