Real-time congestion control using cascaded LSTM deep neural networks for deregulated power markets.

ABSTRACT: In deregulated power markets (DPMs), transmission-line congestion has become more severe and frequent than in traditional power systems. This congestion hinders electricity markets from operating in normal competitive equilibrium. The independent system operator (ISO) is responsible for implementing appropriate measures to mitigate congestion and ensure the proper functioning of the power market. This study utilized generation rescheduling (GR) to address congestion in spot or day-ahead power markets. Rapid alleviation of congestion is essential to prevent tripping of overloaded lines. Owing to their computational inefficiency, evolutionary algorithms (EAs) are ineffective for real-time congestion management, necessitating hybrid models to deliver rapid solutions. This paper proposes a hyb

SUBMITTER: Mohan GM 

PROVIDER: S-EPMC12368097 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature