Managing renewable energy systems and their grid integration is a complex process. Due to multiple factors such as weather dependence, resource location, extreme climate conditions and so on it is not easy to maintain stable electricity flow over time in renewable energy facilities.
This is why it is important to have monitoring systems to ensure proper performance at power plants. These systems, however, are focused mainly on current behavior and also on recording past events. If operating companies want to move from proper performance to optimal performance, then future performance must be addressed. To this end, predictive functionality is gaining in importance within monitoring systems.
In order to make decisions for the optimal operation of the power system, data from different and remote geographical locations on the status of all power plants, substations and other elements of the grid should be known to the operators. This real-time data is obtained by numerous hardware and software elements of SCADA (Supervisory Control And Data Acquisition) systems, which are able to gather and process data. The processed information is presented to the operators via Human Machine Interface (HMI) so that they can monitor the process. Based on the information provided, operators can make decisions and send control commands back to the system, which is another important feature of SCADA systems.
Monitoring systems have been focused on past and current events, but predictive skills are now becoming a key factor.
Moreover, monitoring systems keep track of the logs that have previously been recorded, providing a vast database of events that have occurred in the system in the past. Hence when it is time to revise the system and make strategic decisions, the data obtained provides a solid background.
Until recently, these two functions of monitoring systems, real-time data control and recording of past events, were enough to meet power systems management needs. However, while conventional power sources are available at any time, renewable resources are intermittent and highly dependent on upcoming weather conditions. As renewables are integrated into power systems on a large scale, monitoring systems are facing the challenge of how to implement predictions of future outcomes in their algorithms.
The new challenge for monitoring systems is how to assimilate real-time data in order to improve predictive functionalities.
It is in this department that predictive methods play an important role in the development of monitoring systems—using not only weather forecasts but a variety of collected data to build predictive functionality, such as predictive maintenance, which can be a key factor in maximizing the profits of renewable power plants such as wind farms or solar parks. Providing an electricity output forecast for these facilities is also a key factor when their integration feeds power into the grid, allowing operators and traders to maximize their profits.
It can be concluded that implementing forecasting solutions in monitoring systems will be the next step in the evolution of these systems. By integrating future forecasts alongside past and present data records, provision of relevant data for well-informed decisions—the main value to sustainable power systems—will be advanced.