| 引用本文: | 崔承刚,郝慧玲,杨 宁,等.基于优化Kriging代理模型的场景分析法求解机组组合问题[J].电力系统保护与控制,2020,48(22):49-55.[点击复制] |
| CUI Chenggang,HAO Huiling,YANG Ning,et al.Scenario analysis based on the optimization Kriging model for solving unit commitment problems[J].Power System Protection and Control,2020,48(22):49-55[点击复制] |
|
| 摘要: |
| 由于风电具有很强的波动性和不确定性，为机组组合(Unit Commitment, UC)问题带来许多问题和挑战。因此，提出了一种基于优化Kriging代理模型的场景分析法来处理风电的不确定性。首先通过“预测箱”方法生成大量场景，然后由序列优化的Kriging代理模型估计各场景所对应的经济成本。同时，根据风电不确定性及运行成本对系统的影响，采用重要性采样法削减场景。通过考虑功率平衡和风电爬坡约束的随机机组组合(Stochastic Unit Commitment, SUC)模型验证了该方法的有效性。算例分析结果表明，序列优化Kriging代理模型可以使用较少的场景预测场景运行成本。与Kantorovich 距离法相比，该方法的削减结果选择了较为重要的场景，其求解结果具有更好的经济性和可靠性。 |
| 关键词: 场景分析法 序列优化Kriging代理模型 重要性采样法 机组组合 两阶段随机规划 |
| DOI：DOI: 10.19783/j.cnki.pspc.191518 |
| 投稿时间：2019-12-09修订日期：2020-02-05 |
| 基金项目:国家自然科学基金青年科学基金项目资助(51607111)；上海市科委科研计划项目资助(19DZ1205700) |
|
| Scenario analysis based on the optimization Kriging model for solving unit commitment problems |
| CUI Chenggang,HAO Huiling,YANG Ning,XI Peifeng |
| (1. School of Automation Engineering, Shanghai Electric Power University, Shanghai 200090, China;
2. Shanghai Key Laboratory of Smart Grid Demand Response, Shanghai 200063, China) |
| Abstract: |
| Because wind power has high volatility and uncertainty, it may bring many problems and challenges to the Unit Commitment (UC) problems. Therefore, a scenario analysis method based on the sequence Kriging model is proposed to solve wind power uncertainty. It generates a large number of scenarios by a “forecast bin” method. Then the operational cost of the corresponding scenarios is estimated by a sequence optimization Kriging model. At the same time, an important sampling method is adopted to reduce scenarios given the influence of wind uncertainty combined with the operational cost. The effectiveness of this method is verified by a stochastic unit commitment model considering power balance and wind ramping constraints. It is shown that the sequence optimization Kriging model can use fewer points to estimate the operational cost of the scenario set. Compared to the Kantorovich distance method, the result of the proposed method is more representative, and the resulting solution has better economy and reliability.
This work is supported by Youth Science Fund of National Natural Science Foundation of China (No. 51607111). |
| Key words: scenario analysis sequence optimization Kriging model importance sampling method unit commitment two-step stochastic programming |
