Power Grid Optimization on distribution level and forecasting of load and congestions

AI Quick Summary

collection of flexibility within local energy communites in order to optimize the schedule of the DSO. In addition optimize the forecast in production, consumption as well as grid problems (grid congestions, voltage problems) on the grid level 4,5,6,7 in order to manage peak shaving and peak shifting to further balance the grid on the distribution level. Combine this with the sophisticated digital twin price algorithm from CEEX developed with the ETH zurich Congestion and (Re)Dispatch Management:Flexibility Management: Collect flexible loads (eCars, Batteries, Heat Pumps a.s.o.) to gain an amount of flexibility (this collection will be done within managed LEG’s, ZEV’s, vZEV’s and in the region of the DSO supply areaUtilize historical data (LV7/LV5) to determine the flexibility needed to alleviate grid problems.calculate this amount periodically collecting actual status of battery loads and house temperature status.Use the flexibility in order to be used for schedule optimization for DSObalance grid congestion from external signalbalance grid congestions using dynamic grid prices within the CEEX digital twinAdvanced Forecasting: Gain insights from short-term (1-15 min), medium-term (15 min - 1 hour), and long-term (1 hour - 2 days) forecasts with confidence intervals to support informed decision-making. Forecasting Demand, Supply and Grid Problems (see below)Grid Problem calculation and forecasting: Collection of the needed data Calculation of transformer overload, grid, congestions, voltage problems a.s.o. Forecast the problems in terms of the above taskUtilize historical data (LV7/LV5) to forecast grid problems.

Project Idea Metadata

• Project Idea Name: Power Grid Optimization on distribution level and forecasting of load and congestions
• Date: 8/1/2024 9:56:03 AM
• Administrators:
  • Christian Dollfus

Project Idea Description

1. Congestion and (Re)Dispatch Management:

• Flexibility Management:

1. Collect flexible loads (eCars, Batteries, Heat Pumps a.s.o.) to gain an amount of flexibility (this collection will be done within managed LEG’s, ZEV’s, vZEV’s and in the region of the DSO supply area
2. Utilize historical data (LV7/LV5) to determine the flexibility needed to alleviate grid problems.
3. calculate this amount periodically collecting actual status of battery loads and house temperature status.
4. Use the flexibility in order to be used for
5. schedule optimization for DSO
6. balance grid congestion from external signal
7. balance grid congestions using dynamic grid prices within the CEEX digital twin

• Advanced Forecasting: Gain insights from short-term (1-15 min), medium-term (15 min - 1 hour), and long-term (1 hour - 2 days) forecasts with confidence intervals to support informed decision-making. Forecasting Demand, Supply and Grid Problems (see below)
• Grid Problem calculation and forecasting:

1. Collection of the needed data
2. Calculation of transformer overload, grid, congestions, voltage problems a.s.o.
3. Forecast the problems in terms of the above task
4. Utilize historical data (LV7/LV5) to forecast grid problems.

collection of flexibility within local energy communites in order to optimize the schedule of the DSO. In addition optimize the forecast in production, consumption as well as grid problems (grid congestions, voltage problems) on the grid level 4,5,6,7 in order to manage peak shaving and peak shifting to further balance the grid on the distribution level. Combine this with the sophisticated digital twin price algorithm from CEEX developed with the ETH zurich