Method for evaluating peak regulation sensitivity of water-wind-light complementary system

Abstract

The invention provides a peak regulation sensitivity evaluation method for a water-wind-light complementary system, and the method comprises the steps: firstly defining a water-wind-light independent system and a combined system, carrying out the short-term scheduling of the two systems, and calculating the difference value of the residual load peak-valley difference of the two systems, so as to quantify the peak regulation capability of the water-wind-light complementary system; secondly, peak regulation influence factors capable of representing tap power station runoff, wind power output, photovoltaic output and original load change characteristics are selected; and finally, based on the generalized additive model, providing an evaluation method for the peak regulation sensitivity of the water-wind-light complementary system, and analyzing the influence of different peak regulation influence factors on the peak regulation capability of the water-wind-light complementary system. According to the method, factors which have great influence on peak regulation of the water-wind-solar complementary system can be screened out, and a power grid is helped to formulate a more reasonable scheduling strategy.

Description

Technical Field

[0001] The present invention belongs to the field of water-wind-solar complementary scheduling, and in particular relates to a peak-shaving sensitivity analysis method for a water-wind-solar complementary system. Background Art

[0002] Due to the rapid development of wind and solar power generation, these highly volatile power sources have been incorporated into the power grid in large quantities, which has had a certain impact on the stable operation of the power grid. As a flexible power source, hydropower is often bundled with wind and solar power to smooth out the fluctuations in wind and solar output and reduce the peak-to-valley difference of the residual load. However, the regulation capacity of hydropower will be affected by factors such as rainfall and water inflow, and the output of wind and solar power will be affected by meteorological conditions. These characteristics make the peak-shaving capacity of the hydro-wind-solar complementary system unstable. Therefore, studying the sensitivity of the peak-shaving capacity of the hydro-wind-solar complementary system can provide valuable reference for the power grid and help formulate a more reasonable scheduling strategy. Summary of the invention

[0003] The present invention provides a method for evaluating the peak-shaving sensitivity of a water-wind-solar complementary system. First, an independent water-wind-solar system and a joint system are defined, and the two systems are dispatched in the short term. The difference between the peak-to-valley difference of the residual load of the two systems is calculated to quantify the peak-shaving capacity of the water-wind-solar complementary system. Secondly, the peak-shaving influencing factors that can characterize the runoff, wind power output, photovoltaic output and original load change characteristics of the leading power station are selected. Finally, based on the generalized additive model, an evaluation method for the peak-shaving sensitivity of the water-wind-solar complementary system is proposed, and the influence of different peak-shaving influencing factors on the peak-shaving capacity of the water-wind-solar complementary system is analyzed. The present invention can screen out factors that have a greater impact on the peak-shaving of the water-wind-solar complementary system, which is helpful for the power grid to formulate a more reasonable dispatching strategy.

[0004] A peak-shaving sensitivity assessment method for a water-wind-solar hybrid system, the specific operation steps are as follows: Step 1: Calculate the peak load regulation capacity of the water-wind-solar hybrid system; The independent and combined systems of water, wind and solar power are dispatched to obtain the peak-to-valley difference of the residual load of the two systems. The difference between the peak-to-valley difference of the residual load of the combined system of water, wind and solar power and the independent system is the peak-shaving capacity of the water, wind and solar power complementary system. Step 2: Selection of factors affecting peak load regulation; The peak load influencing factors that can characterize the change characteristics of the runoff, wind power output, photovoltaic output and original load of the leading power station are selected respectively; Step 3: Construction of peak shaving sensitivity assessment method; Based on the generalized additive model, a peak-shaving sensitivity assessment method is proposed, and the impact of different peak-shaving influencing factors on the peak-shaving capacity of the hydro-wind-solar complementary system is analyzed.

[0005] The calculation of the peak load regulation capacity of the water-wind-solar complementary system in step 1 specifically includes the following steps: Step 1-1: The independent hydropower-wind-solar system dispatches only the original load for hydropower; the combined hydropower-wind-solar system dispatches the net load for hydropower. The net load is the original load minus the wind and solar output. The expression is as follows: (1); In the formula, For the t Residual load of the independent water, wind and solar power system in each period, unit MW; For the t The original load of each period, in MW; For the t The hydropower output of the independent water, wind and solar power systems in each period, in MW; For the t Wind power output in each period, in MW; For the t The photovoltaic output of each period, in MW; For the t The residual load of the water-wind-solar combined system in each period, in MW; For the t The hydropower output of the combined water-wind-solar system in each period, in MW; Step 1-2: Taking the minimum variance of the residual load as the objective function, the independent system of hydropower, wind power and solar power and the joint system are dispatched. The objective function expression is as follows: (2); In the formula, F is the objective function of scheduling; It is t The residual load in each period, in MW; T is the total number of periods; The constraint expressions are as follows: Water balance constraints: (3); In the formula, It is i Hydropower Station No. t +1 storage capacity for one period; It is i Hydropower Station No. t Storage capacity for each period, unit: m 3 ; It is i Hydropower Station No. t The inflow flow in each period, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; It is i Hydropower Station No. t The amount of abandoned water flow in each period, in m 3 / s; It is i Hydropower Station No. t The water supply in each period, unit: m 3 / s; It is u The upstream power station The power generation flow in each period, unit: m 3 / s; It is u The upstream power station The amount of abandoned water flow in each period, in m 3 / s; U is the i The total number of upstream power stations of a power station; It is u Upstream power station to i The water stagnation time of a hydropower station is a positive integer; Water level constraints: (4); In the formula, , Respectively i The lower and upper limits of the water level of a hydropower station, in meters; It is i Hydropower Station No. t The water level in each period, in m; Power generation flow constraints: (5); In the formula, , Respectively i The lower and upper limits of the power generation flow of a hydropower station, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; Outbound traffic constraints: (6); In the formula, and They are i The lower and upper limits of the outflow flow of a hydropower station, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m3 / s; It is i Hydropower Station No. t The amount of abandoned water flow in each period, in m 3 / s; Initial and final water level control: (7); In the formula, and They are i The initial and final water levels specified for each hydropower station, in m; and They are i The water level of a hydropower station at the first moment and the Tth period, in m; Hydropower station power head control: (8); In the formula, It is i Hydropower Station No. t The power generation head of each period, in m; It is i Hydropower Station No. t- Water level in one period, in m; It is i Hydropower Station No. t The water level in each period, in m; It is i Hydropower Station No. t Tailwater level of each period, in m; It is i Hydropower Station No. t Head loss in each period, unit: m; The upper and lower limits of hydropower station output: (9); In the formula, It is i Hydropower Station No. t Output in each period, in MW; and It is i The upper and lower limits of the output of each hydropower station, in MW; Step 1-3: Subtract the peak-to-valley difference of the residual load of the independent system from the peak-to-valley difference of the residual load of the joint system. This value quantifies the peak-shaving capacity of the hydro-wind-solar hybrid system. The expression is as follows: (10); In the formula, It is the difference between the peak and valley difference of residual load of the independent system and the combined system, in MW; , They are the residual loads of the independent system and the combined system, in MW.

[0006] The selection of the peak load influencing factors in step 2 specifically includes the following steps: Step 2-1: Using the mean value and variance of the runoff from Longtou Power Station as the peak load influencing factors of the runoff can effectively characterize the characteristics of the runoff from Longtou Power Station. The expression is as follows: (11); In the formula, is the average runoff value of Longtou Power Station, unit: m 3 / s; It is t Runoff of Longtou Power Station in each period, unit: m 3 / s; is the variance of runoff from Longtou Power Station; T is the total number of time periods; Step 2-2: Using the mean value, variance and the difference between the maximum and minimum values ​​of wind power output as the peak load influencing factors of wind power output can effectively characterize the characteristics of wind power output. The expression is as follows: (12); In the formula, is the average value of wind power output, in MW; It is t Wind power output in each period, in MW; is the variance of wind power output; It is the difference between the maximum and minimum wind power output, in MW; is the maximum value of wind power output; is the minimum value of wind power output; T is the total number of time periods; Step 2-3: Using the average value, variance and the difference between the maximum and minimum values ​​of the photovoltaic output as the peak-shaving influencing factors of the photovoltaic output can effectively characterize the characteristics of the photovoltaic output. The expression is as follows: (13); In the formula, is the average value of photovoltaic output, in MW; It is t The photovoltaic output of each period, in MW; is the variance of PV output; It is the difference between the maximum and minimum photovoltaic output, in MW; is the maximum value of photovoltaic output; is the minimum value of photovoltaic output; T is the total number of time periods; Step 2-4: Using the load peak-to-valley difference and the accumulated power as the peak-shaving influencing factors of the original load can effectively characterize the characteristics of the original load. The expression is as follows: (14); In the formula, is the peak-to-valley difference of the original load, in MW; is the original load at t The value of each time period, in MW; E is the cumulative power consumption in T periods, in units ; is the time interval, unit is h.

[0007] The peak shaving sensitivity assessment method in step 3 is constructed, and specifically includes the following steps: Step 3-1: Use the peak load regulation capability of the hydro-wind-solar hybrid system in step 1 As the response variable of the generalized additive model, the ten peak-shaving influencing factors in step 2 are standardized and used as the explanatory variables of the generalized additive model to obtain the nonlinear regression equation based on the generalized additive model, which is expressed as follows: (15); In the formula, is the link function used to map the expected value of the response variable to a suitable range; is the expected value of the response variable; is the intercept term; is a smooth function of the mean value of runoff from Longtou Power Station; It is a smooth function of the average PV output; is a smooth function of the average wind power output; It is a smooth function of the difference between the maximum and minimum wind power output; It is a smooth function of the difference between the maximum and minimum photovoltaic output; It is a smooth function of the original load peak-to-valley difference; is a smoothing function of the variance of runoff from Longtou Power Station; is a smooth function of the PV output variance; is a smooth function of the variance of wind power output; is a smooth function of the accumulated electricity; these smooth functions are used to describe the nonlinear relationship between the response variable and the explanatory variable; Among them, the smoothing function of each explanatory variable is usually a spline function, and the expression is as follows: (16); In the formula, It is j Explanatory variables A smooth function of It is k The coefficients of the spline basis functions; It is kspline basis functions; K is the total number of spline basis functions; Step 3-2: Calculate the partial dependence between each explanatory variable and the response variable. The expression is as follows: (17); in, It is j Explanatory variables The partial dependence value of is the predicted value of the generalized additive model, It is s Group samples except j Explanatory variables The remaining explanatory variables after ; S is the number of samples; Step 3-3: Change the installed capacity of wind and solar power, and use the peak-shaving sensitivity assessment method again to evaluate the peak-shaving capacity of the hydro-wind-solar hybrid system. An analysis is conducted to explore the impact of different wind and solar installed capacities on peak load regulation.

[0008] Beneficial effects of the present invention: 1. The present invention establishes a peak-shaving sensitivity analysis method for a water-wind-solar complementary system, analyzes the impact of factors affecting water-wind-solar load peak-shaving on the peak-shaving capacity of the water-wind-solar complementary system, and further analyzes the impact of wind-solar installed capacity on the peak-shaving capacity of the water-wind-solar complementary system, so as to facilitate the power grid to select a better dispatching method under different power conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0010] Figure 1 It is the overall flow chart of the present invention.

[0011] Figure 2 This is a relationship diagram between the factors affecting the peak load regulation of runoff at Longtou Power Station and the peak load regulation capacity of the water-wind-solar complementary system in an embodiment of the present invention.

[0012] Figure 3 It is a relationship diagram between the factors affecting the peak-shaving of wind power output and the peak-shaving capacity of the water-wind-solar complementary system in an embodiment of the present invention.

[0013] Figure 4 1 is a relationship diagram between the factors affecting photovoltaic output peak regulation and the peak regulation capacity of the water-wind-solar complementary system in an embodiment of the present invention.

[0014] Figure 5 It is a relationship diagram between the original load peak-shaving influencing factors and the peak-shaving capacity of the water-wind-solar complementary system in an embodiment of the present invention.

[0015] Figure 6It is a relationship diagram between the difference between the maximum and minimum wind power output and the peak load regulation capacity of the water-wind-solar complementary system when the wind-solar installed capacity changes in the embodiment of the present invention.

[0016] Figure 7 It is a relationship diagram between the wind power output variance and the peak load regulation capability of the hydro-wind-solar complementary system when the wind-solar installed capacity changes in the embodiment of the present invention.

[0017] Figure 8 It is a relationship diagram between the difference between the maximum and minimum photovoltaic output and the peak-shaving capacity of the water-wind-solar complementary system when the wind-solar installed capacity changes in an embodiment of the present invention.

[0018] Fig. 9 It is a relationship diagram between the photovoltaic output variance and the peak-shaving capability of the hydro-wind-solar complementary system when the wind-solar installed capacity changes in the embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clear, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1: like Figure 1-9 As shown in the figure, firstly, the independent water-wind-solar system and the joint system were defined, and the two systems were short-term scheduled. The difference between the peak-to-valley difference of the residual load of the two systems was calculated to quantify the peak-shaving capacity of the water-wind-solar complementary system; secondly, the peak-shaving influencing factors that can characterize the runoff, wind power output, photovoltaic output and original load change characteristics of the leading power station were selected; finally, based on the generalized additive model, an evaluation method for the peak-shaving sensitivity of the water-wind-solar complementary system was proposed, and the influence of different peak-shaving influencing factors on the peak-shaving capacity of the water-wind-solar complementary system was analyzed.

[0021] A peak-shaving sensitivity assessment method for a water-wind-solar hybrid system, the specific operation steps are as follows: Step 1: Calculate the peak load regulation capacity of the water-wind-solar hybrid system; The independent and combined systems of water, wind and solar power are dispatched to obtain the peak-to-valley difference of the residual load of the two systems. The difference between the peak-to-valley difference of the residual load of the combined system of water, wind and solar power and the independent system is the peak-shaving capacity of the water, wind and solar power complementary system. Step 2: Selection of factors affecting peak load regulation; The peak load influencing factors that can characterize the change characteristics of the runoff, wind power output, photovoltaic output and original load of the leading power station are selected respectively; Step 3: Construction of peak shaving sensitivity assessment method; Based on the generalized additive model, a peak-shaving sensitivity assessment method is proposed, and the impact of different peak-shaving influencing factors on the peak-shaving capacity of the hydro-wind-solar complementary system is analyzed.

[0022] Furthermore, the calculation of the peak load regulation capacity of the water-wind-solar complementary system in step 1 specifically includes the following steps: Step 1-1: The independent hydropower-wind-solar system dispatches only the original load for hydropower; the combined hydropower-wind-solar system dispatches the net load for hydropower. The net load is the original load minus the wind and solar output. The expression is as follows: (1); In the formula, For the t Residual load of the independent water, wind and solar power system in each period, unit MW; For the t The original load of each period, in MW; For the t The hydropower output of the independent water, wind and solar power systems in each period, in MW; For the t Wind power output in each period, in MW; For the t The photovoltaic output of each period, in MW; For the t The residual load of the water-wind-solar combined system in each period, in MW; For the t The hydropower output of the combined water-wind-solar system in each period, in MW; Step 1-2: Taking the minimum variance of the residual load as the objective function, the independent system of hydropower, wind power and solar power and the joint system are dispatched. The objective function expression is as follows: (2); In the formula, F is the objective function of scheduling; It is t The residual load in each period, in MW; T is the total number of periods; The constraint expressions are as follows: Water balance constraints: (3); In the formula, It is i Hydropower Station No. t +1 storage capacity for one period; It is i Hydropower Station No. t Storage capacity for each period, unit: m 3 ; It is i Hydropower Station No.t The inflow flow in each period, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; It is i Hydropower Station No. t The amount of abandoned water flow in each period, in m 3 / s; It is i Hydropower Station No. t The water supply in each period, unit: m 3 / s; It is u The upstream power station The power generation flow in each period, unit: m 3 / s; It is u The upstream power station The amount of abandoned water flow in each period, in m 3 / s; U is the i The total number of upstream power stations of a power station; It is u The upstream power station to i The water stagnation time of a hydropower station is a positive integer; Water level constraints: (4); In the formula, , Respectively i The lower and upper limits of the water level of a hydropower station, in meters; It is i Hydropower Station No. t The water level in each period, in m; Power generation flow constraints: (5); In the formula, , Respectively i The lower and upper limits of the power generation flow of a hydropower station, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; Outbound traffic constraints: (6); In the formula, and They arei The lower and upper limits of the outflow flow of a hydropower station, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; It is i Hydropower Station No. t The amount of abandoned water flow in each period, in m 3 / s; Initial and final water level control: (7); In the formula, and They are i The initial and final water levels specified for each hydropower station, in m; and They are i The water level of a hydropower station at the first moment and the Tth period, in m; Hydropower station power head control: (8); In the formula, It is i Hydropower Station No. t The power generation head of each period, in m; It is i Hydropower Station No. t- Water level in one period, in m; It is i Hydropower Station No. t The water level in each period, in m; It is i Hydropower Station No. t Tailwater level of each period, in m; It is i Hydropower Station No. t Head loss in each period, unit: m; The upper and lower limits of hydropower station output: (9); In the formula, It is i Hydropower Station No. t Output in each period, in MW; and It is i The upper and lower limits of the output of each hydropower station, in MW; Step 1-3: Subtract the peak-to-valley difference of the residual load of the independent system from the peak-to-valley difference of the residual load of the joint system. This value quantifies the peak-shaving capacity of the hydro-wind-solar hybrid system. The expression is as follows: (10); In the formula, It is the difference between the peak and valley difference of residual load of the independent system and the combined system, in MW; , They are the residual loads of the independent system and the combined system, in MW.

[0023] Furthermore, the selection of the peak load influencing factors in step 2 specifically includes the following steps: Step 2-1: Using the mean value and variance of the runoff from Longtou Power Station as the peak load influencing factors of the runoff can effectively characterize the characteristics of the runoff from Longtou Power Station. The expression is as follows: (11); In the formula, is the average runoff value of Longtou Power Station, unit: m 3 / s; It is t Runoff of Longtou Power Station in each period, unit: m 3 / s; is the variance of runoff from Longtou Power Station; T is the total number of time periods; Step 2-2: Using the mean value, variance and the difference between the maximum and minimum values ​​of wind power output as the peak load influencing factors of wind power output can effectively characterize the characteristics of wind power output. The expression is as follows: (12); In the formula, is the average value of wind power output, in MW; It is t Wind power output in each period, in MW; is the variance of wind power output; It is the difference between the maximum and minimum wind power output, in MW; is the maximum value of wind power output; is the minimum value of wind power output; T is the total number of time periods; Step 2-3: Using the average value, variance and the difference between the maximum and minimum values ​​of the photovoltaic output as the peak-shaving influencing factors of the photovoltaic output can effectively characterize the characteristics of the photovoltaic output. The expression is as follows: (13); In the formula, is the average value of photovoltaic output, in MW; It is t The photovoltaic output of each period, in MW; is the variance of PV output; It is the difference between the maximum and minimum photovoltaic output, in MW; is the maximum value of photovoltaic output; is the minimum value of photovoltaic output; T is the total number of time periods; Step 2-4: Using the load peak-to-valley difference and the accumulated power as the peak-shaving influencing factors of the original load can effectively characterize the characteristics of the original load. The expression is as follows: (14); In the formula, is the peak-to-valley difference of the original load, in MW; is the original load at t The value of each time period, in MW; E is the cumulative power consumption in T periods, in units ; is the time interval, unit is h.

[0024] Furthermore, the peak shaving sensitivity assessment method in step 3 is constructed, specifically comprising the following steps: Step 3-1: Use the peak load regulation capability of the hydro-wind-solar hybrid system in step 1 As the response variable of the generalized additive model, the ten peak-shaving influencing factors in step 2 are standardized and used as the explanatory variables of the generalized additive model to obtain the nonlinear regression equation based on the generalized additive model, which is expressed as follows: (15); In the formula, is the link function used to map the expected value of the response variable to a suitable range; is the expected value of the response variable; is the intercept term; is a smooth function of the mean runoff value of Longtou Power Station; It is a smooth function of the average PV output; is a smooth function of the average wind power output; It is a smooth function of the difference between the maximum and minimum wind power output; It is a smooth function of the difference between the maximum and minimum photovoltaic output; It is a smooth function of the original load peak-to-valley difference; is a smoothing function of the variance of runoff from Longtou Power Station; is a smooth function of the PV output variance; is a smooth function of the variance of wind power output; is a smooth function of the accumulated electricity; these smooth functions are used to describe the nonlinear relationship between the response variable and the explanatory variable; Among them, the smoothing function of each explanatory variable is usually a spline function, and the expression is as follows: (16); In the formula, It is j Explanatory variables A smooth function of It is k The coefficients of the spline basis functions; It is k spline basis functions; K is the total number of spline basis functions; Step 3-2: Calculate the partial dependence between each explanatory variable and the response variable. The expression is as follows: (17); in, It is j Explanatory variables The partial dependence value of is the predicted value of the generalized additive model, It is s Group samples except j Explanatory variables The remaining explanatory variables after ; S is the number of samples; Step 3-3: Change the installed capacity of wind and solar power, and use the peak-shaving sensitivity assessment method again to evaluate the peak-shaving capacity of the hydro-wind-solar hybrid system. An analysis is conducted to explore the impact of different wind and solar installed capacities on peak load regulation.

[0025] Embodiment 2: Five cascade hydropower stations and surrounding wind and photovoltaic power stations in a river basin in Yunnan Province of my country were taken as the research objects. The runoff, wind and photovoltaic meteorological data and power grid load were the measured data from 2011 to 2013. The installed capacity of wind and photovoltaic power stations was 6000MW respectively. The wind power output and photovoltaic output were calculated by the wind power output and photovoltaic output formulas respectively.

[0026] Table 1 shows the scheduling results under different input scenarios. Figure 2 The relationship between the factors affecting the peak load regulation of the runoff of Longtou Power Station and the peak load regulation capacity of the water-wind-solar complementary system is shown. It can be seen that when the variance and mean of the runoff of Longtou Power Station change, the peak load regulation capacity of the water-wind-solar complementary system changes little. Because the cascade hydropower station has a strong regulation performance, when the runoff of Longtou Power Station changes, the peak-to-valley difference of the residual load of the independent system and the joint system changes little. The above phenomenon shows that the runoff of Longtou Power Station has no obvious effect on the peak load regulation of the water-wind-solar complementary system.

[0027] Figure 3The relationship between the factors affecting wind power output peak regulation and the peak regulation capacity of the water-wind-solar complementary system is shown. It can be seen that when the average wind power output changes, the peak regulation capacity of the water-wind-solar complementary system changes little, while when the difference between the maximum and minimum wind power output and the variance change, the peak regulation capacity of the water-wind-solar complementary system changes greatly, showing a positive correlation overall. Because when the wind power output fluctuation increases, the peak-to-valley difference of the residual load of the independent system increases, while the combined system takes into account the fluctuation of wind power output, and the peak-to-valley difference of the residual load does not change significantly, and the average wind power output reflects the overall level of wind power output, which has no direct relationship with peak regulation. The above phenomenon shows that when the fluctuation of wind power output increases, the peak regulation effect of using a combined system is better.

[0028] Figure 4 The relationship between the factors affecting the peak-shaving of photovoltaic output and the peak-shaving capacity of the water-wind-solar complementary system is shown. It can be seen that when the average photovoltaic output changes, the peak-shaving capacity of the water-wind-solar complementary system changes little, while when the difference between the maximum and minimum photovoltaic output and the variance change, the peak-shaving capacity of the water-wind-solar complementary system changes greatly, and also shows a positive correlation. Because when the photovoltaic output fluctuation increases, the peak-to-valley difference of the residual load of the independent system increases, while the combined system takes into account the fluctuation of photovoltaic output, and the peak-to-valley difference of the residual load does not change significantly, and the average photovoltaic output reflects the overall level of photovoltaic output, which has no direct relationship with peak-shaving. The above phenomenon shows that when the photovoltaic output fluctuation increases, the peak-shaving effect of using a combined system is better.

[0029] Figure 5 The relationship between the peak-shaving factors of the original load and the peak-shaving capacity of the hydro-wind-solar hybrid system is shown. It can be seen that when the cumulative power and the peak-to-valley difference of the original load change, the peak-shaving capacity of the hydro-wind-solar hybrid system changes little. Because the regulation capacity of the cascade hydropower station is strong, it can also achieve better peak-shaving under a higher peak-to-valley difference of the residual load. The peak-to-valley difference of the residual load of the joint system and the independent system has no obvious change, and the cumulative power is the total value of the power in a day, which has no direct relationship with peak-shaving. The above phenomenon shows that the original load has no obvious effect on the peak-shaving capacity of the hydro-wind-solar hybrid system.

[0030] Figure 6-Figure 9 The influence of the maximum and minimum difference and variance of wind power output, and the maximum and minimum difference and variance of photovoltaic output on the peak-shaving capacity of the water-wind-solar complementary system is shown when the wind-solar installed capacity is 7000MW, 8000MW and 9000MW respectively. It can be seen that with the increase of wind-solar installed capacity, the position of the curve gradually rises. Because the system with a larger wind-solar installed capacity will lead to a larger wind-solar output, the fluctuation of wind-solar output will also increase accordingly, increasing the peak-shaving capacity of the water-wind-solar complementary system. This shows that the larger the wind-solar installed capacity of the system, the more obvious the effect of using the joint system for peak regulation.

[0031] Table 1 Scheduling results under different input scenarios

Claims

1. A method for evaluating the peak load sensitivity of a water-wind-solar hybrid system, characterized in that: The specific steps are as follows: Step 1: Calculate the peak load regulation capacity of the water-wind-solar hybrid system; The independent and combined systems of water, wind and solar power are dispatched to obtain the peak-to-valley difference of the residual load of the two systems. The difference between the peak-to-valley difference of the residual load of the combined system of water, wind and solar power and the independent system is the peak-shaving capacity of the water, wind and solar power complementary system. Step 2: Selection of factors affecting peak load regulation; The peak load influencing factors that can characterize the change characteristics of the runoff, wind power output, photovoltaic output and original load of the leading power station are selected respectively; Step 3: Construction of peak shaving sensitivity assessment method; Based on the generalized additive model, a peak-shaving sensitivity assessment method is proposed, and the impact of different peak-shaving influencing factors on the peak-shaving capacity of the hydro-wind-solar complementary system is analyzed.

2. According to claim 1, a peak-shaving sensitivity assessment method for a water-wind-solar hybrid system is characterized in that: The calculation of the peak load regulation capacity of the water-wind-solar complementary system in step 1 specifically includes the following steps: Step 1-1: The independent hydropower-wind-solar system dispatches only the original load for hydropower; the combined hydropower-wind-solar system dispatches the net load for hydropower. The net load is the original load minus the wind and solar output. The expression is as follows: (1)； In the formula, For the t Residual load of the independent water, wind and solar power system in each period, unit MW; For the t The original load of each period, in MW; For the t The hydropower output of the independent water, wind and solar power systems in each period, in MW; For the t Wind power output in each period, in MW; For the t The photovoltaic output of each period, in MW; For the t The residual load of the water-wind-solar combined system in each period, in MW; For the t The hydropower output of the combined water-wind-solar system in each period, in MW; Step 1-2: Taking the minimum variance of the residual load as the objective function, the independent system of hydropower, wind power and solar power and the joint system are dispatched. The objective function expression is as follows: (2)； In the formula, F is the objective function of scheduling; It is t The residual load in each period, in MW; T is the total number of periods; The constraint expressions are as follows: Water balance constraints: (3)； In the formula, It is i Hydropower Station No. t +1 storage capacity for one period; It is i Hydropower Station No. t Storage capacity for each period, unit: m 3 ; It is i Hydropower Station No. t The inflow flow in each period, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; It is i Hydropower Station No. t The amount of abandoned water flow in each period, in m 3 / s; It is i Hydropower Station No. t The water supply in each period, unit: m 3 / s; It is u The upstream power station The power generation flow in each period, unit: m 3 / s; It is u The upstream power station The amount of abandoned water flow in each period, in m 3 / s; U is the i The total number of upstream power stations of a power station; It is u Upstream power station to i The water stagnation time of a hydropower station is a positive integer; Water level constraints: (4)； In the formula, , Respectively i The lower and upper limits of the water level of a hydropower station, in meters; It is i Hydropower Station No. t The water level in each period, in m; Power generation flow constraints: (5)； In the formula, , Respectively i The lower and upper limits of the power generation flow of a hydropower station, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; Outbound traffic constraints: (6)； In the formula, and They are i The lower and upper limits of the outflow flow of a hydropower station, in m 3 / s; It is i Hydropower Station No. t The power generation flow in each period, unit: m 3 / s; It is i Hydropower Station No. t The amount of abandoned water flow in each period, in m 3 / s; Initial and final water level control: (7)； In the formula, and They are i The initial and final water levels specified for each hydropower station, in m; and They are i The water level of a hydropower station at the first moment and the Tth period, in m; Hydropower station power head control: (8)； In the formula, It is i Hydropower Station No. t The power generation head of each period, in m; It is i Hydropower Station No. t- Water level in one period, in m; It is i Hydropower Station No. t The water level in each period, in m; It is i Hydropower Station No. t Tailwater level of each period, in m; It is i Hydropower Station No. t Head loss in each period, unit: m; The upper and lower limits of hydropower station output: (9)； In the formula, It is i Hydropower Station No. t Output in each period, in MW; and It is i The upper and lower limits of the output of each hydropower station, in MW; Step 1-3: Subtract the peak-to-valley difference of the residual load of the independent system from the peak-to-valley difference of the residual load of the joint system. This value quantifies the peak-shaving capacity of the hydro-wind-solar hybrid system. The expression is as follows: (10)； In the formula, It is the difference between the peak and valley difference of residual load of the independent system and the combined system, in MW; , They are the residual loads of the independent system and the combined system, in MW.

3. A method for evaluating the peak load sensitivity of a water-wind-solar system according to claim 2, characterized in that: The selection of the peak load influencing factors in step 2 specifically includes the following steps: Step 2-1: Using the mean value and variance of the runoff from Longtou Power Station as the peak load influencing factors of the runoff can effectively characterize the characteristics of the runoff from Longtou Power Station. The expression is as follows: (11)； In the formula, is the average runoff value of Longtou Power Station, in m 3 / s; It is t Runoff of Longtou Power Station in each period, unit: m 3 / s; is the variance of runoff from Longtou Power Station; T is the total number of time periods; Step 2-2: Using the mean value, variance and the difference between the maximum and minimum values ​​of wind power output as the peak load influencing factors of wind power output can effectively characterize the characteristics of wind power output. The expression is as follows: (12)； In the formula, is the average value of wind power output, in MW; It is t Wind power output in each period, in MW; is the variance of wind power output; It is the difference between the maximum and minimum wind power output, in MW; is the maximum value of wind power output; is the minimum value of wind power output; T is the total number of time periods; Step 2-3: Using the average value, variance and the difference between the maximum and minimum values ​​of the photovoltaic output as the peak-shaving influencing factors of the photovoltaic output can effectively characterize the characteristics of the photovoltaic output. The expression is as follows: (13)； In the formula, is the average value of photovoltaic output, in MW; It is t The photovoltaic output of each period, in MW; is the variance of PV output; It is the difference between the maximum and minimum photovoltaic output, in MW; is the maximum value of photovoltaic output; is the minimum value of photovoltaic output; T is the total number of time periods; Step 2-4: Using the load peak-to-valley difference and the accumulated power as the peak-shaving influencing factors of the original load can effectively characterize the characteristics of the original load. The expression is as follows: (14)； In the formula, is the peak-to-valley difference of the original load, in MW; is the original load at t The value of each time period, in MW; E is the cumulative power consumption in T periods, in units ; is the time interval, unit is h.

4. A method for evaluating the peak load sensitivity of a water-wind-solar system according to claim 3, characterized in that: The peak shaving sensitivity assessment method in step 3 is constructed, and specifically includes the following steps: Step 3-1: Use the peak load regulation capability of the hydro-wind-solar hybrid system in step 1 As the response variable of the generalized additive model, the ten peak-shaving influencing factors in step 2 are standardized and used as the explanatory variables of the generalized additive model to obtain the nonlinear regression equation based on the generalized additive model, which is expressed as follows: (15)； In the formula, is the link function used to map the expected value of the response variable to a suitable range; is the expected value of the response variable; is the intercept term; is a smooth function of the mean value of runoff from Longtou Power Station; It is a smooth function of the average PV output; is a smooth function of the average wind power output; It is a smooth function of the difference between the maximum and minimum wind power output; It is a smooth function of the difference between the maximum and minimum photovoltaic output; It is a smooth function of the original load peak-to-valley difference; is a smoothing function of the variance of runoff from Longtou Power Station; is a smooth function of the PV output variance; is a smooth function of the variance of wind power output; is a smooth function of the accumulated electricity; these smooth functions are used to describe the nonlinear relationship between the response variable and the explanatory variable; Among them, the smoothing function of each explanatory variable is usually a spline function, and the expression is as follows: (16)； In the formula, It is j Explanatory variables A smooth function of It is k The coefficients of the spline basis functions; It is k spline basis functions; K is the total number of spline basis functions; Step 3-2: Calculate the partial dependence between each explanatory variable and the response variable. The expression is as follows: (17)； in, It is j Explanatory variables The partial dependence value of is the predicted value of the generalized additive model, It is s Group samples except j Explanatory variables The remaining explanatory variables after ; S is the number of samples; Step 3-3: Change the installed capacity of wind and solar power, and use the peak-shaving sensitivity assessment method again to evaluate the peak-shaving capacity of the hydro-wind-solar hybrid system. An analysis is conducted to explore the impact of different wind and solar installed capacities on peak load regulation.

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