高渗透光伏接入下基于近似值函数的主动配电网鲁棒优化

SUN Xinglu

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高渗透光伏接入下基于近似值函数的主动配电网鲁棒优化

更新时间：2025-08-13

- 高渗透光伏接入下基于近似值函数的主动配电网鲁棒优化
- Automation of Electric Power Systems Vol. 42, Issue 15, Pages: 62-69(2018)
- 作者机构：
  
  华南理工大学电力学院,广东省,广州市,510641
- 作者简介：
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- Published：2018
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SUN Xinglu. [J]. Automation of electric power systems, 2018, 42(15): 62-69.
DOI：

SUN Xinglu. [J]. Automation of electric power systems, 2018, 42(15): 62-69. DOI：

摘要

针对高渗透率光伏接入下主动配电网(ADN)运行优化的不确定性问题，提出了一种基于近似值函数的自适应鲁棒优化方法。该方法考虑光伏出力的随机特性和系统运行的鲁棒性，通过自适应鲁棒优化保证系统安全运行，同时最小化运行成本。首先，建立考虑多种可调装置的ADN运行优化模型，并通过凸优化技术将该模型转化为混合整数二阶锥优化模型。然后，利用近似动态规划的值函数思想构造ADN时段解耦的自适应鲁棒优化模型，通过逐次投影近似算法进行近似值函数参数训练。最后，采用列与限制生成算法实现模型的鲁棒逐时段递推求解。对IEEE 33节点和PG&E69节点系统进行算例分析，并与确定性优化、近似动态规划和两阶段鲁棒优化方法进行对比，验证了所提方法的可行性和有效性。

Abstract

In view of the uncertainty in the optimization of active distribution network(ADN)operation under high-penetration photovoltaic(PV)access

an adaptive robust optimization method based on approximate value function is proposed. The method considers the stochastic characteristics of PV output and the robustness of the system operation

ensuring the safe operation of the system through adaptive robust optimization and minimizing the operation cost at the same time. First of all

the optimal operation model is established considering various ADN adjustable devices

and the convex optimization technique is used to transform the original problem into a mixed integer second order cone optimization model. Then

the time decoupling adaptive robust optimization model is constructed by using the approximated value function of approximate dynamic programming(ADP)

and the successive projection approximation routine algorithm is used to train the value function parameters being used in the robust model. Finally

column and constraint generation algorithm is adopted to solve adaptive robust model for each period successively by means of approximated value function. Case study on modified IEEE 33-bus and PG&E69-bus distribution system is analyzed and compared with deterministic optimization

ADP and two-stage robust optimization method to verify the feasibility and effectiveness of the proposed method.

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