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This paper proposes a novel forecasting model designed to accurately forecast the PV power output for both largescale and small-scale PV systems. The proposed model uses available temperature data, approximate and detailed coefficients obtained from the decomposed PV power time series using the stationary wavelet transform (SWT), and statistical features extracted from the historical PV data. The model is comprised of four long-short-term memory (LSTM) recurrent neural networks (RNN) designed to perform multi-step forecasting on the individual approximate and detailed coefficients decomposed by the SWT and a final deep neural network (DNN) designed to perform the next time step PV power forecast. The DNN makes use of the reconstructed values estimated by the four LSTM networks together with temperature data and statistical features to predict the final forecasted value of the next time step PV power. 30-min resolution data from a 12.6 MW PV system located in the state of Florida are used for testing and evaluating the proposed method against several models found in the literature. The results obtained suggest that the proposed model improved the forecasting accuracy significantly in the metrics used to compare with other models while reducing the number of features needed to perform the forecasting operation.
This paper proposes a novel control solution designed to solve the local and grid-connected distributed energy resources (DERs) management problem by developing a generalizable framework capable of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while minimizing the overall cost. The strategy developed aims to find the ideal combination of solar, grid, and energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system. Both offline and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP), and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when compared to the other baseline control algorithms.
INDEX TERMSController Hardware-in-the-Loop (CHIL), distributed energy resources (DER), distributed generation (DG), distributed storage (DS), energy management, real-time pricing, model predictive control (MPC).
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