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Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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基于預測-決策框架的綜合能源系統優化

來源:電工電氣發布時間:2025-01-23 15:23瀏覽次數:324

基于預測-決策框架的綜合能源系統優化

崔國偉
(國網徐州市銅山區供電公司,江蘇 徐州 221000)
 
    摘 要:隨著可再生能源滲透率的不斷提高,大量的不確定性設備給綜合能源系統(IES)的安全高效運行帶來了威脅。一般解決不確定型優化問題的過程是,依靠大量歷史數據并輔助一些人工智能技術進行可再生能源的預測分析,但通常預測和決策分開進行的過程會由于預測誤差過大而導致模型的優化目標產生嚴重惡化。提出了基于K-最近鄰(KNN)算法和魯棒優化(RO)相結合的預測- 決策方法改進 IES 的不確定型優化問題。通過 KNN+ 最小體積(KMV)橢球集的方法構建 KMV 橢球集,求解在該集合下的兩階段魯棒模型,得到最優的多能流解。其中,為了平衡 IES 的魯棒性和經濟性,采用魯棒可調參數表示不確定集的合適水平。通過仿真算例分析,證明了 KMV 橢球集的區間大小與可調參數的變化規律,以及該集合的優越性。
    關鍵詞: 綜合能源系統;機器學習;魯棒優化;不確定型優化
    中圖分類號:TM73     文獻標識碼:A     文章編號:1007-3175(2025)01-0026-10
 
The Optimization of Integrated Energy Systems Based on
Predictive & Prescriptive Framework
 
CUI Guo-wei
(State Grid Xuzhou Tongshan District Power Supply Company, Xuzhou 221000, China)
 
    Abstract: With the increasing permeability of renewable energy, a large number of uncertain devices pose a threat to the safe and efficient operation of the integrated energy system (IES). In general, solving the uncertainty optimization problem relies on a large amount of historical data, and assists some artificial intelligence technology to predict the analysis of renewable energy, but the usual process of separate prediction and decision making can produce serious deterioration of the model's optimization objective due to excessive prediction errors. Therefore, this paper proposes a prediction-decision method based on K-nearest neighbor (KNN) and robust optimization(RO) to improve the uncertainty optimization problem of IES. Then, the KMV ellipsoid set is constructed using the KNN + minimum volume (KMV) ellipsoid set method, and the two-stage robust model under the set is solved to obtain the optimal multi-energy flow solution. In order to balance the robustness and economy of IES, robust adjustable parameters are used to represent the appropriate level of the uncertainty set. Finally, through the simulation example,the change rule of the interval size and the adjustable parameters of the KMV ellipsoid set is proved, and the superiority of the set is proved.
    Key words: integrated energy system; machine learning; robust optimization; uncertain optimization
 
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