Abstract:As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model while in practice the energy forecasting is based on forecasted weather data. In this paper, a study on the uncertainty in weather forecasting for the most commonly used weather variables is presented. The forecasted weather data for six days ahead is compared with the observed data and the results of analysis are quantified by statistical metrics. In addition, the most influential weather predictors in energy forecasting model are selected. The performance of historical and observed weather data errors is assessed using a solar PV generation forecasting model. Finally, a sensitivity test is performed to identify the influential weather variables whose accurate values can significantly improve the results of energy forecasting.

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A solar panel tilt angle plays a great role in the performance of the solar panel which is either fixed at an optimal tilt angle or continuously adjusted using a solar tracking system. Solar tracking systems are not cost efficient especially for residential usage. On the other hand, a fixed tilt angle results in a huge loss of solar energy. One resort to solve this problem is to adjust the tilt angle a limited number of times. In this paper, a novel procedure is proposed to select the number of intervals and their durations by solving an optimisation problem. The proposed algorithm is consisted of four major steps. First, the solar radiation of the next year is predicted using historical data. Second, using a bee algorithm the optimal tilt angle of each interval is computed. Third, an optimisation problem is solved to get new periods for each interval. Finally, a stopping criterion is checked to decide whether the previous step should be repeated or the algorithm has been converged. The effectiveness of the proposed approach is studied at nine different locations across the US. The results show improvement of the solar power generation by using the optimal intervals.

This paper proposes a fuzzy predictive control scheme for controlling power output of a boiler−turbine system in the presence of disturbances and uncertainties. A new model of the boiler−turbine system is introduced based on the modeling approaches of hybrid systems, namely, the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized using the piecewise affine approach. To overcome the deficiency of the model predictive control in presence of disturbance and uncertainty, a fuzzy predictive control scheme is proposed in which a fuzzy supervisor is utilized to adjust the main predictive controller. The proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in nominal conditions and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method. 2 3 1 4 5(2-3)

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