Grid solutions
N-SIDE helps system operators make optimal decisions by providing AI software solutions, from system planning to the control room of the future.
Power exchange solutions
Seamlessly match supply and demand in challenging electricity markets. N-SIDE is the trusted provider of auction-based electricity market clearing algorithms.
Forecasting
N-SIDE uses machine learning to produce a variety of forecasts for system operators, including system imbalance forecast, reserve needs forecast and congestion risk forecast.
Power Matching
This robust market clearing algorithm matches supply and demand bids in a closed-gate auction fashion, considering potential congestion and other constraints.
Outage Planning Optimization
Leverage advanced analytics to determine the optimal strategy and daily schedule for grid interventions and related outages.
Dynamic Reserves Dimensioning
TSOs: dynamically adapt reserves volume based on the real imbalance risk faced by the system using machine learning and advanced analytics.
Professional Services
Whether your challenge can be solved by our existing products or requires the development of new analytics solutions and market design approaches, our team is ready to help.
Resources
The latest insights from N-SIDE
Pauline Martin
On December 7, N-SIDE will have the pleasure to present an innovative topic together with Elia at the Energy Days 2020 (virtual event), organized by Anthony Papavalisiou, Associate Professor at CORE (Center for Operations Research and Econometrics) UCLouvain.
Johan Maricq, Head of Digital workplace and AICoE at ELIA, and Wolf Berwouts, Head of the Energy Analytics Incubation at N-SIDE
will demonstrate the application of reinforcement learning in finding the optimal grid topology.
Abstract
Finding the optimal topology for a transmission network is a complex problem which is in practice solved by highly experienced system operators, backed by approximations and heuristic methods. Elia & N-SIDE research the application of reinforcement learning in a practical innovation project in an attempt to make the decisions faster and better, avoiding unnecessary congestion & redispatch actions. By letting a machine learning model train itself on a digital twin of the Elia network, together with the smart injection of valuable business knowledge, it can learn what optimal actions to take in each situation. Such actions could be the disconnection of lines, splitting of substations, or even acting on the maintenance plan. The ultimate goal is to provide the operators with a tool that can support them in taking these topology decisions in their day-to-day tasks.
Join us at the Energy Days 2020 & learn more about topology optimisation