Congestion management of integrated transmission and distribution network with RES and ESS under stressed condition

Abstract

Summary In this article, the multi‐objective optimization‐based congestion management approach for integrated transmission and distribution system under stressed condition is presented. The congestion is managed by optimized charging and discharging of energy storage systems (ESSs). The coordination among transmission system operator (TSO) and distribution system operator (DSO) takes place for the economic and optimum exchange of power. The main intent of this study is to minimize the amount of load shedding (MW) and its cost ($/MWh) by minimizing the generation cost ($/MWh). These are conflicting objectives as for less load shedding, the system has to have more generation and hence higher cost. The negotiable solution among conflicting objectives is obtained by using the fuzzy min‐max approach. The ESS charging and discharging are optimized for their maximum contribution. This increases the available power (MW), thereby reducing the uncertainty of renewable energy sources and hence lesser load shedding. The analysis is conducted for constant load modeling (CLM) and voltage‐dependent load modeling. The results show that CLM has less load shedding. The proposed approach is implemented on a modified IEEE‐30‐bus test system integrated with the nine‐bus distribution network. Simulations and validations are done using General Algebraic Modeling Solver.

Transmission network congestion control by DESS through interval computation and capacity optimization via hybrid DE‐PSO technique

“…Widely used FACTS devices are Unified Power Flow Controller (UPFC) [5], Static VAR Compensator (SVC) [12], Static Compensator (STATCOM) [5,13,21,[23][24], Thyristor Controlled Series Compensator (TCSC) [5,7,23] etc. But nowadays, Distributed Generators (DGs) are also finding suitable widely adopted and widely adopted for congestion management due to the many technological advancements in modern power system [26][27][28][29][30]. In contrast with the traditional central power plant DG is also used to satisfy the local load [25].…”

“…Benefits of DG is more predominant in the highly congested area and can be attained if its location and capacity is strategically planned. Improper allo-cation of DGs may lead to losses and abrupt changes in the system [26,28,30]. In context to deregulated power market, congestion in the network creates large differences in Locational Marginal Price (LMP) which is an indication to determine the degree of congestion in the network.…”

Development of Reactive Power Allocation Method for Radial Structure Wind Farm Considering Multiple Connections

“…Hierarchical wind farm control is generally used to effectively control the reactive power of wind turbines and manage power capacity [29]. After comparing the output measured at PCC with the reference power received by the grid operator, a curtailment signal is used to control individual wind turbines, and thanks to ESS integration, it is possible to manage output control of wind turbines, hence alleviating transmission congestion [31,32]. The hierarchical wind farm management system (WFMS) measures and manages voltage fluctuations within the wind farm, including the PCC, and thus performs the reactive power control in the same hierarchical control structure.…”

Multiobjective Salp Swarm Algorithm Approach for Transmission Congestion Management

“…Te estimate of the probabilistic energy fow for the regional integrated energy system taking into account the cross-system failures was published in [34]. An energy storage system based multiobjective congestion management has been presented by using GAMS software [35] and genetic algorithm [36]. Hence, in the present work the recently developed multiobjective salp swarm algorithm (MOSSA) [37] with number of strategies has been proposed for solving multiobjective optimization-based CM problem.…”