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The feasibility of renewable energy resources such as solar energy is marked by uncertainties that make it an unpredictable mean of power generation. To guarantee an uninterrupted power supply, solar irradiance modelling can be taken as a useful step towards meeting the operational challenges of electric power grid. This paper presents a dynamic Probabilistic generation model to estimate and generate the time-coupled solar irradiance patterns. Initially, clustering of yearly solar irradiance measurements is performed to obtain a meaningful grouping of similar days. One-hour time step is considered to construct a time-coupled probabilistic model of solar irradiance data based on a Beta distribution. The parameters of beta distribution are found by considering the variations of irradiance patterns at two successive time steps. The probabilistic model is then used to generate a number of aggregate solar irradiance generation scenarios. The effectiveness of proposed scenario generation approach is evaluated through Average Mean Absolute Percentage Error (AMAPE) and comparison with the probabilistic model already available in the literature. [ABSTRACT FROM AUTHOR]

The uncertainty associated with renewable energy sources, particularly wind, makes them an unpredictable means of power generation. To guarantee continuous and reliable power supply, wind speed variability modeling is considered a vital step for meeting the planning and operational challenges of an electric power grid. In this paper, a novel probabilistic generation model is developed to estimate and generate time-coupled wind speed patterns. A 1 h time step is considered to construct the time-coupled probabilistic wind speed patterns based on a two-parameter Weibull distribution. These parameters of the Weibull distribution are found by considering the variations of reference wind speed patterns at two successive time steps. The probabilistic model is then used to create a number of aggregate wind speed generation scenarios. The validity of our proposed approach is evaluated with the help of goodness-of-fit test indicators such as average mean absolute percentage error and the Kolmogorov–Smirnov test error. The results of goodness of fit tests and comparison of output power determined through the proposed model with the existing model in the literature suggest that the proposed model is appropriate for wind speed uncertainty modeling and can be applied in power system planning studies. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) planning studies are generally conducted while assuming constant generation and load models. However, in this paper, optimal allocation of photovoltaic (PV) based DG is carried out while considering probabilistic generation and time varying voltage dependent load models. First, a novel probabilistic generation model is proposed for solar irradiance uncertainty modeling to compute the hourly output power produced via PV units. Subsequently, the obtained power values are used to determine the optimum PV allocation in the distribution system through particle swarm optimization to minimize the multi-objective function. The proposed algorithm has been validated on 33-bus and 69-bus distribution test systems. The results demonstrate that the proposed model is suitable for solar irradiance modeling and can be employed in power system planning studies. [ABSTRACT FROM AUTHOR]

The characterization of electrical demand patterns for aggregated customers is considered as an important aspect for system operators or electrical load aggregators to analyze their behavior. The variation in electrical demand among two consecutive time intervals is dependent on various factors such as, lifestyle of customers, weather conditions, type and time of use of appliances and ambient temperature. This paper proposes an improved methodology for probabilistic characterization of aggregate demand while considering different demand aggregation levels and averaging time step durations. At first, a probabilistic model based on Weibull distribution combined with generalized regression neural networks (GRNN) is developed to extract the inter-temporal behavior of demand variations and, then, this information is used to regenerate aggregate demand patterns. Average Mean Absolute Percentage Error (AMAPE) is used as a statistical indicator to assess the accuracy and effectiveness of proposed probabilistic modeling approach. The results have demonstrated that the performance of proposed approach is better in comparison with an existing Beta distribution-based method to characterize aggregate electrical demand patterns. [ABSTRACT FROM AUTHOR]