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| 典型场景与不确定性下虚拟电厂储能与电制气协同优化调度 |
| Coordinated Optimal Scheduling of Energy Storage and Power-to-gas in Virtual Power Plants Under Typical Scenarios and Uncertainties |
| 投稿时间：2026-01-08 |
| DOI：10.16018/j.cnki.cn32-1650/n.202602004 |
| 中文关键词: 虚拟电厂 优化调度 电制气 协同优化 随机规划 |
| 英文关键词: virtual power plant optimal scheduling power-to-gas coordinated optimization stochastic programming |
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| 中文摘要: |
| 为应对可再生能源波动性,提出基于两阶段随机规划的虚拟电厂(VPP)储能与多类电制气协同配置方法。首先,利用K-means聚类生成场景,并通过经验模态分解将波动功率分频分配给储能与制氢设备,实现灵活资源精准匹配;其次,构建以VPP期望收益最大化为目标的模型,一阶段定容量,二阶段优运行。仿真表明:与确定性规划相比,该方法虽使日均投资成本增加,但在极端场景下弃电成本降低90%以上,显著提升系统鲁棒性与消纳能力。此外,系统经济性对氢价最敏感,提高弃电惩罚将驱动电解槽容量增加。 |
| 英文摘要: |
| To address the volatility of renewable energy, a two-stage stochastic programming-based capacity configuration method for a virtual power plant (VPP) coordinating energy storage and multi-type power-to-gas is proposed. First, K-means clustering is used to generate scenarios, and empirical mode decomposition is applied to decompose fluctuating power into different frequency components, which are then allocated to storage and hydrogen production equipment for precise matching. Subsequently, a model maximizing the VPP's expected profit is constructed, determining capacity in the first stage and optimizing operation in the second stage. Simulations show that, compared with deterministic planning, this method increases daily average investment cost, but reduces curtailment cost by over 90% in extreme scenarios, significantly enhancing system robustness and renewable energy accommodation. Furthermore, system economics are most sensitive to hydrogen price, and higher curtailment penalties drive increased electrolyzer capacity. |
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