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.
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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.
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.
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The latest insights from N-SIDE
Alberte Bouso
The H2020 project FlexPlan aims to establish a new grid planning methodology, considering simultaneously the opportunity to introduce as an alternative to traditional grid investment, such as building new lines or reinforcing existing ones, as detailed in our previous blog.
Aligned with the intentions of the Clean Energy for all Europeans regulatory package from the European Commission, the new planning tool will go beyond the state-of-the-art in planning strategy for TSOs and DSOs. Together with the consortium partners, N-SIDE has contributed to the formulation and design behind this powerful and innovative but complex tool, which main features or characteristics are highlighted in the paper The Innovative FlexPlan Grid-Planning Methodology: How Storage and Flexible Resources Could Help in De-Bottlenecking the European System.
Some of the key features, which are currently being implemented by our team, are:
- Integrated optimization of Transmission and Distribution investment strategies
- Simultaneous mid- and long-term planning decisions over three grid years (2030, 2040, 2050)
- Formulation of a cost minimization function, including multiple terms like environmental impact, reliability of supply, investment and operational costs in terms of net present value
- Probabilistic contingency methodologies, as an alternative to the N-1 criterion
- Numerical decomposition techniques to solve this large-scale mixed-integer optimization problem
- Scenario generation and reduction of the most impactful uncertainties in the system, such as load, renewable energy generation and hydro-condition dependent storage and production, combined with stochastic optimization to account for every representative scenario
The journal paper, covering the details of the aforementioned methodologies, is now published as part of the Special issue “Verifying the Targets—Selected Papers from the 55th International Universities Power Engineering Conference (UPEC 2020)”.
On March 25th, 2021, the comprehensive deliverable D1.2, which contains the overall innovative modeling framework of the FlexPlan project, has finally been published. Do not hesitate to reach out if you want to know more about the details.
