| 引用本文: | 谭荣昊,林舜江,梁炜焜.大规模交直流混联电网中FACTS装置多目标鲁棒优化配置[J].电力系统保护与控制,2026,54(03):144-155.[点击复制] |
| TAN Ronghao,LIN Shunjiang,LIANG Weikun.Multi-objective robust optimization of FACTS device allocation in large-scale hybrid AC/DC power grids[J].Power System Protection and Control,2026,54(03):144-155[点击复制] |
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| 摘要: |
| 为解决大规模交直流混联电网中输电断面的输电能力瓶颈问题，消纳送端更多的新能源出力，可通过在系统中合理配置可控串补(thyristor controlled series compensation, TCSC)和静止同步补偿器(static synchronous compensator, STATCOM)。基于此，考虑新能源出力的不确定性，以最小化年等值投资成本、最大化断面输电能力和新能源消纳容量作为优化目标，建立了大规模交直流混联电网中TCSC和STATCOM的多目标鲁棒优化配置模型。首先，采用凸松弛技术将原混合整数非线性规划模型转化为混合整数二阶锥约束规划模型，以提高模型求解的计算效率和所获得优化解的质量。然后，采用规格化法平面约束法将多目标鲁棒优化模型转化为一系列单目标鲁棒优化模型，再采用列与约束生成算法将单目标模型分解为主问题和子问题交替迭代求解，以得到在新能源场站最大可获得出力不确定波动的最恶劣情况下多目标优化模型的一系列Pareto最优解集，并采用熵权法确定出多目标折中最优解。最后，以修改的IEEE39节点系统和某实际大规模交直流电网为例进行计算，结果验证了所提模型和算法的有效性。 |
| 关键词: 柔性交流输电系统 交直流混联电网 多目标鲁棒优化 凸松弛 |
| DOI：10.19783/j.cnki.pspc.250358 |
| 投稿时间：2025-04-07修订日期：2025-06-17 |
| 基金项目:广东省基础与应用基础研究基金项目资助(2023A1515240075，2024B1515250007) |
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| Multi-objective robust optimization of FACTS device allocation in large-scale hybrid AC/DC power grids |
| TAN Ronghao1,2,LIN Shunjiang1,2,LIANG Weikun1,2 |
| (1. School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China;
2. Guangdong Key Laboratory of Clean Energy Technology, Guangzhou 510630, China) |
| Abstract: |
| To address transmission capacity bottlenecks of critical transmission corridors in large-scale hybrid AC/DC power grids and to accommodate greater renewable energy output at the sending end, thyristor controlled series compensation (TCSC) and static synchronous compensator (STATCOM) can be rationally deployed in the system. Accordingly, considering the uncertainties of renewable energy output, a multi-objective robust optimization model for the allocation of TCSC and STATCOM in large-scale hybrid AC/DC power grids is established, with the objectives of minimizing the annualized equivalent investment cost, maximizing corridor transmission capacity, and maximizing renewable energy accommodation capacity. First, the original mixed integer nonlinear programming model is transformed into mixed integer second-order cone programming model using convex relaxation technology, thereby improving computational efficiency and the quality of the obtained solutions. Then, the multi-objective robust optimization model is transformed into a series of single-objective optimization models using the normalized normal constraint algorithm. Subsequently, the column and constraint generation algorithm is used to decompose each single-objective model into a master problem and subproblem that are solved iteratively, yielding a set of Pareto-optimal solutions for the multi-objective optimization model under the worst-case uncertainty of maximum available renewable generation at renewable energy plants. The entropy weight method is further applied to identify a compromise optimal solution among the Pareto set. Finally, based on the calculation results of the modified IEEE 39-bus system and an actual large-scale AC-DC power grid, the effectiveness of the proposed model and algorithm is verified. |
| Key words: FACTS hybrid AC/DC power grid multi-objective robust optimization convex relaxation |
