Visual Journal of Technical and Vocational Education

Visual Journal of Technical and Vocational Education

Comparison of centralised and decentralised operational costs of multi-microgrid systems considering reliability and flexibility criteria

Document Type : Original Article

Author
Shahriar Abbasi
Department of Electrical and Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran
10.48301/jear.2025.497390.1061
Abstract
The main purpose of formation of multi-microgrid (MMG) in both centralised and decentralised mode is to ensure the microgrid (MG) of providing load-generation balance. Minimising operational cost and maximising profit as well as more efficient and sustainable management in MMGs have been exciting research topics in recent years. However in the previous literature, an accurate and scientific comparison of centralised and decentralised MMGs has still remained as a research gap. Focusing on this gap is the main contribution of the current research; it is focused on comparison of centralised and decentralised MMGs from the operational cost viewpoint, including RESs (PV and WT), fossil fuel generators (FFG), and ESS. Moreover, the operational costs of centralised and decentralised MMGs considering reliability and flexibility criteria will be compared. The study will be done in a test power system with real specifications considering two possible scenarios. The simulations will be done in MATLAB platform using the MATPOWER package and Gurobi solver. The simulations results show that total daily operational cost of centralized MMGs is notably less than this cost in decentralized MMGs. Demand response become better when the MMGs are centrally scheduled. Also the reliability criterion of EENS is less, i.e., the power system becomes more reliable with centralized MMGs. Power losses in centralized MMGs decrease, but the CO2 emission increases.
Keywords
Multi-microgrid
MMG
Reliability
Flexibility
Wind power
Photovoltaic
Subjects
[1] Nejati, S. A., Chong, B., Alinejad, M., & Abbasi, S. (2021). Optimal scheduling of electric vehicles charging and discharging in a smart parking-lot. 2021 56th International Universities Power Engineering Conference (UPEC), https://10.1109/UPEC50034.2021.9548226
[2] Mofidian, R., Hassankhani, I., Jahanshahi, M., Hosseini, S. S., & Miansari, M. (2024). Cost Effective Design of a 200 kW On-grid Rooftop Photovoltaic System Using PVsyst Software in Shiraz. Journal of Engineering and Applied Research, 1(1), 13–24. https://10.48301/jear.2024.194109
[3] Samuel, O., Javaid, N., Khalid, A., Khan, W. Z., Aalsalem, M. Y., Afzal, M. K., & Kim, B.-S. (2020). Towards real-time energy management of multi-microgrid using a deep convolution neural network and cooperative game approach. IEEE access, 8, 161377–161395. https:// 10.1109/ACCESS.2020.3021613
[4] Khosravi, M., Azarinfar, H., & Nejati, S. A. (2022). Microgrids energy management in automated distribution networks by considering consumers’ comfort index. International Journal of Electrical Power & Energy Systems, 139, 108013. https://doi.org/10.1016/j.ijepes.2022.108013
[5] Ton, D. T., & Smith, M. A. (2012). The US department of energy's microgrid initiative. The Electricity Journal, 25(8), 84–94. https://doi.org/10.1016/j.tej.2012.09.013
[6] Arefifar, S. A., Ordonez, M., & Mohamed, Y. A.-R. I. (2016). Energy management in multi-microgrid systems—Development and assessment. IEEE Transactions on Power Systems, 32(2), 910–922. https://doi.org/10.1109/TPWRS.2016.2568858
[7] Rashidi, R., Hatami, A., & Abedini, M. (2021). Multi-microgrid energy management through tertiary-level control: Structure and case study. Sustainable Energy Technologies and Assessments, 47, 101395. https://doi.org/10.1016/j.seta.2021.101395
[8] Theo, W. L., Lim, J. S., Ho, W. S., Hashim, H., & Lee, C. T. (2017). Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods. Renewable and Sustainable Energy Reviews, 67, 531–573. https://doi.org/10.1016/j.rser.2016.09.063
[9] Huo, Y., Bouffard, F., & Joós, G. (2021). Decision tree-based optimization for flexibility management for sustainable energy microgrids. Applied Energy, 290, 116772. https://doi.org/10.1016/j.apenergy.2021.116772
[10] Marzband, M., Alavi, H., Ghazimirsaeid, S. S., Uppal, H., & Fernando, T. (2017). Optimal energy management system based on stochastic approach for a home Microgrid with integrated responsive load demand and energy storage. Sustainable cities and society, 28, 256–264. https://doi.org/10.1016/j.scs.2016.09.017
[11] Rokni, S. G. M., Radmehr, M., & Zakariazadeh, A. (2018). Optimum energy resource scheduling in a microgrid using a distributed algorithm framework. Sustainable cities and society, 37, 222–231. https://doi.org/10.1016/j.scs.2017.11.016
[12] Ding, Y. M., Hong, S. H., & Li, X. H. (2014). A demand response energy management scheme for industrial facilities in smart grid. IEEE Transactions on Industrial Informatics, 10(4), 2257–2269. https://doi.org/10.1109/TII.2014.2330995
[13] Zhao, Z., Lee, W. C., Shin, Y., & Song, K.-B. (2013). An optimal power scheduling method for demand response in home energy management system. IEEE transactions on smart grid, 4(3), 1391–1400. https://doi.org/10.1109/TSG.2013.2251018
[14] Mao, M., Jin, P., Hatziargyriou, N. D., & Chang, L. (2014). Multiagent-based hybrid energy management system for microgrids. IEEE Transactions on Sustainable Energy, 5(3), 938–946. https://doi.org/10.1109/TSTE.2014.2313882
[15] Akram, U., Khalid, M., & Shafiq, S. (2017). An innovative hybrid wind-solar and battery-supercapacitor microgrid system—Development and optimization. IEEE access, 5, 25897–25912. https://doi.org/10.1109/ACCESS.2017.2767618
[16] Zhu, J., Gu, W., Lou, G., Wang, L., Xu, B., Wu, M., & Sheng, W. (2017). Learning automata-based methodology for optimal allocation of renewable distributed generation considering network reconfiguration. IEEE access, 5, 14275–14288. https://doi.org/10.1109/ACCESS.2017.2730850
[17] Meneses, C. A. P., & Mantovani, J. R. S. (2013). Improving the grid operation and reliability cost of distribution systems with dispersed generation. IEEE Transactions on Power Systems, 28(3), 2485–2496. https://doi.org/10.1109/TPWRS.2012.2235863
[18] Kanwar, N., Gupta, N., Niazi, K., & Swarnkar, A. (2018). Optimal distributed resource planning for microgrids under uncertain environment. IET Renewable Power Generation, 12(2), 244–251. https://doi.org/10.1049/iet-rpg.2017.0085
[19] Sharma, S., Bhattacharjee, S., & Bhattacharya, A. (2016). Grey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgrid. IET Generation, Transmission & Distribution, 10(3), 625–637. https://doi.org/10.1049/iet-gtd.2015.0429
[20] Montoya, O. D., Grajales, A., Garces, A., & Castro, C. A. (2017). Distribution systems operation considering energy storage devices and distributed generation. IEEE Latin America Transactions, 15(5), 890–900. https://doi.org/10.1109/TLA.2017.7910203
[21] Aghdam, F. H., Kalantari, N. T., & Mohammadi-Ivatloo, B. (2020). A stochastic optimal scheduling of multi-microgrid systems considering emissions: A chance constrained model. Journal of Cleaner Production, 275, 122965. https://doi.org/10.1016/j.jclepro.2020.122965
[22] Hasankhani, A., & Hakimi, S. M. (2021). Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market. Energy, 219, 119668. https://doi.org/10.1016/j.energy.2020.119668
[23] Cheng, Z., Duan, J., & Chow, M.-Y. (2018). To centralize or to distribute: That is the question: A comparison of advanced microgrid management systems. IEEE Industrial Electronics Magazine, 12(1), 6–24. https://doi.org/10.1109/MIE.2018.2789926
[24] Khavari, F., Badri, A., Zangeneh, A., & Shafiekhani, M. (2017). A comparison of centralized and decentralized energy-management models of multi-microgrid systems. 2017 Smart grid conference (SGC), https://doi.org/10.1109/SGC.2017.8308837
[25] Arabpour, A., & Hojabri, H. (2023). An improved centralized/decentralized accurate reactive power sharing method in AC microgrids. International Journal of Electrical Power & Energy Systems, 148, 108908. https://doi.org/10.1016/j.ijepes.2022.108908
[26] Zhu, J., Zhu, W., Chen, J., Liu, H., Li, G., & Zeng, K. (2023). A DRO-SDDP decentralized algorithm for economic dispatch of multi microgrids with uncertainties. IEEE Systems Journal, 17(4), 6492–6503. https://doi.org/10.1109/JSYST.2023.3305511
[27] Sheikhahmadi, P., Bahramara, S., Mazza, A., Chicco, G., Shafie-Khah, M., & Catalão, J. P. (2022). Multi-microgrids operation with interruptible loads in local energy and reserve markets. IEEE Systems Journal, 17(1), 1292–1303. https://doi.org/10.1109/JSYST.2022.3177637
[28] https://www.mathworks.com/products/connections/product_detail/gurobi-optimizer.html. 
[29] Zimmerman, R. D., Murillo-Sánchez, C. E., & Thomas, R. J. (2010). MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on Power Systems, 26(1), 12–19. https://doi.org/10.1109/TPWRS.2010.2051168
[30] Shengyan, S., Xiaoliu, S., & Yawei, G. (2011). Research on calculation of low voltage distribution network theoretical line loss based on matpower. 2011 International Conference on Advanced Power System Automation and Protection, https://doi.org/10.1109/TPWRS.2010.2051168
[31] Snodgrass, J., Kunkolienkar, S., Habiba, U., Liu, Y., Stevens, M., Safdarian, F., Overbye, T., & Korab, R. (2022). Case study of enhancing the matpower polish electric grid. 2022 IEEE Texas power and energy conference (TPEC), https://doi.org/10.1109/APAP.2011.6180516

  • Receive Date 06 February 2025
  • Revise Date 14 April 2025
  • Accept Date 20 October 2025