26,131 research outputs found
Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data
The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations
Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios
This paper presents and evaluates the performance of an optimal scheduling
algorithm that selects the on/off combinations and timing of a finite set of
dynamic electric loads on the basis of short term predictions of the power
delivery from a photovoltaic source. In the algorithm for optimal scheduling,
each load is modeled with a dynamic power profile that may be different for on
and off switching. Optimal scheduling is achieved by the evaluation of a
user-specified criterion function with possible power constraints. The
scheduling algorithm exploits the use of a moving finite time horizon and the
resulting finite number of scheduling combinations to achieve real-time
computation of the optimal timing and switching of loads. The moving time
horizon in the proposed optimal scheduling algorithm provides an opportunity to
use short term (time moving) predictions of solar power based on advection of
clouds detected in sky images. Advection, persistence, and perfect forecast
scenarios are used as input to the load scheduling algorithm to elucidate the
effect of forecast errors on mis-scheduling. The advection forecast creates
less events where the load demand is greater than the available solar energy,
as compared to persistence. Increasing the decision horizon leads to increasing
error and decreased efficiency of the system, measured as the amount of power
consumed by the aggregate loads normalized by total solar power. For a
standalone system with a real forecast, energy reserves are necessary to
provide the excess energy required by mis-scheduled loads. A method for battery
sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201
Risk-Averse Model Predictive Operation Control of Islanded Microgrids
In this paper we present a risk-averse model predictive control (MPC) scheme
for the operation of islanded microgrids with very high share of renewable
energy sources. The proposed scheme mitigates the effect of errors in the
determination of the probability distribution of renewable infeed and load.
This allows to use less complex and less accurate forecasting methods and to
formulate low-dimensional scenario-based optimisation problems which are
suitable for control applications. Additionally, the designer may trade
performance for safety by interpolating between the conventional stochastic and
worst-case MPC formulations. The presented risk-averse MPC problem is
formulated as a mixed-integer quadratically-constrained quadratic problem and
its favourable characteristics are demonstrated in a case study. This includes
a sensitivity analysis that illustrates the robustness to load and renewable
power prediction errors
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