考虑调度决策非预期性的多阶段随机规划调度策略

doi:10.12204/j.issn.1000-7229.2022.10.009

电力建设 ›› 2022, Vol. 43 ›› Issue (10): 87-97.doi: 10.12204/j.issn.1000-7229.2022.10.009

考虑调度决策非预期性的多阶段随机规划调度策略

赵雪楠¹(), 段凯悦¹(), 李萌²(), 王湘²(), 雷霞²(), 钟鸿鸣²(), 韩玉辉¹(), 陈颖¹(), 刘蒙聪¹()

1.国网内蒙古东部电力有限公司,呼和浩特市 010010
2.西华大学电气与电子信息学院,成都市 610039

收稿日期:2022-02-22 出版日期:2022-10-01 发布日期:2022-09-29
通讯作者: 雷霞 E-mail:zhaoxuenanhd@126.com;duankaiyue98@163.com;576212534@qq.com;xiangwang@xhu.mail.edu.cn;snow_lei246@mail.xhu.edu.cn;383182742@qq.com;hanyuhui0470@163.com;604439278@qq.com;liumengcong@md.sgcc.com.cn
作者简介:赵雪楠(1988),女,硕士,高级工程师,从事发电计划安全校核研究工作,E-mail: zhaoxuenanhd@126.com;
段凯悦(1998),女,本科,从事电网运行管理研究工作,E-mail: duankaiyue98@163.com;
李萌(1996),女,硕士研究生,主要研究方向为微能源网优化运行,E-mail: 576212534@qq.com;
王湘(1988),男,博士,讲师,主要研究方向为电动汽车并网及新型电力系统,E-mail: xiangwang@xhu.mail.edu.cn;
钟鸿鸣(1995),男,硕士研究生,主要研究方向为电力系统及其自动化,E-mail: 383182742@qq.com;
韩玉辉(1974),男,硕士,高级工程师,主要研究方向为电网调度运行管理,E-mail: hanyuhui0470@163.com;
陈颖(1988),女,硕士,高级工程师,主要研究方向为调度计划管理,E-mail: 604439278@qq.com;
刘蒙聪(1988),男,硕士,高级工程师,主要研究方向为调度运行管理,E-mail: liumengcong@md.sgcc.com.cn。
基金资助:
国家自然科学基金项目(51877181);国家电网公司科技项目“含高比例可再生能源接入的源网荷储协同调度技术研究”(52660021000Q)

Scheduling Strategy of Multi-stage Stochastic Programming Considering Non-anticipativity of Scheduling Decision

ZHAO Xuenan¹(), DUAN Kaiyue¹(), LI Meng²(), WANG Xiang²(), LEI Xia²(), ZHONG Hongming²(), HAN Yuhui¹(), CHEN Ying¹(), LIU Mengcong¹()

1. State Grid East Inner Mongolia Electric Power Company Limited, Hohhot 010010, China
2. School of Electric Engineering and Electronic Information, Xihua University, Chengdu 610039, China

Received:2022-02-22 Online:2022-10-01 Published:2022-09-29
Contact: LEI Xia E-mail:zhaoxuenanhd@126.com;duankaiyue98@163.com;576212534@qq.com;xiangwang@xhu.mail.edu.cn;snow_lei246@mail.xhu.edu.cn;383182742@qq.com;hanyuhui0470@163.com;604439278@qq.com;liumengcong@md.sgcc.com.cn
Supported by:
National Natural Science Foundation of China(51877181);State Grid Corporation of China Research Program(52660021000Q)

摘要/Abstract

摘要：

随着可再生能源渗透率的提高,分布式可再生能源带来的波动性、间歇性会传递至主网中,对系统安全运行造成影响,研究不确定性优化方法对系统实际运行具有一定的指导作用。传统的随机优化以及鲁棒优化方法不满足系统实际运行的非预期性要求。文章以日运行期望成本最小为目标,考虑分布式可再生能源发电不确定性,建立多阶段随机规划模型,可以根据之前不确定信息的实现在每个阶段确定预调度决策,不会受到未来不确定信息的影响,符合系统实际运行规律,满足非预期性。为了避免多阶段随机规划问题求解的维数灾难,采用随机对偶动态规划(stochastic dual dynamic programming, SDDP)算法进行求解。仿真结果表明,相比于传统的确定性模型,多阶段随机优化得到的最优调度决策树较之确定性优化得到的单一决策方案具有更广泛的决策空间,可以基于上一阶段不确定信息的实现和决策来更新调度决策,降低系统的运行成本。

关键词: 不确定性优化, 多阶段随机规划, 非预期性, 随机对偶动态规划(SDDP)算法

Abstract:

With the increase of renewable energy penetration, the volatility and intermittence brought by distributed renewable energy will be transferred to the main network, which will affect the safe operation of the system. The study of uncertainty optimization method plays a certain guiding role in the actual operation of the system. However, the traditional stochastic optimization and robust optimization methods do not meet the unpredictable requirements of the actual operation of the system. In this paper, a multi-stage stochastic programming model considering the uncertainty of distributed renewable power generation is established with the goal of minimizing the expected cost of daily operation. The pre-scheduling decision can be made at each stage according to the realization of the previous uncertain information, which will not be affected by the future uncertain information, and is in line with the actual operation law of the system, and meets the unexpected requirements. In order to avoid the disaster of dimensionality in solving multi-stage stochastic programming problems, the stochastic dual dynamic programming algorithm is used to solve the problem. The simulation results show that, compared with the traditional deterministic model, the optimal scheduling decision tree obtained by multi-stage stochastic optimization has wider decision space than the single decision scheme obtained by deterministic optimization. The scheduling decision can be updated according to the implementation and decision of the uncertain information in the previous stage, and the operating cost of the system can be reduced.

Key words: uncertain optimization, multi-stage stochastic programming, non-anticipativity, stochastic dual dynamic programming (SDDP) algorithm

中图分类号:

TM744

赵雪楠, 段凯悦, 李萌, 王湘, 雷霞, 钟鸿鸣, 韩玉辉, 陈颖, 刘蒙聪. 考虑调度决策非预期性的多阶段随机规划调度策略[J]. 电力建设, 2022, 43(10): 87-97.

ZHAO Xuenan, DUAN Kaiyue, LI Meng, WANG Xiang, LEI Xia, ZHONG Hongming, HAN Yuhui, CHEN Ying, LIU Mengcong. Scheduling Strategy of Multi-stage Stochastic Programming Considering Non-anticipativity of Scheduling Decision[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(10): 87-97.

图/表 15

图1 场景树的生成

Fig.1 Construction of scenario tree

图2 向前迭代过程

Fig.2 Forward pass procedure

图3 向后迭代过程

Fig.3 Backward pass procedure

图4 Bender最优割集表示的期望成本函数

Fig.4 Expected cost function represented by Benders’ cut set

图5 可再生能源出力与负荷预测曲线

Fig.5 Predicted curves of load and renewable energy supply

表1 示范电网主要参数

Table 1 Grid parameters

图6 分时电价

Fig.6 Time-of-use electricity price

表2 场景树大小对SDDP算法的影响

Table 2 Effect of scenario-tree size on SDDP algorithm

表3 样本数量对SDDP算法的影响

Table 3 Influence of sample size on SDDP algorithm

图7 SDDP算法收敛特性

Fig.7 Convergence of SDDP algorithm

表4 多阶段随机规划模型与确定性模型的比较

Table 4 Comparison between multi-stage stochastic programming model and deterministic model

图8 可再生能源出力比较

Fig.8 Comparison of renewable energy output

图9 确定性模型的调度计划

Fig.9 Scheduling plan based on deterministic model

图10 电网交互功率比较

Fig.10 Comparison of interactive power of power grid

图11 储能电池充放电功率比较

Fig.11 Comparison of charge and discharge power of storage battery

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考虑调度决策非预期性的多阶段随机规划调度策略

Scheduling Strategy of Multi-stage Stochastic Programming Considering Non-anticipativity of Scheduling Decision

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