Abstract
An integrated energy system (IES) can yield several benefits in energy, environmental impacts, cost, and flexibility over a separate system, although the initial cost may be higher. An IES using gas turbine, solar photovoltaics (PV), heat pumps, electrical cooling, and energy storage units is proposed here to satisfy the electricity, cooling, and heating demands of a residential building. A multi-objective optimization approach is used to find the best solutions considering energy, supply-demand matching and exergo-environmental economic indices with life cycle assessment (LCA) in following electric mode. The maximum benefit from the IES studied is reached with a system yielding 53.08% for energy savings, 99.88% matching, and 43.50% cost savings. The ideal scheme selected by the TOPSIS method has a higher annual total cost than the cost with conventional method, but has a better cost saving ratio, 41.81%. A sensitivity analysis shows that a higher PV use would decrease the fuel consumption, but it would reduce the matching and economic performance. Similar to the effect of natural gas price, the off-grid electricity price has higher impact on the cost saving ratio, but lower influence on the specific exergo-environmental cost.
| Original language | English |
|---|---|
| Article number | 115203 |
| Pages (from-to) | 1-12 |
| Number of pages | 12 |
| Journal | Energy Conversion and Management |
| Volume | 254 |
| DOIs | |
| Publication status | Published - 15 Feb 2022 |
| MoE publication type | A1 Journal article-refereed |
Funding
This research has been supported by National Natural Science Foundation of China (Grant No. 51736006 and 22109022) and Fundamental Research Funds for the Central Universities (Grant No. 2242021k30028).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 12 Responsible Consumption and Production
Keywords
- Electricity matching performance
- Equivalent carbon emission
- Integrated energy system
- Life cycle assessment
- Sensitivity analysis
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