loading page

Capturing Electricity Market Dynamics in the Optimal Trading of Strategic Agents using Neural Network Constrained Optimization
  • +1
  • Mihály Dolányi ,
  • Kenneth Bruninx ,
  • Jean-François Toubeau ,
  • Erik Delarue
Mihály Dolányi
KU Leuven, KU Leuven, KU Leuven, KU Leuven

Corresponding Author:[email protected]

Author Profile
Kenneth Bruninx
Author Profile
Jean-François Toubeau
Author Profile
Erik Delarue
Author Profile


In competitive electricity markets the optimal trading problem of an electricity market agent is commonly formulated as a bi-level program, and solved as mathematical program with equilibrium constraints (MPEC). In this paper, an alternative paradigm, labeled as mathematical program with neural network constraint (MPNNC), is developed to incorporate complex market dynamics in the optimal bidding strategy. This method uses input-convex neural networks (ICNNs) to represent the mapping between the upper-level (agent) decisions and the lower-level (market) outcomes, i.e., to replace the lower-level problem by a neural network. In a comparative analysis, the optimal bidding problem of a load agent is formulated via the proposed MPNNC and via the classical bi-level programming method, and compared against each other.