A Robust Optimization Framework for Energy Management in Energy Hubs: Comparative Analysis of SMA, GA, and MILP with Demand Response Integration
محورهای موضوعی : Journal of Computer & Robotics
Mohammad Reza Ohadi
1
,
Mahdi Hedayati
2
,
Reza Effatnejad
3
,
Abdolreza Dehghani Tafti
4
1 - گروه مهندسي برق،واحد كرج،دانشگاه آزاد اسلامي،كرج، ايران
2 - گروه مهندسی برق، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران
3 - گروه مهندسی برق، واحد کرج، دانشگاه آزاد اسلامی، کرج، ایران
4 - گروه مهندسي برق،واحد كرج،دانشگاه آزاد اسلامي،كرج، ايران
کلید واژه: Energy management, Renewable energy sources, Demand response programs, Slime Mould Algorithm (SMA), Mixed-Integer Linear Programming (MILP), Genetic Algorithm (GA).,
چکیده مقاله :
The growing demand for sustainable development and the integration of renewable energy sources emphasize the critical role of energy hubs (EHs) in modern power systems. EHs synergize electrical, cooling, and heating equipment with demand response (DR) programs and renewable energy resources (RERs) to optimize energy management. This study proposes a combined energy system (CES) framework to manage uncertainties in energy sources and demands, including cooling, heating, wind speed, solar irradiation, and energy prices. The objective is to maximize EH profits by integrating DR programs and RERs across various scenarios. The study utilizes the Slime Mould Algorithm (SMA), the Genetic Algorithm (GA), and the Mixed-Integer Linear Programming (MILP) method to compare performance. The optimization focuses on minimizing operational costs while maximizing renewable energy utilization. Without DR integration, SMA generates 1500 kW from photovoltaic (PV) systems and 2000 kW from wind turbines at a cost of $500,000. Conversely, GA generates 1400 kW from PV and 1800 kW from wind turbines, costing $550,000, while MILP results in 1550 kW from PV and 1950 kW from wind turbines at $510,000. With DR integration, SMA reduces costs to $450,000, GA incurs $500,000, and MILP achieves $460,000. DR programs enhance load management, peak shaving, and load shifting, contributing to cost reduction and efficiency. Simulation results confirm SMA's effectiveness in managing energy resources within a microgrid, optimizing renewables, and significantly reducing costs. SMA consistently outperforms GA and MILP, demonstrating superior cost-effectiveness and resource optimization. This study underscores the importance of advanced optimization algorithms like SMA in modern energy systems, offering a reliable and cost-efficient solution for EHs and supporting sustainable energy management and environmental sustainability.
The growing demand for sustainable development and the integration of renewable energy sources emphasize the critical role of energy hubs (EHs) in modern power systems. EHs synergize electrical, cooling, and heating equipment with demand response (DR) programs and renewable energy resources (RERs) to optimize energy management. This study proposes a combined energy system (CES) framework to manage uncertainties in energy sources and demands, including cooling, heating, wind speed, solar irradiation, and energy prices. The objective is to maximize EH profits by integrating DR programs and RERs across various scenarios. The study utilizes the Slime Mould Algorithm (SMA), the Genetic Algorithm (GA), and the Mixed-Integer Linear Programming (MILP) method to compare performance. The optimization focuses on minimizing operational costs while maximizing renewable energy utilization. Without DR integration, SMA generates 1500 kW from photovoltaic (PV) systems and 2000 kW from wind turbines at a cost of $500,000. Conversely, GA generates 1400 kW from PV and 1800 kW from wind turbines, costing $550,000, while MILP results in 1550 kW from PV and 1950 kW from wind turbines at $510,000. With DR integration, SMA reduces costs to $450,000, GA incurs $500,000, and MILP achieves $460,000. DR programs enhance load management, peak shaving, and load shifting, contributing to cost reduction and efficiency. Simulation results confirm SMA's effectiveness in managing energy resources within a microgrid, optimizing renewables, and significantly reducing costs. SMA consistently outperforms GA and MILP, demonstrating superior cost-effectiveness and resource optimization. This study underscores the importance of advanced optimization algorithms like SMA in modern energy systems, offering a reliable and cost-efficient solution for EHs and supporting sustainable energy management and environmental sustainability.
References
[1] Chen C, Duan S, Cai T, Liu B, Hu G. Smart energy management system for optimal microgrid economic operation. IET Renewable Power Generation 2011;5:258–67. https://doi.org/10.1049/IET-RPG.2010.0052/CITE/REFWORKS.
[2] Di Somma M, Graditi G, Heydarian-Forushani E, Shafie-khah M, Siano P. Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects. Renew Energy 2018;116:272–87. https://doi.org/10.1016/J.RENENE.2017.09.074.
[3] Rezaei N, Kalantar M. Stochastic frequency-security constrained energy and reserve management of an inverter interfaced islanded microgrid considering demand response programs. International Journal of Electrical Power and Energy Systems 2015;69:273–86. https://doi.org/10.1016/j.ijepes.2015.01.023.
[4] Rahman MS, Oo AMT. Distributed multi-agent based coordinated power management and control strategy for microgrids with distributed energy resources. Energy Convers Manag 2017;139:20–32. https://doi.org/10.1016/J.ENCONMAN.2017.02.021.
[5] Rezaei N, Ahmadi A, Khazali AH, Guerrero JM. Energy and Frequency Hierarchical Management System Using Information Gap Decision Theory for Islanded Microgrids. IEEE Transactions on Industrial Electronics 2018;65:7921–32. https://doi.org/10.1109/TIE.2018.2798616.
[6] Mohsenzadeh A, Pang C, Haghifam MR. Determining Optimal Forming of Flexible Microgrids in the Presence of Demand Response in Smart Distribution Systems. IEEE Syst J 2018;12:3315–23. https://doi.org/10.1109/JSYST.2017.2739640.
[7] Arefifar SA, Mohamed YARI, El-Fouly THM. Optimum microgrid design for enhancing reliability and supply-security. IEEE Trans Smart Grid 2013;4:1567–75. https://doi.org/10.1109/TSG.2013.2259854.
[8] Li Z, Bahramirad S, Paaso A, Yan M, Shahidehpour M. Blockchain for decentralized transactive energy management system in networked microgrids. The Electricity Journal 2019;32:58–72. https://doi.org/10.1016/J.TEJ.2019.03.008.
[9] Li Z, Shahidehpour M, Aminifar F, Alabdulwahab A, Al-Turki Y. Networked Microgrids for Enhancing the Power System Resilience. Proceedings of the IEEE 2017;105:1289–310. https://doi.org/10.1109/JPROC.2017.2685558.
[10] Han Y, Zhang K, Li H, Coelho EAA, Guerrero JM. MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview. IEEE Trans Power Electron 2018;33:6488–508. https://doi.org/10.1109/TPEL.2017.2761438.
[11] Li Y, Zhang P, Yue M. Networked microgrid stability through distributed formal analysis. Appl Energy 2018;228:279–88. https://doi.org/10.1016/J.APENERGY.2018.06.038.
[12] Wang Z, Chen B, Wang J, Begovic MM, Chen C. Coordinated energy management of networked microgrids in distribution systems. IEEE Trans Smart Grid 2015;6:45–53. https://doi.org/10.1109/TSG.2014.2329846.
[13] Ahmadi SE, Rezaei N. Reliability-Oriented Optimal Scheduling of Self-Healing in Multi-Microgrids. Proceedings - 2018 Smart Grid Conference, SGC 2018 2018. https://doi.org/10.1109/SGC.2018.8777898.
[14] Hayes BP, Prodanovic M. State Forecasting and Operational Planning for Distribution Network Energy Management Systems. IEEE Trans Smart Grid 2016;7:1002–11. https://doi.org/10.1109/TSG.2015.2489700.
[15] Abrahamse W, Darby S, McComas K. Communication is key: How to discuss energy and environmental issues with consumers. IEEE Power and Energy Magazine 2018;16:29–34. https://doi.org/10.1109/MPE.2017.2759882.
[16] Ur Rehman A, Hafeez G, Albogamy FR, Wadud Z, Ali F, Khan I, et al. An efficient energy management in smart grid considering demand response program and renewable energy sources. IEEE Access 2021;9:148821–44. https://doi.org/10.1109/ACCESS.2021.3124557.
[17] Khazali A, Rezaei N, Ahmadi A, Hredzak B. Information Gap Decision Theory Based Preventive/Corrective Voltage Control for Smart Power Systems With High Wind Penetration. IEEE Trans Industr Inform 2018;14:4385–94. https://doi.org/10.1109/TII.2018.2797105.
[18] Hussain A, Bui VH, Kim HM. Resilience-Oriented Optimal Operation of Networked Hybrid Microgrids. IEEE Trans Smart Grid 2019;10:204–15. https://doi.org/10.1109/TSG.2017.2737024.
[19] Zhou X, Ai Q, Yousif M. Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids. Appl Energy 2019;253:113588. https://doi.org/10.1016/J.APENERGY.2019.113588.
[20] Hussain A, Bui VH, Kim HM. A Resilient and Privacy-Preserving Energy Management Strategy for Networked Microgrids. IEEE Trans Smart Grid 2018;9:2127–39. https://doi.org/10.1109/TSG.2016.2607422.
[21] Ren L, Qin Y, Li Y, Zhang P, Wang B, Luh PB, et al. Enabling resilient distributed power sharing in networked microgrids through software defined networking. Appl Energy 2018;210:1251–65. https://doi.org/10.1016/J.APENERGY.2017.06.006.
[22] Zhao Y, Yu J, Ban M, Liu Y, Li Z. Privacy-Preserving Economic Dispatch for An Active Distribution Network with Multiple Networked Microgrids. IEEE Access 2018;6:38802–19. https://doi.org/10.1109/ACCESS.2018.2854280.
[23] Bazmohammadi N, Tahsiri A, Anvari-Moghaddam A, Guerrero JM. Stochastic Predictive Control of Multi-Microgrid Systems. IEEE Trans Ind Appl 2019;55:5311–9. https://doi.org/10.1109/TIA.2019.2918051.
[24] Bazmohammadi N, Tahsiri A, Anvari-Moghaddam A, Guerrero JM. A hierarchical energy management strategy for interconnected microgrids considering uncertainty. International Journal of Electrical Power & Energy Systems 2019;109:597–608. https://doi.org/10.1016/J.IJEPES.2019.02.033.
[25] Wang Z, Chen B, Wang J, Kim J. Decentralized Energy Management System for Networked Microgrids in Grid-Connected and Islanded Modes. IEEE Trans Smart Grid 2016;7:1097–105. https://doi.org/10.1109/TSG.2015.2427371.
[26] Wang H, Huang J. Joint Investment and Operation of Microgrid. IEEE Trans Smart Grid 2017;8:833–45. https://doi.org/10.1109/TSG.2015.2501818.
[27] Amir V, Jadid S, Ehsan M. Probabilistic Optimal Power Dispatch in Multi-Carrier Networked Microgrids under Uncertainties. Energies 2017, Vol 10, Page 1770 2017;10:1770. https://doi.org/10.3390/EN10111770.
[28] Park SH, Hussain A, Kim HM. Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables. Energies 2019, Vol 12, Page 452 2019;12:452. https://doi.org/10.3390/EN12030452.
[29] Nikmehr N, Najafi-Ravadanegh S, Khodaei A. Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty. Appl Energy 2017;198:267–79. https://doi.org/10.1016/J.APENERGY.2017.04.071.
[30] Wang H, Huang J. Bargaining-based energy trading market for interconnected microgrids. IEEE International Conference on Communications 2015;2015-September:776–81. https://doi.org/10.1109/ICC.2015.7248416.
[31] Wang H, Huang J. Incentivizing Energy Trading for Interconnected Microgrids. IEEE Trans Smart Grid 2018;9:2647–57. https://doi.org/10.1109/TSG.2016.2614988.
[32] Bazmohammadi N, Tahsiri A, Anvari-Moghaddam A, Guerrero JM. Optimal operation management of a regional network of microgrids based on chance-constrained model predictive control. IET Generation, Transmission & Distribution 2018;12:3772–9. https://doi.org/10.1049/IET-GTD.2017.2061.
[33] Esmaeili S, Anvari-Moghaddam A, Jadid S. Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems. Energies 2019, Vol 12, Page 1766 2019;12:1766. https://doi.org/10.3390/EN12091766.
[34] Alavi SA, Ahmadian A, Aliakbar-Golkar M. Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method. Energy Convers Manag 2015;95:314–25. https://doi.org/10.1016/J.ENCONMAN.2015.02.042.
[35] Kuznetsova E, Li YF, Ruiz C, Zio E. An integrated framework of agent-based modelling and robust optimization for microgrid energy management. Appl Energy 2014;129:70–88. https://doi.org/10.1016/J.APENERGY.2014.04.024.
[36] Kuznetsova E, Ruiz C, Li YF, Zio E. Analysis of robust optimization for decentralized microgrid energy management under uncertainty. International Journal of Electrical Power & Energy Systems 2015;64:815–32. https://doi.org/10.1016/J.IJEPES.2014.07.064.
[37] Ghasemi A, Enayatzare M. Optimal energy management of a renewable-based isolated microgrid with pumped-storage unit and demand response. Renew Energy 2018;123:460–74. https://doi.org/10.1016/J.RENENE.2018.02.072.
[38] Nwulu NI, Xia X. Optimal dispatch for a microgrid incorporating renewables and demand response. Renew Energy 2017;101:16–28. https://doi.org/10.1016/J.RENENE.2016.08.026.
[39] Hossain MA, Pota HR, Squartini S, Abdou AF. Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renew Energy 2019;136:746–57. https://doi.org/10.1016/J.RENENE.2019.01.005.
[40] Yang X, Leng Z, Xu S, Yang C, Yang L, Liu K, et al. Multi-objective optimal scheduling for CCHP microgrids considering peak-load reduction by augmented ε-constraint method. Renew Energy 2021;172:408–23. https://doi.org/10.1016/J.RENENE.2021.02.165.
[41] Ramli MAM, Bouchekara HREH, Alghamdi AS. Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renew Energy 2018;121:400–11. https://doi.org/10.1016/J.RENENE.2018.01.058.
[42] Gazijahani FS, Hosseinzadeh H, Tagizadeghan N, Salehi J. A new point estimate method for stochastic optimal operation of smart distribution systems considering demand response programs. 2017 Electrical Power Distribution Networks Conference, EPDC 2017 2017:39–44. https://doi.org/10.1109/EPDC.2017.8012738.
[43] Zakariazadeh A, Jadid S, Siano P. Stochastic multi-objective operational planning of smart distribution systems considering demand response programs. Electric Power Systems Research 2014;111:156–68. https://doi.org/10.1016/J.EPSR.2014.02.021.
[44] Samimi A, Rezaei N. Robust optimal energy and reactive power management in smart distribution networks: An info-gap multi-objective approach. International Transactions on Electrical Energy Systems 2019;29:e12115. https://doi.org/10.1002/2050-7038.12115.
[45] Gao H, Xu S, Liu Y, Wang L, Xiang Y, Liu J. Decentralized optimal operation model for cooperative microgrids considering renewable energy uncertainties. Appl Energy 2020;262:114579. https://doi.org/10.1016/J.APENERGY.2020.114579.
[46] Bahmani R, Karimi H, Jadid S. Stochastic electricity market model in networked microgrids considering demand response programs and renewable energy sources. International Journal of Electrical Power & Energy Systems 2020;117:105606. https://doi.org/10.1016/J.IJEPES.2019.105606.
[47] Wang Y, Lu Y, Ju L, Wang T, Tan Q, Wang J, et al. A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism. Energies 2019, Vol 12, Page 425 2019;12:425. https://doi.org/10.3390/EN12030425.
[48] Ghasemi A. Coordination of pumped-storage unit and irrigation system with intermittent wind generation for intelligent energy management of an agricultural microgrid. Energy 2018;142:1–13. https://doi.org/10.1016/J.ENERGY.2017.09.146.
[49] Marzband M, Alavi H, Ghazimirsaeid SS, Uppal H, Fernando T. Optimal energy management system based on stochastic approach for a home Microgrid with integrated responsive load demand and energy storage. Sustain Cities Soc 2017;28:256–64. https://doi.org/10.1016/J.SCS.2016.09.017.
[50] Shams MH, Shahabi M, Khodayar ME. Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response. Energy 2018;155:326–38. https://doi.org/10.1016/J.ENERGY.2018.04.190.
[51] Daneshvar M, Mohammadi-Ivatloo B, Zare K, Abapour M, Asadi S, Anvari-Moghaddam A. Chance-constrained scheduling of hybrid microgrids under transactive energy control. Int J Energy Res 2021;45:10173–90. https://doi.org/10.1002/ER.6505.
[52] Tooryan F, HassanzadehFard H, Collins ER, Jin S, Ramezani B. Optimization and energy management of distributed energy resources for a hybrid residential microgrid. J Energy Storage 2020;30:101556. https://doi.org/10.1016/J.EST.2020.101556.
[53] Banaei M, Rezaee B. Fuzzy scheduling of a non-isolated micro-grid with renewable resources. Renew Energy 2018;123:67–78. https://doi.org/10.1016/J.RENENE.2018.01.088.
[54] Oree V, Sayed Hassen SZ, Fleming PJ. Generation expansion planning optimisation with renewable energy integration: A review. Renewable and Sustainable Energy Reviews 2017;69:790–803. https://doi.org/10.1016/J.RSER.2016.11.120.
[55] Baziar A, Kavousi-Fard A. Considering uncertainty in the optimal energy management of renewable micro-grids including storage devices. Renew Energy 2013;59:158–66. https://doi.org/10.1016/J.RENENE.2013.03.026.
[56] Chaudhary P, Rizwan M. Energy management supporting high penetration of solar photovoltaic generation for smart grid using solar forecasts and pumped hydro storage system. Renew Energy 2018;118:928–46. https://doi.org/10.1016/J.RENENE.2017.10.113.
[57] Carta JA, Ramírez P, Bueno C. A joint probability density function of wind speed and direction for wind energy analysis. Energy Convers Manag 2008;49:1309–20. https://doi.org/10.1016/J.ENCONMAN.2008.01.010.
[58] Aghajani GR, Shayanfar HA, Shayeghi H. Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy 2017;126:622–37. https://doi.org/10.1016/J.ENERGY.2017.03.051.
[59] Fang X, Hodge BM, Du E, Kang C, Li F. Introducing uncertainty components in locational marginal prices for pricing wind power and load uncertainties. IEEE Transactions on Power Systems 2019;34:2013–24. https://doi.org/10.1109/TPWRS.2018.2881131.
[60] Li S, Chen H, Wang M, Heidari AA, Mirjalili S. Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems 2020;111:300–23. https://doi.org/10.1016/J.FUTURE.2020.03.055.
[61] Chen H, Li C, Mafarja M, Heidari AA, Chen Y, Cai Z. Slime mould algorithm: a comprehensive review of recent variants and applications. Int J Syst Sci 2023;54:204–35. https://doi.org/10.1080/00207721.2022.2153635.
[62] Gharehchopogh FS, Ucan A, Ibrikci T, Arasteh B, Isik G. Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications. Archives of Computational Methods in Engineering 2023 30:4 2023;30:2683–723. https://doi.org/10.1007/S11831-023-09883-3.
[63] Naik MK, Panda R, Abraham A. Adaptive opposition slime mould algorithm. Soft Comput 2021;25:14297–313. https://doi.org/10.1007/S00500-021-06140-2/FIGURES/6.
