Bi-objective operation optimization of regional integrated energy system considering shared energy storage
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View abstract on PubMed
Summary
This summary is machine-generated.Shared energy storage optimizes regional integrated energy systems (RIES). This approach reduces RIES operating costs by millions, enhancing energy efficiency and grid flexibility.
Area Of Science
- Energy Systems Engineering
- Operations Research
- Artificial Intelligence
Background
- The rise of the sharing economy is influencing the power grid sector, prompting exploration of shared energy storage service models.
- Regional Integrated Energy Systems (RIES) require efficient operational strategies to adapt to evolving energy landscapes.
Purpose Of The Study
- To develop an optimization technology for RIES operation that incorporates shared energy storage.
- To minimize operating costs for both the RIES and the Energy Storage Aggregator (ESA).
Main Methods
- Construction of a fundamental RIES structure integrating shared energy storage.
- Establishment of operational optimization objectives and analysis of constraints for RIES and ESA.
- Development of the Chaos Sparrow Search Algorithm (COSSA) by integrating Tent chaos and Gaussian mutation into the traditional Sparrow Search Algorithm.
Main Results
- A case study demonstrated the effectiveness of the proposed optimization model.
- Shared energy storage participation led to a significant reduction in RIES operating costs, amounting to $2.912536 million.
- The proposed method facilitates more efficient RIES operation.
Conclusions
- The integration of shared energy storage into RIES offers substantial economic benefits.
- The developed COSSA algorithm effectively optimizes the operation of RIES with shared energy storage.
- This research supports China's energy strategy transformation by improving energy utilization and system flexibility.
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