IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v338y2023ics0306261923001897.html
   My bibliography  Save this article

Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems

Author

Listed:
  • Srinivasan, Arvind
  • Wu, Raphael
  • Heer, Philipp
  • Sansavini, Giovanni

Abstract

To enable the widespread integration of volatile renewable energy for decarbonization, electricity systems will need to become significantly more flexible to absorb power fluctuations. Therefore, current fossil-fueled flexible power generation will have to be complemented and partially replaced by decentralized flexibility. To address flexibility challenges, multi-energy system (MES) is a promising concept. In this paper, a MES design and operation optimization model is developed considering flexibility provision. MES technologies are harnessed to provide flexibility to the electricity grid in the form of frequency control reserves after mitigating the impact of load and generation forecast errors within the MES. The developed optimization model is applied to a neighborhood MES in Switzerland, participating in the day ahead and intraday electricity spot markets and the daily and weekly tenders for frequency control reserves via the ancillary services market. Results show that the MES designed to achieve 80% decarbonization offers up to 15–45% of its available capacity as flexible reserves to the surrounding electricity grid after reserving 2–10% of its available capacity to mitigate the impact of forecast errors. Battery storage systems (BSS) and heat pumps (HPs) offer flexibility and revenues from flexible reserves provide economic benefits to operations. However, the revenues offered by current electricity markets are not attractive enough to incentivize the MES design. Short ancillary service delivery periods and attractive ancillary service remuneration rates combined with large BSS capacity and high decarbonization targets foster flexible reserves.

Suggested Citation

  • Srinivasan, Arvind & Wu, Raphael & Heer, Philipp & Sansavini, Giovanni, 2023. "Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems," Applied Energy, Elsevier, vol. 338(C).
    • Handle: RePEc:eee:appene:v:338:y:2023:i:c:s0306261923001897
    DOI: 10.1016/j.apenergy.2023.120825
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923001897
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.120825?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. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Mancarella, Pierluigi & Chicco, Gianfranco & Capuder, Tomislav, 2018. "Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services," Energy, Elsevier, vol. 161(C), pages 381-395.
    3. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    4. Wu, Raphael & Sansavini, Giovanni, 2021. "Energy trilemma in active distribution network design: Balancing affordability, sustainability and security in optimization-based decision-making," Applied Energy, Elsevier, vol. 304(C).
    5. Ioan Sarbu & Calin Sebarchievici, 2018. "A Comprehensive Review of Thermal Energy Storage," Sustainability, MDPI, vol. 10(1), pages 1-32, January.
    6. Marco Ratto, 2008. "Analysing DSGE Models with Global Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 115-139, March.
    7. Moret, Stefano & Codina Gironès, Víctor & Bierlaire, Michel & Maréchal, François, 2017. "Characterization of input uncertainties in strategic energy planning models," Applied Energy, Elsevier, vol. 202(C), pages 597-617.
    8. Panos, Evangelos & Densing, Martin, 2019. "The future developments of the electricity prices in view of the implementation of the Paris Agreements: Will the current trends prevail, or a reversal is ahead?," Energy Economics, Elsevier, vol. 84(C).
    9. Braeuer, Fritz & Rominger, Julian & McKenna, Russell & Fichtner, Wolf, 2019. "Battery storage systems: An economic model-based analysis of parallel revenue streams and general implications for industry," Applied Energy, Elsevier, vol. 239(C), pages 1424-1440.
    10. Connolly, D., 2017. "Heat Roadmap Europe: Quantitative comparison between the electricity, heating, and cooling sectors for different European countries," Energy, Elsevier, vol. 139(C), pages 580-593.
    11. Wang, Jueying & Hu, Zhijian & Xie, Shiwei, 2019. "Expansion planning model of multi-energy system with the integration of active distribution network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    12. Sinsel, Simon R. & Riemke, Rhea L. & Hoffmann, Volker H., 2020. "Challenges and solution technologies for the integration of variable renewable energy sources—a review," Renewable Energy, Elsevier, vol. 145(C), pages 2271-2285.
    13. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    14. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    15. Gabrielli, Paolo & Gazzani, Matteo & Mazzotti, Marco, 2018. "Electrochemical conversion technologies for optimal design of decentralized multi-energy systems: Modeling framework and technology assessment," Applied Energy, Elsevier, vol. 221(C), pages 557-575.
    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. Zhou, Yizhou & Wei, Zhinong & Sun, Guoqiang & Cheung, Kwok W. & Zang, Haixiang & Chen, Sheng, 2018. "A robust optimization approach for integrated community energy system in energy and ancillary service markets," Energy, Elsevier, vol. 148(C), pages 1-15.
    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. Moon-Jong Jang & Eunsung Oh, 2024. "Deep-Reinforcement-Learning-Based Vehicle-to-Grid Operation Strategies for Managing Solar Power Generation Forecast Errors," Sustainability, MDPI, vol. 16(9), pages 1-18, May.
    2. Yang, Sheng & Liu, Beilin & Li, Xiaolong & Liu, Zhiqiang & Liu, Yue & Xie, Nan & Ren, Jingzheng, 2023. "Flexibility index for a distributed energy system design optimization," Renewable Energy, Elsevier, vol. 219(P1).
    3. Zhao, Yi-Bo & Dong, Xiao-Jian & Shen, Jia-Ni & He, Yi-Jun, 2024. "Simultaneous sizing and scheduling optimization for PV-wind-battery hybrid systems with a modified battery lifetime model: A high-resolution analysis in China," Applied Energy, Elsevier, vol. 360(C).
    4. Zhang, Bidan & He, Guannan & Du, Yang & Wen, Haoran & Huan, Xintao & Xing, Bowen & Huang, Jingsi, 2024. "Assessment of the economic impact of forecasting errors in Peer-to-Peer energy trading," Applied Energy, Elsevier, vol. 374(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. Diana Enescu & Gianfranco Chicco & Radu Porumb & George Seritan, 2020. "Thermal Energy Storage for Grid Applications: Current Status and Emerging Trends," Energies, MDPI, vol. 13(2), pages 1-21, January.
    2. Petkov, Ivalin & Gabrielli, Paolo, 2020. "Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems," Applied Energy, Elsevier, vol. 274(C).
    3. Brodnicke, Linda & Gabrielli, Paolo & Sansavini, Giovanni, 2023. "Impact of policies on residential multi-energy systems for consumers and prosumers," Applied Energy, Elsevier, vol. 344(C).
    4. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    5. 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.
    6. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    7. Dong, Haoxin & Shan, Zijing & Zhou, Jianli & Xu, Chuanbo & Chen, Wenjun, 2023. "Refined modeling and co-optimization of electric-hydrogen-thermal-gas integrated energy system with hybrid energy storage," Applied Energy, Elsevier, vol. 351(C).
    8. Kai Xu & Youguang Guo & Gang Lei & Jianguo Zhu, 2023. "A Review of Flywheel Energy Storage System Technologies," Energies, MDPI, vol. 16(18), pages 1-32, September.
    9. Hu, Wenxuan & Scholz, Yvonne & Yeligeti, Madhura & Deng, Ying & Jochem, Patrick, 2024. "Future electricity demand for Europe: Unraveling the dynamics of the Temperature Response Function," Applied Energy, Elsevier, vol. 368(C).
    10. Laugs, Gideon A.H. & Benders, René M.J. & Moll, Henri C., 2024. "Maximizing self-sufficiency and minimizing grid interaction: Combining electric and molecular energy storage for decentralized balancing of variable renewable energy in local energy systems," Renewable Energy, Elsevier, vol. 229(C).
    11. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(C).
    12. Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    13. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    14. Joseph Oyekale & Mario Petrollese & Vittorio Tola & Giorgio Cau, 2020. "Impacts of Renewable Energy Resources on Effectiveness of Grid-Integrated Systems: Succinct Review of Current Challenges and Potential Solution Strategies," Energies, MDPI, vol. 13(18), pages 1-48, September.
    15. John Boland & Sleiman Farah, 2021. "Probabilistic Forecasting of Wind and Solar Farm Output," Energies, MDPI, vol. 14(16), pages 1-15, August.
    16. Nie, Yuhao & Li, Xiatong & Paletta, Quentin & Aragon, Max & Scott, Andea & Brandt, Adam, 2024. "Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    17. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
    18. Elena Collino & Dario Ronzio, 2021. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System," Energies, MDPI, vol. 14(3), pages 1-30, February.
    19. Fan, Guangyao & Liu, Zhijian & Liu, Xuan & Shi, Yaxin & Wu, Di & Guo, Jiacheng & Zhang, Shicong & Yang, Xinyan & Zhang, Yulong, 2022. "Two-layer collaborative optimization for a renewable energy system combining electricity storage, hydrogen storage, and heat storage," Energy, Elsevier, vol. 259(C).
    20. Yinhe Bu & Xingping Zhang, 2021. "On the Way to Integrate Increasing Shares of Variable Renewables in China: Experience from Flexibility Modification and Deep Peak Regulation Ancillary Service Market Based on MILP-UC Programming," Sustainability, MDPI, vol. 13(5), pages 1-22, 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:appene:v:338:y:2023:i:c:s0306261923001897. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.