考虑负荷约束的新能源电网资源分阶段运行优化研究

陈祎超; 殷章桃

陈祎超,殷章桃.考虑负荷约束的新能源电网资源分阶段运行优化研究[J].电测与仪表,2026,63(2):111-119.
CHEN Yichao,YIN Zhangtao.Research on staged operation optimization of new energy grid resources considering load constraints[J].Electrical Measurement & Instrumentation,2026,63(2):111-119.

考虑负荷约束的新能源电网资源分阶段运行优化研究

Research on staged operation optimization of new energy grid resources considering load constraints

DOI：10.19753/j.issn1001-1390.2026.02.012

中文关键词: 新能源电网资源运行优化功率预测分阶段建模粒子群优化

英文关键词:New energy grid Resource operation optimization Power prediction Staged modeling Particle Swarm Optimization

基金项目:国家自然科学基金：基于压电结构多模态振动特性的宽频带振动能量高效收集系统理论与关键技术研究（51867021）

作者中文名	作者英文名	单位
陈祎超	CHEN Yichao	国网宁夏电力有限公司建设分公司
殷章桃	YIN Zhangtao	国网新疆送变电有限公司调试中心

摘要点击次数: 83

中文摘要:

在新能源电网中，受到风、光等自然资源的间歇性的影响，使得电网的功率具有随机性，导致存在多个目标需要同时优化的问题。传统的粒子群算法结合单一模型的方式，往往需要放弃考虑负荷这一关键因素，以保证单一模型求解过程不会陷入局部最优解。对此，提出考虑负荷约束的新能源电网资源分阶段运行优化研究。通过差分自回归移动平均模型（ARIMA）预测负荷的低频部分，采用深度信念网络（DBN）预测负荷高频部分，将两部分叠加重构生成新能源电网负荷预测结果。设计日前与实时两阶段的电网资源优化运行模型。最后，利用自适应变异粒子群优化算法，针对两阶段配置模型，进行全局最优求解，以实现新能源电网资源运行优化。实验结果表明：所提算法在24小时内每个时段的弃风光电量均低于对比算法，新能源消纳率在3h时达到最高97.6%，可控负荷时移率与运行成本远低于对比算法，提高了电网的自动化和智能化水平。

英文摘要:

In the new energy grid, the intermittent impact of natural resources such as wind and light makes the power of the grid stochastic, leading to the problem of multiple objectives needing to be optimized simultaneously. Traditional particle swarm optimization combined with a single model often needs to give up the consideration of load, which is a key factor, to ensure that the single model solution process will not fall into the local optimal solution. In this regard, a research on phased operation optimization of new energy grid resources considering load constraints is proposed. By using the Differential Autoregressive Moving Average (ARIMA) model to predict the low-frequency part of the load, and using a Deep Belief Network (DBN) to predict the high-frequency part of the load, the two parts are superimposed and reconstructed to generate the load prediction results for the new energy grid. Design a two-stage optimization operation model for power grid resources, including day ahead and real-time. Finally, using the adaptive mutation particle swarm optimization algorithm, a global optimal solution is performed for the two-stage configuration model to achieve optimization of resource operation in the new energy grid. The experimental results show that the proposed algorithm has a lower amount of abandoned wind and solar power in each time period within 24 hours compared to the comparative algorithm. The new energy consumption rate reaches the highest 97.6% at 3 hours, and the controllable load time shift rate and operating cost are much lower than the comparative algorithm, improving the automation and intelligence level of the power grid.

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