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MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems

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  • Mavromatidis, Georgios
  • Petkov, Ivalin

Abstract

This study presents MANGO (Multi-stAge eNerGy Optimization), a novel optimization model that incorporates a multi-year planning horizon, along with flexible, multi-stage investment strategies for the effective, long-term design of decentralized multi-energy systems (D-MES). By considering the dynamic surrounding energy and techno-economic landscape that evolves over time, MANGO harnesses the strategic value of investment flexibility and can optimally phase D-MES investments in order to benefit, for instance, from projected future reduced technology costs and technical improvements. To achieve this, the model considers the most relevant dynamic aspects, such as year-to-year variations in energy demands, changing energy carrier and technology prices, technical improvements and equipment degradation. MANGO is also capable of optimizing the design of complex configurations composed of multiple, interconnected D-MES installed at different locations. Finally, the model’s formulation also addresses end-of-horizon effects that can distort solutions in multi-stage energy system models.

Suggested Citation

  • Mavromatidis, Georgios & Petkov, Ivalin, 2021. "MANGO: A novel optimization model for the long-term, multi-stage planning of decentralized multi-energy systems," Applied Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:appene:v:288:y:2021:i:c:s030626192100129x
    DOI: 10.1016/j.apenergy.2021.116585
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    1. Nils Korber & Maximilian Rohrig & Andreas Ulbig, 2022. "A stakeholder-oriented multi-criteria optimization model for decentral multi-energy systems," Papers 2204.06545, arXiv.org.
    2. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
    3. Li, Ximei & Gao, Jianmin & Chen, Bingyuan & You, Shi & Zheng, Yi & Du, Qian & Qin, Yukun, 2023. "Multi-objective optimization of district heating systems with turbine-driving fans and pumps considering economic, exergic, and environmental aspects," Energy, Elsevier, vol. 277(C).
    4. Ren, Fukang & Wei, Ziqing & Zhai, Xiaoqiang, 2022. "A review on the integration and optimization of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof & Allan, James & Hoffmann, Volker H., 2022. "MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits," Applied Energy, Elsevier, vol. 314(C).
    6. Lei, Dayong & Zhang, Zhonghui & Wang, Zhaojun & Zhang, Liuyu & Liao, Wei, 2023. "Long-term, multi-stage low-carbon planning model of electricity-gas-heat integrated energy system considering ladder-type carbon trading mechanism and CCS," Energy, Elsevier, vol. 280(C).
    7. Richarz, Jan & Henn, Sarah & Osterhage, Tanja & Müller, Dirk, 2022. "Optimal scheduling of modernization measures for typical non-residential buildings," Energy, Elsevier, vol. 238(PA).
    8. Lerbinger, Alicia & Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof, 2023. "Optimal decarbonization strategies for existing districts considering energy systems and retrofits," Applied Energy, Elsevier, vol. 352(C).
    9. Daniel Akinyele & Abraham Amole & Elijah Olabode & Ayobami Olusesi & Titus Ajewole, 2021. "Simulation and Analysis Approaches to Microgrid Systems Design: Emerging Trends and Sustainability Framework Application," Sustainability, MDPI, vol. 13(20), pages 1-26, October.
    10. Serrano-Arévalo, Tania Itzel & López-Flores, Francisco Javier & Raya-Tapia, Alma Yunuen & Ramírez-Márquez, César & Ponce-Ortega, José María, 2023. "Optimal expansion for a clean power sector transition in Mexico based on predicted electricity demand using deep learning scheme," Applied Energy, Elsevier, vol. 348(C).
    11. Wu, Min & Xu, Jiazhu & Zeng, Linjun & Li, Chang & Liu, Yuxing & Yi, Yuqin & Wen, Ming & Jiang, Zhuohan, 2022. "Two-stage robust optimization model for park integrated energy system based on dynamic programming," Applied Energy, Elsevier, vol. 308(C).
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    13. Hoettecke, Lukas & Schuetz, Thomas & Thiem, Sebastian & Niessen, Stefan, 2022. "Technology pathways for industrial cogeneration systems: Optimal investment planning considering long-term trends," Applied Energy, Elsevier, vol. 324(C).

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