Abstract
This paper proposes a recurrent neural network (RNN)-based maximum frequency deviation forecasting model for power systems with high photovoltaic power (PV) penetration. The proposed RNN model extracts the nonlinear features and invariant structures exhibited in regional PV power output data and time-variable frequency data in case of contingency. To capture the regularity and random characteristics of PV power output, a probability power flow-dynamic tool (PPDT) for uncertain power system modeling has been developed. This tool considers all possible combinations of PV power generation patterns, even those with low probability, such as those caused by passing clouds. The results are verified by a comparison of various artificial intelligence methods using case studies from the South Korean power system. An online dispatch algorithm that considers the frequency constraints for a designated contingency can be implemented by using the proposed model.
Highlights
A high rate of change of frequency (RoCoF) of 6 Hz/s was recorded during the South Australian blackout of September 28, 2016, which was caused by the “lightness” of the South Australian power system
Note that has a range of to + σ during the simulation timeframe; we can configure the recurrent neural network (RNN) model with datasets derived by the probability power flow-dynamic tool (PPDT) and the return frequency data is calculated by capturing the inherent daily regularity and randomness of the regional PV power supply
The volume of renewable energy has grown to the extent that its effects on the power system can no longer be neglected
Summary
When many small nonsynchronous power generation units replace synchronous power generation units, the total rotational inertia of the synchronous generators decreases significantly. This causes large frequency variations that can result in an unstable and insecure grid. As the penetration of variable renewable energy generation into power systems increases, planning for the effects of contingency events will become increasingly important. From the perspective of the power system operator, frequency response forecasting for contingencies plays a crucial role in power systems with high renewable penetration. The proposed forecasting model in power systems can be used to manage rapid and large frequency deviations arising from sizeable faults and can ensure that planned systems are capable of managing an unexpected contingency event
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