Research On Optimal Scheduling Of Combined Power Generation Systems Based On Photovoltaic Power Generation Power Prediction

Aiming at the energy crisis and environmental pollution,the paper proposes ways to develop and utilize renewable energy,especially the application of photovoltaic power generation.However,the uncertainty and randomness of photovoltaic power generation will bring challenges to the operation of power systems and grid scheduling.Therefore,based on accurate power load forecasting and photovoltaic power generation forecasting data,this paper conducts short-term load forecasting and photovoltaic power generation forecasting for a "PV-Fire" combined power generation system with photovoltaic power generation connected to the grid,and constructs an optimal scheduling model.By optimizing scheduling,it is possible to achieve the full utilization of solar energy resources,reduce the light rejection rate,and mitigate the impact and impact of photovoltaic power grid connection on the power system,thereby contributing to building an environmentally friendly society.The main work and innovative results of this article include:(1)An IHHO-LSTM power load forecasting model is proposed to solve the problem of inaccurate load forecasting accuracy.The model uses long short term memory(LSTM)to construct a time series prediction model,and uses Harris Hawks Optimization(HHO)to optimize the super parameters of LSTM for optimization,thereby improving prediction accuracy.The example verification results show that the model can well achieve accurate short-term load forecasting,and provide accurate load forecasting data for the economic optimal scheduling of the subsequent "light fire" combined power generation system.(2)To improve the prediction accuracy of photovoltaic power generation,this paper proposes a photovoltaic output power prediction model that combines empirical mode decomposition(EMD),principal component analysis(PCA),and sparrow search method(SSA).Firstly,analyze the historical data collected by the experimental platform to obtain key climate impact factors.Then,EMD is used to decompose the original meteorological factor signal sequence into modes.Thirdly,PCA method is used to extract the key factors of the sequence.Further,the sparrow search algorithm is used to optimize the BP neural network.Finally,based on measured data from a certain location,this paper verifies the effectiveness of the proposed photovoltaic power prediction model.The results show that the model can effectively reduce the impact of photovoltaic power fluctuations,mitigate the adverse effects caused by uncertainty in the prediction process,and effectively improve the prediction effect and accuracy.(3)In order to better solve the randomness and volatility of photovoltaic grid connection and improve the accuracy of photovoltaic power generation power prediction,this paper proposes a hybrid prediction model,which combines an improved sparrow search algorithm and a short-term and short-term memory network.Firstly,the original signal is denoised through synchronous compression wavelet transform.Then,using the improved sparrow search algorithm,a short-term and short-term memory network prediction model is developed.Finally,an example is verified.The photovoltaic power prediction model proposed in this paper can effectively remove noise from photovoltaic power generation data,achieve smooth processing of original signals,and improve prediction accuracy.(4)By comprehensively utilizing various single prediction models,a combined prediction model is established for predicting photovoltaic power generation.The model uses EMD-PCA-SSA-BP model,SWT-ISSA-LSTM model,and BP model as basic single prediction models,and determines the weighting coefficients of each single prediction model through the entropy method.The experimental results show that the combined prediction model has higher prediction accuracy compared to the single prediction model.And can provide more accurate photovoltaic power generation power prediction data for the subsequent optimization and scheduling problems of the "PV-Fire" combined power generation system.(5)A dynamic environmental economic scheduling model for a " PV-Fire" combined power generation system with photovoltaic power generation is established,with the goal of minimizing economic costs and environmental pollutant emissions reduction,and is solved using the NSGA-II algorithm with an elite strategy.The experimental results show that the model successfully obtains a comprehensive optimal solution that meets the environmental and economic indicators for optimal scheduling of combined power generation systems.