With the increasing awareness of environmental protection and the support of national policy, as well as the maturing of solar power generation technology, solar power generation has become one of the most promising renewable energies. However, due to changes in external factors such as season, time, weather, cloud cover, etc., solar radiation is uncertain, and it is difficult to predict energy output, even for the next hour. This inherent instability is a particularly difficult issue for the prediction of energy output in the effective operation of solar power systems. This paper proposes a grey wolf optimization (GWO)-based general regression neural network (GRNN), which is expected to provide more accurate predictions with shorter computational times. Therefore, a self-organizing map (SOM) is utilized to realize the weather clustering and the training of the GRNN with a GWO model. The performance of the proposed model is investigated using short-term and ultra-short-term forecasting in different seasons. It is very important to accurately predict the PV power output. Moreover, the numerical results demonstrate that the proposed approach can significantly enhance the prediction accuracy of PV systems.
Accurately predicting the power produced during solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for its balanced operation and optimized dispatch and reduces operating costs. Solar PV power generation depends on the weather conditions, such as temperature, relative humidity, rainfall (precipitation), global solar radiation, wind speed, etc., and it is prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility, and intermittency. Recently, the demand for further investigation into the uncertainty of short-term solar PV power generation prediction and its effective use in many applications in renewable energy sources has increased. In order to improve the predictive accuracy of the output power of solar PV power generation and develop a precise predictive model, the authors used predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is an important aspect of optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods identified in the reviewed literature are classified according to the input data source, and the case studies and examples proposed are analyzed in detail. The contributions, advantages, and disadvantages of the predictive probabilistic methods are compared. Finally, future studies on short-term solar PV power forecasting are proposed.
This paper mainly discusses the demand bidding and risk management of user aggregators by considering profit and risk. The covariance matrix of demand price was used to analyze the risk model under an uncertain demand price. By considering revenue and cost, the demand bidding strategy of user aggregators was derived to obtain the maximum profit. By using a risk-tolerance parameter β, a new demand bidding model for the user aggregators that takes both risk and profit into consideration was formulated. We simulated the risk posed by fluctuating demand prices for user aggregators using this model. Finally, this paper proposes Feasible Particle Swarm Optimization (FPSO) to solve the demand bidding model of user aggregators. Through the dynamic adjustment of control factor parameters in the FPSO, we changed the behavioral characteristics of various types of particles, improved the search efficiency and stability of particles in high-dimensional space, and sought the optimal solution for the system as a whole. This paper provides a parameter adjustment mechanism, improves the capability of algorithm implementation, and increases the probability of finding the optimal solution. The simulation results suggest that a tradeoff between profit and risk needs to be considered in the search process. By doing so, enterprises’ abilities in terms of operation and management control can be enhanced, and effective demand management can be achieved.
Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.
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