考虑源荷不确定性的区域综合能源系统优化调度方法

Optimization scheduling method for regional integrated energy systems considering source and load uncertainties

综合能源系统(RIES)是解决未来能源问题的重要技术手段之一,区域一体化是未来综合能源系统发展的重要方向。然而,可再生能源的大量接入和负荷类型的多元化带来的多重不确定性将对电网运行产生不可忽视的影响。针对上述问题,本文提出了一种基于多元不确定性和综合需求响应(IDR)的多能耦合综合能源系统优化模型。首先,介绍了包括可再生能源、耦合装置、储能设备以及电、热负荷在内的RIES优化模型。然后,通过比较分析源、荷不确定性的数学特征,分别选取鲁棒优化(RO)对新能源不确定性进行建模,采用随机优化(SO)对负荷不确定性进行描述。最后,用实际的RIES对模型进行了对比验证。研究结果表明,采用RO和SO方法对源、荷不确定性进行建模具有更好的鲁棒性和一定的经济性,考虑源荷不确定性的区域综合能源系统模型能够有效减小负荷峰谷差,降低设备供能压力,保证系统的协同优化运行,在保证可靠性的前提下,仍有一定的经济价值。

The Regional Integrated Energy System(RIES)represents a crucial technological strategy for future energy solutions,with an emphasis on regional integration as a key developmental direction.The substantial integration of renewable energy and the diversification of load types introduce multifaceted uncertainties that significantly impact the operation of the electrical grid.In response to these issues,this paper presents an optimized RIES model that incorporates multiple uncertainties,multi-energy coupling,and Integrated Demand Response(IDR).The proposed RIES optimization model encompasses renewable energy sources,coupling devices,energy storage units,and both electrical and thermal loads.This model undergoes a comparative analysis of the mathematical characteristics of source and load uncertainties.To address these uncertainties,Robust Optimization(RO)is applied to new energy source variability,while Stochastic Optimization(SO)was utilized for load uncertainty characterization.The model was then empirically validated using actual RIES data.Research findings indicated that the application of RO and SO methods for modeling source and load uncertainties enhances robustness and economic efficiency.The RIES model,considering these uncertainties,effectively reduces the disparity between peak and valley loads,decreases the pressure on equipment supply,and guarantees collaborative optimization of the system's operations,thereby improving both the reliability and economic viability of the system.