Partnerships revealed
Probabilistic Congestion Forecast for Grid Operators
N-SIDE developed a methodology that reinforces the existing nodal forecasts, and takes into account all the uncertainties on the nodes when calculating potential congestions on the full network. This allows for a well-informed risk based decision on the actions to take by the planners and operators. The solution is data driven, uses both machine learning as sophisticated power flow simulations and probabilistic methods, and it can run on day-ahead or in near-real-time.
Digitalising the Dutch network through intelligent flexibility
KrakenFlex’ solution focuses on real time Distributed Energy Resource (DER) management and distribution network monitoring technology capable of MV and LV grid monitoring and controlling DER assets. The solution can be broken down into two elements:
- Flexibility management by which all types of domestic and commercial DER such as EVs, EV charging stations, heat pumps, BESS (battery energy storage systems), renewable generation and beyond can be controlled and monitored in real time, via a SaaS platform.
- Network Intelligence via an computing solution, enabling complex power systems algorithms and analytics at the grid edge, coupled with cloud based contextualisation. The KrakenFlex grid intelligence solution monitors both the LV and MV grid levels (from the LV side of the secondary substation transformer), then through edge computing and advanced power system algorithms enables operationally important insights including: real time grid signals, monitoring of voltages, currents, harmonics and congestions, fault detection, power quality, improved forecasting and flexibility. These two elements can run independently with many benefits, however for this specific project, we recommend that these two components are combined to unlock the maximum benefits of the solution to solve the transport capacity problem. For this we recommend working with third party retailers who can utilise our solution to help unlock untapped smart capacity and dynamic network pricing.
Extending grid capacity through Dynamic Asset Rating
Siemens Energy aims to enroll Dynamic Asset Rating (DAR) calculations for targeted grid assets and make operational recommendations to grid operators using the calculated data. The idea is to extend the capacity of the grid by building upon the current Dynamic Line Rating (DLR) scheme currently in use by the TSO and DSOs which together allow grid operators to find additional capacity in the current grid infrastructure during times of high congestion. Most grid assets, such as transformers, overhead lines and tie lines, or busbars tend to be under-utilized for extended time periods because only static limits are considered. The software will optimize the asset utilization of targeted assets through intelligent processing of sensored data collected from the asset as well as virtual data from digital twins. The solution will be vendor independent and interface with the existing digital twins, data, and models that TenneT already has as well as leveraging existing data from TenneT’s data platform. Additionally, the recommendation system will tie directly into the Energy Management System (EMS) architecture. A significant reduction in redispatch costs can be achieved through the combined use of DLR and DAR, resulting in extended capacity, connection of new customers, reduced congestion, better asset maintenance cycles, and reduction of unplanned outages.
From these submissions, the experts from TenneT and the DSOs have selected the three most promising solutions, after our 2 days of pitches and deep dives, which are:
The three selected solutions
We received 57 submissions with each a pitch video and supporting information. We appreciate all the efforts invested by the participating companies – it’s great to see all the enthusiasm and the willingness to solve the challenge together.
Thank you for your contribution and positive turn-out.
Partnerships revealed
From these submissions, the experts from TenneT and the DSOs have selected the three most promising solutions, after our 2 days of pitches and deep dives, which are:
Probabilistic Congestion Forecast for Grid Operators
N-SIDE developed a methodology that reinforces the existing nodal forecasts, and takes into account all the uncertainties on the nodes when calculating potential congestions on the full network. This allows for a well-informed risk based decision on the actions to take by the planners and operators. The solution is data driven, uses both machine learning as sophisticated power flow simulations and probabilistic methods, and it can run on day-ahead or in near-real-time.
Digitalising the Dutch network through intelligent flexibility
KrakenFlex’ solution focuses on real time Distributed Energy Resource (DER) management and distribution network monitoring technology capable of MV and LV grid monitoring and controlling DER assets. The solution can be broken down into two elements:
- Flexibility management by which all types of domestic and commercial DER such as EVs, EV charging stations, heat pumps, BESS (battery energy storage systems), renewable generation and beyond can be controlled and monitored in real time, via a SaaS platform.
- Network Intelligence via an computing solution, enabling complex power systems algorithms and analytics at the grid edge, coupled with cloud based contextualisation. The KrakenFlex grid intelligence solution monitors both the LV and MV grid levels (from the LV side of the secondary substation transformer), then through edge computing and advanced power system algorithms enables operationally important insights including: real time grid signals, monitoring of voltages, currents, harmonics and congestions, fault detection, power quality, improved forecasting and flexibility. These two elements can run independently with many benefits, however for this specific project, we recommend that these two components are combined to unlock the maximum benefits of the solution to solve the transport capacity problem. For this we recommend working with third party retailers who can utilise our solution to help unlock untapped smart capacity and dynamic network pricing.
Extending grid capacity through Dynamic Asset Rating
Siemens Energy aims to enroll Dynamic Asset Rating (DAR) calculations for targeted grid assets and make operational recommendations to grid operators using the calculated data. The idea is to extend the capacity of the grid by building upon the current Dynamic Line Rating (DLR) scheme currently in use by the TSO and DSOs which together allow grid operators to find additional capacity in the current grid infrastructure during times of high congestion. Most grid assets, such as transformers, overhead lines and tie lines, or busbars tend to be under-utilized for extended time periods because only static limits are considered. The software will optimize the asset utilization of targeted assets through intelligent processing of sensored data collected from the asset as well as virtual data from digital twins. The solution will be vendor independent and interface with the existing digital twins, data, and models that TenneT already has as well as leveraging existing data from TenneT’s data platform. Additionally, the recommendation system will tie directly into the Energy Management System (EMS) architecture. A significant reduction in redispatch costs can be achieved through the combined use of DLR and DAR, resulting in extended capacity, connection of new customers, reduced congestion, better asset maintenance cycles, and reduction of unplanned outages.
The three selected solutions
We received 57 submissions with each a pitch video and supporting information. We appreciate all the efforts invested by the participating companies – it’s great to see all the enthusiasm and the willingness to solve the challenge together.
Thank you for your contribution and positive turn-out.
