考虑预测误差不确定性的源-荷-广义储低碳经济动态优化调度
Dynamic optimal scheduling of source-load-generalized storage low carbon economy considering prediction error uncertainty
吴佩芝 1徐天奇 2李琰 2李晓兰 1崔琳1
作者信息
- 1. 云南民族大学 电气信息工程学院 云南省高校电力信息物理融合系统重点实验室, 昆明 650504
- 2. 云南民族大学 电气信息工程学院 云南省高校电力信息物理融合系统重点实验室, 昆明 650504;云南省无人自主系统重点实验室, 昆明 650504
- 折叠
摘要
基于确定性模型的低碳经济调度方法无法准确描述不确定因素对电网碳排放的影响.据此,从系统层面考虑源-荷两侧不确定性,构建了一种低碳经济动态优化调度模型.采用混合高斯概率密度估计刻画风、光预测误差的不确定性,提出一种考虑净负荷预测误差的正负旋转备用容量概率约束方法;为减少系统的碳排放量,一从发电端考虑,引入碳交易机制,建立含有阶梯型碳交易成本的系统总运行成本最低的调度目标函数;二从用户层面考虑,将需求响应负荷同实际储能设备视为广义储能参与日前-日内滚动优化调度;在IEEE39节点系统进行不同场景分析,验证了所提的调度模型实现系统低碳、经济运行目标的有效性,也挖掘了需求响应负荷的减碳潜力.
Abstract
With the integration of a high proportion of new energy into the power grid, the current low-carbon economy scheduling method based on deterministic model is unable to accurately describe the impact of uncertainties on carbon emissions.With the consideration of the uncertainties on both sides of the source-load from the system level, a dynamic optimal scheduling model for low-carbon economy is built.First , the uncertainty of wind and light prediction errors is described by mixed Gaussian probability density estimation, and a probabilistic constraint method of positive and negative rotating reserve capacity considering net load forecasting errors is proposed.Second, to reduce the carbon emissions of the system:on the power generation side, the carbon trading mechanism is introduced to build the scheduling objective function with the lowest total operating cost of the system with stepped carbon transaction cost; on the users' side, the demand response load and the actual energy storage equipment are regarded as generalized energy storage to participate in the day-to-day rolling optimal dispatching.Finally, different scenarios are analyzed in the IEEE39 node system to verify the effectiveness of the proposed scheduling model to achieve low-carbon and efficient operation goals, tapping the carbon reduction potential of the demand response load.
关键词
碳交易机制/混合高斯/需求响应负荷/广义储能/净负荷预测误差/优化调度Key words
carbon trading mechanism/mixed Gaussian/demand response load/generalized energy storage/the equivalent load forecast error/optimal dispatching引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据