Decentralized optimal multi-area generation scheduling considering renewable resources mix and dynamic tie line rating

Abstract

Electrical power networks usually consist of multi-area systems, which are connected by tie lines. Dynamic economic/environmental dispatch of multi-area approaches are used to schedule such networks. The goal of multi-area scheduling is determining the optimal operation of generation units and the tie line power flow in a certain period by minimizing environmental constraints. Such operation problem is limited by technical constraints of thermal generation units, tie line constraints, and transmission losses. A major limitation of multi-area economic/environmental dispatch problem is the capacity of tie lines. The worst scenario of weather condition is generally employed to set this capacity. This value is called static line rating (SLR). However, the SLR is less than the real-time capacity of overhead line. The capacity of overhead line can be determined by estimation of weather condition data for better utilization of overhead lines. This value is called dynamic line rating (DLR). In this paper, a decentralized methodology is proposed for optimal scheduling generation units taking into consideration environmental constraint, dynamic line rating, wind power generations; compressed air energy storage and power pool market. The ε-constraint approach is applied to solve proposed dynamic economic/environmental dispatch of multi-area model. In addition, a fuzzy satisfying technique is used to select the best compromise solution. An interconnected, multi-area power system with cross-border trading in the presence of wind power uncertainty and the storage unit is considered for evaluating the proposed method. The proposed decentralized approach is based on the integrated simplicial decomposition method (SDM), the nonlinear block Gauss-Seidel (GS) method and the augmented Lagrangian method (ALM) (SDM-GS-ALM) decomposition algorithm, because we need to schedule a multi-area network without central operation intervention.