In our previous post, we discussed how to manage an extensive building portfolio using Energy Twin and machine learning. This time we will show you how it can be further applied to another real-world example.
Energy Twin models can be used to forecast the future energy consumption profile, from a few hours to a few days ahead. This prediction was used as part of the control system for a virtual power plant’s Combined Heat and Power (CHP).
The virtual power plant’s goal is to use CHP and sell electrical energy when it is beneficial. In this case, heat is generated by both gas boilers and CHPs; however, CHPs have a lower gas-to-heat efficiency than gas boilers. As a result, the operator must prioritise gas boilers even if it contradicts the virtual power plant’s trading plan. Exceeding the contracted daily maximum gas consumption is far more expensive than the discrepancy in electrical energy delivery.
To avoid costly penalties, the model predicts daily gas consumption and is updated every hour. If a risk of exceeding occurs, the operator receives a notification with detailed information and suggested actions so that local technicians can respond in advance, if needed.