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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.
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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.
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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.
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The latest insights from N-SIDE
Alberte Bouso
Working Together: Developing Probabilistic Models with Nordic TSOs for Multi-Area Reserves Dimensioning
The Transmission System Operator (TSO) in power systems ensures that there is a balance between power generation and consumption within its designated control area. Operating reserves are available capacity that can be used to adjust power levels as needed to maintain this balance. Insufficient reserve capacity heightens the possibility of having to take emergency actions, like reducing demand or cutting back on power generation, which should be avoided.
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It's increasingly hard to find the right balance of capacity to run the system reliably without increasing costs. This challenge is exacerbated by the growing presence of weather-dependent generation in the system, leading to greater forecast inaccuracies. However, these uncertainties should not block the transition to a fully renewable energy system. Implementing dynamic reserve sizing is a practical method for performing balancing activities in the future energy system. Some of the advantages of adjusting the balancing capacity dynamically are: |
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- Anticipate high-risk and low-risk periods based on day-ahead conditions
- Calibration of the desired reliability objectives and trade-off with other adequacy KPIs
- Account for cooperation agreements with neighboring control zones
- Use of state-of-the-art predictive models and prognosis of the expected risk
