Water-hydrogen-wind-solar multi-energy complementary system joint scheduling rule derivation method and system

Abstract

The invention discloses a joint scheduling rule derivation method and system for a water-hydrogen-wind-solar multi-energy complementary system. The method comprises the following steps: determining the output of the multi-energy complementary system; establishing a deterministic optimization scheduling model based on the output of the multi-energy complementary system; solving the deterministic optimization scheduling model to obtain variables of the combined scheduling rule of the multi-energy complementary system; and determining a parameter value of the combined scheduling rule based on the variable of the combined scheduling rule of the multi-energy complementary system, and optimizing the parameter value of the combined scheduling rule to obtain an optimal parameter value so as to deduce an optimal combined scheduling rule based on the optimal parameter value. The method can accurately evaluate the long-term operation economy and power supply reliability of the multi-energy complementary system.

Description

Technical Field

[0001] The present invention relates to the field of multi-energy complementary technology, and in particular to a method and system for deriving joint dispatching rules for a water-hydrogen-wind-solar multi-energy complementary system. Background Art

[0002] The development of clean, low-carbon and renewable energy such as wind power and photovoltaics has become a major strategic measure for the world to alleviate the energy crisis, respond to climate change and improve the ecological environment. However, wind power and photovoltaics are affected by many natural factors such as wind speed, solar radiation and temperature, and their output power has significant randomness, volatility and intermittent characteristics, which increases the peak load and frequency regulation pressure of the power grid, is not conducive to the safe, economical and stable operation of the power system, and limits the power grid's absorption of wind power and photovoltaics. Therefore, building a power generation system with multi-energy complementarity and coordinated supply and demand is an important way to improve the utilization rate of renewable energy.

[0003] In the study of multi-energy complementary dispatching rules, existing studies are mostly aimed at single adjustable energy complementary systems based on hydropower, such as hydro-solar, hydro-wind-solar, etc., and their function forms are often relatively simple. There are fewer studies on dispatching rules for multiple adjustable energy complementary systems containing hydropower, hydrogen energy, etc., and there is also a lack of analysis of typical cases. Therefore, for multiple adjustable energy complementary systems containing hydropower, hydrogen energy, etc., the joint dispatching rules in the existing technology cannot accurately evaluate the economy and power supply reliability of the system's long-term operation. Summary of the invention

[0004] The present invention aims to solve the technical problems in the related art at least to a certain extent. To this end, the first object of the present invention is to provide a method for deriving joint dispatching rules for a water-hydrogen-wind-solar multi-energy complementary system, which can accurately evaluate the economy and power supply reliability of the long-term operation of the multi-energy complementary system.

[0005] The second object of the present invention is to provide a joint dispatching rule derivation system for a water-hydrogen-wind-solar multi-energy complementary system.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A method for deriving joint dispatching rules for a water-hydrogen-wind-solar multi-energy complementary system, comprising:

[0008] Determine the output of the multi-energy complementary system;

[0009] Establish a deterministic optimization scheduling model based on the output of the multi-energy complementary system;

[0010] Solving the deterministic optimization scheduling model to obtain variables of the joint scheduling rules of the multi-energy complementary system;

[0011] Based on the variables of the joint dispatching rule of the multi-energy complementary system, the parameter values ​​of the joint dispatching rule are determined, and the parameter values ​​of the joint dispatching rule are optimized to obtain the optimal parameter values, so as to derive the optimal joint dispatching rule based on the optimal parameter values.

[0012] Preferably, the output of the multi-energy complementary system includes hydropower output, hydrogen production, wind power output and photovoltaic power output.

[0013] Preferably, the hydropower output is determined based on the power generation efficiency, the power generation flow and the power generation head; the hydrogen production is determined based on the efficiency coefficient of the electrolyzer and the output allocated to hydrogen production; the wind power output is determined based on the wind speed; the photovoltaic power generation output is determined based on the rated photovoltaic output, the output temperature coefficient of the solar cell module, the temperature difference between the actual test conditions and the standard test conditions, the actual intensity of solar radiation and the intensity of solar radiation under the standard test conditions.

[0014] Preferably, the objective function of the deterministic optimization scheduling model includes maximizing the total scheduling benefit function and maximizing the guarantee rate function; wherein the total scheduling benefit includes the scheduling benefit obtained from power generation and the scheduling benefit obtained from hydrogen production; the guarantee rate is the ratio of the total number of cycles in which the total output of the multi-energy complementary system is greater than or equal to the guaranteed output to the total number of cycles.

[0015] Preferably, the constraints of the deterministic optimization scheduling model include: water balance constraints, reservoir water storage constraints, reservoir discharge constraints, hydropower output constraints, transmission capacity constraints of the multi-energy complementary system and output constraints allocated to hydrogen production.

[0016] Preferably, the variables of the joint dispatching rules of the multi-energy complementary system include input variables and output variables; the input variables include available energy and available water; the output variables include decision variables and the proportion of power generation allocated to hydrogen production; wherein the decision variables include hydropower output, reservoir discharge, water level and reservoir water storage capacity.

[0017] Preferably, determining the parameter value of the joint dispatch rule based on the variables of the joint dispatch rule of the multi-energy complementary system includes:

[0018] Obtaining the decision variables of the current period, the decision variables of the next period, and the proportion of power generation allocated to hydrogen production from the solved historical data;

[0019] The available energy of the current period is calculated based on the decision variables of the current period;

[0020] The parameter values ​​of the joint dispatching rules are obtained according to the available energy in the current period, the decision variables in the next period, the proportion of power generation allocated to hydrogen production, and the pre-built linear fitting model.

[0021] Preferably, an implicit random method is used to optimize the parameter values ​​of the joint scheduling rules to obtain the optimal parameter values; wherein the implicit random method is a non-dominated sorting genetic algorithm II.

[0022] Preferably, deriving an optimal joint scheduling rule based on the optimal parameter value includes: substituting the optimal parameter value into the linear fitting model to derive the optimal joint scheduling rule.

[0023] To achieve the above-mentioned object, the second aspect of the present invention provides a joint dispatching rule derivation system for a water-hydrogen-wind-solar multi-energy complementary system, comprising:

[0024] A first determination module, used to determine the output of the multi-energy complementary system;

[0025] A building block for establishing a deterministic optimal scheduling model based on the output of a multi-energy complementary system;

[0026] A solution module, used for solving the deterministic optimization scheduling model to obtain variables of the joint scheduling rules of the multi-energy complementary system;

[0027] A second determination module is used to determine the parameter value of the joint dispatch rule based on the variables of the joint dispatch rule of the multi-energy complementary system;

[0028] An optimization module is used to optimize the parameter values ​​of the joint scheduling rules to obtain the optimal parameter values;

[0029] A derivation module is used to derive an optimal joint scheduling rule based on the optimal parameter value.

[0030] The present invention has at least the following technical effects:

[0031] The present invention provides a method and system for deriving joint dispatching rules for a water-hydrogen-wind-solar multi-energy complementary system, specifically, first determining the output of the multi-energy complementary system, then establishing a deterministic optimization dispatching model according to the output of the multi-energy complementary system, then solving the deterministic optimization dispatching model to obtain the input variables and output variables of the joint dispatching rules of the multi-energy complementary system, then obtaining the parameter values ​​of the joint dispatching rules according to the output variables, input variables and linear fitting models, and then optimizing the parameter values ​​of the joint dispatching rules to obtain the optimal parameter values, and then returning and substituting them into the linear fitting model to obtain the optimal linear fitting model, i.e., the optimal joint dispatching rules, thereby, the output variable prediction of the multi-energy complementary system in the future period can be realized through the optimal linear fitting model. Therefore, the present invention can derive a joint dispatching rule, wherein the objective function of the deterministic optimization dispatching model includes maximizing the total dispatching benefit function and maximizing the guarantee rate function, thereby, the economy and power supply reliability of the long-term operation of the multi-energy complementary system can be accurately evaluated through the joint dispatching rule derivation method.

[0032] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of a method for deriving joint dispatching rules for a water-hydrogen-wind-solar multi-energy complementary system according to an embodiment of the present invention.

[0034] Figure 2 Schematic diagram comparing the operating results of the optimal joint scheduling rule and conventional scheduling.

[0035] Figure 3 This is a structural block diagram of the joint dispatching rule derivation system of the water-hydrogen-wind-solar multi-energy complementary system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The present embodiment is described in detail below, and examples of the embodiment are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0037] The following describes the method and system for deriving joint scheduling rules for the water-hydrogen-wind-solar multi-energy complementary system of this embodiment with reference to the accompanying drawings.

[0038] Figure 1 Flow chart of the method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0039] Step S1: Determine the output of the multi-energy complementary system.

[0040] In this embodiment, the output of the multi-energy complementary system includes hydropower output, hydrogen production, wind power output and photovoltaic power output.

[0041] Among them, the hydropower output is determined based on the power generation efficiency, power generation flow and power generation head; the hydrogen production is determined based on the efficiency coefficient of the electrolyzer and the output allocated to hydrogen production; the wind power output is determined based on the wind speed; the photovoltaic power generation output is determined based on the rated photovoltaic output, the output temperature coefficient of the solar cell module, the temperature difference between the actual test conditions and the standard test conditions, the actual intensity of solar radiation and the intensity of solar radiation under standard test conditions.

[0042] 1) Hydropower output is determined by the interaction between reservoir discharge and water head, and the calculation formula is as follows:

[0043]

[0044] In the formula, is the hydropower output in the tth period; K is the comprehensive output coefficient of the hydropower station; is the reservoir discharge used for power generation in the tth period; and are the average water levels of the upstream and downstream, respectively; is the head loss in the tth period. Therefore, the hydropower output is determined based on the power generation efficiency, power generation flow and power generation head.

[0045] 2) The amount of hydrogen produced is affected by the output input and characteristics of the electrolyzer. The formula for calculating the amount of hydrogen produced is as follows:

[0046]

[0047] In the formula, G t is the amount of hydrogen produced; η is the efficiency coefficient of the electrolyzer; is the output allocated to hydrogen production in the tth period.

[0048] 3) Calculate wind power output. Wind power output is determined by wind speed and can be calculated using a piecewise linear function:

[0049]

[0050] In the formula, is the wind power output in the tth period; v t is the wind speed in the tth period; v in 、v out and v rated They are cut-in, cut-out and rated wind speed; is the rated power of wind power.

[0051] 4) Calculate the photovoltaic power generation output. Considering the difference between general conditions and standard test conditions, the photovoltaic power generation output calculation formula is as follows:

[0052]

[0053] In the formula, is the photovoltaic power generation output in the tth period; is the rated photovoltaic output; α is the output temperature coefficient of the solar cell module; θ t is the temperature difference between the actual test conditions and the standard test conditions; R t is the actual intensity of solar radiation in the tth period; R stc is the intensity of solar radiation under standard test conditions.

[0054] Step S2: Establish a deterministic optimization scheduling model based on the output of the multi-energy complementary system.

[0055] In this embodiment, the long sequence data of reservoir inflow, wind power output and photovoltaic power output can be used to allocate available energy (including water resources and power generation) in the long term to maximize the dispatching efficiency and guarantee rate. To this end, a deterministic optimization dispatching model can be established according to the output of the multi-energy complementary system, and then the deterministic optimization dispatching model can be solved according to dynamic programming.

[0056] Among them, the objective function of the deterministic optimization scheduling model includes maximizing the total scheduling benefit function and maximizing the guarantee rate function; among them, the total scheduling benefit includes the scheduling benefit obtained from power generation and the scheduling benefit obtained from hydrogen production; the guarantee rate is the ratio of the total number of cycles in which the total output of the multi-energy complementary system is greater than or equal to the guaranteed output to the total number of cycles.

[0057] Specifically, the maximum total scheduling benefit function is expressed as follows:

[0058]

[0059] In the formula, max is the maximum value function, B is the total scheduling benefit; T is the total number of cycles; Dispatch benefits obtained for power generation; dispatch benefits obtained for hydrogen production; is the electricity price; f LO (.) is the energy loss function, which converts the output into output reduction rate considering short-term scheduling; is the amount of electricity generated that is distributed to the grid; is the hydrogen price; is the density of hydrogen; ΔT t is the time step of the tth period.

[0060] The maximum guaranteed rate function is expressed as follows:

[0061]

[0062] In the formula, C is the guarantee rate; N t is the total output in the tth period; is the guaranteed output for the tth period; N t Greater than or equal to The total number of cycles.

[0063] Furthermore, the constraints of the deterministic optimization scheduling model include: water balance constraints, reservoir water storage constraints, reservoir discharge constraints, hydropower output constraints, transmission capacity constraints of the multi-energy complementary system and output constraints allocated to hydrogen production.

[0064] 1) The water balance constraint is expressed as follows:

[0065]

[0066] Where V t and V t+1 is the reservoir water storage capacity in the tth and t+1th periods; Q t is the reservoir inflow in the tth period; is the amount of water discharged from the reservoir in the tth period.

[0067] 2) The reservoir water storage capacity constraints are expressed as follows:

[0068] V t min <V t <V t max (8)

[0069] Where V t min and V t max are the lower and upper limits of the reservoir storage capacity respectively.

[0070] 3) The reservoir discharge constraint conditions are expressed as follows:

[0071]

[0072] In the formula, and They are the lower and upper limits of the total discharge of the reservoir respectively.

[0073] 4) The hydropower output constraint conditions are as follows:

[0074]

[0075] In the formula, and They are the lower and upper limits of hydropower output respectively.

[0076] 5) The transmission capacity constraint of the multi-energy complementary system is expressed as follows:

[0077]

[0078] In the formula, It is the upper limit of the transmission capacity of the multi-energy complementary system.

[0079] 6) The output constraints allocated to hydrogen production are expressed as follows:

[0080]

[0081] In the formula, It is the upper limit of the output allocated to hydrogen production.

[0082] Step S3: Solve the deterministic optimization scheduling model to obtain the variables of the joint scheduling rules of the multi-energy complementary system.

[0083] In this embodiment, the input variables and output variables of the joint dispatching rules of the multi-energy complementary system can be obtained by solving the maximum total dispatching benefit function and the maximum guarantee rate function based on the above-mentioned constraints.

[0084] Among them, the input variables include available energy and available water, which can be directly derived from the hydrological and meteorological data; the output variables include decision variables and the proportion of power generation allocated to hydrogen production; among them, the decision variables include hydropower output, reservoir discharge, water level and reservoir storage, and the output variables can convert the input variables into the system's power generation.

[0085] Step S4: Determine the parameter values ​​of the joint dispatching rules based on the variables of the joint dispatching rules of the multi-energy complementary system, and optimize the parameter values ​​of the joint dispatching rules to obtain the optimal parameter values, so as to derive the optimal joint dispatching rules based on the optimal parameter values.

[0086] In this embodiment, the parameter values ​​of the joint dispatching rules are determined based on the variables of the joint dispatching rules of the multi-energy complementary system, including: obtaining the decision variables of the current time period, the decision variables of the next time period and the proportion of power generation allocated to hydrogen production from the solved historical data; calculating the available energy of the current time period according to the decision variables of the current time period; obtaining the parameter values ​​of the joint dispatching rules according to the available energy of the current time period, the decision variables of the next time period and the proportion of power generation allocated to hydrogen production, and a pre-constructed linear fitting model.

[0087] Specifically, since available energy can effectively utilize prediction information, available energy is selected as the input variable. In this embodiment, the available energy of the current period can be calculated according to the decision variables of the current period by the following formula:

[0088]

[0089] In the formula, is the available energy in the tth period; is the available water and electricity in the tth period.

[0090] in, It consists of initial potential energy and potential power generation output, calculated as follows:

[0091]

[0092] In the formula, ΔH t is the average water head in the tth period.

[0093] In this embodiment, because the linear fitting model is easy to operate in practice, a linear scheduling rule can be used. In the multi-energy complementary system scheduling, power generation is the basis of hydrogen production. Therefore, the linear fitting model, i.e., the joint scheduling rule, is expressed as follows:

[0094]

[0095] Where D t+1 is the decision variable for the t+1th period; a j,t (j=1,2,3,4) is the parameter of the jth joint scheduling rule; u t+1 is the proportion of electricity generation allocated to hydrogen production in the t+1th period; N t+1 is the total output in the t+1th period; It is the output allocated to hydrogen production in the t+1th period.

[0096] That is to say, the decision variable D of the current period is obtained from the historical data to be solved. t , the decision variable D for the next period t+1 , u in the next period t+1 , and then according to the decision variable D in the current period t Calculate the available energy for the current period Then D t+1 、u t+1 and Substituting into the linear fitting model, we can get the parameter a of the joint dispatch rule: j,t .

[0097] Furthermore, an implicit random method is used to optimize the parameter values ​​of the joint scheduling rule to obtain the optimal parameter values; wherein the implicit random method is a non-dominated sorting genetic algorithm II.

[0098] The objective function and constraints are consistent with the deterministic optimization scheduling model. The non-dominated sorting genetic algorithm II is used to optimize the parameters. The formula is as follows:

[0099]

[0100] Where L is the two-dimensional objective function vector, L = [BC]; argmax is the maximum inverse function.

[0101] in, is the vector from January to December (T=12) corresponding to the jth optimal parameter; a j =[a j,1 a j,2 ...a j,t ...a j,T ](j=1,2,3,4),a j is the vector from January to December corresponding to the jth optimal parameter.

[0102] After obtaining the optimal parameter values, the optimal joint scheduling rules can be derived based on the optimal parameter values, and then used to predict the output variables of the multi-energy complementary system in the future period.

[0103] Specifically, the optimal parameter value can be substituted into the linear fitting model represented by formula (15), that is, the joint scheduling rule, so as to obtain the optimal linear fitting model, that is, the optimal joint scheduling rule based on the optimal parameter value, and then obtain the available energy of the current period, and substitute the available energy of the current period into the optimal linear fitting model to derive the decision variables of the future period, and use the derived decision variables of the future period to further obtain the proportion of power generation allocated to hydrogen production in the future period based on the optimal joint scheduling rule.

[0104] Therefore, this embodiment can obtain the joint dispatching rules through the joint dispatching rule derivation method, and based on the joint dispatching rules, the economy and power supply reliability of the long-term operation of the multi-energy complementary system can be accurately evaluated.

[0105] Next, taking the Ertan water-hydrogen-wind-solar complementary power station as an example, the data from January 1980 to December 2011 were used as input, and the initial and final water levels of the reservoir were set to 1180m.

[0106] Figure 2 The operating results obtained according to the optimal joint dispatch rule and the conventional dispatch diagram were compared. The water level change trends of the two operating schemes are similar. However, the hydrogen production ratios of the optimal joint dispatch rule and the conventional dispatch diagram are very different. The conventional dispatch diagram does not take into account the complex interaction between hydropower and hydrogen power. The hydrogen production ratio of the conventional dispatch diagram is fixed according to the installed capacity ratio. In contrast, the proposed joint dispatch rule takes into account the interaction between hydropower and hydrogen power. Therefore, the hydrogen production ratio can be flexibly adjusted to maximize the total operating benefits of power generation and hydrogen production. The results of the embodiment show that it is necessary to derive the water-hydrogen-wind-solar joint dispatch rule, which provides a new way for the efficient operation of complementary systems.

[0107] Figure 3 : is a structural block diagram of the system for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to an embodiment of the present invention. Figure 3 As shown, the water-hydrogen-wind-solar multi-energy complementary system joint scheduling rule derivation system 100 includes a first determination module 10, a construction module 20, a solution module 30, a second determination module 40, an optimization module 50 and a derivation module 60 which are connected in sequence.

[0108] In this embodiment, the first determination module 10 first determines the output of the multi-energy complementary system, and the construction module 20 establishes a deterministic optimization scheduling model based on the output of the multi-energy complementary system. The solution module 30 solves the deterministic optimization scheduling model to obtain the variables of the joint scheduling rules of the multi-energy complementary system, wherein the variables of the joint scheduling rules of the multi-energy complementary system include input variables and output variables. The second determination module 40 then determines the parameter values ​​of the joint scheduling rules based on the variables of the joint scheduling rules of the multi-energy complementary system. After determining the parameter values ​​of the joint scheduling rules, the optimization module 50 optimizes the parameter values ​​of the joint scheduling rules to obtain the optimal parameter values, so that the derivation module 60 derives the optimal joint scheduling rules based on the optimal parameter values, thereby facilitating the prediction of the output variables of the multi-energy complementary system in the future period. Therefore, the economy and power supply reliability of the multi-energy complementary system can be accurately evaluated through the above-mentioned joint scheduling rule derivation system.

[0109] It should be noted that the specific implementation manner of the system for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system in an embodiment of the present invention can be referred to the specific implementation manner of the method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system mentioned above. To avoid redundancy, it will not be repeated here.

[0110] In summary, the present invention provides a method and system for deriving joint dispatching rules for a water-hydrogen-wind-solar multi-energy complementary system, specifically, first determining the output of the multi-energy complementary system, then establishing a deterministic optimization dispatching model based on the output of the multi-energy complementary system, and then solving the deterministic optimization dispatching model to obtain the input variables and output variables of the joint dispatching rules of the multi-energy complementary system, and then obtaining the parameter values ​​of the joint dispatching rules based on the output variables, input variables and linear fitting models, and then optimizing the parameter values ​​of the joint dispatching rules to obtain the optimal parameter values, and then returning and substituting them into the linear fitting model to obtain the optimal linear fitting model, that is, the optimal joint dispatching rules, thereby, the output variable prediction of the multi-energy complementary system in the future period can be realized through the optimal linear fitting model. Therefore, the present invention can derive a joint dispatching rule, wherein the objective function of the deterministic optimization dispatching model includes maximizing the total dispatching benefit function and maximizing the guarantee rate function, thereby, the economy and power supply reliability of the long-term operation of the multi-energy complementary system can be accurately evaluated through the joint dispatching rule derivation method.

[0111] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0112] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. A method for deriving joint dispatching rules for a water-hydrogen-wind-solar multi-energy complementary system, characterized in that: include: Determine the output of the multi-energy complementary system; Establish a deterministic optimization scheduling model based on the output of the multi-energy complementary system; Solving the deterministic optimization scheduling model to obtain variables of the joint scheduling rules of the multi-energy complementary system; Based on the variables of the joint dispatching rule of the multi-energy complementary system, the parameter values ​​of the joint dispatching rule are determined, and the parameter values ​​of the joint dispatching rule are optimized to obtain the optimal parameter values, so as to derive the optimal joint dispatching rule based on the optimal parameter values.

2. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 1 is characterized in that: The output of the multi-energy complementary system includes hydropower output, hydrogen production, wind power output and photovoltaic power output.

3. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 2 is characterized in that: The hydropower output is determined based on the power generation efficiency, power generation flow and power generation head; the hydrogen production is determined based on the efficiency coefficient of the electrolyzer and the output allocated to hydrogen production; the wind power output is determined based on the wind speed; the photovoltaic power generation output is determined based on the rated photovoltaic output, the output temperature coefficient of the solar cell module, the temperature difference between the actual test conditions and the standard test conditions, the actual intensity of solar radiation and the intensity of solar radiation under standard test conditions.

4. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 1 is characterized in that: The objective function of the deterministic optimization scheduling model includes maximizing the total scheduling benefit function and maximizing the guarantee rate function; wherein the total scheduling benefit includes the scheduling benefit obtained from power generation and the scheduling benefit obtained from hydrogen production; the guarantee rate is the ratio of the total number of cycles in which the total output of the multi-energy complementary system is greater than or equal to the guaranteed output to the total number of cycles.

5. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 4 is characterized in that: The constraints of the deterministic optimization scheduling model include: water balance constraints, reservoir water storage constraints, reservoir discharge constraints, hydropower output constraints, transmission capacity constraints of the multi-energy complementary system and output constraints allocated to hydrogen production.

6. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 1 is characterized in that: The variables of the joint dispatching rules of the multi-energy complementary system include input variables and output variables; the input variables include available energy and available water; the output variables include decision variables and the proportion of power generation allocated to hydrogen production; wherein the decision variables include hydropower output, reservoir discharge, water level and reservoir water storage capacity.

7. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 6 is characterized in that: The parameter values ​​of the joint dispatch rule are determined based on the variables of the joint dispatch rule of the multi-energy complementary system, including: Obtaining the decision variables of the current period, the decision variables of the next period, and the proportion of power generation allocated to hydrogen production from the solved historical data; The available energy of the current period is calculated based on the decision variables of the current period; The parameter values ​​of the joint dispatching rules are obtained according to the available energy in the current period, the decision variables in the next period, the proportion of power generation allocated to hydrogen production, and the pre-built linear fitting model.

8. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 7 is characterized in that: An implicit random method is used to optimize the parameter values ​​of the joint scheduling rule to obtain the optimal parameter values; wherein the implicit random method is a non-dominated sorting genetic algorithm II.

9. The method for deriving joint dispatching rules for the water-hydrogen-wind-solar multi-energy complementary system according to claim 8 is characterized in that: The optimal joint scheduling rule is derived based on the optimal parameter value, including: substituting the optimal parameter value into the linear fitting model to derive the optimal joint scheduling rule.

10. A joint dispatching rule derivation system for a water-hydrogen-wind-solar multi-energy complementary system, characterized in that: include: A first determination module, used to determine the output of the multi-energy complementary system; A building block for establishing a deterministic optimal scheduling model based on the output of a multi-energy complementary system; A solution module, used for solving the deterministic optimization scheduling model to obtain variables of the joint scheduling rules of the multi-energy complementary system; A second determination module is used to determine the parameter value of the joint dispatch rule based on the variables of the joint dispatch rule of the multi-energy complementary system; The optimization module is used to optimize the parameter values ​​of the joint scheduling rules to obtain the optimal parameter values; the derivation module is used to derive the optimal joint scheduling rules based on the optimal parameter values.

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