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Overview of Natural Gas Boiler Optimization Technologies and Potential Applications on Gas Load Balancing Services

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  • Georgios I. Tsoumalis

    (School of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

  • Zafeirios N. Bampos

    (School of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

  • Georgios V. Chatzis

    (School of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

  • Pandelis N. Biskas

    (School of Electrical and Computer Engineering, Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

Abstract

Natural gas is a fossil fuel that has been widely used for various purposes, including residential and industrial applications. The combustion of natural gas, despite being more environmentally friendly than other fossil fuels such as petroleum, yields significant amounts of greenhouse gas emissions. Therefore, the optimization of natural gas consumption is a vital process in order to ensure that emission targets are met worldwide. Regarding residential consumption, advancements in terms of boiler technology, such as the usage of condensing boilers, have played a significant role in moving towards this direction. On top of that, the emergence of technologies such as smart homes, Internet of Things, and artificial intelligence provides opportunities for the development of automated optimization solutions, which can utilize data acquired from the boiler and various sensors in real-time, implement consumption forecasting methodologies, and accordingly provide control instructions in order to ensure optimal boiler functionality. Apart from energy consumption minimization, manual and automated optimization solutions can be utilized for balancing purposes, including natural gas demand response, which has not been sufficiently covered in the existing literature, despite its potential for the gas balancing market. Despite the existence of few research works and solutions regarding pure gas DR, the concept of an integrated demand response has been more widely researched, with the existing literature displaying promising results from the co-optimization of natural gas along with other energy sources, such as electricity and heat.

Suggested Citation

  • Georgios I. Tsoumalis & Zafeirios N. Bampos & Georgios V. Chatzis & Pandelis N. Biskas, 2022. "Overview of Natural Gas Boiler Optimization Technologies and Potential Applications on Gas Load Balancing Services," Energies, MDPI, vol. 15(22), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8461-:d:970765
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    References listed on IDEAS

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    1. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    2. Yue Xin & Ke Wang & Yindi Zhang & Fanjin Zeng & Xiang He & Shadrack Adjei Takyi & Paitoon Tontiwachwuthikul, 2021. "Numerical Simulation of Combustion of Natural Gas Mixed with Hydrogen in Gas Boilers," Energies, MDPI, vol. 14(21), pages 1-15, October.
    3. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
    4. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    5. Liu, Peiyun & Ding, Tao & Zou, Zhixiang & Yang, Yongheng, 2019. "Integrated demand response for a load serving entity in multi-energy market considering network constraints," Applied Energy, Elsevier, vol. 250(C), pages 512-529.
    6. Liu, Fengguo & Zheng, Longfeng & Zhang, Rui, 2020. "Emissions and thermal efficiency for premixed burners in a condensing gas boiler," Energy, Elsevier, vol. 202(C).
    7. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Biskas, Pandelis N. & Keranidis, Stratos D. & Symeonidis, Polychronis A. & Voulgarakis, Dimitrios K., 2022. "A novel system for providing explicit demand response from domestic natural gas boilers," Applied Energy, Elsevier, vol. 317(C).
    8. 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.
    9. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    10. Brkić, Dejan & Tanasković, Toma I., 2008. "Systematic approach to natural gas usage for domestic heating in urban areas," Energy, Elsevier, vol. 33(12), pages 1738-1753.
    11. Lina Montuori & Manuel Alcázar-Ortega, 2021. "District Heating as Demand Response Aggregator: Estimation of the Flexible Potential in the Italian Peninsula," Energies, MDPI, vol. 14(21), pages 1-19, October.
    12. Andrew Speake & Paul Donohoo-Vallett & Eric Wilson & Emily Chen & Craig Christensen, 2020. "Residential Natural Gas Demand Response Potential during Extreme Cold Events in Electricity-Gas Coupled Energy Systems," Energies, MDPI, vol. 13(19), pages 1-19, October.
    13. Konstantinos Papageorgiou & Elpiniki I. Papageorgiou & Katarzyna Poczeta & Dionysis Bochtis & George Stamoulis, 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 13(9), pages 1-32, May.
    14. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    15. Bai, Linquan & Li, Fangxing & Cui, Hantao & Jiang, Tao & Sun, Hongbin & Zhu, Jinxiang, 2016. "Interval optimization based operating strategy for gas-electricity integrated energy systems considering demand response and wind uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 270-279.
    16. Cauchi, Nathalie & Macek, Karel & Abate, Alessandro, 2017. "Model-based predictive maintenance in building automation systems with user discomfort," Energy, Elsevier, vol. 138(C), pages 306-315.
    17. Montuori, Lina & Alcázar-Ortega, Manuel, 2021. "Demand response strategies for the balancing of natural gas systems: Application to a local network located in The Marches (Italy)," Energy, Elsevier, vol. 225(C).
    18. Aste, Niccolò & Adhikari, R.S. & Compostella, Junia & Pero, Claudio Del, 2013. "Energy and environmental impact of domestic heating in Italy: Evaluation of national NOx emissions," Energy Policy, Elsevier, vol. 53(C), pages 353-360.
    19. Mustafa Akpinar & M. Fatih Adak & Nejat Yumusak, 2017. "Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey," Energies, MDPI, vol. 10(6), pages 1-20, June.
    20. Harriet Thomson & Carolyn Snell & Stefan Bouzarovski, 2017. "Health, Well-Being and Energy Poverty in Europe: A Comparative Study of 32 European Countries," IJERPH, MDPI, vol. 14(6), pages 1-20, May.
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    Cited by:

    1. Marcin Trojan & Piotr Dzierwa & Jan Taler & Mariusz Granda & Karol Kaczmarski & Dawid Taler & Tomasz Sobota, 2023. "Analysis of the Causes of the Emergency Shutdown of Natural Gas-Fired Water Peak Boilers at the Large Municipal Combined Heat and Power Plant," Energies, MDPI, vol. 16(17), pages 1-21, August.

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