Can the LSTM-Kan model predict future power generation?

Table 1. Partial environmental data. In this study, following the prediction of future power generation using the LSTM-KAN model, the projected wind and photovoltaic (PV) generation outputs were incorporated as uncontrollable inputs into the Model Predictive Control (MPC) framework.

Is battery energy storage a promising control strategy for a unified generation unit?

By fully exploiting the potential of battery energy storage technology, we proposed a promising control strategy for a unified generation unit consisting of a boiler-turbine unit and a BESS.

How can LSTM-Kan improve the learning and prediction of photovoltaic power output?

Utilizing environmental data from a designated period in Shanghai, China, this approach enhances the learning and prediction of photovoltaic power output. Compared to the standard LSTM model, LSTM-KAN demonstrates a faster convergence rate during training and achieves fewer prediction errors.

Can battery energy storage systems improve peaking load shaving and power regulation quality?

To improve the capability of the peaking load shaving and the power regulation quality, battery energy storage systems (BESS) can be used to cooperate power units to satisfy the multi-objective regulation needs.

What is model predictive control (MPC)?

This approach allows the Model Predictive Control (MPC) framework to handle these inputs with greater accuracy and reliability, as the predictions of renewable energy generation are directly integrated into the optimization process without introducing the complexities associated with their inherent variability and control requirements.

Can a unified control scheme smooth the power output of a power plant?

This paper proposes a novel unified control scheme to smooth the power output of the power plant and meet the strict power load demands distributed from the automatic generation centre (AGC).