IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v226y2021ics0360544221005077.html
   My bibliography  Save this article

A novel multi-objective stochastic risk co-optimization model of a zero-carbon multi-energy system (ZCMES) incorporating energy storage aging model and integrated demand response

Author

Listed:
  • Alabi, Tobi Michael
  • Lu, Lin
  • Yang, Zaiyue

Abstract

To model a realistic and highly flexible zero-carbon multi-energy system (ZCMES), a novel modelling strategy for ZCMES incorporating energy storage aging influence and integrated demand response (IDR) is proposed. Firstly, an integrated clustering-scenario generation and reduction approach (IC-SGRA) is developed to quantify the datasets uncertainties while selecting a representative day for the model. Secondly, the model is formulated as a multi-objective optimization problem to evaluate the influence of decision-maker preference concerning investment cost and operation cost on the optimal planning, and then weighting sum method is adopted to solve the problem. Finally, a Markowitz portfolio risk theory approach is adopted to mitigate the risk associated with uncertainties during decision-making, then an illustrative case study is used to analyse the proposed model. The simulation results reveal that the energy storage is overdesigned when aging effects are not considered, and the proposed approach can reduce the investment cost and the operation cost by 10.86% and 80.66% respectively, while the overall expenditure is reduced by 23.09%. Moreover, it was observed that the optimal total economic cost is obtained when high preference is given to the operation expenditure by the decision-makers while an equal preference resulted in a 0.24% reduction in investment cost and a 0.49% increase in total expenditure. Furthermore, the effect of BES lifetime and IDR load factors are also examined on ZCMES optimal planning. This study concluded that IDR is a promising strategy to encourage adopting zero-carbon policies flexibly and economically while choosing BES with high lifetime and tolerable capacity loss contribute to optimal planning.

Suggested Citation

  • Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2021. "A novel multi-objective stochastic risk co-optimization model of a zero-carbon multi-energy system (ZCMES) incorporating energy storage aging model and integrated demand response," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221005077
    DOI: 10.1016/j.energy.2021.120258
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221005077
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120258?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mazzoni, Stefano & Ooi, Sean & Nastasi, Benedetto & Romagnoli, Alessandro, 2019. "Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems," Applied Energy, Elsevier, vol. 254(C).
    2. Cardoso, Gonçalo & Brouhard, Thomas & DeForest, Nicholas & Wang, Dai & Heleno, Miguel & Kotzur, Leander, 2018. "Battery aging in multi-energy microgrid design using mixed integer linear programming," Applied Energy, Elsevier, vol. 231(C), pages 1059-1069.
    3. Acuña, Luceny Guzmán & Padilla, Ricardo Vasquez & Mercado, Alcides Santander, 2017. "Measuring reliability of hybrid photovoltaic-wind energy systems: A new indicator," Renewable Energy, Elsevier, vol. 106(C), pages 68-77.
    4. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    5. Chen, Cong & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & Gharehpetian, G.B., 2018. "A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2128-2143.
    7. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    8. Patteeuw, Dieter & Bruninx, Kenneth & Arteconi, Alessia & Delarue, Erik & D’haeseleer, William & Helsen, Lieve, 2015. "Integrated modeling of active demand response with electric heating systems coupled to thermal energy storage systems," Applied Energy, Elsevier, vol. 151(C), pages 306-319.
    9. Ren, Hongbo & Zhou, Weisheng & Nakagami, Ken'ichi & Gao, Weijun & Wu, Qiong, 2010. "Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 87(12), pages 3642-3651, December.
    10. Zatti, Matteo & Gabba, Marco & Freschini, Marco & Rossi, Michele & Gambarotta, Agostino & Morini, Mirko & Martelli, Emanuele, 2019. "k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization," Energy, Elsevier, vol. 181(C), pages 1051-1063.
    11. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    12. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    13. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    14. Wang, Yongli & Qi, Chengyuan & Dong, Huanran & Wang, Shuo & Wang, Xiaohai & Zeng, Ming & Zhu, Jinrong, 2020. "Optimal design of integrated energy system considering different battery operation strategy," Energy, Elsevier, vol. 212(C).
    15. Mazzeo, Domenico & Matera, Nicoletta & De Luca, Pierangelo & Baglivo, Cristina & Maria Congedo, Paolo & Oliveti, Giuseppe, 2020. "Worldwide geographical mapping and optimization of stand-alone and grid-connected hybrid renewable system techno-economic performance across Köppen-Geiger climates," Applied Energy, Elsevier, vol. 276(C).
    16. Steen, David & Stadler, Michael & Cardoso, Gonçalo & Groissböck, Markus & DeForest, Nicholas & Marnay, Chris, 2015. "Modeling of thermal storage systems in MILP distributed energy resource models," Applied Energy, Elsevier, vol. 137(C), pages 782-792.
    17. Correa-Florez, Carlos Adrian & Gerossier, Alexis & Michiorri, Andrea & Kariniotakis, Georges, 2018. "Stochastic operation of home energy management systems including battery cycling," Applied Energy, Elsevier, vol. 225(C), pages 1205-1218.
    18. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2016. "Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory," Energy, Elsevier, vol. 114(C), pages 846-861.
    19. Yongli Wang & Haiyang Yu & Mingyue Yong & Yujing Huang & Fuli Zhang & Xiaohai Wang, 2018. "Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss," Energies, MDPI, vol. 11(7), pages 1-21, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Song, Yuguang & Xia, Mingchao & Yang, Liu & Chen, Qifang & Su, Su, 2023. "Multi-granularity source-load-storage cooperative dispatch based on combined robust optimization and stochastic optimization for a highway service area micro-energy grid," Renewable Energy, Elsevier, vol. 205(C), pages 747-762.
    2. Li, Qiang & Wei, Fanchao & Zhou, Yongcheng & Li, Jiajia & Zhou, Guowen & Wang, Zhonghao & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2023. "A scheduling framework for VPP considering multiple uncertainties and flexible resources," Energy, Elsevier, vol. 282(C).
    3. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2021. "Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and," Energy, Elsevier, vol. 232(C).
    5. Christina Papadimitriou & Marialaura Di Somma & Chrysanthos Charalambous & Martina Caliano & Valeria Palladino & Andrés Felipe Cortés Borray & Amaia González-Garrido & Nerea Ruiz & Giorgio Graditi, 2023. "A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks," Energies, MDPI, vol. 16(10), pages 1-46, May.
    6. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2022. "Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy," Applied Energy, Elsevier, vol. 314(C).
    7. Alabi, Tobi Michael & Lawrence, Nathan P. & Lu, Lin & Yang, Zaiyue & Bhushan Gopaluni, R., 2023. "Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system," Applied Energy, Elsevier, vol. 333(C).
    8. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    9. Cristobal Morales & Augusto Lismayes & Hector Chavez & Harold R. Chamorro & Lorenzo Reyes-Chamorro, 2021. "The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards," Energies, MDPI, vol. 14(21), pages 1-13, November.
    10. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    11. Li, Ye & Liu, Zihan & Sang, Yufeng & Hu, Jingfan & Li, Bojia & Zhang, Xinyu & Jurasz, Jakub & Zheng, Wandong, 2023. "Optimization of integrated energy system for low-carbon community considering the feasibility and application limitation," Applied Energy, Elsevier, vol. 348(C).
    12. Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2023. "From prosumer to flexumer: Case study on the value of flexibility in decarbonizing the multi-energy system of a manufacturing company," Papers 2301.07997, arXiv.org.
    13. He, Shuaijia & Gao, Hongjun & Chen, Zhe & Liu, Junyong & Zhao, Liang & Wu, Gang & Xu, Song, 2022. "Low-carbon distribution system planning considering flexible support of zero-carbon energy station," Energy, Elsevier, vol. 244(PB).
    14. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    15. Tan, Caixia & Wang, Jing & Geng, Shiping & Pu, Lei & Tan, Zhongfu, 2021. "Three-level market optimization model of virtual power plant with carbon capture equipment considering copula–CVaR theory," Energy, Elsevier, vol. 237(C).
    16. Mardan Dezfouli, Amir Hossein & Niroozadeh, Narjes & Jahangiri, Ali, 2023. "Energy, exergy, and exergoeconomic analysis and multi-objective optimization of a novel geothermal driven power generation system of combined transcritical CO2 and C5H12 ORCs coupled with LNG stream i," Energy, Elsevier, vol. 262(PB).
    17. Mimica, Marko & Giménez de Urtasun, Laura & Krajačić, Goran, 2022. "A robust risk assessment method for energy planning scenarios on smart islands under the demand uncertainty," Energy, Elsevier, vol. 240(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2021. "Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and," Energy, Elsevier, vol. 232(C).
    2. Chen, Cong & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Pilotti, L. & Colombari, M. & Castelli, A.F. & Binotti, M. & Giaconia, A. & Martelli, E., 2023. "Simultaneous design and operational optimization of hybrid CSP-PV plants," Applied Energy, Elsevier, vol. 331(C).
    4. Heendeniya, Charitha Buddhika & Sumper, Andreas & Eicker, Ursula, 2020. "The multi-energy system co-planning of nearly zero-energy districts – Status-quo and future research potential," Applied Energy, Elsevier, vol. 267(C).
    5. Marzi, Emanuela & Morini, Mirko & Saletti, Costanza & Vouros, Stavros & Zaccaria, Valentina & Kyprianidis, Konstantinos & Gambarotta, Agostino, 2023. "Power-to-Gas for energy system flexibility under uncertainty in demand, production and price," Energy, Elsevier, vol. 284(C).
    6. Christina Papadimitriou & Marialaura Di Somma & Chrysanthos Charalambous & Martina Caliano & Valeria Palladino & Andrés Felipe Cortés Borray & Amaia González-Garrido & Nerea Ruiz & Giorgio Graditi, 2023. "A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks," Energies, MDPI, vol. 16(10), pages 1-46, May.
    7. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    8. Jiaxin Lu & Weijun Wang & Yingchao Zhang & Song Cheng, 2017. "Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER," Energies, MDPI, vol. 10(10), pages 1-17, October.
    9. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Sustainability analysis for the design of distributed energy systems: A multi-objective optimization approach," Applied Energy, Elsevier, vol. 290(C).
    10. Martelli, Emanuele & Freschini, Marco & Zatti, Matteo, 2020. "Optimization of renewable energy subsidy and carbon tax for multi energy systems using bilevel programming," Applied Energy, Elsevier, vol. 267(C).
    11. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    12. Constantino Dário Justo & José Eduardo Tafula & Pedro Moura, 2022. "Planning Sustainable Energy Systems in the Southern African Development Community: A Review of Power Systems Planning Approaches," Energies, MDPI, vol. 15(21), pages 1-28, October.
    13. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    14. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    15. Yan, Rujing & Wang, Jiangjiang & Wang, Jiahao & Tian, Lei & Tang, Saiqiu & Wang, Yuwei & Zhang, Jing & Cheng, Youliang & Li, Yuan, 2022. "A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties," Energy, Elsevier, vol. 247(C).
    16. van der Heijde, Bram & Vandermeulen, Annelies & Salenbien, Robbe & Helsen, Lieve, 2019. "Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage," Applied Energy, Elsevier, vol. 248(C), pages 79-94.
    17. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    18. Ramos-Teodoro, Jerónimo & Rodríguez, Francisco & Berenguel, Manuel & Torres, José Luis, 2018. "Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example," Applied Energy, Elsevier, vol. 229(C), pages 537-550.
    19. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    20. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221005077. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.