Issue
Natl Sci Open
Volume 3, Number 3, 2024
Special Topic: Energy Systems of Low Carbon Buildings
Article Number 20230051
Number of page(s) 36
Section Engineering
DOI https://doi.org/10.1360/nso/20230051
Published online 17 November 2023
  • Huo T, Ma Y, Xu L, et al. Carbon emissions in China’s urban residential building sector through 2060: A dynamic scenario simulation. Energy 2022; 254: 124395. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Liu Z, Deng Z, He G, et al. Challenges and opportunities for carbon neutrality in China. Nat Rev Earth Environ 2022; 3: 141-155. [Article] [Google Scholar]
  • Li L, Zhang Y, Zhou T, et al. Mitigation of China’s carbon neutrality to global warming. Nat Commun 2022; 13: 5315. [Article] [Google Scholar]
  • Karunathilake H, Hewage K, Brinkerhoff J, et al. Optimal renewable energy supply choices for net-zero ready buildings: A life cycle thinking approach under uncertainty. Energy Build 2019; 201: 70-89. [Article] [CrossRef] [Google Scholar]
  • Lopes RA, Martins J, Aelenei D, et al. A cooperative net zero energy community to improve load matching. Renew Energy 2016; 93: 1-13. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Perera ATD, Nik VM, Chen D, et al. Quantifying the impacts of climate change and extreme climate events on energy systems. Nat Energy 2020; 5: 150-159. [Article] [CrossRef] [Google Scholar]
  • Zhang Z, Chen M, Zhong T, et al. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nat Commun 2023; 14: 2347. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Winkler L, Pearce D, Nelson J, et al. The effect of sustainable mobility transition policies on cumulative urban transport emissions and energy demand. Nat Commun 2023; 14: 2357. [Article] [Google Scholar]
  • Chowdhury AFMK, Deshmukh R, Wu GC, et al. Enabling a low-carbon electricity system for Southern Africa. Joule 2022; 6: 1826-1844. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Camarasa C, Mata É, Navarro JPJ, et al. A global comparison of building decarbonization scenarios by 2050 towards 1.5–2 °C targets. Nat Commun 2022; 13: 3077. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Zhao W, Diao H, Li P, et al. Transactive energy-based joint optimization of energy and flexible reserve for integrated electric-heat systems. IEEE Access 2021; 9: 14491-14503. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wang Y, Ren H, Dong L, et al. Smart solutions shape for sustainable low-carbon future: A review on smart cities and industrial parks in China. Technol Forecast Soc Change 2019; 144: 103-117. [Article] [Google Scholar]
  • Sasse JP, Trutnevyte E. Regional impacts of electricity system transition in Central Europe until 2035. Nat Commun 2020; 11: 4972. [Article] [Google Scholar]
  • Pena-Bello A, Parra D, Herberz M, et al. Integration of prosumer peer-to-peer trading decisions into energy community modelling. Nat Energy 2022; 7: 74-82. [Article] [Google Scholar]
  • Heptonstall PJ, Gross RJK. A systematic review of the costs and impacts of integrating variable renewables into power grids. Nat Energy 2021; 6: 72-83. [Article] [Google Scholar]
  • Guo J, Wu D, Wang Y, et al. Co-optimization method research and comprehensive benefits analysis of regional integrated energy system. Appl Energy 2023; 340: 121034. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wu D, Guo J. Optimal design method and benefits research for a regional integrated energy system. Renew Sustain Energy Rev 2023; 186: 113671. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Chatzivasileiadi A, Ampatzi E, Knight I. Characteristics of electrical energy storage technologies and their applications in buildings. Renew Sustain Energy Rev 2013; 25: 814-830. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Berrill P, Wilson EJH, Reyna JL, et al. Decarbonization pathways for the residential sector in the United States. Nat Clim Chang 2022; 12: 712-718. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zhuo Z, Du E, Zhang N, et al. Cost increase in the electricity supply to achieve carbon neutrality in China. Nat Commun 2022; 13: 3172. [Article] [Google Scholar]
  • Odenweller A, Ueckerdt F, Nemet GF, et al. Probabilistic feasibility space of scaling up green hydrogen supply. Nat Energy 2022; 7: 854-865. [Article] [CrossRef] [Google Scholar]
  • Ustaoglu A, Yaras A, Hekimoğlu G, et al. Expanded glass sphere/lauryl alcohol composite in gypsum: Physico-mechanical properties, energy storage performance and environmental impact assessment. Sol Energy 2023; 262: 111925. [Article] [Google Scholar]
  • Mulugetta Y, Sokona Y, Trotter PA, et al. Africa needs context-relevant evidence to shape its clean energy future. Nat Energy 2022; 7: 1015-1022. [Article] [CrossRef] [Google Scholar]
  • Gruber K, Gauster T, Laaha G, et al. Profitability and investment risk of Texan power system winterization. Nat Energy 2022; 7: 409-416. [Article] [Google Scholar]
  • Iyer G, Ou Y, Edmonds J, et al. Ratcheting of climate pledges needed to limit peak global warming. Nat Clim Chang 2022; 12: 1129-1135. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Abhyankar N, Lin J, Kahrl F, et al. Achieving an 80% carbon-free electricity system in China by 2035. iScience 2022; 25: 105180. [Article] [CrossRef] [PubMed] [Google Scholar]
  • Sepulveda NA, Jenkins JD, Edington A, et al. The design space for long-duration energy storage in decarbonized power systems. Nat Energy 2021; 6: 506-516. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Kittner N, Lill F, Kammen DM. Energy storage deployment and innovation for the clean energy transition. Nat Energy 2017; 2: 17125. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Li P, Wang Z, Liu H, et al. Bi-level optimal configuration strategy of community integrated energy system with coordinated planning and operation. Energy 2021; 236: 121539. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Guo M, Mu Y, Jia H, et al. Electric/thermal hybrid energy storage planning for park-level integrated energy systems with second-life battery utilization. Adv Appl Energy 2021; 4: 100064. [Article] [CrossRef] [Google Scholar]
  • Yang X, Nielsen CP, Song S, et al. Breaking the hard-to-abate bottleneck in China’s path to carbon neutrality with clean hydrogen. Nat Energy 2022; 7: 955-965. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Manoharan P, Subramaniam U, Babu TS, et al. Improved perturb and observation maximum power point tracking technique for solar photovoltaic power generation systems. IEEE Syst J 2021; 15: 3024-3035. [Article] [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  • Xu L, Wang Z, Liu Y, et al. Energy allocation strategy based on fuzzy control considering optimal decision boundaries of standalone hybrid energy systems. J Clean Prod 2021; 279: 123810. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Guo J, Liu Z, Wu X, et al. Two-layer co-optimization method for a distributed energy system combining multiple energy storages. Appl Energy 2022; 322: 119486. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Yan Y, Zhang C, Li K, et al. An integrated design for hybrid combined cooling, heating and power system with compressed air energy storage. Appl Energy 2018; 210: 1151-1166. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Vivek V, Malghan D, Mukherjee K. Toward achieving persistent behavior change in household water conservation. Proc Natl Acad Sci USA 2021; 118: e2023014118. [Article] [Google Scholar]
  • Goldstein B, Gounaridis D, Newell JP. The carbon footprint of household energy use in the United States. Proc Natl Acad Sci USA 2020; 117: 19122-19130. [Article] [Google Scholar]
  • Shen Y, Shi X, Zhao Z, et al. Measuring the low-carbon energy transition in Chinese cities. iScience 2023; 26: 105803. [Article] [CrossRef] [PubMed] [Google Scholar]
  • Way R, Ives MC, Mealy P, et al. Empirically grounded technology forecasts and the energy transition. Joule 2022; 6: 2057-2082. [Article] [CrossRef] [Google Scholar]
  • Hsu A, Wang X, Tan J, et al. Predicting European cities’ climate mitigation performance using machine learning. Nat Commun 2022; 13: 7487. [Article] [Google Scholar]
  • Barrett J, Pye S, Betts-Davies S, et al. Energy demand reduction options for meeting national zero-emission targets in the United Kingdom. Nat Energy 2022; 7: 726-735. [Article] [CrossRef] [Google Scholar]
  • Wiser R, Rand J, Seel J, et al. Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050. Nat Energy 2021; 6: 555-565. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Sovacool BK, Upham P, Martiskainen M, et al. Policy prescriptions to address energy and transport poverty in the United Kingdom. Nat Energy 2023; 8: 273-283. [Article] [Google Scholar]
  • Luderer G, Madeddu S, Merfort L, et al. Impact of declining renewable energy costs on electrification in low-emission scenarios. Nat Energy 2022; 7: 32-42. [Article] [Google Scholar]
  • Nikam V, Kalkhambkar V. A review on control strategies for microgrids with distributed energy resources, energy storage systems, and electric vehicles. Int Trans Electr Energ Syst 2021; 31: e12607. [Article] [CrossRef] [Google Scholar]
  • Bistline JET, Blanford G, Grant J, et al. Economy-wide evaluation of CO2 and air quality impacts of electrification in the United States. Nat Commun 2022; 13: 6693. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Pratama YW, Patrizio P, Mac Dowell N. National priorities in the power system transition to net-zero: No one size fits all. iScience 2022; 25: 105260. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Wang J, Ma C, Wu J. Thermodynamic analysis of a combined cooling, heating and power system based on solar thermal biomass gasification. Appl Energy 2019; 247: 102-115. [Article] [CrossRef] [Google Scholar]
  • He J, Shi C, Wei T, et al. Stochastic model predictive control of hybrid energy storage for improving AGC performance of thermal generators. IEEE Trans Smart Grid 2022; 13: 393-405. [Article] [Google Scholar]
  • Caliano M, Bianco N, Graditi G, et al. Design optimization and sensitivity analysis of a biomass-fired combined cooling, heating and power system with thermal energy storage systems. Energy Convers Manage 2017; 149: 631-645. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wei X, Qiu R, Liang Y, et al. Roadmap to carbon emissions neutral industrial parks: energy, economic and environmental analysis. Energy 2022; 238: 121732. [Article] [CrossRef] [Google Scholar]
  • Koohi-Fayegh S, Rosen MA. A review of energy storage types, applications and recent developments. J Energy Storage 2020; 27: 101047. [Article] [Google Scholar]
  • Sengupta S, Adams PJ, Deetjen TA, et al. Subnational implications from climate and air pollution policies in India’s electricity sector. Science 2022; 378: eabh1484. [Article] [Google Scholar]
  • Ren C, Zhou X, Wang C, et al. Ageing threatens sustainability of smallholder farming in China. Nature 2023; 616: 96-103. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Hannan MA, Hoque MM, Mohamed A, et al. Review of energy storage systems for electric vehicle applications: Issues and challenges. Renew Sustain Energy Rev 2017; 69: 771-789. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zheng J, Duan H, Zhou S, et al. Limiting global warming to below 1.5°C from 2°C: An energy-system-based multi-model analysis for China. Energy Economics 2021; 100: 105355. [Article] [CrossRef] [Google Scholar]
  • Rahman MM, Oni AO, Gemechu E, et al. Assessment of energy storage technologies: A review. Energy Convers Manage 2020; 223: 113295. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • De Rosa M, Afanaseva O, Fedyukhin AV, et al. Prospects and characteristics of thermal and electrochemical energy storage systems. J Energy Storage 2021; 44: 103443. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Hambel C, Kraft H, Schwartz E. Optimal carbon abatement in a stochastic equilibrium model with climate change. Eur Economic Rev 2021; 132: 103642. [Article] [CrossRef] [Google Scholar]
  • Gou X, Chen Q, He KL. Real-time quantification for dynamic heat storage characteristic of district heating system and its application in dispatch of integrated energy system. Energy 2022; 259: 124960. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zakeri B, Syri S. Electrical energy storage systems: a comparative life cycle cost analysis. Renew Sustain Energy Rev 2015; 42: 569-596. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Liu J, Chen X, Cao S, et al. Overview on hybrid solar photovoltaic-electrical energy storage technologies for power supply to buildings. Energy Convers Manage 2019; 187: 103-121. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Moy K, Lee SB, Harris S, et al. Design and validation of synthetic duty cycles for grid energy storage dispatch using lithium-ion batteries. Adv Appl Energy 2021; 4: 100065. [Article] [CrossRef] [Google Scholar]
  • Albertus P, Manser JS, Litzelman S. Long-duration electricity storage applications, economics, and technologies. Joule 2020; 4: 21-32. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Javed MS, Ma T, Jurasz J, et al. Solar and wind power generation systems with pumped hydro storage: Review and future perspectives. Renew Energy 2020; 148: 176-192. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Krishan O, Suhag S. An updated review of energy storage systems: Classification and applications in distributed generation power systems incorporating renewable energy resources. Int J Energy Res 2019; 43: 6171-6210. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Lin X, Zamora R. Controls of hybrid energy storage systems in microgrids: Critical review, case study and future trends. J Energy Storage 2022; 47: 103884. [Article] [CrossRef] [Google Scholar]
  • Chong LW, Wong YW, Rajkumar RK, et al. Hybrid energy storage systems and control strategies for stand-alone renewable energy power systems. Renew Sustain Energy Rev 2016; 66: 174-189. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Tie D, Huang S, Wang J, et al. Hybrid energy storage devices: Advanced electrode materials and matching principles. Energy Storage Mater 2019; 21: 22-40. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Jing W, Lai CH, Wong WSH, et al. A comprehensive study of battery-supercapacitor hybrid energy storage system for standalone PV power system in rural electrification. Appl Energy 2018; 224: 340-356. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Fan M, Wang J, Kong X, et al. Experimental evaluation of the cascaded energy storage radiator for constructing indoor thermal environment in winter. Appl Energy 2023; 332: 120503. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Borri E, Zsembinszki G, Cabeza LF. Recent developments of thermal energy storage applications in the built environment: A bibliometric analysis and systematic review. Appl Therm Eng 2021; 189: 116666. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Pan G, Hu Q, Gu W, et al. Assessment of plum rain’s impact on power system emissions in Yangtze-Huaihe River basin of China. Nat Commun 2021; 12: 6156. [Article] [Google Scholar]
  • Pomerantseva E, Bonaccorso F, Feng X, et al. Energy storage: The future enabled by nanomaterials. Science 2019; 366: eaan8285. [Article] [Google Scholar]
  • Palacios A, Barreneche C, Navarro ME, et al. Thermal energy storage technologies for concentrated solar power—A review from a materials perspective. Renew Energy 2020; 156: 1244-1265. [Article] [CrossRef] [Google Scholar]
  • Mansø M, Petersen AU, Wang Z, et al. Molecular solar thermal energy storage in photoswitch oligomers increases energy densities and storage times. Nat Commun 2018; 9: 1945. [Article] [Google Scholar]
  • Li Z, Lu Y, Huang R, et al. Applications and technological challenges for heat recovery, storage and utilisation with latent thermal energy storage. Appl Energy 2021; 283: 116277. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Guelpa E, Verda V. Thermal energy storage in district heating and cooling systems: A review. Appl Energy 2019; 252: 113474. [Article] [CrossRef] [Google Scholar]
  • Nie B, Palacios A, Zou B, et al. Review on phase change materials for cold thermal energy storage applications. Renew Sustain Energy Rev 2020; 134: 110340. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Jouhara H, Żabnieńska-Góra A, Khordehgah N, et al. Latent thermal energy storage technologies and applications: A review. Int J Thermofluids 2020; 5-6: 100039. [Article] [CrossRef] [Google Scholar]
  • Koçak B, Fernandez AI, Paksoy H. Review on sensible thermal energy storage for industrial solar applications and sustainability aspects. Sol Energy 2020; 209: 135-169. [Article] [Google Scholar]
  • Chauhan VK, Shukla SK, Rathore PKS. A systematic review for performance augmentation of solar still with heat storage materials: A state of art. J Energy Storage 2022; 47: 103578. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Qiu L, Liu JZ, Chang SLY, et al. Biomimetic superelastic graphene-based cellular monoliths. Nat Commun 2012; 3: 1241. [Article] [Google Scholar]
  • Elberry AM, Thakur J, Santasalo-Aarnio A, et al. Large-scale compressed hydrogen storage as part of renewable electricity storage systems. Int J Hydrogen Energy 2021; 46: 15671-15690. [Article] [Google Scholar]
  • Erickson ED, Tominac PA, Zavala VM. Biogas production in United States dairy farms incentivized by electricity policy changes. Nat Sustain 2023; 6: 438-446. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Krevor S, de Coninck H, Gasda SE, et al. Subsurface carbon dioxide and hydrogen storage for a sustainable energy future. Nat Rev Earth Environ 2023; 4: 102-118. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zivar D, Kumar S, Foroozesh J. Underground hydrogen storage: A comprehensive review. Int J Hydrogen Energy 2021; 46: 23436-23462. [Article] [Google Scholar]
  • Lang C, Jia Y, Yao X. Recent advances in liquid-phase chemical hydrogen storage. Energy Storage Mater 2020; 26: 290-312. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Tarhan C, Çil MA. A study on hydrogen, the clean energy of the future: hydrogen storage methods. J Energy Storage 2021; 40: 102676. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Hassan IA, Ramadan HS, Saleh MA, et al. Hydrogen storage technologies for stationary and mobile applications: Review, analysis and perspectives. Renew Sustain Energy Rev 2021; 149: 111311. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Fan G, Liu Z, Liu X, et al. Energy management strategies and multi-objective optimization of a near-zero energy community energy supply system combined with hybrid energy storage. Sustain Cities Soc 2022; 83: 103970. [Article] [Google Scholar]
  • Liu Z, Guo J, Li Y, et al. Multi-scenario analysis and collaborative optimization of a novel distributed energy system coupled with hybrid energy storage for a nearly zero-energy community. J Energy Storage 2021; 41: 102992. [Article] [CrossRef] [Google Scholar]
  • Shin H, Hansen KU, Jiao F. Techno-economic assessment of low-temperature carbon dioxide electrolysis. Nat Sustain 2021; 4: 911-919. [Article] [Google Scholar]
  • Luo Z, Peng J, Cao J, et al. Demand flexibility of residential buildings: Definitions, flexible loads, and quantification methods. Engineering 2022; 16: 123-140. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Kohlhepp P, Harb H, Wolisz H, et al. Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies. Renew Sustain Energy Rev 2019; 101: 527-547. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Ding Y, Bai Y, Tian Z, et al. Coordinated optimization of robustness and flexibility of building heating systems for demand response control considering prediction uncertainty. Appl Therm Eng 2023; 223: 120024. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Chen Z, Sun Y, Xin A, et al. Integrated demand response characteristics of industrial park: A review. J Modern Power Syst Clean Energy 2020; 8: 15-26. [Article] [Google Scholar]
  • Zheng X, Qiu Y, Zhan X, et al. Optimization based planning of urban energy systems: retrofitting a Chinese industrial park as a case-study. Energy 2017; 139: 31-41. [Article] [CrossRef] [Google Scholar]
  • Nik VM, Moazami A. Using collective intelligence to enhance demand flexibility and climate resilience in urban areas. Appl Energy 2021; 281: 116106. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Gonzalez JM, Tomlinson JE, Martínez Ceseña EA, et al. Designing diversified renewable energy systems to balance multisector performance. Nat Sustain 2023; 6: 415-427. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Parrish B, Heptonstall P, Gross R, et al. A systematic review of motivations, enablers and barriers for consumer engagement with residential demand response. Energy Policy 2020; 138: 111221. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Sovacool BK, Kester J, Noel L, et al. Actors, business models, and innovation activity systems for vehicle-to-grid (V2G) technology: A comprehensive review. Renew Sustain Energy Rev 2020; 131: 109963. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wang H, Feng K, Wang P, et al. China’s electric vehicle and climate ambitions jeopardized by surging critical material prices. Nat Commun 2023; 14: 1246. [Article] [Google Scholar]
  • Guan Y, Yan J, Shan Y, et al. Burden of the global energy price crisis on households. Nat Energy 2023; 8: 304-316. [Article] [CrossRef] [Google Scholar]
  • Nunes A, Woodley L, Rossetti P. Re-thinking procurement incentives for electric vehicles to achieve net-zero emissions. Nat Sustain 2022; 5: 527-532. [Article] [Google Scholar]
  • Singh K, Singh A. Behavioural modelling for personal and societal benefits of V2G/V2H integration on EV adoption. Appl Energy 2022; 319: 119265. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Noorollahi Y, Golshanfard A, Aligholian A, et al. Sustainable energy system planning for an industrial zone by integrating electric vehicles as energy storage. J Energy Storage 2020; 30: 101553. [Article] [CrossRef] [Google Scholar]
  • Lyu Y, Liu Y, Guo Y, et al. Review of green development of Chinese industrial parks. Energy Strategy Rev 2022; 42: 100867. [Article] [CrossRef] [Google Scholar]
  • Hassija V, Chamola V, Garg S, et al. A blockchain-based framework for lightweight data sharing and energy trading in V2G network. IEEE Trans Veh Technol 2020; 69: 5799-5812. [Article] [CrossRef] [Google Scholar]
  • Tepe B, Figgener J, Englberger S, et al. Optimal pool composition of commercial electric vehicles in V2G fleet operation of various electricity markets. Appl Energy 2022; 308: 118351. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Mahmoud M, Ramadan M, Olabi AG, et al. A review of mechanical energy storage systems combined with wind and solar applications. Energy Convers Manage 2020; 210: 112670. [Article] [CrossRef] [Google Scholar]
  • Chen H, Cong TN, Yang W, et al. Progress in electrical energy storage system: A critical review. Prog Nat Sci 2009; 19: 291-312. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Harsini AE. Resilience-oriented district energy system integrated with renewable energy and multi-level seasonal energy storage. J Energy Storage 2023; 72: 108645. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Lennon A, Lunardi M, Hallam B, et al. The aluminium demand risk of terawatt photovoltaics for net zero emissions by 2050. Nat Sustain 2022; 5: 357-363. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Su H, Zio E, Zhang J, et al. A systematic method for the analysis of energy supply reliability in complex Integrated Energy Systems considering uncertainties of renewable energies, demands and operations. J Clean Prod 2020; 267: 122117. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wang J, Lu Z, Li M, et al. Energy, exergy, exergoeconomic and environmental (4E) analysis of a distributed generation solar-assisted CCHP (combined cooling, heating and power) gas turbine system. Energy 2019; 175: 1246-1258. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Pahle M, Tietjen O, Osorio S, et al. Safeguarding the energy transition against political backlash to carbon markets. Nat Energy 2022; 7: 290-296. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zibunas C, Meys R, Kätelhön A, et al. Cost-optimal pathways towards net-zero chemicals and plastics based on a circular carbon economy. Comput Chem Eng 2022; 162: 107798. [Article] [CrossRef] [Google Scholar]
  • Jafari M, Botterud A, Sakti A. Decarbonizing power systems: A critical review of the role of energy storage. Renew Sustain Energy Rev 2022; 158: 112077. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Gür TM. Review of electrical energy storage technologies, materials and systems: Challenges and prospects for large-scale grid storage. Energy Environ Sci 2018; 11: 2696-2767. [Article] [CrossRef] [Google Scholar]
  • Mildenberger M, Howe PD, Trachtman S, et al. The effect of public safety power shut-offs on climate change attitudes and behavioural intentions. Nat Energy 2022; 7: 736-743. [Article] [CrossRef] [Google Scholar]
  • Fang J, Xu Q, Tang R, et al. Research on demand management of hybrid energy storage system in industrial park based on variational mode decomposition and Wigner-Ville distribution. J Energy Storage 2021; 42: 103073. [Article] [CrossRef] [Google Scholar]
  • Yuan J, Li Y, Luo X, et al. A new hybrid multi-criteria decision-making approach for developing integrated energy systems in industrial parks. J Clean Prod 2020; 270: 122119. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Shen W, Zeng B, Zeng M. Multi-timescale rolling optimization dispatch method for integrated energy system with hybrid energy storage system. Energy 2023; 283: 129006. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Liu J, Cao X, Xu Z, et al. Resilient operation of multi-energy industrial park based on integrated hydrogen-electricity-heat microgrids. Int J Hydrogen Energy 2021; 46: 28855-28869. [Article] [CrossRef] [Google Scholar]
  • Tang R, Xu Q, Fang J, et al. Optimal configuration strategy of hybrid energy storage system on industrial load side based on frequency division algorithm. J Energy Storage 2022; 50: 104645. [Article] [CrossRef] [Google Scholar]
  • Yang A, Wang H, Li B, et al. Capacity optimization of hybrid energy storage system for microgrid based on electric vehicles’ orderly charging/discharging strategy. J Clean Prod 2023; 411: 137346. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Li P, Wang Z, Wang J, et al. A multi-time-space scale optimal operation strategy for a distributed integrated energy system. Appl Energy 2021; 289: 116698. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zhu D, Yang B, Ma C, et al. Stochastic gradient-based fast distributed multi-energy management for an industrial park with temporally-coupled constraints. Appl Energy 2022; 317: 119107. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zhang Z, Zhou K, Yang S. Optimal selection of energy storage system sharing schemes in industrial parks considering battery degradation. J Energy Storage 2023; 57: 106215. [Article] [CrossRef] [Google Scholar]
  • Zhu S, Mac Kinnon M, Carlos-Carlos A, et al. Decarbonization will lead to more equitable air quality in California. Nat Commun 2022; 13: 5738. [Article] [Google Scholar]
  • Zhang S, Chen W. Assessing the energy transition in China towards carbon neutrality with a probabilistic framework. Nat Commun 2022; 13: 87. [Article] [Google Scholar]
  • Ulpiani G, Vetters N, Melica G, et al. Towards the first cohort of climate-neutral cities: expected impact, current gaps, and next steps to take to establish evidence-based zero-emission urban futures. Sustain Cities Soc 2023; 95: 104572. [Article] [Google Scholar]
  • Luo B, Ye D, Wang L. Recent progress on integrated energy conversion and storage systems. Adv Sci 2017; 4: 1700104. [Article] [CrossRef] [Google Scholar]
  • Matar W, Shabaneh R. Viability of seasonal natural gas storage in the Saudi energy system. Energy Strategy Rev 2020; 32: 100549. [Article] [CrossRef] [Google Scholar]
  • Li Y, Zhang F, Li Y, et al. An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties. Energy 2021; 223: 120048. [Article] [CrossRef] [Google Scholar]
  • Helveston JP, He G, Davidson MR. Quantifying the cost savings of global solar photovoltaic supply chains. Nature 2022; 612: 83-87. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Liu C, Wang H, Wang ZY, et al. Research on life cycle low carbon optimization method of multi-energy complementary distributed energy system: A review. J Clean Prod 2022; 336: 130380. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • He Y, Guo S, Zhou J, et al. Multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages. Renew Energy 2022; 184: 776-790. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • He W, Wang J. Optimal selection of air expansion machine in Compressed Air Energy Storage: A review. Renew Sustain Energy Rev 2018; 87: 77-95. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Gao J, Chen JJ, Qi BX, et al. A cost-effective two-stage optimization model for microgrid planning and scheduling with compressed air energy storage and preventive maintenance. Int J Electr Power Energy Syst 2021; 125: 106547. [Article] [CrossRef] [Google Scholar]
  • Dang Q, Wu D, Boulet B. EV fleet as virtual battery resource for community microgrid energy storage planning. IEEE Can J Electr Comput Eng 2021; 44: 431-442. [Article] [CrossRef] [Google Scholar]
  • Ma K, Zhang R, Yang J, et al. Collaborative optimization scheduling of integrated energy system considering user dissatisfaction. Energy 2023; 274: 127311. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Al-Ghussain L, Darwish Ahmad A, Abubaker AM, et al. An integrated photovoltaic/wind/biomass and hybrid energy storage systems towards 100% renewable energy microgrids in university campuses. Sustain Energy Technol Assess 2021; 46: 101273. [Article] [Google Scholar]
  • Naderipour A, Ramtin AR, Abdullah A, et al. Hybrid energy system optimization with battery storage for remote area application considering loss of energy probability and economic analysis. Energy 2022; 239: 122303. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Liu Z, Fan G, Sun D, et al. A novel distributed energy system combining hybrid energy storage and a multi-objective optimization method for nearly zero-energy communities and buildings. Energy 2022; 239: 122577. [Article] [CrossRef] [Google Scholar]
  • Gjorgievski VZ, Markovska N, Abazi A, et al. The potential of power-to-heat demand response to improve the flexibility of the energy system: an empirical review. Renew Sustain Energy Rev 2021; 138: 110489. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Guelpa E, Bellando L, Giordano A, et al. Optimal configuration of power-to-cool technology in district cooling systems. Proc IEEE 2020; 108: 1612-1622. [Article] [Google Scholar]
  • Shi T, Han F, Chen L, et al. Study on value co-creation and evolution game of low-carbon technological innovation ecosystem. J Clean Prod 2023; 414: 137720. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Javadi EA, Joorabian M, Barati H. A sustainable framework for resilience enhancement of integrated energy systems in the presence of energy storage systems and fast-acting flexible loads. J Energy Storage 2022; 49: 104099. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Heidari A, Mortazavi SS, Bansal RC. Stochastic effects of ice storage on improvement of an energy hub optimal operation including demand response and renewable energies. Appl Energy 2020; 261: 114393. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Bi SL, Bauer N, Jewell J. Coal-exit alliance must confront freeriding sectors to propel Paris-aligned momentum. Nat Clim Chang 2023; 13: 130-139. [Article] [Google Scholar]
  • Meng F, Dillingham G. Life cycle analysis of natural gas-fired distributed combined heat and power versus centralized power plant. Energy Fuels 2018; 32: 11731-11741. [Article] [CrossRef] [Google Scholar]
  • Izadi A, Shahafve M, Ahmadi P. Neural network genetic algorithm optimization of a transient hybrid renewable energy system with solar/wind and hydrogen storage system for zero energy buildings at various climate conditions. Energy Convers Manage 2022; 260: 115593. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Jia K, Liu C, Li S, et al. Modeling and optimization of a hybrid renewable energy system integrated with gas turbine and energy storage. Energy Convers Manage 2023; 279: 116763. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Babatunde OM, Munda JL, Hamam Y. Off-grid hybrid photovoltaic—micro wind turbine renewable energy system with hydrogen and battery storage: Effects of sun tracking technologies. Energy Convers Manage 2022; 255: 115335. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Liu X. Research on optimal placement of low-carbon equipment capacity in integrated energy system considering carbon emission and carbon trading. Intl J Energy Res 2022; 46: 20535-20555. [Article] [CrossRef] [Google Scholar]
  • Pan G, Gu W, Lu Y, et al. Optimal planning for electricity-hydrogen integrated energy system considering power to hydrogen and heat and seasonal storage. IEEE Trans Sustain Energy 2020; 11: 2662-2676. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wang L, Liang DH, Crossland AF, et al. Coordination of multiple energy storage units in a low-voltage distribution network. IEEE Trans Smart Grid 2015; 6: 2906-2918. [Article] [CrossRef] [Google Scholar]
  • Luo F, Shao J, Jiao Z, et al. Research on optimal allocation strategy of multiple energy storage in regional integrated energy system based on operation benefit increment. Int J Electr Power Energy Syst 2021; 125: 106376. [Article] [CrossRef] [Google Scholar]
  • Wang H, Xie Z, Pu L, et al. Energy management strategy of hybrid energy storage based on Pareto optimality. Appl Energy 2022; 327: 120095. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Nord N, Shakerin M, Tereshchenko T, et al. Data informed physical models for district heating grids with distributed heat sources to understand thermal and hydraulic aspects. Energy 2021; 222: 119965. [Article] [CrossRef] [Google Scholar]
  • van der Heijde B, Fuchs M, Ribas Tugores C, et al. Dynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems. Energy Convers Manage 2017; 151: 158-169. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Bird TJ, Jain N. Dynamic modeling and validation of a micro-combined heat and power system with integrated thermal energy storage. Appl Energy 2020; 271: 114955. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Eladl AA, El-Afifi MI, El-Saadawi MM, et al. A review on energy hubs: models, methods, classification, applications, and future trends. Alexandria Eng J 2023; 68: 315-342. [Article] [CrossRef] [Google Scholar]
  • Salimi M, Ghasemi H, Adelpour M, et al. Optimal planning of energy hubs in interconnected energy systems: A case study for natural gas and electricity. IET Gener Transm Dis 2015; 9: 695-707. [Article] [CrossRef] [Google Scholar]
  • Hu J, Liu X, Shahidehpour M, et al. Optimal operation of energy hubs with large-scale distributed energy resources for distribution network congestion management. IEEE Trans Sustain Energy 2021; 12: 1755-1765. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Chen X, Kang C, O’Malley M, et al. Increasing the flexibility of combined heat and power for wind power integration in china: modeling and implications. IEEE Trans Power Syst 2015; 30: 1848-1857. [Article] [Google Scholar]
  • Ma T, Wu J, Hao L, et al. Energy flow matrix modeling and optimal operation analysis of multi energy systems based on graph theory. Appl Therm Eng 2019; 146: 648-663. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Chen B, Wu W, Guo Q, et al. An efficient optimal energy flow model for integrated energy systems based on energy circuit modeling in the frequency domain. Appl Energy 2022; 326: 119923. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Du Y, Xue Y, Wu W, et al. Coordinated planning of integrated electric and heating system considering the optimal reconfiguration of district heating network. IEEE Trans Power Syst 2023; 39: 794-808. [Article] [Google Scholar]
  • Jaxa-Rozen M, Trutnevyte E. Sources of uncertainty in long-term global scenarios of solar photovoltaic technology. Nat Clim Chang 2021; 11: 266-273. [Article] [Google Scholar]
  • Asensio EM, Magallán GA, De Angelo CH, et al. Energy management on battery/ultracapacitor hybrid energy storage system based on adjustable bandwidth filter and sliding-mode control. J Energy Storage 2020; 30: 101569. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zhang M, Guo C, Fang T, et al. Hybrid energy storage power allocation based on moving average filtering and VMD. In: Proceedings of the2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT). Lanzhou: IEEE, 2021 [Google Scholar]
  • Karrari S, Ludwig N, De Carne G, et al. Sizing of hybrid energy storage systems using recurring daily patterns. IEEE Trans Smart Grid 2022; 13: 3290-3300. [Article] [Google Scholar]
  • Roy PKS, Karayaka HB, Yan Y, et al. Investigations into best cost battery-supercapacitor hybrid energy storage system for a utility scale PV array. J Energy Storage 2019; 22: 50-59. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Eckert JJ, Silva LCA, Dedini FG, et al. Electric vehicle powertrain and fuzzy control multi-objective optimization, considering dual hybrid energy storage systems. IEEE Trans Veh Technol 2020; 69: 3773-3782. [Article] [CrossRef] [Google Scholar]
  • Boudia A, Messalti S, Harrag A, et al. New hybrid photovoltaic system connected to superconducting magnetic energy storage controlled by PID-fuzzy controller. Energy Convers Manage 2021; 244: 114435. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Shi J, Wang L, Lee WJ, et al. Hybrid energy storage system (HESS) optimization enabling very short-term wind power generation scheduling based on output feature extraction. Appl Energy 2019; 256: 113915. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Liu J, Cao S, Chen X, et al. Energy planning of renewable applications in high-rise residential buildings integrating battery and hydrogen vehicle storage. Appl Energy 2021; 281: 116038. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zou B, Peng J, Li S, et al. Comparative study of the dynamic programming-based and rule-based operation strategies for grid-connected PV-battery systems of office buildings. Appl Energy 2022; 305: 117875. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Di Somma M, Yan B, Bianco N, et al. Multi-objective design optimization of distributed energy systems through cost and exergy assessments. Appl Energy 2017; 204: 1299-1316. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Li M, Zhou M, Feng Y, et al. Integrated design and optimization of natural gas distributed energy system for regional building complex. Energy Build 2017; 154: 81-95. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Khanna TM, Baiocchi G, Callaghan M, et al. A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings. Nat Energy 2021; 6: 925-932. [Article] [CrossRef] [Google Scholar]
  • Zhang H, Zhang F, Yang L, et al. Multi-parameter collaborative power prediction to improve the efficiency of supercapacitor-based regenerative braking system. IEEE Trans Energy Convers 2021; 36: 2612-2622. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Zhou Z, Zhang J, Liu P, et al. A two-stage stochastic programming model for the optimal design of distributed energy systems. Appl Energy 2013; 103: 135-144. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wang Y, Wang Y, Huang Y, et al. Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network. Appl Energy 2019; 251: 113410. [Article] [CrossRef] [Google Scholar]
  • Li P, Wang Z, Wang N, et al. Stochastic robust optimal operation of community integrated energy system based on integrated demand response. Int J Electr Power Energy Syst 2021; 128: 106735. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Yamchi HB, Safari A, Guerrero JM. A multi-objective mixed integer linear programming model for integrated electricity-gas network expansion planning considering the impact of photovoltaic generation. Energy 2021; 222: 119933. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Ogunsina AA, Petinrin MO, Petinrin OO, et al. Optimal distributed generation location and sizing for loss minimization and voltage profile optimization using ant colony algorithm. SN Appl Sci 2021; 3: 248. [Article] [Google Scholar]
  • Zhang L, Kuang J, Sun B, et al. A two-stage operation optimization method of integrated energy systems with demand response and energy storage. Energy 2020; 208: 118423. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Mavromatidis G, Orehounig K, Carmeliet J. Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach. Appl Energy 2018; 222: 932-950. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Sadamoto T, Chakrabortty A, Ishizaki T, et al. Dynamic modeling, stability, and control of power systems with distributed energy resources: Handling faults using two control methods in tandem. IEEE Control Syst 2019; 39: 34-65. [Article] [CrossRef] [MathSciNet] [Google Scholar]
  • Li F, Sun B, Zhang C, et al. A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage. Energy 2019; 188: 115948. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Li K, Wei X, Yan Y, et al. Bi-level optimization design strategy for compressed air energy storage of a combined cooling, heating, and power system. J Energy Storage 2020; 31: 101642. [Article] [CrossRef] [Google Scholar]
  • Cherp A, Vinichenko V, Tosun J, et al. National growth dynamics of wind and solar power compared to the growth required for global climate targets. Nat Energy 2021; 6: 742-754. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Tong K, Ramaswami A, Xu CK, et al. Measuring social equity in urban energy use and interventions using fine-scale data. Proc Natl Acad Sci USA 2021; 118: e2023554118. [Article] [Google Scholar]
  • Shupler M, Mangeni J, Tawiah T, et al. Modelling of supply and demand-side determinants of liquefied petroleum gas consumption in peri-urban Cameroon, Ghana and Kenya. Nat Energy 2021; 6: 1198-1210. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Wang Z, Tao H, Cai W, et al. Study on the multitime scale rolling optimization operation of a near-zero energy building energy supply system. Energy Convers Manage 2022; 270: 116255. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Hu J, Zhou H, Li Y, et al. Multi-time scale energy management strategy of aggregator characterized by photovoltaic generation and electric vehicles. J Modern Power Syst Clean Energy 2020; 8: 727-736. [Article] [Google Scholar]
  • Luo Z, Wu Z, Li Z, et al. A two-stage optimization and control for CCHP microgrid energy management. Appl Therm Eng 2017; 125: 513-522. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Bao Z, Zhou Q, Yang Z, et al. A multi time-scale and multi energy-type coordinated microgrid scheduling solution—Part II: Optimization algorithm and case studies. IEEE Trans Power Syst 2015; 30: 2267-2277. [Article] [Google Scholar]
  • Forough AB, Roshandel R. Lifetime optimization framework for a hybrid renewable energy system based on receding horizon optimization. Energy 2018; 150: 617-630. [Article] [NASA ADS] [CrossRef] [Google Scholar]
  • Yang Z, Jing J, Zhou F. Multi-time scale collaborative scheduling strategy of distributed energy systems for energy internet. In: Proceedings of the2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). Shenyang: IEEE, 2021 [Google Scholar]
  • Watari D, Taniguchi I, Goverde H, et al. Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics. Appl Energy 2021; 289: 116671. [Article] [NASA ADS] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.