Grid congestion and locational marginal pricing (LMP) are key factors in wholesale electricity markets, where transmission constraints create barriers that increase costs and compromise reliability. Grid congestion occurs when demand exceeds a line or transformer’s safe capacity, necessitating operators to reroute power or reduce generation, often at higher costs. LMP, comprising energy, congestion, and loss components, indicates these cost differences at each node, providing clear price signals that help guide generation dispatch and support long-term investments in transmission and generation assets.
Traditional methods for managing congestion—such as security-constrained economic dispatch and post-event redispatch—are inherently reactive, computationally intensive, and increasingly insufficient due to the rapid variability of renewable energy sources and the growing complexity of the grid. Forecasting errors caused by intermittent wind and solar, combined with the high dimensionality of optimal power flow (OPF) problems, leave operators without reliable probabilistic insights. These challenges increase congestion rents, burden consumers, and underscore the need for proactive, data-driven strategies to predict and mitigate grid stress before it occurs.
Artificial intelligence and machine learning provide transformative capabilities by analyzing large datasets—such as historical load, generation, weather, and market signals—to deliver short-term price forecasts, probabilistic congestion risk assessments, and real-time decision support. Techniques such as deep neural networks, reinforcement learning for topology optimization, and surrogate OPF models accelerate scenario analysis and suggest remedial actions, while ensemble methods enhance robustness. Early implementations, including wind farm bidding optimization, contingency analysis tools, and renewable forecasting by ISOs, have shown measurable improvements in price stability and system reliability. As these AI solutions evolve, regulatory and workforce adjustments will be necessary to ensure safety, transparency, and fair market access, thereby paving the way for a smarter, more resilient grid.