基于风-光-储联合优化的最优潮流

刘中华; 牛玉刚; 贾廷纲

doi:10.14135/j.cnki.1006-3080.20201214001

基于风-光-储联合优化的最优潮流

doi: 10.14135/j.cnki.1006-3080.20201214001

1.
华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237
2.
上海电气自动化集团，上海 200070

详细信息

作者简介:
刘中华（1996—），男，安徽人，硕士生，主要研究方向为微电网能量调度与控制。E-mail：liuzhonghua2333@163.com

通讯作者:
牛玉刚，E-mail：acniuyg@ecust.edu.cn

计量
- 文章访问数: 290
- HTML全文浏览量: 174
- PDF下载量: 12
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-14
- 网络出版日期: 2021-03-24

Optimal Power Flow Based on Optimization of Wind-Photovoltaic-Storage Hybrid System

1.
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
2.
Shanghai Electric Automation Group, Shanghai 200070, China

摘要

摘要: 针对分布式能源发电的间歇性和不确定性，提出了风、光互补发电和储能实时在线优化结合的方法。以风、光日前预测发电与实时发电误差最小、储能出力最少为目标，建立风-光-储联合优化模型；使用改进PSO算法对风、光出力进行实时优化，优化后的分布式发电可以有效降低分布式电源带来的日前调度偏差。通过储能优化后的风、光出力在进行潮流计算时可直接处理为负荷模型，可以有效降低分布式能源建模复杂度。最后，以IEEE30节点系统为例，以发电机发电费用最低为目标函数，将风-光-储联合出力等效为单个节点，利用GA算法对最优潮流模型进行求解，验证了本文方法的正确性和有效性。
- 风力发电 /
- 光伏发电 /
- 储能优化 /
- 最优潮流 /
- 能量调度
Abstract: With the vigorous development and application of distributed energy, major challenges have been raised to the planning and operation of traditional power grids. The intermittent influence of distributed energy such as wind power and photovoltaic power generation on energy dispatch and the impact of uncertainty on the power grid are issues that need to be solved urgently. In view of the intermittency and uncertainty of distributed power generation, the wind-solar energy-storage joint optimization model is established with the goal of minimizing wind-solar forecast power generation error and energy storage output, and a method of real-time online optimization of wind-solar complementary power generation and energy storage is proposed. Improved PSO (Particle Swarm Optimization) algorithm is used to optimize the model in real time. Based on the establishment of the wind-solar-storage complementary model, the co-generation of wind-solar storage is regarded as an equivalent node to construct an optimal power flow model with the optimal output of diesel generators as the objective function. GA (Genetic Algorithm) is used to solve the optimal power flow model. Finally, the IEEE30-node system is taken as an example to verify the correctness and effectiveness of the strategy.
- wind power generation /
- photovoltaic power generation /
- energy storage optimization /
- optimal power flow /
- energy management

HTML全文

图 1 风-光-储最优潮流结构图

Figure 1. Structure of wind-solar-storage optimal power flow

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图 2 风-光-储粒子群优化结构图

Figure 2. PSO structure of wind-solar-storage

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图 3 最优潮流遗传算法优化结构图

Figure 3. GA optimal structure of optimal power flow

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图 4 风速历史数据

Figure 4. Wind speed history data

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图 5 风速概率分布拟合偏差

Figure 5. Fitting deviation of wind speed probability distribution

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图 6 实际风力发电与光伏发电

Figure 6. Actual wind and photovoltaic power generation

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图 7 实际发电与预测发电

Figure 7. Actual generation with predictive power

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图 8 储能补偿效果

Figure 8. Energy storage compensation effect

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图 9 储能补偿前后风光出力预测绝对偏差

Figure 9. Forecast deviation of wind and solar output before and after energy storage compensation

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图 10 储能荷电状态

Figure 10. Energy storage state of charge

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图 11 IEEE30节点系统

Figure 11. IEEE30 bus system

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图 12 负荷需求信息

Figure 12. Information of load

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图 13 单个发电机出力

Figure 13. Single generation output

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图 14 优化前后偏差

Figure 14. Deviation before and after optimization

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图 15 各时段节点电压

Figure 15. Node voltage in each period

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图 16 GA算法求解最优潮流收敛情况

Figure 16. GA to solve OPF convergence

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表 1 两种优化结果对比

Table 1. Result comparsion between two methods

Method Matching degree with planned/% Energy storage charge/% Power difference/MW

This paper 99.48 8.57 18.2255
Literature[14] 93.26 19.32 2.2041

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表 2 发电机损耗参数

Table 2. Generation parameters setting

Node a b c Minimum
generation/MW Maximum
generation/MW

1 0.0375 3.00 0 10 80
2 0.0475 1.15 0 10 80
5 0.0325 1.20 0 10 80
8 0.0430 1.25 0 10 80
11 0.0450 2.00 0 10 80
13 0.0350 2.50 0 10 80

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