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Emerging Trends and Approaches for Designing Net-Zero Low-Carbon Integrated Energy Networks: A Review of Current Practices

  • Review Article-Electrical Engineering
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Abstract

The incorporation of net-zero technology into preexisting energy networks is crucial for facilitating the shift toward an ecologically conscious and sustainable energy infrastructure. The primary objective of this integration is to effectively decrease carbon footprints and to provide a comprehensive understanding of the current approaches and trends related to the design and management frameworks of integrated energy networks. The initial section of this study establishes the foundation for a comprehensive examination of the particular challenges associated with decarbonization in the strategic and operational aspects of integrated energy networks. The subsequent analysis proceeds to elucidate the fundamental framework and technological architecture upon which these energy networks are constructed. This provides significant insights into the operational complexity and efficacy of the system. In addition, the paper provides a concise examination of prominent frameworks and alternative approaches that tackle the issue of low-carbon design and administration. The degree of accuracy facilitates individuals when selecting systems that align with the specific requirements of unique circumstances. Furthermore, this study provides explicit suggestions for future research based on an examination of the distinct attributes and framework of integrated energy networks. The anticipated outcome of implementing these recommendations is to enable the advancement of sustainable development and expedite the shift toward energy infrastructure with reduced carbon emissions. This will make a significant contribution to the collaborative endeavor of mitigating climate change and fostering a sustainable energy future. This study further elucidates the significant contribution of integrated energy networks in addressing climate change and enhancing energy efficiency. It achieves this by synthesizing a complete range of concepts sourced from many academic papers, industry reports, and case studies. This statement offers an examination of the multifaceted technological, legislative, and planning factors that contribute to the attainment of net-zero objectives.

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Data Availability Statement

The data will be made available on request.

References

  1. Aktar, A.K.; Taşcıkaraoğlu, A.; Gürleyük, S.S.; Catalão, J.P.: A framework for dispatching of an electric vehicle fleet using vehicle-to-grid technology. Sustain. Energy Grids Netw., 100991 (2023)

  2. Gola, K.K.; Gupta, B.: Underwater acoustic sensor networks: an energy efficient and void avoidance routing based on grey wolf optimization algorithm. Arab. J. Sci. Eng. 46(4), 3939–3954 (2021)

    Google Scholar 

  3. Putz, D.; Gumhalter, M.; Auer, H.: The true value of a forecast: assessing the impact of accuracy on local energy communities. Sustain. Energy Grids Netw., 100983 (2022)

  4. Ocampo-Toro, J.; Garzon-Rivera, O.; Grisales-Noreña, L.; Montoya-Giraldo, O.; Gil-González, W.: Optimal power dispatch in direct current networks to reduce energy production costs and co 2 emissions using the antlion optimization algorithm. Arab. J. Sci. Eng. 46(10), 9995–10006 (2021)

    Google Scholar 

  5. Du, E.; Zhang, N.; Hodge, B.-M.; Wang, Q.; Kang, C.; Kroposki, B.; Xia, Q.: The role of concentrating solar power toward high renewable energy penetrated power systems. IEEE Trans. Power Syst. 33(6), 6630–6641 (2018). https://doi.org/10.1109/TPWRS.2018.2834461

    Article  Google Scholar 

  6. Ho-Van, K.; Do-Dac, T.: Overlay networks with jamming and energy harvesting: security analysis. Arab. J. Sci. Eng. 46(10), 9713–9724 (2021)

    Google Scholar 

  7. Pulazza, G.; Zhang, N.; Kang, C.; Nucci, C.A.: Transmission planning with battery-based energy storage transportation for power systems with high penetration of renewable energy. IEEE Trans. Power Syst. 36(6), 4928–4940 (2021). https://doi.org/10.1109/TPWRS.2021.3069649

    Article  Google Scholar 

  8. Le-Thanh, T.; Ho-Van, K.: Security-and-reliability trade-off of energy harvesting-based underlay relaying networks with transmit antenna selection and jamming. Arab. J. Sci. Eng. 47(11), 13711–13727 (2022)

    Google Scholar 

  9. Liu, Y.; Wang, Y.; Yong, P.; Zhang, N.; Kang, C.; Lu, D.: Fast power system cascading failure path searching with high wind power penetration. IEEE Trans. Sustain. Energy 11(4), 2274–2283 (2020). https://doi.org/10.1109/TSTE.2019.2953867

    Article  Google Scholar 

  10. Clegg, S.; Mancarella, P.: Integrated electrical and gas network flexibility assessment in low-carbon multi-energy systems. IEEE Trans. Sustain. Energy 7(2), 718–731 (2016). https://doi.org/10.1109/TSTE.2015.2497329

    Article  Google Scholar 

  11. Qi, J.; Liu, L.; Shen, Z.; Xu, B.; Leung, K.-S.; Sun, Y.: Low-carbon community adaptive energy management optimization toward smart services. IEEE Trans. Industr. Inf. 16(5), 3587–3596 (2020). https://doi.org/10.1109/TII.2019.2950511

    Article  Google Scholar 

  12. Wang, H.; Ji, C.; Shi, C.; Ge, Y.; Meng, H.; Yang, J.; Chang, K.; Yang, Z.; Wang, S.; Wang, X.: Modeling and parametric study of the performance-emissions trade-off of a hydrogen Wankel rotary engine. Fuel 318, 123662 (2022)

    Google Scholar 

  13. Cheng, Y.; Zhang, N.; Zhang, B.; Kang, C.; Xi, W.; Feng, M.: Low-carbon operation of multiple energy systems based on energy-carbon integrated prices. IEEE Trans. Smart Grid 11(2), 1307–1318 (2020). https://doi.org/10.1109/TSG.2019.2935736

    Article  Google Scholar 

  14. Yin, N.: Multiobjective optimization for vehicle routing optimization problem in low-carbon intelligent transportation. IEEE Trans. Intell. Transp. Syst. (2022). https://doi.org/10.1109/TITS.2022.3193679

    Article  Google Scholar 

  15. De Caro, F.; De Stefani, J.; Vaccaro, A.; Bontempi, G.: Daft-e: feature-based multivariate and multi-step-ahead wind power forecasting. IEEE Trans. Sustain. Energy 13(2), 1199–1209 (2022). https://doi.org/10.1109/TSTE.2021.3130949

    Article  Google Scholar 

  16. Yadav, D.; Mekhilef, S.; Singh, B.; Rawa, M.: Carbon trading analysis and impacts on economy in market-to-market coordination with higher PV penetration. IEEE Trans. Ind. Appl. 57(6), 5582–5592 (2021). https://doi.org/10.1109/TIA.2021.3105495

    Article  Google Scholar 

  17. Arregui, J.; Perarnaud, C.: A new dataset on legislative decision-making in the European union: the DEU III dataset. J. Eur. Publ. Policy 29(1), 12–22 (2022)

    Google Scholar 

  18. Rösch, C.; Villarreal, J.V.: Perception of EU citizens on engineered biocatalytic solar fuels. Renew. Sustain. Energy Rev. 149, 111366 (2021)

    Google Scholar 

  19. Nematchoua, M.K.; Sendrahasina, R.M.; Malmedy, C.; Orosa, J.A.; Simo, E.; Reiter, S.: Analysis of environmental impacts and costs of a residential building over its entire life cycle to achieve nearly zero energy and low emission objectives. J. Clean. Prod. 373, 133834 (2022)

    Google Scholar 

  20. Narayanamoorthy, S.; Brainy, J.; Shalwala, R.A.; Alsenani, T.R.; Ahmadian, A.; Kang, D.: An enhanced fuzzy decision making approach for the assessment of sustainable energy storage systems. Sustain. Energy Grids Netw. 33, 100962 (2023)

    Google Scholar 

  21. Verma, N.; Sharma, V.; Badar, M.A.: Entropy-based lean, energy and six sigma approach to achieve sustainability in manufacturing system. Arab. J. Sci. Eng. 46(8), 8105–8117 (2021)

    Google Scholar 

  22. Chang, N.-B.; Hossain, U.; Valencia, A.; Qiu, J.; Kapucu, N.: The role of food-energy-water nexus analyses in urban growth models for urban sustainability: a review of synergistic framework. Sustain. Cities Soc. 63, 102486 (2020)

    Google Scholar 

  23. Mao, W.; Zhao, Z.; Chang, Z.; Min, G.; Gao, W.: Energy-efficient industrial internet of things: overview and open issues. IEEE Trans. Industr. Inf. 17(11), 7225–7237 (2021)

    Google Scholar 

  24. Raja, S.: Green computing and carbon footprint management in the it sectors. IEEE Trans. Comput. Soc. Syst. 8(5), 1172–1177 (2021)

    Google Scholar 

  25. Wu, X.; Hu, X.; Yin, X.; Moura, S.J.: Stochastic optimal energy management of smart home with PEV energy storage. IEEE Trans. Smart Grid 9(3), 2065–2075 (2018). https://doi.org/10.1109/TSG.2016.2606442

    Article  Google Scholar 

  26. Li, S.; Zhu, J.; Dong, H.: A novel energy sharing mechanism for smart microgrid. IEEE Trans. Smart Grid 12(6), 5475–5478 (2021). https://doi.org/10.1109/TSG.2021.3094329

    Article  Google Scholar 

  27. Yang, L.; Chen, X.; Zhang, J.; Poor, H.V.: Cost-effective and privacy-preserving energy management for smart meters. IEEE Trans. Smart Grid 6(1), 486–495 (2015). https://doi.org/10.1109/TSG.2014.2343611

    Article  Google Scholar 

  28. Tao, Y.; Qiu, J.; Lai, S.; Zhao, J.; Xue, Y.: Carbon-oriented electricity network planning and transformation. IEEE Trans. Power Syst. 36(2), 1034–1048 (2020)

    Google Scholar 

  29. Raiker, G.A.; Reddy B. S.; Loganathan, U.; Agrawal, S.; Thakur, A.S.; K., A.; Barton, J.P.; Thomson, M.: Energy disaggregation using energy demand model and IoT-based control. IEEE Trans. Ind. Appl. 57(2), 1746–1754 (2021). https://doi.org/10.1109/TIA.2020.3047016

  30. Neyestani, N.; Yazdani-Damavandi, M.; Shafie-khah, M.; Chicco, G.; Catalão, J.P.S.: Stochastic modeling of multienergy carriers dependencies in smart local networks with distributed energy resources. IEEE Trans. Smart Grid 6(4), 1748–1762 (2015). https://doi.org/10.1109/TSG.2015.2423552

    Article  Google Scholar 

  31. Schiera, D.S.; Barbierato, L.; Lanzini, A.; Borchiellini, R.; Pons, E.; Bompard, E.; Patti, E.; Macii, E.; Bottaccioli, L.: A distributed multimodel platform to cosimulate multienergy systems in smart buildings. IEEE Trans. Ind. Appl. 57(5), 4428–4440 (2021). https://doi.org/10.1109/TIA.2021.3094497

    Article  Google Scholar 

  32. Pirson, T.; Delhaye, T.P.; Pip, A.; Brun, G.L.; Raskin, J.-P.; Bol, D.: The environmental footprint of IC production: review, analysis and lessons from historical trends. IEEE Trans. Semicond. Manuf. (2022). https://doi.org/10.1109/TSM.2022.3228311

    Article  Google Scholar 

  33. Liu, N.; Tan, L.; Sun, H.; Zhou, Z.; Guo, B.: Bilevel heat-electricity energy sharing for integrated energy systems with energy hubs and prosumers. IEEE Trans. Industr. Inf. 18(6), 3754–3765 (2022). https://doi.org/10.1109/TII.2021.3112095

    Article  Google Scholar 

  34. Schick, C.; Klempp, N.; Hufendiek, K.: Role and impact of prosumers in a sector-integrated energy system with high renewable shares. IEEE Trans. Power Syst. 37(4), 3286–3298 (2022). https://doi.org/10.1109/TPWRS.2020.3040654

    Article  Google Scholar 

  35. Xu, Z.; Han, G.; Liu, L.; Martínez-García, M.; Wang, Z.: Multi-energy scheduling of an industrial integrated energy system by reinforcement learning-based differential evolution. IEEE Trans. Green Commun. Network. 5(3), 1077–1090 (2021). https://doi.org/10.1109/TGCN.2021.3061789

    Article  Google Scholar 

  36. Liu, N.; Wang, J.; Wang, L.: Hybrid energy sharing for multiple microgrids in an integrated heat-electricity energy system. IEEE Trans. Sustain. Energy 10(3), 1139–1151 (2019). https://doi.org/10.1109/TSTE.2018.2861986

    Article  Google Scholar 

  37. Yap, J.T.; Gabriola, A.J.P.; Herrera, C.F.: Managing the energy trilemma in the Philippines. Energy, Sustain. Soc. 11, 1–17 (2021)

    Google Scholar 

  38. Ahmed, I.; Irshad, A.; Zafar, S.; Khan, B.A.; Raza, M.; Ali, P.R.: The role of environmental initiatives and green value co-creation as mediators: promoting corporate entrepreneurship and green innovation. SN Bus. Econ. 3(4), 85 (2023)

    Google Scholar 

  39. Rauf, H.; Khalid, M.; Arshad, N.: A novel smart feature selection strategy of lithium-ion battery degradation modelling for electric vehicles based on modern machine learning algorithms. J. Energy Storage 68, 107577 (2023)

    Google Scholar 

  40. Ahmed, I.; Hasan, S.R.; Ashfaq, B.; Raza, M.; Mukhtar, S.; et al.: Adaptive swarm intelligence-based optimization approach for smart grids power dispatch. In: 2022 International Conference on Emerging Technologies in Electronics, Computing and Communication (ICETECC), pp. 1–6 (2022). IEEE

  41. Ali, M.; Iqbal, A.; Khalid, M.; et al.: A review on recent advances in matrix converter technology: Topologies, control, applications, and future prospects. Int. J. Energy Res. 2023 (2023)

  42. Ali, M.; Abdulgalil, M.A.; Habiballah, I.; Khalid, M.: Optimal scheduling of isolated microgrids with hybrid renewables and energy storage systems considering demand response. IEEE Access (2023)

  43. Nadeem, T.B.; Siddiqui, M.; Khalid, M.; Asif, M.: Distributed energy systems: a review of classification, technologies, applications, and policies: current policy, targets and their achievements in different countries (continued). Energ. Strat. Rev. 48, 101096 (2023)

    Google Scholar 

  44. Alqahtani, M.; Marimuthu, P.; Moorthy, V.; Pangedaiah, B.; Reddy, C.R.; Kiran Kumar, M.; Khalid, M.: Investigation and minimization of power loss in radial distribution network using gray wolf optimization. Energies 16(12), 4571 (2023)

    Google Scholar 

  45. Ahmed, I.; Rehan, M.; Basit, A.; Tufail, M.; Hong, K.-S.: Neuro-fuzzy and networks-based data driven model for multi-charging scenarios of plug-in-electric vehicles. IEEE Access (2023)

  46. Ahmed, I.; Rehan, M.; Basit, A.; Tufail, M.; Hong, K.-S.: A dynamic optimal scheduling strategy for multi-charging scenarios of plug-in-electric vehicles over a smart grid. IEEE Access 11, 28992–29008 (2023)

    Google Scholar 

  47. Rosales-Asensio, E.; de la Puente-Gil, Á.; García-Moya, F.-J.; Blanes-Peiró, J.; de Simón-Martín, M.: Decision-making tools for sustainable planning and conceptual framework for the energy-water-food nexus. Energy Rep. 6, 4–15 (2020)

    Google Scholar 

  48. Zhang, Y.; Zhang, X.; Lan, L.: Robust optimization-based dynamic power generation mix evolution under the carbon-neutral target. Resour. Conserv. Recycl. 178, 106103 (2022)

    Google Scholar 

  49. Miri, M.; Saffari, M.; Arjmand, R.; McPherson, M.: Integrated models in action: analyzing flexibility in the Canadian power system toward a zero-emission future. Energy 261, 125181 (2022)

    Google Scholar 

  50. Cobo, S.; Galán-Martín, Á.; Tulus, V.; Huijbregts, M.A.; Guillén-Gosálbez, G.: Human and planetary health implications of negative emissions technologies. Nat. Commun. 13(1), 2535 (2022)

    Google Scholar 

  51. Kirkegaard, J.K.; Nyborg, S.; Georg, S.; Horst, M.: Towards failed renewable energy communities? Activist attempts to change market conditions in the Danish wind energy market. Energy Res. Soc. Sci. 102, 103152 (2023)

    Google Scholar 

  52. Wang, Y.; Cao, Q.; Liu, L.; Wu, Y.; Liu, H.; Gu, Z.; Zhu, C.: A review of low and zero carbon fuel technologies: achieving ship carbon reduction targets. Sustain. Energy Technol. Assess. 54, 102762 (2022)

    Google Scholar 

  53. Maraaba, L.; Almuhaini, M.; Habli, M.; Khalid, M.: Neural networks based dynamic load modeling for power system reliability assessment. Sustainability 15(6), 5403 (2023)

    Google Scholar 

  54. Khalid, M.; Savkin, A.V.: Closure to discussion on “a method for short-term wind power prediction with multiple observation points’’. IEEE Trans. Power Syst. 28(2), 1898–1899 (2013). https://doi.org/10.1109/TPWRS.2013.2255351

    Article  Google Scholar 

  55. Abdulgalil, M.A.; Khalid, M.: Enhancing the reliability of a microgrid through optimal size of battery ESS. IET Gener. Transm. Distribut. 13(9), 1499–1508 (2019)

    Google Scholar 

  56. Dahbi, S.; Aboutni, R.; Aziz, A.; Benazzi, N.; Elhafyani, M.; Kassmi, K.: Optimised hydrogen production by a photovoltaic-electrolysis system DC/DC converter and water flow controller. Int. J. Hydrogen Energy 41(45), 20858–20866 (2016)

    Google Scholar 

  57. Şahin, M.E.; Blaabjerg, F.; Sangwongwanich, A.: A comprehensive review on supercapacitor applications and developments. Energies 15(3), 674 (2022)

    Google Scholar 

  58. Argyrou, M.C.; Marouchos, C.C.; Kalogirou, S.A.; Christodoulides, P.: Modeling a residential grid-connected PV system with battery-supercapacitor storage: control design and stability analysis. Energy Rep. 7, 4988–5002 (2021)

    Google Scholar 

  59. Alshehri, J.; Khalid, M.; Alzahrani, A.: An intelligent battery energy storage-based controller for power quality improvement in microgrids. Energies 12(11), 2112 (2019)

    Google Scholar 

  60. Khalid, M.: Wind power economic dispatch-impact of radial basis functional networks and battery energy storage. IEEE Access 7, 36819–36832 (2019)

    Google Scholar 

  61. Gull, M.S.; Mehmood, N.; Rauf, H.; Khalid, M.; Arshad, N.: Soft load shedding based demand control of residential consumers. Electronics 11(4), 615 (2022)

    Google Scholar 

  62. Alhumaid, Y.; Khan, K.; Alismail, F.; Khalid, M.: Multi-input nonlinear programming based deterministic optimization framework for evaluating microgrids with optimal renewable-storage energy mix. Sustainability 13(11), 5878 (2021)

    Google Scholar 

  63. Salman, U.; Khan, K.; Alismail, F.; Khalid, M.: Techno-economic assessment and operational planning of wind-battery distributed renewable generation system. Sustainability 13(12), 6776 (2021)

    Google Scholar 

  64. Maaruf, M.; Khalid, M.: Global sliding-mode control with fractional-order terms for the robust optimal operation of a hybrid renewable microgrid with battery energy storage. Electronics 11(1), 88 (2021)

    Google Scholar 

  65. Ali, R.; Khan, K.; Khalid, M.; Khan, A.: Multi-input boost converter for parallel connected renewable energy systems. Renew. Energy Power Qual. J. 18, 403–408 (2020)

    Google Scholar 

  66. Suriyan, K.; Ramalingam, N.; Jayaraman, M.K.; Gunasekaran, R.: Recent developments of smart energy networks and challenges. Smart Energy Electric Power Syst., 37–47 (2023)

  67. Qi, S.; Wang, X.; Li, X.; Qian, T.; Zhang, Q.: Enhancing integrated energy distribution system resilience through a hierarchical management strategy in district multi-energy systems. Sustainability 11(15), 4048 (2019)

    Google Scholar 

  68. Clegg, S.; Mancarella, P.: Integrated modeling and assessment of the operational impact of power-to-gas (P2G) on electrical and gas transmission networks. IEEE Trans. Sustain. Energy 6(4), 1234–1244 (2015)

    Google Scholar 

  69. Liu, X.; Li, X.; Tian, J.; Cao, H.: Low-carbon economic dispatch of integrated electricity and natural gas energy system considering carbon capture device. Trans. Institute of Measurement and Control, 01423312211060572 (2021)

  70. Yang, W.; Liu, W.; Chung, C.Y.; Wen, F.: Coordinated planning strategy for integrated energy systems in a district energy sector. IEEE Trans. Sustain. Energy 11(3), 1807–1819 (2019)

    Google Scholar 

  71. Chevallier, J.; Goutte, S.; Ji, Q.; Guesmi, K.: Green finance and the restructuring of the oil-gas-coal business model under carbon asset stranding constraints. Energy Policy 149, 112055 (2021)

    Google Scholar 

  72. Mišík, M.: The EU needs to improve its external energy security. Energy Policy 165, 112930 (2022)

    Google Scholar 

  73. Liu, N.; Hu, X.; Ma, L.; Yu, X.: Vulnerability assessment for coupled network consisting of power grid and EV traffic network. IEEE Trans. Smart Grid 13(1), 589–598 (2021)

    Google Scholar 

  74. Wang, X.; Yao, F.; Wen, F.: Applications of blockchain technology in modern power systems: a brief survey. Energies 15(13), 4516 (2022)

    Google Scholar 

  75. Abomazid, A.M.; El-Taweel, N.A.; Farag, H.E.Z.: Optimal energy management of hydrogen energy facility using integrated battery energy storage and solar photovoltaic systems. IEEE Trans. Sustain. Energy 13(3), 1457–1468 (2022). https://doi.org/10.1109/TSTE.2022.3161891

    Article  Google Scholar 

  76. Şahin, M.E.; Okumuş, H.İ: Parallel-connected buck-boost converter with FLC for hybrid energy system. Electric Power Compon. Syst. 48(19–20), 2117–2129 (2021)

    Google Scholar 

  77. Şahin, M.E.; Blaabjerg, F.: Pv powered hybrid energy storage system control using bidirectional and boost converters. Electric Power Compon. Syst. 49(15), 1260–1277 (2022)

    Google Scholar 

  78. Şahin, M.E.; Blaabjerg, F.: A hybrid PV-battery/supercapacitor system and a basic active power control proposal in matlab/simulink. Electronics 9(1), 129 (2020)

    Google Scholar 

  79. Sahin, M.; Okumus, H.: The design steps of a hybrid energy system (2017)

  80. Kumar, A.; Sah, B.; Deng, Y.; He, X.; Bansal, R.; Kumar, P.: Autonomous hybrid renewable energy system optimization for minimum cost (2015)

  81. Sahin, M.E.: Fuzzy logic controlled synchronous buck dc-dc converter IOR solar energy-hydrogen systems. INISTA 2009, 200 (2009)

    Google Scholar 

  82. Sahin, M.E.; Okumus, H.İ.: Fuzzy logic controlled buck-boost dc-dc converter for solar energy-battery system. In: 2011 International Symposium on Innovations in Intelligent Systems and Applications, pp. 394–397 (2011). IEEE

  83. Koçyiğit, N.; Şahin, M.: Design of a laboratory unit air-conditioning system with matlab/simulink software. Acta Phys. Pol., A 132(3), 839–842 (2017)

    Google Scholar 

  84. Geng, J.; Du, W.; Yang, D.; Chen, Y.; Liu, G.; Fu, J.; He, G.; Wang, J.; Chen, H.: Construction of energy internet technology architecture based on general system structure theory. Energy Rep. 7, 10–17 (2021)

    Google Scholar 

  85. Lin, L.; Guan, X.; Peng, Y.; Wang, N.; Maharjan, S.; Ohtsuki, T.: Deep reinforcement learning for economic dispatch of virtual power plant in internet of energy. IEEE Internet Things J. 7(7), 6288–6301 (2020). https://doi.org/10.1109/JIOT.2020.2966232

    Article  Google Scholar 

  86. Ahmed, I.; Rehan, M.; Hong, K.-S.; Basit, A.: A consensus-based approach for economic dispatch considering multiple fueling strategy of electricity production sector over a smart grid. In: 2022 13th Asian Control Conference (ASCC), pp. 1196–1201 (2022). IEEE

  87. Basit, A.; Tufail, M.; Rehan, M.; Rashid, H.u.: A non-uniform event-triggered distributed filtering scheme for discrete-time nonlinear systems over wireless sensor networks. Transactions of the Institute of Measurement and Control, 01423312221126233 (2022)

  88. Guo, Y.; Chen, C.; Tong, L.: Pricing multi-interval dispatch under uncertainty part I: dispatch-following incentives. IEEE Trans. Power Syst. 36(5), 3865–3877 (2021). https://doi.org/10.1109/TPWRS.2021.3055730

    Article  Google Scholar 

  89. Ahmed, I.; Rehan, M.; Basit, A.; Hong, K.-S.: Greenhouse gases emission reduction for electric power generation sector by efficient dispatching of thermal plants integrated with renewable systems. Sci. Rep. 12(1), 1–21 (2022)

    Google Scholar 

  90. Mahmoodi, M.; Shamsi, P.; Fahimi, B.: Economic dispatch of a hybrid microgrid with distributed energy storage. IEEE Trans. Smart Grid 6(6), 2607–2614 (2015). https://doi.org/10.1109/TSG.2014.2384031

    Article  Google Scholar 

  91. Ahmed, I.; Rehan, M.; Basit, A.; Malik, S.H.; Hong, K.-S.; et al.: Multi-area economic emission dispatch for large-scale multi-fueled power plants contemplating inter-connected grid tie-lines power flow limitations. Energy 261, 125178 (2022)

    Google Scholar 

  92. Botterud, A.; Zhou, Z.; Wang, J.; Sumaili, J.; Keko, H.; Mendes, J.; Bessa, R.J.; Miranda, V.: Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of illinois. IEEE Trans Sustai Energy 4(1), 250–261 (2013). https://doi.org/10.1109/TSTE.2012.2215631

    Article  Google Scholar 

  93. Ahmed, I.; Basit, A.; Rehan, M.; Hong, K.-S.; et al.: Multi-objective whale optimization approach for cost and emissions scheduling of thermal plants in energy hubs. Energy Rep. 8, 9158–9174 (2022)

    Google Scholar 

  94. Cheng, S.; Gu, C.; Yang, X.; Li, S.; Fang, L.; Li, F.: Network pricing for multienergy systems under long-term load growth uncertainty. IEEE Trans. Smart Grid 13(4), 2715–2729 (2022). https://doi.org/10.1109/TSG.2022.3159647

    Article  Google Scholar 

  95. Yin, N.: Multiobjective optimization for vehicle routing optimization problem in low-carbon intelligent transportation. IEEE Trans. Intell. Transp. Syst. (2022). https://doi.org/10.1109/TITS.2022.3193679

    Article  Google Scholar 

  96. Saeidpour Parizy, E.; Choi, S.; Bahrami, H.R.: Grid-specific co-optimization of incentive for generation planning in power systems with renewable energy sources. IEEE Trans. Sustain. Energy 11(2), 947–957 (2020). https://doi.org/10.1109/TSTE.2019.2914875

    Article  Google Scholar 

  97. Yang, X.; Zhang, Y.; He, H.; Ren, S.; Weng, G.: Real-time demand side management for a microgrid considering uncertainties. IEEE Trans. Smart Grid 10(3), 3401–3414 (2019). https://doi.org/10.1109/TSG.2018.2825388

    Article  Google Scholar 

  98. Jahani, A.; Zare, K.; Khanli, L.M.; Karimipour, H.: Optimized power trading of reconfigurable microgrids in distribution energy market. IEEE Access 9, 48218–48235 (2021). https://doi.org/10.1109/ACCESS.2021.3064634

    Article  Google Scholar 

  99. Al-Awami, A.T.; Amleh, N.A.; Muqbel, A.M.: Optimal demand response bidding and pricing mechanism with fuzzy optimization: application for a virtual power plant. IEEE Trans. Ind. Appl. 53(5), 5051–5061 (2017). https://doi.org/10.1109/TIA.2017.2723338

    Article  Google Scholar 

  100. Liu, J.; Singh, R.; Pal, B.C.: Distribution system state estimation with high penetration of demand response enabled loads. IEEE Trans. Power Syst. 36(4), 3093–3104 (2021). https://doi.org/10.1109/TPWRS.2020.3047269

    Article  Google Scholar 

  101. Yang, H.; Gao, Y.; Ma, Y.; Zhang, D.: Optimal modification of peak-valley period under multiple time-of-use schemes based on dynamic load point method considering reliability. IEEE Trans. Power Syst. 37(5), 3889–3901 (2022). https://doi.org/10.1109/TPWRS.2021.3131519

    Article  Google Scholar 

  102. Yan, X.; Zhang, H.; Gu, C.; Liu, N.; Li, F.; Song, Y.: Truncated strategy based dynamic network pricing for energy storage. J. Modern Power Syst. Clean Energy (2022). https://doi.org/10.35833/MPCE.2021.000631

  103. Gu, H.; Yu, J.; Shen, Y.; Li, Y.; Guan, D.; Ye, P.: Bi-level decentralized optimal economic dispatch for urban regional integrated energy system under carbon emission constraints. IEEE Access 10, 62341–62364 (2022). https://doi.org/10.1109/ACCESS.2022.3177723

    Article  Google Scholar 

  104. Liu, J.; Xu, Z.; Wu, J.; Liu, K.; Sun, X.; Guan, X.: Optimal planning of internet data centers decarbonized by hydrogen-water-based energy systems. IEEE Trans. Autom. Sci. Eng. (2022). https://doi.org/10.1109/TASE.2022.3213672

    Article  Google Scholar 

  105. Chen, B.; Guo, Q.; Yin, G.; Wang, B.; Pan, Z.; Chen, Y.; Wu, W.; Sun, H.: Energy circuit-based integrated energy management system: theory, implementation, and application. Proc. IEEE (2022). https://doi.org/10.1109/JPROC.2022.3216567

    Article  Google Scholar 

  106. Li, H.; Hong, T.: A semantic ontology for representing and quantifying energy flexibility of buildings. Adv. Appl. Energy 8, 100113 (2022)

    Google Scholar 

  107. Lin, Y.-H.: Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand-side management. Int. Trans. Electric. Energy Syst. 29(6), 12014 (2019)

    Google Scholar 

  108. Couraud, B.; Andoni, M.; Robu, V.; Norbu, S.; Chen, S.; Flynn, D.: Responsive flexibility: a smart local energy system. Renew. Sustain. Energy Rev. 182, 113343 (2023)

    Google Scholar 

  109. Zou, W.; Sun, Y.; Gao, D.-C.; Zhang, X.; Liu, J.: A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: impacts analysis, collaborative management technologies, and future perspective. Appl. Energy 331, 120393 (2023)

    Google Scholar 

  110. Hussain, S.; Lai, C.; Eicker, U.: Flexibility: Literature review on concepts, modeling, and provision method in smart grid. Sustain. Energy Grids Netw. 101113 (2023)

  111. Suthar, S.; Cherukuri, S.H.C.; Pindoriya, N.M.: Peer-to-peer energy trading in smart grid: frameworks, implementation methodologies, and demonstration projects. Electric Power Syst. Res. 214, 108907 (2023)

    Google Scholar 

  112. Chen, Y.; Zhao, C.: Review of energy sharing: business models, mechanisms, and prospects. IET Renew. Power Gener. 16(12), 2468–2480 (2022)

    Google Scholar 

  113. Yan, M.; Shahidehpour, M.; Alabdulwahab, A.; Abusorrah, A.; Gurung, N.; Zheng, H.; Ogunnubi, O.; Vukojevic, A.; Paaso, E.A.: Blockchain for transacting energy and carbon allowance in networked microgrids. IEEE Trans. Smart Grid 12(6), 4702–4714 (2021)

    Google Scholar 

  114. Benjaafar, S.; Li, Y.; Daskin, M.: Carbon footprint and the management of supply chains: insights from simple models. IEEE Trans. Autom. Sci. Eng. 10(1), 99–116 (2012)

    Google Scholar 

  115. Basit, A.; Tufail, M.; Rehan, M.; Ahmed, I.: A new event-triggered distributed state estimation approach for one-sided Lipschitz nonlinear discrete-time systems and its application to wireless sensor networks. ISA Trans. 137, 74–86 (2023)

    Google Scholar 

  116. Zhao, X.; Shang, Y.; Ma, X.; Xia, P.; Shahzad, U.: Does carbon trading lead to green technology innovation: recent evidence from Chinese companies in resource-based industries. IEEE Trans. Eng. Manage. (2022). https://doi.org/10.1109/TEM.2022.3186905

    Article  Google Scholar 

  117. La Viña, A.G.; Tan, J.M.; Guanzon, T.I.M.; Caleda, M.J.; Ang, L.: Navigating a trilemma: energy security, equity, and sustainability in the Philippines’ low-carbon transition. Energy Res. Soc. Sci. 35, 37–47 (2018)

    Google Scholar 

  118. Goldthau, A.; Sovacool, B.K.: The uniqueness of the energy security, justice, and governance problem. Energy Policy 41, 232–240 (2012)

    Google Scholar 

  119. Basit, A.; Tufail, M.; Rehan, M.: An adaptive gain based approach for event-triggered state estimation with unknown parameters and sensor nonlinearities over wireless sensor networks. ISA Trans. 129, 41–54 (2022)

    Google Scholar 

  120. Guo, Z.; Wei, W.; Chen, L.; Dong, Z.Y.; Mei, S.: Impact of energy storage on renewable energy utilization: a geometric description. IEEE Trans. Sustain. Energy 12(2), 874–885 (2021). https://doi.org/10.1109/TSTE.2020.3023498

    Article  Google Scholar 

  121. Said, D.: Intelligent photovoltaic power forecasting methods for a sustainable electricity market of smart micro-grid. IEEE Commun. Mag. 59(7), 122–128 (2021)

    Google Scholar 

  122. Wei, X.; Zhang, X.; Sun, Y.; Qiu, J.: Carbon emission flow oriented tri-level planning of integrated electricity-hydrogen-gas system with hydrogen vehicles. IEEE Trans. Ind. Appl. 58(2), 2607–2618 (2021)

    Google Scholar 

  123. Li, X.; Li, C.; Chen, G.; Dong, Z.Y.: A risk-averse energy sharing market game for renewable energy microgrid aggregators. IEEE Trans. Power Syst. 37(5), 3528–3539 (2022). https://doi.org/10.1109/TPWRS.2021.3137898

    Article  Google Scholar 

  124. Chen, Q.; Wang, W.; Wang, H.; Wu, J.; Li, X.; Lan, J.: A social beetle swarm algorithm based on grey target decision-making for a multiobjective distribution network reconfiguration considering partition of time intervals. IEEE Access 8, 204987–205013 (2020). https://doi.org/10.1109/ACCESS.2020.3036898

    Article  Google Scholar 

  125. Shi, B.; Dai, W.; Luo, C.; Goh, H.H.; Li, J.: Modeling and impact analysis for solar road integration in distribution networks. IEEE Trans. Sustain. Energy (2022). https://doi.org/10.1109/TSTE.2022.3230686

    Article  Google Scholar 

  126. Zhang, Y.; Zhao, Y.; Gao, S.: A novel hybrid model for wind speed prediction based on VMD and neural network considering atmospheric uncertainties. IEEE Access 7, 60322–60332 (2019). https://doi.org/10.1109/ACCESS.2019.2915582

    Article  Google Scholar 

  127. Mohy-ud-din, G.; Muttaqi, K.M.; Sutanto, D.: Adaptive and predictive energy management strategy for real-time optimal power dispatch from VPPS integrated with renewable energy and energy storage. IEEE Trans. Ind. Appl. 57(3), 1958–1972 (2021). https://doi.org/10.1109/TIA.2021.3057356

    Article  Google Scholar 

  128. Kumar, S.; Krishnasamy, V.; Kaur, R.; Kandasamy, N.K.: Virtual energy storage-based energy management algorithm for optimally sized dc nanogrid. IEEE Syst. J. 16(1), 231–239 (2022). https://doi.org/10.1109/JSYST.2021.3050779

    Article  Google Scholar 

  129. Mohy-ud-din, G.; Muttaqi, K.M.; Sutanto, D.: Adaptive and predictive energy management strategy for real-time optimal power dispatch from VPPS integrated with renewable energy and energy storage. IEEE Trans. Ind. Appl. 57(3), 1958–1972 (2021). https://doi.org/10.1109/TIA.2021.3057356

    Article  Google Scholar 

  130. Chen, W.; Li, T.: Distributed economic dispatch for energy internet based on multiagent consensus control. IEEE Trans. Autom. Control 66(1), 137–152 (2021). https://doi.org/10.1109/TAC.2020.2979749

    Article  MathSciNet  Google Scholar 

  131. Müller, S.C.; Häger, U.; Rehtanz, C.: A multiagent system for adaptive power flow control in electrical transmission systems. IEEE Trans. Industr. Inf. 10(4), 2290–2299 (2014). https://doi.org/10.1109/TII.2014.2315499

    Article  Google Scholar 

  132. Han, Y.; Zeng, Z.: Impulsive communication with full and partial information for adaptive tracking consensus of uncertain second-order multiagent systems. IEEE Trans. Cybern. 52(10), 10302–10313 (2022). https://doi.org/10.1109/TCYB.2021.3064765

    Article  Google Scholar 

  133. Liu, Z.; Su, C.; Høidalen, H.K.; Chen, Z.: A multiagent system-based protection and control scheme for distribution system with distributed-generation integration. IEEE Trans. Power Deliv. 32(1), 536–545 (2017). https://doi.org/10.1109/TPWRD.2016.2585579

    Article  Google Scholar 

  134. Li, Y.; Ding, Y.; Liu, Y.; Yang, T.; Wang, P.; Wang, J.; Yao, W.: Dense skip attention based deep learning for day-ahead electricity price forecasting. IEEE Trans. Power Syst. (2022). https://doi.org/10.1109/TPWRS.2022.3217579

    Article  Google Scholar 

  135. Zhuo, Z.; Zhang, N.; Hou, Q.; Du, E.; Kang, C.: Backcasting technical and policy targets for constructing low-carbon power systems. IEEE Trans. Power Syst. 37(6), 4896–4911 (2022). https://doi.org/10.1109/TPWRS.2022.3150040

    Article  Google Scholar 

  136. Zhang, S.; Andrews-Speed, P.: State versus market in china’s low-carbon energy transition: an institutional perspective. Energy Res. Soc. Sci. 66, 101503 (2020)

    Google Scholar 

  137. Chen, S.; Mao, H.; Sun, J.: Low-carbon city construction and corporate carbon reduction performance: evidence from a quasi-natural experiment in china. J. Bus. Ethics 180(1), 125–143 (2022)

    Google Scholar 

  138. Kulmer, V.; Seebauer, S.; Hinterreither, H.; Kortschak, D.; Theurl, M.C.; Haas, W.: Transforming the s-shape: identifying and explaining turning points in market diffusion curves of low-carbon technologies in austria. Res. Policy 51(1), 104371 (2022)

    Google Scholar 

  139. Wang, Y.; Qiu, J.; Tao, Y.; Zhao, J.: Carbon-oriented operational planning in coupled electricity and emission trading markets. IEEE Trans. Power Syst. 35(4), 3145–3157 (2020). https://doi.org/10.1109/TPWRS.2020.2966663

    Article  Google Scholar 

  140. Jiang, J.; Xie, D.; Ye, B.; Shen, B.; Chen, Z.: Research on china’s cap-and-trade carbon emission trading scheme: overview and outlook. Appl. Energy 178, 902–917 (2016)

    Google Scholar 

  141. Lecain, D.R.; Morgan, J.A.; Schuman, G.E.; Reeder, J.D.; Hart, R.H.: Carbon exchange rates in grazed and ungrazed pastures of wyoming. Rangel. Ecol. Manage. J. Range Manage. Archiv. 53(2), 199–206 (2000)

    Google Scholar 

  142. Ellis, A.V.; Wilson, M.A.: Carbon exchange in hot alkaline degradation of glucose. J. Org. Chem. 67(24), 8469–8474 (2002)

    Google Scholar 

  143. He, S.; Liu, N.; Li, R.: Multiobjective coordinated operation for multienergy hubs: a bargaining game approach. IEEE Trans. Ind. Appl. 58(6), 7892–7906 (2022)

    Google Scholar 

  144. Yuan, Q.; Ye, Y.; Tang, Y.; Liu, X.; Tian, Q.: Low carbon electric vehicle charging coordination in coupled transportation and power networks. IEEE Trans. Ind. Appl. (2022). https://doi.org/10.1109/TIA.2022.3230014

    Article  Google Scholar 

  145. Liang, Z.; Mieth, R.; Dvorkin, Y.: Inertia pricing in stochastic electricity markets. IEEE Trans. Power Syst. (2022). https://doi.org/10.1109/TPWRS.2022.3189548

    Article  Google Scholar 

  146. Heydarian-Forushani, E.; Ben Elghali, S.; Zerrougui, M.; La Scala, M.; Mestre, P.: An auction-based local market clearing for energy management in a virtual power plant. IEEE Trans. Ind. Appl. 58(5), 5724–5733 (2022). https://doi.org/10.1109/TIA.2022.3188226

    Article  Google Scholar 

  147. Kargarian, A.; Mohammadi, J.; Guo, J.; Chakrabarti, S.; Barati, M.; Hug, G.; Kar, S.; Baldick, R.: Toward distributed/decentralized dc optimal power flow implementation in future electric power systems. IEEE Trans. Smart Grid 9(4), 2574–2594 (2016)

    Google Scholar 

  148. Chen, L.; Li, Y.; Huang, M.; Hui, X.; Gu, S.: Robust dynamic state estimator of integrated energy systems based on natural gas partial differential equations. IEEE Trans. Ind. Appl. 58(3), 3303–3312 (2022). https://doi.org/10.1109/TIA.2022.3161607

    Article  Google Scholar 

  149. Yan, S.; Wang, W.; Li, X.; Zhao, Y.: Research on a cross-regional robust trading strategy based on multiple market mechanisms. Energy 261, 125253 (2022)

    Google Scholar 

  150. Dolatabadi, A.; Jadidbonab, M.; Mohammadi-ivatloo, B.: Short-term scheduling strategy for wind-based energy hub: a hybrid stochastic/IGDT approach. IEEE Trans. Sustain. Energy 10(1), 438–448 (2018)

    Google Scholar 

  151. Li, P.; Sheng, W.; Duan, Q.; Li, Z.; Zhu, C.; Zhang, X.: A lyapunov optimization-based energy management strategy for energy hub with energy router. IEEE Trans. Smart Grid 11(6), 4860–4870 (2020)

  152. Zuo, T.; Zhang, Y.; Xie, X.; Meng, K.; Tong, Z.; Dong, Z.Y.; Jia, Y.: A review of optimization technologies for large-scale wind farm planning with practical and prospective concerns. IEEE Trans. Ind. Inf. (2022). https://doi.org/10.1109/TII.2022.3217282

    Article  Google Scholar 

  153. Qiu, Y.; Zhou, S.; Wang, J.; Chou, J.; Fang, Y.; Pan, G.; Gu, W.: Feasibility analysis of utilising underground hydrogen storage facilities in integrated energy system: case studies in china. Appl. Energy 269, 115140 (2020)

    Google Scholar 

  154. Xu, Z.; Han, G.; Liu, L.; Martínez-García, M.; Wang, Z.: Multi-energy scheduling of an industrial integrated energy system by reinforcement learning-based differential evolution. IEEE Trans. Green Commun. Network. 5(3), 1077–1090 (2021)

    Google Scholar 

  155. Chen, S.; Conejo, A.J.; Wei, Z.: Conjectural-variations equilibria in electricity, natural-gas, and carbon-emission markets. IEEE Trans. Power Syst. 36(5), 4161–4171 (2021)

    Google Scholar 

  156. Zhang, S.; Wang, S.; Zhang, Z.; Lyu, J.; Cheng, H.; Huang, M.; Zhang, Q.: Probabilistic multi-energy flow calculation of electricity-gas integrated energy systems with hydrogen injection. IEEE Trans. Ind. Appl. 58(2), 2740–2750 (2021)

    Google Scholar 

  157. Liu, N.; Tan, L.; Sun, H.; Zhou, Z.; Guo, B.: Bilevel heat-electricity energy sharing for integrated energy systems with energy hubs and prosumers. IEEE Trans. Industr. Inf. 18(6), 3754–3765 (2021)

    Google Scholar 

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Acknowledgements

The authors would like to acknowledge the support provided by the Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) at King Fahd University of Petroleum and Minerals under Project No. INRE2220.

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Aziz, S., Ahmed, I., Khan, K. et al. Emerging Trends and Approaches for Designing Net-Zero Low-Carbon Integrated Energy Networks: A Review of Current Practices. Arab J Sci Eng 49, 6163–6185 (2024). https://doi.org/10.1007/s13369-023-08336-0

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