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Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use

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  • Thomas T. D. Tran

    (Indiana Institute of Technology, 1600 E Washington Blvd, Fort Wayne, IN 46803, USA)

  • Amanda D. Smith

    (Site-Specific Energy Systems Laboratory, Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USA)

Abstract

Stochastic optimization of a district energy system (DES) is investigated with renewable energy systems integration and uncertainty analysis to meet all three major types of energy consumption: electricity, heating, and cooling. A district of buildings on the campus of the University of Utah is used as a case study for the analysis. The proposed DES incorporates solar photovoltaics (PV) and wind turbines for power generation along with using the existing electrical grid. A combined heat and power (CHP) system provides the DES with power generation and thermal energy for heating. Natural gas boilers supply the remaining heating demand and electricity is used to run all of the cooling equipment. A Monte Carlo study is used to analyze the stochastic power generation from the renewable energy resources in the DES. The optimization of the DES is performed with the Particle Swarm Optimization (PSO) algorithm based on a day-ahead model. The objective of the optimization is to minimize the operating cost of the DES. The results of the study suggest that the proposed DES can achieve operating cost reductions (approximately 10% reduction with respect to the current system). The uncertainty of energy loads and power generation from renewable energy resources heavily affects the operating cost. The statistical approach shows the potential to identify probable operating costs at different time periods, which can be useful for facility managers to evaluate the operating costs of their DES.

Suggested Citation

  • Thomas T. D. Tran & Amanda D. Smith, 2019. "Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use," Energies, MDPI, vol. 12(3), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:533-:d:204209
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    References listed on IDEAS

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    Cited by:

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    3. Quetzalcoatl Hernandez-Escobedo & Alida Ramirez-Jimenez & Jesús Manuel Dorador-Gonzalez & Miguel-Angel Perea-Moreno & Alberto-Jesus Perea-Moreno, 2020. "Sustainable Solar Energy in Mexican Universities. Case Study: The National School of Higher Studies Juriquilla (UNAM)," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    4. Ghaemi, Zahra & Tran, Thomas T.D. & Smith, Amanda D., 2022. "Comparing classical and metaheuristic methods to optimize multi-objective operation planning of district energy systems considering uncertainties," Applied Energy, Elsevier, vol. 321(C).
    5. Yingying Chen & Jian Zhu, 2019. "A Graph Theory-Based Method for Regional Integrated Energy Network Planning: A Case Study of a China–U.S. Low-Carbon Demonstration City," Energies, MDPI, vol. 12(23), pages 1-17, November.
    6. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.

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