| 引用本文: | 陆 瑜,何兆磊,李 琰,等.计及风光不确定性的综合能源系统容量配置双层优化[J].电力系统保护与控制,2025,53(19):127-138.[点击复制] |
| LU Yu,HE Zhaolei,LI Yan,et al.Bi-level optimization for capacity allocation of integrated energy systems considering wind-solar uncertainty[J].Power System Protection and Control,2025,53(19):127-138[点击复制] |
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| 摘要: |
| 综合能源系统在能源结构转型中具有重要作用，但风速和辐照强度的随机波动对系统容量配置与投资决策的影响显著，现有方法难以兼顾波动适应性与全生命周期成本优化。为此，提出了一种容量配置双层优化模型，以全生命周期成本最小化为目标，实现容量配置与运行调度的协调优化。上层优化通过遗传算法进行设备容量配置，采用自适应核密度估计结合自回归模型生成精准的风光典型场景，并基于Wasserstein距离进行场景削减，提高场景代表性与计算效率。下层优化通过混合整数线性规划实现设备运行调度，平衡经济性与鲁棒性，并将调度结果传递至上层，引导容量配置方案迭代更新，形成双层交互优化机制。仿真结果表明，与传统优化方法相比，所提模型在降低全生命周期成本的同时，提高了风光资源利用率和系统运行可靠性，为综合能源系统优化配置提供了理论支撑与实践参考。 |
| 关键词: 综合能源系统 风光不确定性 双层优化 随机优化 Wasserstein距离 |
| DOI：10.19783/j.cnki.pspc.241592 |
| 投稿时间：2024-11-29修订日期：2025-02-17 |
| 基金项目:国家自然科学基金项目资助(62062068)；云南省中青年学术和技术带头人后备人才项目资助(202305AC160077)；云南省教育厅科学研究基金项目资助(2023J0587) |
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| Bi-level optimization for capacity allocation of integrated energy systems considering wind-solar uncertainty |
| LU Yu1,2,HE Zhaolei3,LI Yan1,2,CUI Lin1,XU Tianqi1,2 |
| (1. Key Laboratory of Cyber-Physical Power System of Yunnan Colleges and Universities, School of Electrical and Information
Engineering, Yunnan Minzu University, Kunming 650504, China; 2. Yunnan Key Laboratory of Unmanned Autonomous
System, Kunming 650504, China; 3. Metering Center of Yunnan Power Grid Co., Ltd., Kunming 650200, China) |
| Abstract: |
| Integrated energy systems play a crucial role in the energy transition, but the stochastic fluctuations of wind speed and solar irradiance significantly affect capacity planning and investment decisions. Existing methods struggle to balance adaptability and lifecycle cost optimization. To address this, a bi-level optimization model for capacity allocation is proposed, aiming to minimize the total lifecycle cost while coordinating optimization of capacity allocation and operation scheduling. In the upper-level optimization, a genetic algorithm is used for equipment capacity allocation. Typical wind and solar scenarios are generated using adaptive kernel density estimation combined with an autoregressive model, while Wasserstein distance ensures representativeness and computational efficiency through scenario reduction. The lower-level optimization employs mixed-integer linear programming for operation scheduling, balancing economic efficiency and robustness. The scheduling results are then fed back to the upper level, guiding iterative updates of capacity allocation and forming a bi-level interactive optimization loop. Simulation results show that, compared to traditional optimization methods, the proposed model reduces total lifecycle costs while improving wind and solar utilization and system reliability. This provides both theoretical support and practical reference for the optimal planning of integrated energy systems. |
| Key words: integrated energy system wind-solar uncertainty bi-level optimization stochastic optimization Wasserstein |
