The Optimal Power Flow (OPF), as an optimization problem, is indispensable for the economic and secure operation of power distribution networks. The OPF is crucial for achieving a multitude of operational objectives including:
- efficient generation, transmission, and load expansion planning,
- mitigating power network losses,
- minimizing voltage deviations and maintaining voltage stability, and
- delivering on corporate social responsibility objectives by strategic emissions reductions and decarbonization of the energy value chain.
Solution techniques for the OPF problem have traditionally gravitated around classical deterministic and stochastic mathematical programming methods. These methods allow practitioners to succinctly integrate operational constraints as mathematical expressions. The operational constraints that characterize the OPF problem give rise to a highly nonlinear and nonconvex optimization model. The solution quickly becomes intractable for large-scale power distribution networks and yet, system operators rely on successive OPF solutions to establish the most economical and secure operating point for the power network over time. The nonlinearity of the OPF problem implies that classical methods often get trapped in suboptimal basins or are unable to drift to a feasible solution depending on the initial conditions. To address these shortcomings, metaheuristic optimization techniques such as Particle Swarm optimization and Genetic Algorithms can be employed to solve the OPF problem. While planning studies have more leeway in their requirements for solution times, operational optimization applications that work in real-time require more stringent convergence guarantees.
Optimizing power distribution networks with growing penetration of renewable energy sources (RES) is an application that requires time-bound solution to the OPF problem. Power injections from RES such as wind and solar are inextricably linked to weather patterns. While predictable to an extent, the uncertainty in power generation and load profiles manifests as fast-moving voltage incursions that need to be quickly mitigated by the incumbent compensators. This is where machine learning (ML) approaches to OPF truly shine. Framing OPF as an inference problem allows for quicker turn-around that is orders of magnitude faster than conventional mathematical techniques. The question remains, how do we design and train agents that not only learn to optimize grids but grasp the intricate operational constraints spanning the problem space?
A recent paper by the Grid Foresight Lab [1] addresses real-time optimization of unbalanced power distribution grids using a hybrid Deep Reinforcement Learning (DRL) framework. DRL is a paradigm of machine learning for data-driven sequential decision-making problems. In the context of power system applications, DRL encompasses the development of intelligent agents using historical grid data and network parameters for optimizing competing objectives such as economic dispatch, voltage deviations, and emissions abatement. Model-free DRL methods allow optimizing processes where a precise model of the environment isn’t readily available whereas model-based DRL methods can be used for predictive control. The solution proposed in [1] combines the best of both worlds, along with other algorithmic innovations, for an integrated physics-informed ML framework that can be trained in an end-to-end fashion. The agent is designed for multi-objective optimization of network efficiency and Volt-Var control by combining mathematical models with data-driven inference. Once trained, the agent can yield instantaneous solutions to the OPF problem that automatically adapts to the evolving grid conditions. This enables real-time optimization that not only respects critical operational constraints but helps operators distill intelligent control policies for responding swiftly to uncertainties. The enthusiastic readers are referred to [1] for a detailed exposition on the topic.
To recap, this article touched on utility of OPF applications, the traditional approaches to solving the problem, and the need for machine learning-based approaches to OPF. Looking ahead, researchers envision a future where intelligent agents will be critical for deploying robust operational frameworks. The development and refinement of data-driven methodologies for power system networks implies that intelligent agents may well become an indispensable part of the future.
[1] S. Hussain, M. Farrokhabadi and H. Zareipour, “A Hybrid Imitation–Reinforcement Learning Framework for Optimal Operation of Soft Open Points in Unbalanced Distribution Networks,” in IEEE Transactions on Smart Grid, vol. 16, no. 6, pp. 4563-4575, Nov. 2025, doi: 10.1109/TSG.2025.3600714.
