With the recent push to integrate renewable energy into the existing energy infrastructures, it is becoming clear that there is a need to adjust its operation mode. This need is apparent because most renewable energy sources depend on the weather and are not easy to predict or plan with. Moreover, the power generated from such sources as wind energy and solar energy is highly stochastic. This situation calls for the application of advanced technologies for renewable energy forecasting and scheduling.
Renewable energy forecasting helps foresee what changes are expected in the amount of energy that will be generated in the future. This prior knowledge is informative for energy suppliers to plan the input they put into generating systems. Renewable energy scheduling also works side-by-side with forecasting because it is mainly determined by the predictions made by the energy forecasting models.
How renewable energy forecasting and scheduling work
Recent advancements in artificial intelligence have improved the job of weather forecasting (done by meteorologists) through machine learning. As a result, grid operators can leverage machine learning techniques to determine the amount of renewable energy that will be used and purchased by consumers at a particular time.
Machine learning (which is used for renewable energy forecasting) works because a software system learns patterns from recent data and develops an improved analysis for the future. In order to achieve this, a forecasting model is designed to fit a particular situation over several days. In addition, the data collected must be valid, accurate, reliable, consistent, and complete to be effective.
What You Should Know About Renewable Energy Forecasting And Scheduling
Here are five important things about renewable energy forecasting and scheduling you should know;
1. Renewable energy forecasting is built around short-term forecasting
Forecasting can be done with different horizons: short, medium, and long-term. Short-term forecasting involves forecasting from a few minutes to a few days ahead. It is used for day-to-day activities, and this time frame applies to renewable energy prediction.
The lead times in short-term forecasting are such that the changes in weather over a short period can be analyzed and used to predict the data to be used the next time. Renewable energy forecasting and scheduling require updated and recent data as frequently as possible, and short-term forecasting achieves that. The amazing part of it all is that there would be little or no human interruption with the presence of technology. Such way, errors would be significantly minimized.
2. Decentralized computing plays a prominent role in renewable energy scheduling.
Decentralized computing involves the allocation of both software and hardware to various points of duty. It is not like centralized computing, where all activities stem from a particular place. This form of computing (decentralized computing) is necessary for renewable energy scheduling because of the nature of locations in renewable energy generation and consumption.
For example, in an energy community, several houses may produce power at a time, and some utilities need power. The allotment of power to different places can be done effectively with decentralized computing technologies like the blockchain. It is effective because control has to happen independently from various locations when forecasting predictions require an adjustment.
3. Smart grids allow for renewable energy forecasting and scheduling.
Smart grids are electrical distribution points that are not like the conventional grid. The difference is that they contain many operation and control systems, advanced metering systems, intelligent circuit breakers and boards, and most importantly, renewable energy sources fit in well. Their operations are more efficient and can be readily evaluated because of the availability of needed information at the click of the finger. It is in such a system that renewable energy forecasting and scheduling can thrive.
Advanced forecasting models can be introduced and used to plan how the plants would run, whether solar photovoltaics or wind turbines. The ease in integration occurs because the smart grid already has smart IoT devices for thermal sensing, smart meters, phasor management networks, and the likes.
4. Grids with renewable energy attain stability easier with forecasting models in place.
Grid stability is of utmost importance for the sake of the life span of grids. Grid operators cannot consistently have variations in input and output in the grid happen repeatedly. With accurate renewable energy forecasting models, proper preparation and scheduling would be done, and there would be less frequent stability problems. Renewable energy forecasting and scheduling cut back most excesses when it comes to grid management.
5. The weather is a significant factor in renewable energy forecasting and scheduling.
Renewable energy forecasts are usually a combination of accurate weather predictions and the availability of plants and systems. The weather is a great factor, as the weather changes cause significant changes in the renewable power generated. For example, the variable speed of the wind is proportional to the amount of power generated by wind turbines. In the same way, the intensity of sun rays and the positioning of clouds play a big role in the fluctuations when it comes to solar power.
What makes renewable energy forecasting and scheduling interesting is that it studies the highly influencing factors of effective power generation. This kind of study is immediately applied, rather than just being carried out for nothing. It turns out that analyzing the weather, as Meteomatics does, has a vital role in the forecasting done by Hive Power’s Forecaster.
Renewable Energy Forecasting and Scheduling Solution – Hive Power
Hive Power’s Forecaster is one of our Flexibility Operator’s modules that performs short-term forecasting in a very accurate manner. It simply considers various factors involved in renewable energy-based power generation and uses them in forecasting. Its machine learning models make predictions on the amount of energy that would be used and generated in the future, based on previous data. This data is real-time data which is very helpful because it is used as soon as it is delivered.
Renewable energy forecasting and scheduling are essential for the effectiveness of renewable energy systems. With more observations in the needs of a renewable energy system, new technologies keep springing up, and it fosters development. Therefore, it is crucial to embrace these technologies as they come, especially when they are practical and efficient like this (in renewable energy forecasting and scheduling).