Grid Dispatch Planning with Adaptive Forecast Horizons
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Solution Overview
Problem
Conventional dispatch planning in electrical grids relies on a fixed 48-hour timeframe for optimization, which may not account for varying forecasting error rates, leading to inefficiencies in reducing carbon emissions and fossil fuel consumption.
Innovation Solution
An algorithm that selects a forecast horizon based on the forecasting error rate by simulating operation of power sources across different time periods, optimizing dispatch planning to minimize costs, including carbon emissions and fossil fuel consumption, by weighting costs differently across the horizon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed 48-hour forecast horizon is used for dispatch planning, then the planning process is simple and consistent, but it does not account for varying forecasting error rates leading to higher carbon emissions and fossil fuel consumption
Solution Approach 1:
The patent applies dynamics by making the forecast horizon adjustable rather than fixed. The system dynamically selects the forecast horizon based on current forecasting error rates, allowing the planning window to expand or contract depending on forecast reliability. This resolves the contradiction by maintaining simplicity through automation while adapting to varying conditions to reduce emissions.
Solution Approach 2:
The patent changes the parameter of forecast horizon length based on forecasting error rate thresholds. When error rates are low, a longer horizon is selected to enable more comprehensive optimization; when error rates are high, a shorter horizon is used to maintain reliability. This parameter adaptation reduces carbon emissions while keeping the planning process manageable.
2Loss of time
If a longer forecast horizon is selected for dispatch planning, then more time is available for optimization and cost reduction, but forecasting errors increase leading to less reliable dispatch plans
Solution Approach 1:
The system dynamically adjusts the forecast horizon length based on real-time assessment of forecasting error rates. By making the horizon variable rather than static, the system can extend the planning window when forecasts are reliable (capturing more optimization time) and shorten it when reliability deteriorates, thus balancing time availability with forecast accuracy.
Solution Approach 2:
The patent implements feedback by continuously monitoring forecasting error rates and using this information to select appropriate forecast horizons. The system evaluates forecast reliability and adjusts the planning window accordingly, creating a closed-loop control mechanism that balances optimization time with forecasting precision.
3Measurement precision
If a shorter forecast horizon is used for dispatch planning, then forecasting accuracy is maintained, but the ability to reduce carbon emissions and fossil fuel consumption is limited
Solution Approach 1:
The patent changes the forecast horizon parameter based on forecasting error rate thresholds. When error rates indicate high reliability, the system extends the horizon to enable more comprehensive emission reduction strategies. This parameter adaptation allows the system to maintain accuracy while capturing sufficient optimization time to reduce carbon emissions and fossil fuel consumption.
Solution Approach 2:
The system dynamically adjusts the forecast horizon to balance accuracy and emission reduction potential. Rather than using a static short horizon, the system expands the window when forecast reliability permits, enabling more effective long-term dispatch optimization that reduces harmful emissions while maintaining planning precision.
4Object-generated harmful factors
If custom forecast horizons are selected based on forecasting error rates, then carbon emissions and fossil fuel consumption are reduced, but the complexity of the dispatch planning process increases
Solution Approach 1:
The patent applies self-service by implementing automated algorithms that independently select appropriate forecast horizons based on forecasting error rates. The system performs the complex evaluation and selection process autonomously without requiring manual intervention, thus reducing emissions through optimized planning while keeping operational complexity manageable through automation.
Solution Approach 2:
The system uses feedback mechanisms to automatically adjust forecast horizons based on monitored forecasting error rates. This closed-loop control handles the complexity of custom horizon selection through automated decision-making, reducing carbon emissions and fossil fuel consumption while maintaining process manageability through systematic algorithmic control.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for operating sources of electricity in an electrical grid having a forecasting error rate are described. In some implementations, a method includes selecting a forecast horizon based on the forecasting error rate; performing an optimization process for dispatch planning of the electrical grid for the selected forecast horizon; automatically operating one or more power sources of the electrical grid in accordance with the dispatch planning for a first time increment; and performing an optimization process for dispatch planning for subsequent time increments within the selected horizon after the first time increment has elapsed.