A multi-stage congestion management strategy in deregulated power markets using generator rescheduling and demand-side interventions

Congestion in deregulated power markets poses a significant challenge to power system reliability, economic dispatch, and market efficiency. This paper proposes a novel multi-stage congestion management framework that integrates generator rescheduling using Particle Swarm Optimisation (PSO) with a severity-based, hierarchical application of demand-side strategies, specifically Real-Time Pricing (RTP) and Adaptive Load Shedding. The methodology is designed to progressively relieve congestion by activating increasingly stringent measures only when preceding steps prove insufficient. Simulations conducted on the IEEE 30-bus and IEEE 118-bus test systems under artificially induced congestion conditions demonstrate the effectiveness and scalability of the proposed framework. In the IEEE 30-bus case, the total generation cost decreases from 2280.90 USD/hr (post-congestion) to 1633.09 USD/hr, with all line flows restored within thermal limits. Application to the larger IEEE 118-bus system further validates the approach, reducing the generation cost from 335,965 USD/hr (congested) to 193,681 USD/hr after the complete three-stage process, i.e., a 42.35 % reduction, while total demand falls moderately from 6055 MW to 5579 MW, i.e., a 7.86 % as congestion is fully eliminated. Results show that although PSO-based generator rescheduling significantly reduces overloads, it is insufficient for full congestion clearance, therefore necessitating the successive deployment of RTP and adaptive load shedding. Compared to conventional single-method solutions, the proposed strategy achieves enhanced technical efficiency, demand-side flexibility, and operational robustness. This work contributes a scalable and adaptive solution for congestion management in emerging electricity markets, particularly in environments transitioning to market-based operation.