分布式能源2025，Vol.10Issue(2)：12-24,13.DOI:10.16513/j.2096-2185.DE.(2025)010-02-0012-13
基于K近邻算法和混合BiLSTM功率预测的微电网运行策略
Microgrid Operation Strategy Based on K-Nearest Neighbor Algorithm and Hybrid BiLSTM Power Prediction
摘要
Abstract
The uncertainty of renewable energy output poses significant challenges to the optimization and scheduling of microgrids.At the same time,traditional optimization methods and scheduling time scales are too single,resulting in large errors in scheduling results,making it difficult to ensure the reliability and economy of system operation.A two-stage optimization operation strategy for microgrids based on K-nearest neighbor(K-NN)algorithm,variational mode decomposition(VMD),convolutional neural network(CNN),and bidirectional long short-term memory(BiLSTM)neural network is proposed to address the above issues.Firstly,a power prediction model based on K-nearest neighbor algorithm and hybrid BiLSTM neural network is established to provide accurate wind and solar prediction data for the two-stage optimization scheduling model.Secondly,a two-stage optimal scheduling model is established.In the day ahead scheduling phase,a stepped carbon trading mechanism and incentive demand response are introduced to develop a day ahead scheduling plan with the goal of minimizing the total operating cost of the system;In the intra day scheduling phase,an intra day rolling optimal scheduling strategy based on model predictive control is established to achieve rolling correction of the intra day scheduling plan with the goal of minimizing the adjustment of the intra day scheduling plan,and reduce the power fluctuation caused by the prediction error.Finally,taking a microgrid as an example for simulation analysis,the results show that the proposed method effectively improves the prediction accuracy while enhancing the economic,environmental,and stability of the microgrid.关键词
K-近邻(K-NN)算法/微电网/功率预测/两阶段运行策略/激励型需求响应/模型预测控制Key words
K-nearest neighbor(K-NN)algorithm/microgrids/power prediction/two-stage operation strategy/incentive demand response/model predictive control分类
能源与动力引用本文复制引用
毛睿,马辉,向昆,范李平,赵剑楠,王灿,席磊..基于K近邻算法和混合BiLSTM功率预测的微电网运行策略[J].分布式能源,2025,10(2):12-24,13.基金项目
国家自然科学基金项目(52377191) （52377191）
湖北省自然科学基金项目(2024AFB584)This work is supported by National Natural Science Foundation of China(52377191) （2024AFB584）
Natural Science Foundation of Hubei Province(2024AFB584) （2024AFB584）
