We propose a neural network architecture that emulates the behavior of a physics solver that solves electricity differential equations to compute electricity flow in power grids (so-called "load flow"). Load flow computation is a well studied and understood problem, but current methods (based on Newton-Raphson) are slow. With increasing usage expectations of the current infrastructure, it is important to find methods to accelerate computations. One avenue we are pursuing in this paper is to use proxies based on "graph neural networks". In contrast with previous neural network approaches, which could only handle fixed grid topologies, our novel graph-based method, trained on data from power grids of a given size, generalizes to larger or smaller ones. We experimentally demonstrate viability of the method on randomly connected artificial grids of size 30 nodes. We achieve better accuracy than the DC-approximation (a standard benchmark linearizing physical equations) on random power grids whose size range from 10 nodes to 110 nodes, the scale of real-world power grids. Our neural network learns to solve the load flow problem without overfitting to a specific instance of the problem.
Recent trends in power systems and those envisioned for the next few decades push Transmission System Operators to develop probabilistic approaches to risk estimation. However, current methods to solve power flows are too slow to fully attain this objective. Thus we propose a novel artificial neural network architecture that achieves a more suitable balance between computational speed and accuracy in this context. Improving on our previous work on Graph Neural Solver for Power System [10], our architecture is based on Graph Neural Networks and allows for fast and parallel computations. It learns to perform a power flow computation by directly minimizing the violation of Kirchhoff's law at each bus during training. Unlike previous approaches, our graph neural solver learns by itself and does not try to imitate the output of a Newton-Raphson solver. It is robust to variations of injections, power grid topology, and line characteristics. We experimentally demonstrate the viability of our approach on standard IEEE power grids (case9, case14, case30 and case118) both in terms of accuracy and computational time.
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