Research On Stochastic Optimization Schduling Of Multi-Energy Power System With High Proportion Of Wind Power And Photovoltaic Power Generation

Wind and photovoltaic(PV)power generation have strong uncertainties.With the increasing proportion of wind and PV power generation,the operational uncertainty of power system increases.The strong uncertainty is shown at both the source and the load in the power system with high proportion of wind and PV power generation,so the concept of power and electricity balance and capacity adequacy will be transformed from the current deterministic to the probabilistic.At that time,the traditional power system optimization scheduling model will be difficult to adapt to the new requirements of power system scheduling under the situation of high proportion of wind and PV power generation.In the multi-energy power system with high proportion of wind and PV,it is necessary to study the new stochastic optimization method and establish a new optimization scheduling model,in order to develop a safe and economical scheduling scheme considering the influence of source-load uncertainty.Based on this,chance-constrained dependent chance goal programming is taken as the mathematical theory and method support,and the stochastic optimization scheduling of multi-energy power system with high proportion of wind and PV is taken as the research topic.The main work and achievements are as follows:Aiming at the asymmetry and multi-peak characteristics of wind and PV power forecast error distribution,the general Gaussian mixture model is proposed to expand the applicability of Gaussian mixture model in describing the forecast error distribution.Based on the proposed general Gaussian mixture model,the refined wind power forecast error distribution model is established.The case study shows that the proposed model can not only describe the peak of the forecast error distribution,but also has a flexible waist curve,which has the best fitting effect on the statistical characteristics of forecast error.Considering the influence of meteorological factors such as weather type,ambient temperature and temperature difference on the forecast error of PV power generation,and analyzing the influence of forecast error on predicted output amplitude and climbing speed,the refined PV output forecast error distribution model considering output characteristics and meteorological factors is proposed,based on the proposed general Gaussian mixture model.The effectiveness of the proposed refined PV power generation forecast error distribution model is analyzed in case study.The proposed model considering the output characteristics and meteorological factors can give the refined error distribution of the forecasting day,and also provide a reliable confidence interval for predicted output.In addition,models of coal-fired units,combined heat and power(CHP)unit,gas turbines,and combined cycle gas turbines have been established.In order to tap the hydropower adjustment capacity to consume wind and solar renewable energy,hydropower unit model has been established considering the output characteristics of units under different water levels.Based on the model,the cascade hydropower station output model and the pumped storage unit output model are established.Since the analytic method is not applicable to the deterministic transformation of chance constraints with multiple random variables,the sampling-based chance constraint deterministic transformation method is proposed,which breaks through the requirements of analytic methods for random variables and relized the deterministic transformation of chance constraints with multiple random variables subject to arbitrary distribution.The case study shows that the proposed deterministic transformation method can transform the chance constraints into the mixed integer constraints with enough sampling points.When the random variables in the chance constraints can be separated from the optimized variables,the resulting mixed integer constraints can be further simplified,so that the model can be solved quickly.In addition,in order to achieve the efficient solution of optimization model with multiple objective function,the goal programming is introduced to transform the multi-objective optimization problem into the single-objective optimization problem that minimizes the distance between the vector target and the target vector.While improving the speed of the model,it can also be solved by commercial solvers such as Cplex and Grubi.Based on the influence of wind and PV output uncertainty on the multi-energy power system scheduling model with high proportion of wind and PV,the influence of source-load uncertainty on power balance constraint and spinning reserve capacity demand in scheduling model is analyzed.Considering the influence of source-load uncertainty on power balance equation in power system,the concept of probabilistic power balance is proposed.Probabilistic power balance model is established based on dependent chance programming.Considering the influence of source-load uncertainty on the system spinning reserve capacity,the chance-constrained spinning reserve capacity constraint model is established.Combining the above two aspects,considering the energy balance of water,heat and electricity,the stochastic optimization scheduling model of multi-energy power system with high proportion of wind and PV based on chance-constrained dependent chance goal optimization is established,and compared with other existing stochastic optimization scheduling models and traditional scheduling modes of CHP unit "determining power by heat"and cascade hydropower "determining power by water".The results show that in the multi-energy power system with large-scale uncertain power supply,the source-load uncertainty will directly affect the formulation of the scheduling scheme.When the probabilistic power balance model proposed in this paper is applied in practice,instead of getting larger,the deviation of power supply and demand of obtained scheduling scheme will be smaller than that scheduled by the traditional power balance equation.In addition,the stochastic optimization scheduling model of multi-energy power system can utilize the complementary characteristics between different power sources to improve the comsumption of renewable energy,and reduce the operation of high-energy and high-cost units to improve the economy of the system operation.Considering the variation characteristics of wind and PV power forecast error with prediction lead time,the rolling scheduling model of multi-energy power system with high proportion of wind and PV considering prediction accuracy change and periodical update of wind and PV power is proposed.Firstly,the variation characteristics of wind and PV power forecast error with prediction lead time are analyzed quantitatively.Based on this,the rolling updating mechanism of the scheduling model and the confidence level of the chance constraints of spinning reserve capacity at each moment are designed.Then,considering the influence of the source-load uncertainty on the rolling scheduling model,based on the proposed probabilistic power balance theory,the rolling scheduling model of multi-energy power system with high proportion of wind and PV considering the accuracy change and periodical update change of wind and PV power generation is established.The case study shows that the proposed rolling scheduling model improves the economical operation of the system without increasing the risk of load loss.In the scheduling process,the proposed rolling scheduling model can make full use of the characteristics of "nearly small" that the forecast error shows with the prediction lead time,and focus on the operational safety and economy in different periods of the scheduling scheme to realize the global optimization.