Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/3/533 |
| _version_ | 1818001575626407936 |
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| author | Thomas T. D. Tran Amanda D. Smith |
| author_facet | Thomas T. D. Tran Amanda D. Smith |
| author_sort | Thomas T. D. Tran |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:34:56Z |
| format | Article |
| id | doaj.art-b9754081c7774972b597df1b07d2253a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T03:34:56Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b9754081c7774972b597df1b07d2253a2022-12-22T02:14:48ZengMDPI AGEnergies1996-10732019-02-0112353310.3390/en12030533en12030533Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective UseThomas T. D. Tran0Amanda D. Smith1Indiana Institute of Technology, 1600 E Washington Blvd, Fort Wayne, IN 46803, USASite-Specific Energy Systems Laboratory, Department of Mechanical Engineering, University of Utah, Salt Lake City, UT 84112, USAStochastic 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.https://www.mdpi.com/1996-1073/12/3/533district energy systemoptimizationrenewable energy systemscombined heat and poweroperating costuncertainty |
| spellingShingle | Thomas T. D. Tran Amanda D. Smith Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use Energies district energy system optimization renewable energy systems combined heat and power operating cost uncertainty |
| title | Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use |
| title_full | Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use |
| title_fullStr | Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use |
| title_full_unstemmed | Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use |
| title_short | Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use |
| title_sort | stochastic optimization for integration of renewable energy technologies in district energy systems for cost effective use |
| topic | district energy system optimization renewable energy systems combined heat and power operating cost uncertainty |
| url | https://www.mdpi.com/1996-1073/12/3/533 |
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