| In a competitive market environment,the Available Transfer Capability(ATC)is not only an important technical reference indicator for measuring the safety and stability of the power system,but also a key market information for guiding the optimization of power resource allocation.With the sharp increase in uncertainty factors brought by the high proportion of wind and photovoltaic grid integration,failure to accurately evaluate the ATC of transmission channels may lead to transmission blockage or system instability.Therefore,it is of great practical significance and value to fully consider the impact of the uncertainty of wind and photovoltaic power output in ATC calculation on the safety and stability of system operation and dispatching economy.Based on this,this paper takes the calculation of ATC in wind and photovoltaic joint grid-connected system as the background,studies the impact of the volatility,correlation and forecasting error on ATC,the main contents are as follows:1.In order to quantify and analyze the uncertainty of wind and solar power,the uncertainty of wind and solar power output is modeled,and it is transformed into various deterministic scenario combinations based on scenario analysis.For the volatility of wind and solar,the non-parametric kernel density estimation method is used to describe the marginal probability distribution functions of wind and photovoltaic power output.On this basis,considering the correlation between wind and photovoltaic power output,the FrankCopula function is used to construct a joint distribution model of wind and solar power output.In the process of generating wind and solar power output scenarios,Latin hypercube sampling is used to sample the distribution functions extensively,and the scenario reduction technique based on synchronous backtracking is introduced to reduce the initial sample size,ensuring sampling accuracy and reducing computational burden.The accuracy and effectiveness of the wind and solar uncertainty modeling method and the correlation scenario generation method are verified through simulation based on measured wind and solar data.2.To improve the speed and accuracy of ATC evaluation in wind and solar interregional power systems,the ATC model accounting for wind and solar power uncertainty is established,and an improved aquila optimization algorithm is constructed for efficient solution.Firstly,the ATC model based on the optimal powerflow is established by considering the relative constraints of wind and solar output,and the ATC model is linearized by the dynamic penalty functions to reduce the complexity.Secondly,to address the problem of traditional algorithms easily falling into local extremes in ATC solutions,an improved aquila optimization algorithm is constructed by integrating chaotic mapping and differential evolution strategies to achieve accurate and fast evaluation of ATC.Finally,the IEEE-118 node system is used as an example to verify the high efficiency of the improved aquila optimization algorithm and analyze the impact of wind and photovoltaic power output on the inter-regional ATC evaluation.3.To ensure the economic efficiency of the ATC indicator,a two-layer ATC economic dispatch model considering wind and solar power forecasting errors is proposed.The upperlayer model is the ATC calculation model,and the lower-layer model is the economic dispatch model considering wind and solar power forecasting errors,which can ensure the lowest economic dispatch cost while maximizing inter-regional ATC.In view of the difficulty of solving the two-layer model,the Kuhn-Tucker conditions are introduced to equivalently transform the lower-layer economic dispatch model into an additional complementary constraint of the upper-layer ATC calculation model,and the strong duality theory and the big M method are used to eliminate the nonlinear terms in the ATC economic dispatch model.Combined with the IEEE-14 node example system,the necessity of considering wind and solar power forecasting errors in the ATC economic dispatch mdel is analyzed,and the validity of the constructed two-layer model is verified. |
