Model Predictive Control-Based Energy Management System with Load Curtailment for Standalone Microgrid

Muhammad Sharir Fathullah Mohd Yunus; Siti Hajar Yusoff; Siti Nadiah Mohd Sapihie; Mohd Shahrin Abu Hanifah; Teddy Surya Gunawan; Suriza Ahmad Zabidi

doi:10.31436/iiumej.v26i1.3230

Authors

Muhammad Sharir Fathullah Mohd Yunus International Islamic University Malaysia https://orcid.org/0009-0000-2361-8653
Siti Hajar Yusoff International Islamic University Malaysia https://orcid.org/0000-0002-7323-126X
Siti Nadiah Mohd Sapihie Petronas Research Sdn Bhd
Mohd Shahrin Abu Hanifah https://orcid.org/0000-0001-9615-4239
Teddy Surya Gunawan International Islamic University Malaysia https://orcid.org/0000-0003-3345-4669
Suriza Ahmad Zabidi International Islamic University Malaysia https://orcid.org/0000-0002-1584-2636

DOI:

https://doi.org/10.31436/iiumej.v26i1.3230

Keywords:

Model Predictive Controller (MPC), Energy Management System, Microgrid, Mixed Integer Quadratic Programming (MIQP), Load Curtailment

Abstract

The standalone microgrid emerges as an economical solution for providing electricity to remote areas disconnected from the main grid. However, the intermittent nature of renewable energy sources (RES) and unpredictable load demands can disrupt its balance. Diesel generators are commonly used to supply auxiliary power and serve as a backup for the microgrid. Nevertheless, inefficient utilization of diesel generators can lead to high operational costs, shortened lifespan, and environmental pollution. An energy management system (EMS) is implemented to optimize the operational cost and reliability of the microgrid by incorporating model predictive control (MPC), which has grown in popularity in recent years due to its ability to support multiple inputs and multiple outputs. The MPC-based EMS is developed using MATLAB/Simulink software, with the optimization problem formulated using mixed integer quadratic programming (MIQP). A comparative analysis is conducted between the MPC-based EMS with and without load curtailment capability, revealing a significant improvement of 52.21% in diesel fuel cost savings with the inclusion of load curtailment capability.

ABSTRAK: Grid mikro berdiri sendiri muncul sebagai penyelesaian ekonomi untuk menyediakan perkhidmatan elektrik kepada kawasan terpencil yang jauh daripada grid utama. Walau bagaimanapun, ciri-ciri ketidaktentuan sumber tenaga boleh diperbaharui (RES) dan permintaan beban yang tidak dapat diramalkan boleh mengganggu keseimbangan dalam grid mikro. Jentera diesel selalunya digunakan untuk menyediakan kuasa tambahan dan berfungsi sebagai sandaran untuk grid mikro. Namun begitu, penggunaan jentera diesel yang tidak cekap boleh menyebabkan kos operasi yang berlebihan, pengurangan jangka hayat dan pencemaran alam sekitar. Pengurusan tenaga (EMS) dilaksanakan untuk mengoptimumkan kos operasi grid mikro dan kebolehpercayaan dengan menggunakan kawal perkiraan model (MPC) yang semakin popular kerana keupayaannya dalam menyokong input dan output yang pelbagai. EMS berasaskan MPC dibangunkan menggunakan perisian MATLAB/Simulink, dengan masalah pengoptimuman diformulasikan menggunakan pengaturcaraan kuadratik berbilangan campuran (MIQP). Analisis perbandingan dijalankan antara EMS berasaskan MPC dengan dan tanpa keupayaan pemangkasan beban, yang mendedahkan pengurangan yang signifikan sebanyak 52.21% dalam penjimatan kos bahan api diesel dengan penambahan keupayaan pemangkasan beban.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

J. M. Raya-Armenta, N. Bazmohammadi, J. G. Avina-Cervantes, D. Sáez, J. C. Vasquez, and J. M. Guerrero (2021). Energy management system optimization in islanded microgrids: An overview and future trends. Renewable and Sustainable Energy Reviews, 149: 111327. https://doi.org/10.1016/j.rser.2021.111327 DOI: https://doi.org/10.1016/j.rser.2021.111327

Y. Zahraoui, I. Alhamrouni, S. Mekhilef, M. R. Basir Khan, M. Seyedmahmoudian, A. Stojcevski, and B. Horan, (2021). Energy Management System in Microgrids: A Comprehensive Review. Sustainability, 13(19). https://doi.org/10.3390/su131910492 DOI: https://doi.org/10.3390/su131910492

A. Badawi, N. Hasbullah, S. Yusoff, and A. Hashim (2019). Energy and power estimation for three different locations in Palestine. Indonesian Journal of Electrical Engineering and Computer Science, 14: 1049-1056. https://doi.org/10.11591/ijeecs.v14.i3.pp1049-1056 DOI: https://doi.org/10.11591/ijeecs.v14.i3.pp1049-1056

A. Badawi, A. Zyoud, S. Khan, A. Hashim, and N. Hasbullah (2019). Evaluation of Wind Power for Electrical Energy Generation in the Mediterranean Coast of Palestine for 14 Years. International Journal of Electrical and Computer Engineering, 9: 2212-2219. https://doi.org/10.11591/ijece.v9i4.pp2212-2219 DOI: https://doi.org/10.11591/ijece.v9i4.pp2212-2219

M. F. Zia, E. Elbouchikhi, and M. Benbouzid (2018). Microgrids energy management systems: A critical review on methods, solutions, and prospects. Applied Energy, 222: 1033-1055. https://doi.org/10.1016/j.apenergy.2018.04.103 DOI: https://doi.org/10.1016/j.apenergy.2018.04.103

U. R. Nair and R. Costa-Castelló (2020). A Model Predictive Control-Based Energy Management Scheme for Hybrid Storage System in Islanded Microgrids. IEEE Access, 8: 97809-97822. https://doi.org/10.1109/ACCESS.2020.2996434 DOI: https://doi.org/10.1109/ACCESS.2020.2996434

S. Rathor and D. Saxena (2020). Energy management system for smart grid: An overview and key issues. International Journal of Energy Research, 44. https://doi.org/10.1002/er.4883

K. S. Joshal and N. Gupta, (2023). Microgrids with Model Predictive Control: A Critical Review. Energies, 16(13). https://doi.org/10.3390/en16134851 DOI: https://doi.org/10.3390/en16134851

S. K. Rathor and D. Saxena (2020). Energy management system for smart grid: An overview and key issues. International Journal of Energy Research, 44(6): 4067-4109. DOI: https://doi.org/10.1002/er.4883

J. Hu, Y. Shan, J. M. Guerrero, A. Ioinovici, K. W. Chan, and J. Rodriguez (2021). Model predictive control of microgrids – An overview. Renewable and Sustainable Energy Reviews, 136: 110422. https://doi.org/10.1016/j.rser.2020.110422 DOI: https://doi.org/10.1016/j.rser.2020.110422

C. Essayeh, M. Raiss El-Fenni, H. Dahmouni, and M. A. Ahajjam (2021). Energy Management Strategies for Smart Green MicroGrid Systems:? A Systematic Literature Review. Journal of Electrical and Computer Engineering, 2021: 6675975. https://doi.org/10.1155/2021/6675975 DOI: https://doi.org/10.1155/2021/6675975

A. M. Jasim, B. H. Jasim, B.-C. Neagu, and B. N. Alhasnawi, (2023). Efficient Optimization Algorithm-Based Demand-Side Management Program for Smart Grid Residential Load. Axioms, 12(1). https://doi.org/10.3390/axioms12010033 DOI: https://doi.org/10.3390/axioms12010033

S. Ahmad, M. Shafiullah, C. B. Ahmed, and M. Alowaifeer (2023). A Review of Microgrid Energy Management and Control Strategies. IEEE Access, 11: 21729-21757. https://doi.org/10.1109/ACCESS.2023.3248511 DOI: https://doi.org/10.1109/ACCESS.2023.3248511

H. He, S. Huang, Y. Liu, and T. Zhang (2021). Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction. Discrete Dynamics in Nature and Society, 2021: 2198846. https://doi.org/10.1155/2021/2198846 DOI: https://doi.org/10.1155/2021/2198846

K.-S. Ryu, D.-J. Kim, H. Ko, C.-J. Boo, J. Kim, Y.-G. Jin, and H.-C. Kim (2021). MPC Based Energy Management System for Hosting Capacity of PVs and Customer Load with EV in Stand-Alone Microgrids. Energies, 14(13). https://doi.org/10.3390/en14134041 DOI: https://doi.org/10.3390/en14134041

T. Pippia, J. Sijs, and B. D. Schutter (2020). A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids. IEEE Transactions on Control Systems Technology, 28(6): 2364-2376. https://doi.org/10.1109/TCST.2019.2945023 DOI: https://doi.org/10.1109/TCST.2019.2945023

I. Novickij and G. Joós, (2019). Model Predictive Control Based Approach for Microgrid Energy Management. IEEE: 1-4. https://doi.org/10.1109/CCECE.2019.8861781 DOI: https://doi.org/10.1109/CCECE.2019.8861781

P. K. Ndwali, J. G. Njiri, and E. M. Wanjiru (2021). Economic Model Predictive Control of Microgrid Connected Photovoltaic-Diesel Generator backup Energy System Considering Demand side Management. Journal of Electrical Engineering & Technology, 16(5): 2297-2312. https://doi.org/10.1007/s42835-021-00801-w DOI: https://doi.org/10.1007/s42835-021-00801-w

A. Salazar, D. Arcos-Aviles, J. Llanos, D. Ortiz, M. Rodríguez, E. Motoasca, and W. Martinez, (2021). Model predictive control-based energy management system for isolated electro-thermal microgrids in rural areas of Ecuador. IEEE: 1-10. https://doi.org/10.23919/EPE21ECCEEurope50061.2021.9570196 DOI: https://doi.org/10.23919/EPE21ECCEEurope50061.2021.9570196

S. Polimeni, L. Meraldi, L. Moretti, S. Leva, and G. Manzolini (2021). Development and experimental validation of hierarchical energy management system based on stochastic model predictive control for Off-grid Microgrids. Advances in Applied Energy, 2: 100028. https://doi.org/10.1016/j.adapen.2021.100028 DOI: https://doi.org/10.1016/j.adapen.2021.100028

M. Abbasi, E. Abbasi, L. Li, R. P. Aguilera, D. Lu, and F. Wang, (2023). Review on the Microgrid Concept, Structures, Components, Communication Systems, and Control Methods. Energies, 16(1). https://doi.org/10.3390/en16010484 DOI: https://doi.org/10.3390/en16010484

M. A. Alarcón, R. G. Alarcón, A. H. González, and A. Ferramosca, (2021). Economic model predictive control for energy management in a hybrid storage microgrid. IEEE: 1-6. https://doi.org/10.1109/RPIC53795.2021.9648465 DOI: https://doi.org/10.1109/RPIC53795.2021.9648465

S. U. Ali, A. Waqar, M. Aamir, S. M. Qaisar, and J. Iqbal (2023). Model predictive control of consensus-based energy management system for DC microgrid. PLoS One, 18(1): e0278110. https://doi.org/10.1371/journal.pone.0278110 DOI: https://doi.org/10.1371/journal.pone.0278110

M. Ajani (2020). Model Predictive Control Approach to Microgrid Operation. Master Thesis. McGill University (Canada).

M. S. Taha, H. H. Abdeltawab, and Y. A. R. I. Mohamed (2018). An Online Energy Management System for a Grid-Connected Hybrid Energy Source. IEEE Journal of Emerging and Selected Topics in Power Electronics, 6(4): 2015-2030. http://doi.org/10.1109/JESTPE.2018.2828803 DOI: https://doi.org/10.1109/JESTPE.2018.2828803

G. S. Thirunavukkarasu, M. Seyedmahmoudian, E. Jamei, B. Horan, S. Mekhilef, and A. Stojcevski (2022). Role of optimization techniques in microgrid energy management systems—A review. Energy Strategy Reviews, 43: 100899. https://doi.org/10.1016/j.esr.2022.100899 DOI: https://doi.org/10.1016/j.esr.2022.100899

A. Elmouatamid, R. Ouladsine, M. Bakhouya, N. E. Kamoun, K. Zine-Dine, and M. Khaidar, (2019). A Model Predictive Control Approach for Energy Management in Micro-Grid Systems. IEEE: 1-6. https://doi.org/10.1109/SEST.2019.8848995 DOI: https://doi.org/10.1109/SEST.2019.8848995

J. L.-L. X. A. L. R. B. G. M. D. J. Morrow (2021). Annual timeseries (1-minute resolution) of per-unit profiles of residential electric load and 8 PV systems: a case study in Northern Ireland, UK.). https://doi.org/10.21227/edzt-vh05