The increasing penetration of distributed energy resources (DERs) in power systems has aroused interest in distributed economic dispatch (DED). While quadratic cost functions are usually adopted, those of DERs are not necessarily quadratic in practice. Existing distributed algorithms perform unsatisfying convergence rates under nonquadratic cost functions. Therefore, this article proposes a secant approximation-based method (SAM) for general convex cost functions to achieve efficient convergence. The marginal cost functions are represented by linear functions in particular price intervals. With a dynamic ratio consensus (DRC) approach, the DERs with linear marginal cost functions can rapidly reach price consensus. The approximated prices can, in turn, determine new price intervals, which are narrower and more accurate.Case studies based on real-world data verify the improved convergence rate under general convex cost functions and exhibit growing performance in larger-scale cases. The proposed algorithm can be applied to DED in systems with high penetration of DERs and massive agents with general convex cost functions.

中文翻译：

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适应一般凸成本函数的实时分布式经济调度：基于割线近似的方法

电力系统中分布式能源（DER）的渗透率不断提高，引起了人们对分布式经济调度（DED）的兴趣。尽管通常采用二次成本函数，但实际中DER的函数不一定是二次函数。现有的分布式算法在非二次成本函数下的收敛速度不令人满意。因此，本文针对一般凸成本函数提出了一种基于割线近似的方法（SAM），以实现有效的收敛。边际成本函数由特定价格区间的线性函数表示。通过动态比率共识（DRC）方法，具有线性边际成本函数的DER可以快速达到价格共识。近似的价格又可以确定新的价格区间，该区间更窄，更准确。基于实际数据的案例研究证明了在一般凸成本函数下的收敛速度有所提高，并且在大规模案例中表现出不断增长的性能。所提出的算法可以应用于具有较高DERs渗透率和具有一般凸成本函数的大规模代理的系统中的DED。