Double-Layer Optimization of External Derivative Response for Multi-Energy Microgrid with Shared Energy Storage Stations
The strong uncertainty introduced by a high proportion of renewable energy sources integrated into the energy system complicates the internal optimization of system operation and may lead to the spillover of uncertainty risks, affecting the stable operation of the higher-level power grid. To address...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2025-02-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202408007 |
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| Summary: | The strong uncertainty introduced by a high proportion of renewable energy sources integrated into the energy system complicates the internal optimization of system operation and may lead to the spillover of uncertainty risks, affecting the stable operation of the higher-level power grid. To address this issue, a two-layer coordinated optimization strategy for the external response of a multi-energy complementary micro-energy grid system based on a shared energy storage station is proposed. Firstly, operational models for energy equipment within the micro-energy grid system are constructed, and operational modes and profit mechanisms for the shared energy storage station are proposed. Secondly, a two-layer coordinated optimization model considering two different stakeholders is established, with the micro-energy grid system operator as the upper layer and the shared energy storage station operator as the lower layer. Subsequently, the Hong's (2m+1) point estimation method is used to quantify the uncertainty of wind and solar power, and the two-layer nonlinear optimization model is transformed into a single-layer mixed-integer optimization model using the KKT conditions and Big-M method. Finally, simulation results demonstrate that the proposed strategy can effectively prevent the spillover of uncertainty risks associated with wind and solar power, reducing the operational costs of the micro-energy grid operator by 6.3%. |
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| ISSN: | 1004-9649 |