文章摘要
王会超,秦昊,周昶,等.计及新能源预测不确定性的跨区域日前-日内调度模型[J].电力系统自动化. DOI: 10.7500/AEPS20190122003.
WANG Huichao,QIN Hao,ZHOU Chang, et al.Cross-regional Day-ahead to Intra-day Scheduling Model Considering Forecasting Uncertainty of Renewable Energy[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20190122003.
计及新能源预测不确定性的跨区域日前-日内调度模型
Cross-regional Day-ahead to Intra-day Scheduling Model Considering Forecasting Uncertainty of Renewable Energy
DOI:10.7500/AEPS20190122003
关键词: 增量模型  不确定性  多时间尺度  随机优化模型  调整模型
KeyWords: incremental model  uncertainty  multi-time scale  stochastic optimization model  adjustment model
上网日期:2019-08-12
基金项目:基于送受端多源发电潜力分析的新能源跨区域互动消纳与协调控制技术研究
作者单位E-mail
王会超 中国电力科学研究院有限公司 17600271330 @163.com 
秦昊 中国电力科学研究院有限公司 qinhao@epri.sgcc.com.cn 
周昶 中国电力科学研究院有限公司 zhouchang@epri.sgcc.com.cn 
栗峰 中国电力科学研究院有限公司 lifeng@epri.sgcc.com.cn 
许晓慧 中国电力科学研究院有限公司 xuxiaohui@epri.sgcc.com.cn 
潘旭 中国电力科学研究院有限公司 yuqiaizi1986@163.com 
摘要:
      为简化现有直流线路运行等效模型,精细化地对新能源跨区消纳进行仿真,构建了直流线路输送功率增量模型。为应对两区域新能源预测不确定性对新能源跨区消纳的影响,利用基于场景分析的随机优化模型和多时间尺度滚动优化思想建立了计及新能源预测不确定性的跨区域日前-日内调度模型。利用自回归滑动平均模型模型、场景缩减技术和场景组合构建描述两区域新能源日前预测不确定性的场景集,建立日前随机优化模型求取统计意义上新能源弃电最小的两区域机组启停计划和直流线路输送计划;基于新能源日内预测数据,建立日内调整模型滚动优化两区域机组发电计划和直流线路调整计划。最后,通过某省实际数据改建的跨区域系统算例,验证所建模型的有效性。
Abstract:
      In order to simplify the existing equivalent operation model of DC line and simulate the cross-regional accommodation of renewable energy accurately, the incremental model of DC line transmission power is constructed. A cross-regional day-ahead to intra-day scheduling model considering forecasting uncertainty of renewable energy is established to cope with the impact of forecasting uncertainty of renewable energy on cross-regional accommodation of renewable energy. The model uses the stochastic optimization model based on scenario analysis and multi-time scale rolling optimization concept. The auto-regressive and moving average model, scenario reduction technology and scenario combination concept are used to construct scenario sets for describing the day-ahead forecasting uncertainty of renewable energy in two regions. The day-ahead stochastic optimization model is established to optimize the unit start-stop plan in two regions and the DC line transmission plan with the goal of minimizing abandoned power of renewable energy statistically. The intra-day adjustment model is established to optimize the unit power generation schedule in two regions and the DC line transmission power adjustment plan based on the intra-day prediction data of renewable energy wavily. Finally, the validity of the model is verified by a cross-regional system example reconstructed from the actual data of a province.
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