Khairul Eahsun Fahim Incorporating solar power forecasting into a solar-integrated economic load dispatch model involves considering the predicted solar power generation values for different time intervals. Here's a step-by-step guide on how you can effectively integrate solar power forecasting into your economic load dispatch model:

By following these steps, you can successfully incorporate solar power forecasting capabilities into your economic load dispatch model, enabling more efficient and cost-effective management of power generation resources, particularly in the context of solar-integrated energy systems.

According to ieeexplore.ieee.org, incorporating solar power forecasting into a solar-integrated economic load dispatch involves several steps:

By incorporating solar power forecasting into the ELD problem in this way, it is possible to more efficiently use solar power and reduce the reliance on non-renewable energy sources.

Incorporating solar power forecasting into a solar integrated economic load dispatch (SIED) can significantly improve the efficiency and cost-effectiveness of the power generation system. Solar power forecasting helps in predicting the future solar power generation, enabling more accurate scheduling of energy resources and optimizing the economic load dispatch. Here's a step-by-step guide on how to incorporate solar power forecasting into a SIED:

1. **Data Collection:** Collect historical solar power generation data, weather data, and other relevant parameters that influence solar power generation, such as temperature, cloud cover, and solar irradiance.

2. **Select Forecasting Model:** Choose an appropriate forecasting model based on the available data and the specific requirements of the SIED system. Commonly used models include statistical models (like autoregressive integrated moving average - ARIMA), machine learning models (like artificial neural networks - ANN), and physical models (like numerical weather prediction models).

3. **Feature Selection:** Identify the relevant features that affect solar power generation, such as weather conditions, time of day, historical generation patterns, and seasonal variations.

4. **Model Training:** Train the selected forecasting model using historical data. Use techniques such as cross-validation to ensure the model's accuracy and reliability.

5. **Integration with SIED:** Integrate the solar power forecasting model into the SIED system. This integration should allow for real-time or near-real-time updating of the solar power forecast.

6. **Optimization Algorithm Adjustment:** Modify the SIED optimization algorithm to incorporate the solar power forecast as an input parameter. This ensures that the economic load dispatch takes into account the predicted solar power generation when scheduling the energy resources.

7. **Real-Time Monitoring and Feedback Loop:** Implement a system for real-time monitoring of actual solar power generation and continuous feedback to the forecasting model. This helps in continuously improving the accuracy of the forecasting model over time.

8. **Implementation of Control Strategies:** Incorporate control strategies that can adjust the power generation schedule dynamically based on the deviations between the actual and forecasted solar power generation. This might involve re-optimizing the economic load dispatch periodically throughout the day.

9. **Performance Evaluation:** Regularly assess the performance of the solar power forecasting model and the overall SIED system. Use metrics such as mean absolute percentage error (MAPE) and root mean square error (RMSE) to evaluate the accuracy of the forecasts.

10. **Continuous Improvement:** Continuously refine the forecasting model and the SIED system by incorporating feedback from the performance evaluation and incorporating any advancements in forecasting techniques and optimization algorithms.

By following these steps, you can effectively integrate solar power forecasting into a solar integrated economic load dispatch, leading to a more efficient and optimized power generation system.

We have developed machine learning algorithms for this specific purpose. Unfortunately Covid prevented us from commercializing. We reported in the iea-pvps task 13 journal available for free download-see chapter 3 and 4: https://iea-pvps.org/key-topics/improving-efficiency-of-pv-systems-using-statistical-performance-monitoring/

we also wrote a compendium of the types of algorithms currently in use by the PV community: https://iea-pvps.org/key-topics/the-use-of-advanced-algorithms-in-pv-failure-monitoring/. I am certain this information will be of use.