Optimal Decision-making And Control For Non-Gaussian Power Systems

For complex industrial processes in modern,random uncertainty,especially non-Gaussian uncertainty,it is very common and the impact is unavoidable in the industrial system.In previous research,the random variable is assumed to obey the Gaussian distribution.But the system output doesn't obey Gaussian distribution in the actual system.So the original methods of mean and variance are not applicable to completely describe the characteristics of nonGaussian systems.The non-Gaussian systems are studied by SDC theory.The research is in the context of the power system,where the uncertainty is increased due to the intermittency,randomness and volatility of solar and wind power generation in the power system.In response to such problems,the influence of non-Gaussian uncertainty on output power and system frequency is reduced by using SDC theory for wind energy conversion system and AGC system in this paper.Since non-Gaussian uncertainty affect the emission economic dispatch of the power system.Furthermore,the CCP theory and intelligent algorithms are combined to minimize cost function and pollution emissions to obtain the optimal dispatch scheme.The research work can be listed as follows:1.For the hybrid wind-solar generation system which is affected by environmental factors and which output characteristics are nonlinear.To improve the conversion efficiency of new energy and ensure the maximum power output of the hybrid wind-solar generation system.The MPPT control is carried out for the wind subsystem and photovoltaic subsystem respectively.Firstly,the photovoltaic and wind energy conversion systems are modeled.Then,a MPPT control method based on SDC theory is designed for the non-Gaussian wind energy conversion system.The P&O method is used for MPPT control in the photovoltaic subsystem.Finally,the rationality and effectiveness of the proposed control algorithm are verified by simulation.2.The AGC system with uncertain wind power output is modeled and controlled.To ensure that the AGC system can keep the frequency within the specified range and the power system can operate safely and stably when it is affected by uncertain factors.Firstly,the It(?) process is used to describe the uncertainty distribution of non-Gaussian wind power,and combined with the ordinary differential equation model of AGC system to form a generalized stochastic system.This will then be followed by the development of stochastic distribution control model that links the power sources with the PDF of the system area control error using the FPK equations.The minimum entropy performance index is established and the optimal control law for the performance index with the limited control input is solved by the penalty function.Finally,the feasibility effectiveness of the proposed algorithm through the experiment is verified.3.For the optimal dispatch of wind power grid-connected,to ensure the global optimization of both the cost function and atmospheric pollutants emission in the whole dispatching cycle.Firstly,based on multi-objective CPP theory,a DEED model is formulated and the cost function and emission of atmospheric pollutants are considered as objective functions.Then,to avoid solving the CPP model,a GMM is established to deal with nonGaussian random variables.According to the cumulative distribution function of non-Gaussian random variables,this multi-objective CPP model is transformed into a deterministic one.Finally,the effectiveness and the superiority of the proposed algorithm are verified by simulation.