A two-stage capacity allocation method for hydro-wind-solar hybrid system with energy uncertainty

Abstract

The present invention discloses a two-stage capacity configuration method for a water-wind-solar complementary system with energy uncertainty, comprising the following steps: step (1), establishing a typical wind power and photovoltaic output scenario model and calculating the output process; step (2), setting a runoff scenario and calculating the corresponding inflow runoff value; step (3), according to the characteristics of cross-network power transmission of ultra-high voltage transmission lines of a large-scale water-wind-solar complementary system and the regulation capacity of a hydropower station, using time parameters and characteristic parameters to describe the total output process of the water-wind-solar complementary system under the ultra-high voltage transmission demand, and setting a two-stage complementary strategy for the coordinated operation of water, wind and light; step (4), constructing a multi-objective optimization model for capacity configuration of the water-wind-solar complementary system; step (5), solving by a multi-objective optimization algorithm; step (6), determining the optimal capacity configuration scheme of the water-wind-solar system. The present invention can realize the adaptive complementary wind and photovoltaic output of hydropower and effectively improve the stability of the hydropower output process.

Description

Technical field:

[0001] The present invention relates to the field of wind power and photovoltaic capacity configuration of a new energy power generation system, and in particular to a two-stage capacity configuration method for a water-wind-solar complementary system taking into account energy uncertainty. Background technology:

[0002] With the deepening adjustment of my country's energy structure and the continuous drive of the "dual carbon" goal, clean and low-carbon energy represented by hydropower, wind power, photovoltaic power, etc. plays an increasingly important and irreplaceable role in energy supply. Due to the randomness, volatility, and intermittency of wind power and photovoltaic power generation, photovoltaic power generation is difficult to be accepted by the power grid. In order to improve the power generation stability of wind power and photovoltaic energy, the complementary development of water, wind, and light energy is proposed. The wind and light resources in the "Three Norths" and southwest regions of my country are densely distributed, and there is a good foundation for hydropower development. It is the main construction site for the large-scale grid-connected base of the water, wind, and light multi-energy complementary system. Through long-distance ultra-high voltage transmission lines, electric energy is transmitted across the grid to the eastern region of my country, which improves the stability of wind power and photovoltaic output while improving the utilization rate of transmission lines, with significant economic benefits. Based on the randomness and intermittency of water, wind, and light resources and their complementary characteristics in time and space, the capacity of each power source is reasonably configured from the planning level, which can effectively improve the economy and reliability of coordinated operation of the complementary system. Therefore, capacity optimization research for the water, wind, and light complementary system is an important part of improving the complementary performance of the system.

[0003] The uncertainties of wind and photovoltaic energy, such as randomness and volatility, are mainly reflected in the fluctuation frequency and amplitude of the system output power. Once the fluctuation frequency or amplitude of wind power and photovoltaic power is too large and the peak-shaving operation capacity of hydropower in the system is insufficient, wind and photovoltaic power will be abandoned, and even the power supply reliability of other power sources will be affected. Therefore, the uncertainty of wind power and photovoltaic output needs to be studied in depth. Most of the current capacity configuration research focuses on the economic benefits and costs of the system, ignoring factors such as the uncertainty of wind power and photovoltaic energy output, ultra-high voltage transmission requirements, inflow characteristics of hydropower stations, and regulation characteristics of reservoirs; in addition, the complementary strategies or methods involved in existing research rarely consider the stability of hydropower output after complementation, which reduces the feasibility of the research results. In order to clarify the impact of energy output uncertainty on the system and optimize the stability of system output, it is necessary to propose a two-stage capacity configuration method for water-wind-solar complementary systems considering energy uncertainty. Summary of the invention:

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and to propose a two-stage capacity configuration method for a water-wind-solar complementary system with energy uncertainty. The method of the present invention can be implemented under the conditions of the determination of the hydropower installed capacity, reservoir regulation characteristics and wind-solar power generation scenarios, taking into account the uncertainty of wind power and photovoltaic energy in a large-scale water-wind-solar multi-energy complementary system and the characteristics of ultra-high voltage cross-grid power transmission, and utilizing the proposed two-stage complementary strategy for the water-wind-solar complementary system to formulate the optimal capacity configuration scheme for the water-wind-solar multi-energy complementary system and its corresponding system operation mode. In the said mode, the hydropower adaptively complementary wind and light output can be realized, and the stability of the hydropower output process can be effectively improved. The present invention can provide technical support for the planning and construction of a water-wind-solar complementary system, and is suitable for promotion and application in the configuration of wind power and photovoltaic capacity in a large-scale water-wind-solar complementary system.

[0005] The present invention solves the practical problem by adopting the following technical solutions:

[0006] A two-stage capacity configuration method for a water-wind-solar hybrid system with energy uncertainty comprises the following steps:

[0007] Step (1), the historical measured daily output process of the existing wind farms and photovoltaic power stations in the study area is divided into four categories according to seasons, and typical wind power and photovoltaic output scenario models are generated respectively; according to the wind power-photovoltaic joint output characteristics and the utilization rate of the power transmission channel, a reasonable wind power and photovoltaic capacity ratio is selected to combine the typical wind power and photovoltaic output scenario model, and the multiple ratio method is used to obtain the daily output process of the typical wind power-photovoltaic joint scenario model in each season under different planned wind and light capacities;

[0008] Step (2), dividing the historical average daily runoff data of the hydrological station upstream of the hydropower station into four categories according to season, counting the frequency of the historical average daily runoff respectively, and selecting the runoff corresponding to the typical frequency according to the runoff frequency distribution characteristics to generate the runoff scenario model of each season;

[0009] Step (3), according to the characteristics of cross-grid power transmission of ultra-high voltage transmission lines of large-scale hydro-wind-solar complementary systems and the regulation capacity of hydropower stations, time parameters and characteristic parameters are used to describe the total output process of the hydro-wind-solar complementary system under ultra-high voltage transmission demand, and a two-stage complementary strategy for coordinated operation of water, wind and solar is set;

[0010] Step (4), constructing a multi-objective optimization model for capacity configuration of the water-wind-solar complementary system based on the complementary strategy of step (3);

[0011] Step (5), using the daily power generation process of the typical wind power-photovoltaic joint scenario model obtained in step (1) and the runoff scenario model under different guarantee rates obtained in step (2) as input variables of the multi-objective optimization model established in step (4), using the multi-objective optimization algorithm to solve the model to obtain the optimal coordinated operation mode under different planned installed capacity conditions, and calculating the power generation process of the hydropower station and the reservoir water level change process;

[0012] Step (6) selects reliability indicators to evaluate the reliability of the coordinated operation mode of the water-wind-solar system under different planned installed capacity conditions, and determines the optimal capacity configuration plan of the water-wind-solar system based on the goal orientation of managers and decision makers.

[0013] Furthermore, in step (1), spring is March, April, and May, summer is June, July, and August, autumn is September, October, and November, and winter is December, January, and February; the scenario generation step specifically includes: using the Copula function to construct a wind power-photovoltaic power joint probability distribution model for each season and each period; using the Latin hypercube sampling method to sample the output data for each season and each period, and obtaining a wind-solar joint output data set for each season; based on the extracted samples of random features that can represent uncertainty, generating a large number of wind-solar joint output initial scenario sets that meet the fluctuation characteristics of wind power and photovoltaic output; using the eigenvalue dimensionality reduction clustering method to reduce the initial scenario set to obtain a typical wind-solar joint output scenario.

[0014] Furthermore, step (3) specifically includes: according to the ultra-high voltage transmission characteristics, the total output process of the water-wind-solar complementary system is divided into I stages by using time parameters, each stage is subdivided into several time periods according to the decision time step, the output of the water-wind-solar complementary system in all time periods in each stage is a constant value, and characteristic parameters are used to characterize the output characteristics of different stages; the two-stage complementary strategy specifically includes: in the first complementary stage, the hydropower compensates the wind-solar output curve into a multi-segment line (the number of segments is I), and determines the time parameters of the segmented nodes of the complementary system output process; in the second complementary stage, the hydropower uses the remaining water volume of the day to increase the total output of each segment of the multi-segment line, and finally determines the total output process of the water-wind-solar complementary system.

[0015] Furthermore, in step (4), the water-wind-solar complementary system capacity configuration mathematical model is a multi-objective nested optimization model of a nested collaborative operation optimization model, which is composed of an objective function, decision variables and constraints:

[0016] a. Objective function:

[0017] The total hydropower output in the first complementary stage of f1 is the smallest:

[0018]

[0019]

[0020] The total output variation coefficient of f2 hydropower in the two complementary stages is the smallest:

[0021]

[0022]

[0023] Where:

[0024] represents the hydropower output of the water-wind-solar multi-energy complementary system in the first complementary stage in the jth period, (MW);

[0025] Represents the output value of the uth segment of the system output polyline in the first complementary stage of the water-wind-solar multi-energy complementary system, (MW);

[0026] represents the wind power-photovoltaic combined output in the water-wind-solar multi-energy complementary system in the jth period, (MW);

[0027] C wp Represents the total wind and solar capacity of the planned water-wind-solar multi-energy complementary system, (MW);

[0028] γ represents the proportion of photovoltaic power to the total wind and solar power capacity in the water-wind-solar multi-energy complementary system;

[0029] represents the output of wind power in megawatts in the jth period, (MW);

[0030] represents the output of installed PV capacity per megawatt in the jth period, (MW);

[0031] represents the actual output of the hydropower-wind-solar multi-energy complementary system in the jth period of the hydropower day, (MW);

[0032] Represents the average value of actual daily hydropower output of the water-wind-solar multi-energy complementary system, (MW);

[0033] Represents the total system output in the jth period of the second complementary stage in the water-wind-solar multi-energy complementary system, (MW);

[0034] t u Represents the time parameters of the polyline;

[0035] U represents the number of time parameters of the polyline;

[0036] J represents the total number of time periods, where J = 24 × 60 / Δt;

[0037] Δt represents the decision time step, (10min);

[0038] b. Decision variables

[0039] Select the total output of the water-wind-solar multi-energy complementary system The output polyline division node time parameters (t1, t2, ..., t U ) and the total wind and solar capacity of the water-wind-solar multi-energy complementary system (C wp ) as decision variables;

[0040] c. Constraints, as follows:

[0041] ①Energy balance constraints of hydropower stations:

[0042]

[0043] ② System power balance constraints:

[0044]

[0045] ③Reservoir water balance constraints:

[0046]

[0047] ④Hydropower station output constraints:

[0048]

[0049] ⑤ Constraints on the operating area of ​​hydropower station units:

[0050]

[0051] ⑥ Constraints on ramp rate of hydropower units:

[0052]

[0053] ⑦ Constraints on the discharge flow allowed by the hydropower station:

[0054]

[0055] ⑧Hydropower station power generation head constraints:

[0056]

[0057] ⑨Reservoir water level constraints:

[0058]

[0059] ⑩Reservoir water level constraints at the beginning and end of the reservoir operation period:

[0060] Z1=Z start , Z J =Z end

[0061] Total wind and solar capacity constraints:

[0062]

[0063] Where:

[0064] Represents the comprehensive output coefficient of the hydropower station in the jth period of the day, which is related to the unit output and the generating head; represents the average power generation flow of the hydropower station in the jth period of the day, (m3 / s);

[0065] represents the average power generation head of the hydropower station in the jth period of the day, (m);

[0066] J represents the total number of time periods, where J = 24 × 60 / Δt;

[0067] V j represents the reservoir capacity at the beginning of the jth period within the day, (m3);

[0068] represents the average inflow of the hydropower station in the jth period of the day, (m3 / s);

[0069] represents the average water discharge of the hydropower station in the jth period of the day (equal to 0, i.e. no water discharge), (m3 / s);

[0070] represents the minimum output of the hydropower station in the jth period of the day, (MW);

[0071] represents the maximum output of the hydropower station in the jth period of the day, (MW);

[0072] Represents the average output of the kth unit of the hydropower station in the jth period of the day, (MW);

[0073] Represents the minimum output of the kth unit of the hydropower station in the jth period of the day, (MW);

[0074] Represents the maximum output of the kth unit of the hydropower station in the jth period of the day, (MW);

[0075] Represents the lower limit of the mth vibration zone of the kth unit of the hydropower station, (MW);

[0076] Represents the upper limit of the mth vibration zone of the kth unit of the hydropower station, (MW);

[0077] M k Represents the number of vibration zones of the kth unit in the hydropower station;

[0078] represents the ramp rate (load increase) of the kth unit of the hydropower station, (MW);

[0079] represents the ramp rate (load reduction) of the kth unit of the hydropower station, (MW);

[0080] represents the minimum discharge flow allowed in the jth period of the hydropower station day, (m3 / s);

[0081] represents the maximum discharge flow allowed in the jth period of the hydropower station day, (m3 / s);

[0082] represents the minimum generating head of the hydropower station in the jth period of the day, (m);

[0083] represents the maximum generating head of the hydropower station in the jth period of the day, (m);

[0084] Z j represents the average reservoir water level in the jth period of the reservoir day, (m);

[0085] represents the lower limit water level of the reservoir, (m);

[0086] represents the lower limit water level of the reservoir, (m);

[0087] Z start represents the initial reservoir water level during the reservoir operation period, (m);

[0088] Z end represents the reservoir water level at the end of the reservoir operation period, (m);

[0089] Represents the maximum total wind and solar capacity of the planned hydro-wind-solar multi-energy complementary system (MW).

[0090] Furthermore, in step (5), the total wind and solar capacity value used as an input variable is any value between the maximum value and the minimum value of the wind and solar capacity that can be complemented by the hydropower station. It starts from the minimum allowed wind and solar capacity and increases in equal increments until the upper limit of the wind and solar capacity that satisfies both objectives is calculated.

[0091] Furthermore, in step (5), the optimization algorithm adopts one of dynamic programming and its improved algorithm or heuristic algorithm.

[0092] The dynamic programming and its improved algorithm include discrete differential dynamic programming, successive asymptotic dynamic programming and successive optimization method.

[0093] The heuristic algorithms include genetic algorithm, artificial neural network algorithm, particle swarm algorithm and ant colony algorithm.

[0094] Beneficial effects:

[0095] Compared with the prior art, the beneficial effects brought about by the technical solution of the present invention are as follows: the present invention proposes a two-stage capacity configuration method for a water-wind-solar complementary system that takes into account energy uncertainty. The daily output process of a typical joint wind power-photovoltaic output scenario is generated based on the historical measured daily output process of the wind farms and photovoltaic power stations built in the study area, and the runoff scenario is generated based on the historical daily average runoff data. According to the characteristics of cross-network power transmission of ultra-high voltage transmission lines of large-scale water-wind-solar complementary systems and the regulation capacity of hydropower stations, a two-stage complementary strategy for the coordinated operation of hydropower stations and wind-solar energy is set. A mathematical model for the capacity configuration of a water-wind-solar complementary system is established, and an optimization algorithm is used to solve it, and the optimal coordinated operation mode of wind farms and photovoltaic power stations under different planned installed capacity conditions is obtained. Reliability indicators are selected to evaluate the reliability of the coordinated operation mode of the water-wind-solar system under different planned installed capacity conditions, and the optimal capacity configuration scheme of the water-wind-solar system is determined according to the goal orientation of managers and decision makers.

[0096] The capacity configuration method proposed in the present invention takes into account the uncertainty of energy output, and optimizes the capacity configuration scheme of the water-wind-solar complementary system under the premise of ensuring the stability of hydropower output. It can provide technical support for the planning and construction of the water-wind-solar complementary system, and is suitable for promotion and application in the wind power and photovoltaic capacity configuration of large-scale water-wind-solar complementary systems. Description of the drawings:

[0097] Figure 1 It is a flowchart for executing the technical solution of the present invention;

[0098] Figure 2 a. Figure 2 b. Figure 2 c and Figure 2 d are respectively the daily power generation processes (in megawatts) of typical combined wind-solar power output scenarios in spring, summer, autumn and winter in step (1) of the method of the present invention.

[0099] Figure 3 This is a schematic diagram of the two-stage complementary strategy of the water-wind-solar multi-energy complementary system;

[0100] Figure 4is the empirical frequency curve of inflow runoff in different seasons;

[0101] Figure 5 a. Figure 5 b. Figure 5 c and Figure 5 d are the energy output processes and storage capacity change processes of the water-wind-solar hybrid system in spring, summer, autumn and winter respectively;

[0102] Figure 6 a. Figure 6 b. Figure 6 c and Figure 6 d shows the change process of each reliability index of the hydro-wind-solar hybrid system under different capacity configuration schemes in spring, summer, autumn and winter respectively; Specific implementation method:

[0103] The present invention proposes a distributed power supply trusted capacity assessment method based on the distribution network security domain. Figure 1 The implementation process of the patent of this invention is further described in detail.

[0104] To further illustrate the technical solutions and features of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments, but the content of the present invention is not limited to the content described in the specific embodiments.

[0105] This example takes a hydropower station (regulating reservoir capacity 150 million m3) as an example. 3 , hydropower installed capacity 4200MW) as an example, Figure 1 As shown in the figure, a two-stage capacity configuration method for a water-wind-solar hybrid system considering energy uncertainty is proposed. The specific implementation steps are as follows:

[0106] (1) The historical measured daily output process (time step of 10 minutes) of the existing wind farms and photovoltaic power stations in the study area is divided into seasons (spring is March, April, and May, summer is June, July, and August, autumn is September, October, and November, and winter is December, January, and February). The Copula function is used to construct a joint probability distribution model of wind power and photovoltaic power output in each season and time period. The Latin hypercube sampling method is used to sample the output data of each season and time period, and the wind-solar joint output data set of each season is obtained. Based on the samples of random features that can represent uncertainty, a large number of wind-solar joint output initial scenario sets that meet the fluctuation characteristics of wind power and photovoltaic output are generated. Finally, the clustering method of eigenvalue dimensionality reduction is used to reduce the initial scenario set to obtain typical wind-solar joint output scenarios in each season (see Figure 2 a. Figure 2 b. Figure 2 c. Figure 2d) The multiple ratio method is used to obtain the daily output process of typical combined wind power-photovoltaic output scenarios in each season under different planned installed capacity conditions of wind farms and photovoltaic power stations.

[0107] (2) The historical average daily runoff data of the hydrological station upstream of the hydropower station were divided into four categories according to season, and the frequency of historical average daily runoff was calculated respectively (see Figure 3 ), the runoff under typical frequencies (90%, 50%, 10%) were selected to generate the runoff scenarios of each season, which were recorded as Runoff-1, Runoff-2, and Runoff-3 (see Table 1).

[0108] Table 1 Runoff scenarios in different seasons

[0109]

[0110] (3) The utilization efficiency of the transmission channel under three different wind power and photovoltaic capacity ratios (1:2, 1:1, and 2:1) is calculated to be 27%, 53%, and 72% respectively, and the wind power and photovoltaic capacity ratio is selected as 2:1.

[0111] (4) According to the characteristics of UHV transmission, the total output process of the hydro-wind-solar complementary system is divided into I stages using time parameters. Each stage is subdivided into several time periods according to the decision time step. The output of the hydro-wind-solar complementary system in all time periods in each stage is a constant value. The two-stage complementary strategy is adopted as follows: In the first complementary stage, hydropower compensates the wind-solar output curve into a multi-segment line (the number of segments is U+1), and determines U time parameters of the segment nodes of the complementary system output process; in the second complementary stage, hydropower uses the remaining water volume of the day to increase the total output of each segment of the multi-segment line, and finally determines the total output process of the hydro-wind-solar complementary system (see Figure 4 ).

[0112] (5) Construct a mathematical model for capacity configuration of the water-wind-solar complementary system.

[0113] The multi-objective optimization model is composed of objective function, decision variables and constraints:

[0114] a. Objective function:

[0115] The total hydropower output in the first complementary stage of f1 is the smallest:

[0116]

[0117]

[0118] The total output variation coefficient of f2 hydropower in the two complementary stages is the smallest:

[0119]

[0120]

[0121] Where:

[0122] represents the hydropower output of the water-wind-solar multi-energy complementary system in the first complementary stage in the jth period, (MW);

[0123] Represents the output value of the uth segment of the system output polyline in the first complementary stage of the water-wind-solar multi-energy complementary system, (MW);

[0124] represents the wind power-photovoltaic combined output in the water-wind-solar multi-energy complementary system in the jth period, (MW);

[0125] C wp Represents the total wind and solar capacity of the planned water-wind-solar multi-energy complementary system, (MW);

[0126] γ represents the proportion of photovoltaic power to the total wind and solar power capacity in the water-wind-solar multi-energy complementary system;

[0127] represents the output of wind power in megawatts in the jth period, (MW);

[0128] represents the output of installed PV capacity per megawatt in the jth period, (MW);

[0129] represents the actual output of the hydropower-wind-solar multi-energy complementary system in the jth period of the hydropower day, (MW);

[0130] Represents the average value of actual daily hydropower output of the water-wind-solar multi-energy complementary system, (MW);

[0131] Represents the total system output in the jth period of the second complementary stage in the water-wind-solar multi-energy complementary system, (MW);

[0132] t u Represents the time parameters of the polyline;

[0133] U represents the number of time parameters of the polyline;

[0134] J represents the total number of time periods, where J = 24 × 60 / Δt;

[0135] Δt represents the decision time step, (10min);

[0136] b. Decision variables

[0137] Select the total output of the water-wind-solar multi-energy complementary system The output polyline division node time parameters (t1, t2, ..., t U ) and the total wind and solar capacity of the water-wind-solar multi-energy complementary system (C wp ) as decision variables;

[0138] c. Constraints, as follows:

[0139] ①Energy balance constraints of hydropower stations:

[0140]

[0141] ② System power balance constraints:

[0142]

[0143] ③Reservoir water balance constraints:

[0144]

[0145] ④Hydropower station output constraints:

[0146]

[0147] ⑤ Constraints on the ramp rate of hydropower units:

[0148]

[0149] ⑥ Constraints on the discharge flow allowed by the hydropower station:

[0150]

[0151] ⑦Hydropower station power head constraints:

[0152]

[0153] ⑧Reservoir water level constraints:

[0154]

[0155] ⑨Reservoir water level constraints at the beginning and end of the reservoir operation period:

[0156] Z1=2446m,Z J =2446m

[0157] ⑩Constraints on total wind and solar capacity:

[0158]

[0159] Where:

[0160] Represents the comprehensive output coefficient of the hydropower station in the jth period of the day, which is related to the unit output and the generating head;

[0161] represents the average power generation flow of the hydropower station in the jth period of the day, (m 3 / s);

[0162] represents the average power generation head of the hydropower station in the jth period of the day, (m);

[0163] J represents the total number of time periods, where J = 24 × 60 / Δt;

[0164] V j represents the reservoir capacity at the beginning of the jth period of the day, (m 3 );

[0165] represents the average inflow of the hydropower station in the jth period of the day, (m 3 / s);

[0166] represents the average water discharge of the hydropower station in the jth period of the day (equal to 0, i.e. no water discharge), (m 3 / s);

[0167] represents the minimum output of the hydropower station in the jth period of the day, (MW);

[0168] represents the maximum output of the hydropower station in the jth period of the day, (MW);

[0169] Represents the average output of the kth unit of the hydropower station in the jth period of the day, (MW);

[0170] represents the ramp rate (load increase) of the kth unit of the hydropower station, (MW);

[0171] represents the ramp rate (load reduction) of the kth unit of the hydropower station, (MW);

[0172] Z j represents the average reservoir water level in the jth period of the reservoir day, (m);

[0173] Represents the maximum total wind and solar capacity of the planned hydro-wind-solar multi-energy complementary system (MW).

[0174] (6) The daily output process of the typical combined wind power-photovoltaic output scenario obtained in step (1), the runoff scenarios under different guarantee rates obtained in step (2), and the wind power and photovoltaic capacity ratio obtained in step (3) are used as input variables of the multi-objective optimization model established in step (6), and the optimization algorithm is used to solve the optimal coordinated operation mode of the water-wind-photovoltaic complementary system under different scenario combinations, and the output process of the hydropower station and the reservoir water level change process are calculated (see Figure 5 a. Figure 5 b. Figure 5 c. Figure 5 d).

[0175] (7) Calculate and count the output reliability evaluation indicators of the hydropower-wind-solar hybrid system under the wind-solar and runoff combination scenarios in different seasons (α hydropower output variation coefficient in the first complementary stage, δ hydropower output variation coefficient in the second complementary stage, λ total output peak-to-valley difference rate). Since the changing trends of different scenarios in the same season are similar, one scenario is selected as an example in each season (see Figure 6 a. Figure 6 b. Figure 6 c. Figure 6 d). Determine the optimal capacity configuration plan for the hydropower, wind power and photovoltaic system based on the goal orientation of managers and decision makers. For example, if the goal orientation of managers and decision makers is to achieve the most stable system output, the optimal wind power capacity ratio of the hydropower, wind power and photovoltaic system is 0.32, and the corresponding system capacity configuration plan is: hydropower 4200MW, wind power 1318MW, photovoltaic 659MW.

[0176] The present invention is not limited to the embodiments described above. The above description of the specific embodiments is intended to describe and illustrate the technical solution of the present invention. The above specific embodiments are merely illustrative and not restrictive. Without departing from the scope of the present invention and the scope of protection of the claims, a person of ordinary skill in the art can also make many forms of specific changes under the guidance of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. A two-stage capacity configuration method for a water-wind-solar hybrid system with energy uncertainty, characterized in that: The following steps are involved: Step (1), the historical measured daily output process of the existing wind farms and photovoltaic power stations in the study area is divided into four categories according to seasons, and typical wind power and photovoltaic output scenario models are generated respectively; according to the wind power-photovoltaic joint output characteristics and the utilization rate of the power transmission channel, a reasonable wind power and photovoltaic capacity ratio is selected to combine the typical wind power and photovoltaic output scenario model, and the multiple ratio method is used to obtain the daily output process of the typical wind power-photovoltaic joint scenario model in each season under different planned wind and light capacities; Step (2), dividing the historical average daily runoff data of the hydrological station upstream of the hydropower station into four categories according to season, counting the frequency of the historical average daily runoff respectively, and selecting the runoff corresponding to the typical frequency according to the runoff frequency distribution characteristics to generate the runoff scenario model of each season; Step (3), according to the characteristics of cross-grid power transmission of ultra-high voltage transmission lines of large-scale hydro-wind-solar complementary systems and the regulation capacity of hydropower stations, time parameters and characteristic parameters are used to describe the total output process of the hydro-wind-solar complementary system under ultra-high voltage transmission demand, and a two-stage complementary strategy for coordinated operation of water, wind and solar is set; Step (4), based on the complementary strategy of step (3), construct a multi-objective optimization model for the capacity configuration of the water-wind-solar complementary system; the multi-objective optimization model includes the objective function The total hydropower output in the first complementary stage of f1 is the smallest: The total output variation coefficient of f2 hydropower in the two complementary stages is the smallest: Where: represents the hydropower output of the water-wind-solar multi-energy complementary system in the first complementary stage in the jth period, (MW); Represents the output value of the uth segment of the system output polyline in the first complementary stage of the water-wind-solar multi-energy complementary system, (MW); represents the wind power-photovoltaic combined output in the water-wind-solar multi-energy complementary system in the jth period, (MW); C wp Represents the total wind and solar capacity of the planned water-wind-solar multi-energy complementary system, (MW); γ represents the proportion of photovoltaic power to the total wind and solar power capacity in the water-wind-solar multi-energy complementary system; represents the output of wind power in megawatts in the jth period, (MW); represents the output of installed PV capacity in megawatts in the jth period, (MW); represents the actual output of the hydropower-wind-solar multi-energy complementary system in the jth period of the hydropower day, (MW); Represents the average value of actual daily hydropower output of the water-wind-solar multi-energy complementary system, (MW); Represents the total system output in the jth period of the second complementary stage in the water-wind-solar multi-energy complementary system, (MW); t u Represents the time parameters of the polyline; U represents the number of time parameters of the polyline; J represents the total number of time periods, where J = 24 × 60 / △t; Δt represents the decision time step, (10min); Step (5), using the daily power generation process of the typical wind power-photovoltaic joint scenario model obtained in step (1) and the runoff scenario model under different guarantee rates obtained in step (2) as input variables of the multi-objective optimization model established in step (4), using the multi-objective optimization algorithm to solve the model to obtain the optimal coordinated operation mode under different planned installed capacity conditions, and calculating the power generation process of the hydropower station and the reservoir water level change process; Step (6) selects reliability indicators to evaluate the reliability of the coordinated operation mode of the water-wind-solar system under different planned installed capacity conditions, and determines the optimal capacity configuration plan of the water-wind-solar system based on the goal orientation of managers and decision makers.

2. According to the two-stage capacity configuration method of a water-wind-solar hybrid system with energy uncertainty according to claim 1, it is characterized in that: In step (1), spring is March, April, and May, summer is June, July, and August, autumn is September, October, and November, and winter is December, January, and February. The scene generation step specifically includes: The Copula function is used to construct the joint probability distribution model of wind power and photovoltaic power output in each season and time period; The Latin hypercube sampling method is used to sample the output data of each season and each period to obtain the wind-solar joint output data set of each season; Based on the samples of random features that can represent uncertainty, a large number of wind-solar combined output initial scenario sets that meet the fluctuation characteristics of wind power and photovoltaic output are generated; The clustering method of eigenvalue dimensionality reduction is used to reduce the initial scene set and obtain a typical wind-solar joint output scene model.

3. According to the two-stage capacity configuration method of a water-wind-solar hybrid system with energy uncertainty in claim 1, it is characterized in that: The step (3) specifically comprises: According to the characteristics of UHV transmission, the total output process of the hydro-wind-solar complementary system is divided into I stages using time parameters. Each stage is subdivided into several time periods according to the decision time step. The output of the hydro-wind-solar complementary system in all time periods in each stage is a constant value, and characteristic parameters are used to characterize the output characteristics of different stages. The two-stage complementary strategy specifically includes: in the first complementary stage, hydropower compensates the wind-solar output curve into a multi-segment line, and determines the time parameters of the segmented nodes of the complementary system output process; in the second complementary stage, hydropower uses the remaining water volume of the day to increase the total output of each segment of the multi-segment line, and finally determines the total output process of the water-wind-solar complementary system.

4. According to the two-stage capacity configuration method of a water-wind-solar hybrid system with energy uncertainty in claim 1, it is characterized in that: In step (5), the multi-objective optimization model of the water-wind-solar complementary system is a multi-objective nested optimization model of a nested collaborative operation optimization model, and the multi-objective optimization model is also composed of decision variables and constraints: Decision variables Select the total output of the water-wind-solar multi-energy complementary system The output multi-segment line division node time parameters t1, t2, ..., t U The total wind and solar capacity of the hydro-wind-solar multi-energy complementary system C wp as decision variables; c. Constraints, as follows: ①Energy balance constraints of hydropower stations: ② System power balance constraints: ③Reservoir water balance constraints: ④Hydropower station output constraints: ⑤ Constraints on the operating area of ​​hydropower station units: ⑥Hydropower unit climbing rate constraint: ⑦ Constraints on the discharge flow allowed by the hydropower station: ⑧Hydropower station power head constraints: ⑨Reservoir water level constraints: ⑩Reservoir water level constraints at the beginning and end of the reservoir operation period: Z1=Z start ,WITH J =Z end Total wind and solar capacity constraints: Where: Represents the comprehensive output coefficient of the hydropower station in the jth period of the day, which is related to the unit output and the generating head; represents the average power generation flow of the hydropower station in the jth period of the day, (m 3 / s); represents the average power generation head of the hydropower station in the jth period of the day, (m); J represents the total number of time periods, where J = 24 × 60 / △t; V j represents the reservoir capacity at the beginning of the jth period of the day, (m 3 ); represents the average inflow of the hydropower station in the jth period of the day, (m 3 / s); represents the average water discharge of the hydropower station in the jth period of the day (equal to 0, i.e. no water discharge), (m 3 / s); represents the minimum output of the hydropower station in the jth period of the day, (MW); represents the maximum output of the hydropower station in the jth period of the day, (MW); Represents the average output of the kth unit of the hydropower station in the jth period of the day, (MW); Represents the minimum output of the kth unit of the hydropower station in the jth period of the day, (MW); Represents the maximum output of the kth unit of the hydropower station in the jth period of the day, (MW); Represents the lower limit of the mth vibration zone of the kth unit of the hydropower station, (MW); Represents the upper limit of the mth vibration zone of the kth unit of the hydropower station, (MW); M k Represents the number of vibration zones of the kth unit in the hydropower station; represents the ramp rate (load increase) of the kth unit of the hydropower station, (MW); represents the ramp rate (load reduction) of the kth unit of the hydropower station, (MW); represents the minimum discharge flow allowed in the jth period of the hydropower station day, (m 3 / s); represents the maximum discharge flow allowed in the jth period of the hydropower station day, (m 3 / s); represents the minimum generating head of the hydropower station in the jth period of the day, (m); represents the maximum generating head of the hydropower station in the jth period of the day, (m); Z j represents the average reservoir water level in the jth period of the reservoir day, (m); represents the lower limit water level of the reservoir, (m); represents the lower limit water level of the reservoir, (m); Z start represents the initial reservoir water level during the reservoir operation period, (m); Z end represents the reservoir water level at the end of the reservoir operation period, (m); Represents the maximum total wind and solar capacity of the planned hydro-wind-solar multi-energy complementary system (MW).

5. According to the two-stage capacity configuration method of a water-wind-solar hybrid system with energy uncertainty in claim 1, it is characterized in that: In step (5), the total wind and solar capacity value used as an input variable is any value between the maximum and minimum values ​​of the wind and solar capacity that can be complemented by the hydropower station. It starts from the minimum allowed wind and solar capacity and increases in equal increments until the upper limit of the wind and solar capacity that satisfies both objectives is calculated.

6. The two-stage capacity configuration method for a water-wind-solar hybrid system with energy uncertainty according to claim 1 is characterized in that: In step (6), the optimization algorithm adopts one of dynamic programming and its improved algorithm or heuristic algorithm: The dynamic programming and its improved algorithm include discrete differential dynamic programming, successive progressive dynamic programming and successive optimization method. The heuristic algorithms include genetic algorithm, artificial neural network algorithm, particle swarm algorithm and ant colony algorithm.

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