Novel power system resource scheduling method considering heterogeneity influence of hydrogen energy storage technology

Abstract

The invention discloses a novel power system resource scheduling method considering hydrogen energy storage technology heterogeneity influence, and the method comprises the steps: constructing a novel power system with wind and light as a power generation main body and hydrogen energy storage as an energy storage system, analyzing the technical current situation of each assembly in the system, and depicting the hydrogen energy storage technology heterogeneity; during modeling, characteristics of system resources are analyzed, a novel power system resource scheduling model considering hydrogen energy storage technology heterogeneity is constructed with minimization of system daily operation cost and wind curtailment and light curtailment rate as a target, and an 'NSGA-II + scene simulation' integration method is designed to solve the scheduling model; finally, case data is introduced for multi-scene clustering analysis, sensitivity analysis is conducted on hydrogen energy storage technology heterogeneity and power system resource allocation according to typical daily data, and the method has high theoretical value for application of resource allocation and hydrogen energy storage in a novel power system; and related management suggestions for promoting hydrogen energy storage development can be given.

Description

Technical Field

[0001] The present invention relates to the field of power dispatching of new power systems, and in particular to a new power system resource dispatching method that takes into account the influence of heterogeneity of hydrogen energy storage technology. Background Art

[0002] With the large-scale development and utilization of new energy, the development of new energy faces multiple challenges such as high-level consumption problems and ensuring the stability of power supply. In the new power system, energy storage is a flexible regulation resource with the dual attributes of power supply and load, which is conducive to the large-scale access and effective consumption of renewable energy. Hydrogen energy storage, as a new energy storage technology, has shown unique advantages in solving long-term, large-scale energy storage needs and renewable energy integration; and the technical heterogeneity of hydrogen energy storage limits its development and large-scale application. The key technologies of the current hydrogen energy storage system mainly include hydrogen production, hydrogen storage and fuel cell power generation. The heterogeneity of these hydrogen energy storage technologies will have a great impact on the cost, renewable energy consumption rate and grid stability in the resource scheduling problem of the new power system, restricting the high-quality development and large-scale application of hydrogen energy storage in the new power system. Therefore, the impact of the technical heterogeneity of hydrogen energy storage in the resource scheduling of the new power system is an issue that needs to be studied urgently, which will make an important contribution to the application and large-scale development of hydrogen energy storage. Summary of the invention

[0003] The purpose of the present invention is to provide a new power system resource scheduling method that takes into account the impact of the heterogeneity of hydrogen energy storage technology. The method analyzes the impact of the technical heterogeneity of hydrogen energy storage in the power system, realizes the resource scheduling of the power system containing hydrogen energy storage, and promotes the large-scale application of hydrogen energy storage in the power system.

[0004] In order to achieve the above functions, the present invention designs a new power system resource scheduling method that takes into account the influence of the heterogeneity of hydrogen energy storage technology. For a power system with wind and solar power as the main power generation and hydrogen energy storage as the energy storage system, the following steps S1 to S4 are performed to complete the resource scheduling of the power system containing hydrogen energy storage:

[0005] Step S1: for the power system, based on the daily operation cost, daily investment and construction cost, daily operation and maintenance cost, transaction cost, and wind and solar power abandonment rate of the power system, a dispatch model of the power system under constraints is established, wherein the dispatch model aims to minimize the daily operation cost and wind and solar power abandonment rate of the power system;

[0006] Step S2: Collect meteorological data and load data of the power system within a preset time period, and select a typical day using a clustering algorithm;

[0007] Step S3: designing an NSGA-II+ operation simulation integrated algorithm for solving the dispatch model, using typical day meteorological data, local electricity prices, hydrogen prices, load data of the power system, scale parameters of each component and heterogeneity parameters of hydrogen energy storage technology as inputs of the NSGA-II+ operation simulation integrated algorithm, wherein the scale parameters of each component include photovoltaic scale parameters, wind scale parameters, electrolyzer scale parameters, hydrogen storage tank scale parameters and fuel cell scale parameters;

[0008] By running the simulation process, the daily operating cost and wind and solar power abandonment rate of the power system under a set of decision variables are obtained. The decision variables are optimized by the NSGA-II algorithm with the daily operating cost and wind and solar power abandonment rate of the power system as the fitness value, and the optimal scale parameters of each component of the power system are obtained.

[0009] Step S4: Conduct a sensitivity analysis of power system resource configuration based on the optimal scale parameters of each component of the power system to complete resource scheduling of the target power system.

[0010] Beneficial effects: Compared with the prior art, the advantages of the present invention include:

[0011] 1. The present invention considers the new energy storage hydrogen storage with broad development prospects, analyzes the heterogeneity of its key technologies and its characteristics, conducts heterogeneity modeling, studies the resource scheduling problem after it is connected to the new power system, and ensures the stable operation of the power grid through the fast response charging and discharging function, and realizes the friendly consumption of renewable energy, and obtains the optimal electrolytic cell technology and fuel cell technology in the hydrogen energy storage system during resource scheduling;

[0012] 2. When modeling the scheduling problem, the design goals are from the two perspectives of minimizing the daily operating cost of the system and the wind and solar power abandonment rate. The present invention designs the "NSGA-II+operation simulation" integrated algorithm to solve the scheduling plan. The operation process of the system is characterized by simulation operation, which better copes with the complexity of resource scheduling of the new power system. It has certain reference value and theoretical significance for the modeling and solution of resource scheduling problems of the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a structural diagram of a new power system based on hydrogen energy storage provided according to an embodiment of the present invention;

[0014] Figure 2 is a schematic diagram of the NSGA-II+ operation simulation integration algorithm provided according to an embodiment of the present invention;

[0015] Figure 3 is a flow chart of the NSGA-II+ operation simulation integration algorithm provided according to an embodiment of the present invention;

[0016] Figure 4 is a diagram of electrolyzer technology heterogeneity scheduling results provided according to an embodiment of the present invention;

[0017] Figure 5 is a diagram of heterogeneous scheduling results of hydrogen fuel cell technology provided according to an embodiment of the present invention;

[0018] Figure 6 is a diagram of the scheduling result of the energy storage system sensitivity analysis provided by an embodiment of the present invention;

[0019] Figure 7 is a power sensitivity analysis diagram of an electrolytic cell provided according to an embodiment of the present invention;

[0020] Figure 8 It is a sensitivity analysis diagram of the upper limit of power of a hydrogen storage tank provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0022] The novel power system resource scheduling method considering the influence of the heterogeneity of hydrogen energy storage technology provided in the embodiment of the present invention is for a power system with wind and solar power as the main power generation and hydrogen energy storage as the energy storage system, and performs the following steps S1 to S4 to complete the resource scheduling of the power system containing hydrogen energy storage:

[0023] Step S1: for the power system, based on the daily operation cost, daily investment and construction cost, daily operation and maintenance cost, transaction cost, and wind and solar power abandonment rate of the power system, a dispatch model of the power system under constraints is established, wherein the dispatch model aims to minimize the daily operation cost and wind and solar power abandonment rate of the power system;

[0024] Reference diagram of new power system structure based on hydrogen energy storage Figure 1 , the dispatch model of the power system is as follows:

[0025] minC s =C sc +C om +C deal

[0026]

[0027] In the formula, C s is the daily operating cost of the system; C sc C is the daily investment and construction cost of the system; om is the daily operation and maintenance cost of the system; C deal The transaction cost of buying electricity from the electricity market or selling hydrogen to the hydrogen market for the system; c b,scis the unit construction cost of equipment b, is the rated power of equipment b during operation, r is the discount rate, and Y b b is the service life of equipment, c is b,om is the operation and maintenance cost coefficient of equipment b, c e,t is the electricity price at time t, c H is the cost of purchasing hydrogen, P lack,t P is the amount of electricity purchased from the grid when power supply is insufficient at time t, H is the hydrogen produced on that day; θ rec is the system’s wind and solar curtailment rate, which represents the power system’s ability to absorb renewable energy. rec Refers to the percentage of renewable energy output that is not effectively utilized by the electricity load and the electrolytic cell in the total theoretical output of renewable energy; when the sum of the electricity load and the input power of the electrolytic cell is greater than the sum of the renewable energy output, θ rec =0;P loss,t is the amount of wind and solar power abandoned at time t, P PV,t is the theoretical output power of the solar photovoltaic system at time t, P WT,t is the theoretical output power of the wind turbine at time t.

[0028] The constraints of the power system include hydrogen storage technology heterogeneity constraints, renewable energy generation constraints, and power balance constraints.

[0029] The hydrogen energy storage technology heterogeneity constraints include water electrolysis hydrogen production technology heterogeneity constraints, hydrogen storage tank constraints, and fuel cell power generation technology heterogeneity constraints, which are as follows:

[0030] The heterogeneity constraint of water electrolysis hydrogen production technology is established based on the electrolyzer scale parameters and hydrogen energy storage technology heterogeneity parameters, as shown in the following formula:

[0031] P ET,t =P et,t (ω AE η AE +ω PEM η PEM )

[0032]

[0033] Where P ET,t represents the hydrogen produced by the electrolyzer at time t; P et,t represents the power input into the electrolytic cell at time t; η AE , η PEM They represent the efficiency of electrolyzer AE and electrolyzer PEM respectively; Indicates the rated power of the electrolytic cell; the above parameters belong to the scale parameters of the electrolytic cell;

[0034] ω AE,ω PEM ∈{0, 1}, indicating that one of the electrolyzers AE and PEM is selected as the electrolyzer of the power system, which belongs to the heterogeneity parameter of hydrogen energy storage technology;

[0035] The hydrogen storage tank constraint is established based on the hydrogen storage tank scale parameter, as shown in the following formula:

[0036]

[0037] Where P HYS,t and P HYS,t-1 Respectively represent the energy of hydrogen in the hydrogen storage tank at time t and time t-1; P ET,t It represents the energy of hydrogen produced by the electrolyzer and entering the hydrogen storage tank at time t; η HYS,in Indicates the efficiency of hydrogen produced by the electrolyzer entering the hydrogen storage tank, η HYS,out Indicates the efficiency of releasing hydrogen from the hydrogen storage tank for fuel cell power generation; P hfc,t represents the hydrogen energy released from the hydrogen storage tank to the fuel cell at time t; μ HYS ∈{0, 1}, to control the charging and discharging states of the hydrogen storage tank to be different at the same time; Indicates the rated hydrogen storage capacity of the hydrogen storage tank;

[0038] The above parameters belong to the scale parameters of hydrogen storage tanks;

[0039] The heterogeneity constraint of fuel cell power generation technology is established based on the fuel cell scale parameter and the hydrogen energy storage technology heterogeneity parameter, as shown in the following formula:

[0040] P HFC,t =P hfc,t (ω PEMFC η PEMFC +ω PAFC η PAFC +ω SOFC η SOFC +ω MCFC η MCFC +ω AFC η AFC )

[0041]

[0042] Where P HFC,t represents the electrical energy output by the fuel cell at time t; P hfc,t represents the hydrogen energy input into the fuel cell at time t; η PEMFC , η PAFC , η SOFC , η MCFC , η AFC Respectively represent the power generation efficiency of five types of fuel cells; Indicates the rated power of the fuel cell system;

[0043] The above parameters belong to fuel cell scale parameters;

[0044] ω PEMFC ,ω PAFC ,ω SOFC ,ω MCFC ,ω AFC ∈{0, 1}, indicating that one of the five fuel cells is selected to be configured in the power system, which is a heterogeneity parameter of hydrogen energy storage technology.

[0045] The renewable energy generation constraints include wind turbine generation constraints and solar photovoltaic generation constraints, which are as follows:

[0046] Wind turbine power generation constraints are established based on wind power size parameters as follows:

[0047] Without considering the time lag effect and wake effect of wind power generation, the theoretical output of the wind turbine is expressed as a function of wind speed:

[0048]

[0049] Where P WT,t represents the theoretical output power of the wind turbine at time t; P wt,t represents the actual output power of the wind turbine at time t; Indicates the rated power of the wind turbine; V W,t V is the wind speed of the wind turbine during the period t; rated is the rated wind speed of the wind turbine; V in is the cut-in wind speed for wind turbines; V out Cut out wind speed for wind turbines; the above parameters belong to wind scale parameters;

[0050] The solar photovoltaic power generation constraints are established based on the photovoltaic scale parameters, as shown in the following formula:

[0051]

[0052] Where P PV,t Represents the theoretical output power of the solar photovoltaic system at time t; P pv,t Represents the actual output power of the solar photovoltaic system at time t; Indicates the rated power of the photovoltaic system; f PV Represents the power reduction coefficient of the photovoltaic system; G t represents the light intensity at time t; G ref represents the reference light intensity; α represents the photovoltaic system power temperature coefficient; T t Indicates the actual temperature of the photovoltaic panel at time t; T refIndicates the operating reference temperature of the photovoltaic panel; the above parameters belong to photovoltaic scale parameters.

[0053] The power balance constraint is specifically as follows:

[0054] P wt,t +P pv,t +P HFC,t +P lack,t =P PEM,t +P load,t +P loss,t

[0055] Where P wt,t , P pv,t , P HFC,t They represent the actual output power of wind turbines, solar photovoltaics and hydrogen fuel cells at time t respectively; P PEM,t represents the input power of the electrolytic cell at time t; P load,t represents the electrical load at time t; P lack,t P represents the power shortage of the power system at time t, that is, the amount of power purchased from the power grid; loss,t Indicates the amount of wind and solar power curtailment at time t.

[0056] Step S2: Collect meteorological data and load data of the power system within a preset time period, and select a typical day using a clustering algorithm;

[0057] In step S2, meteorological data and load data of the power system within one year are collected, wherein the meteorological data includes wind speed data, light intensity data, and temperature data; based on the meteorological data and load data, a clustering algorithm is used to cluster the scenes, and the number of days corresponding to each type of scene and the probability of occurrence of the scene are obtained, wherein the probability of occurrence of each type of scene = the number of days the scene occurs / 365; according to the date on which each type of scene occurs, each type of scene is counted again into four seasons, and the number of days each type of scene occurs in each season is obtained by counting, and the scene with the most days is selected as the typical day data of the season, and finally the wind speed data, light intensity data, temperature data, and load data of the power system on typical days in the four seasons are obtained;

[0058] In one embodiment, the Kmeans clustering algorithm is used to cluster four types of data, namely wind speed data, light intensity data, temperature data and load data, into eight scenes, and the occurrence probability of each scene is greater than 1%.

[0059] Step S3: designing an NSGA-II+ operation simulation integrated algorithm for solving the dispatch model, using typical day meteorological data, local electricity prices, hydrogen prices, load data of the power system, scale parameters of each component and heterogeneity parameters of hydrogen energy storage technology as inputs of the NSGA-II+ operation simulation integrated algorithm, wherein the scale parameters of each component include photovoltaic scale parameters, wind scale parameters, electrolyzer scale parameters, hydrogen storage tank scale parameters and fuel cell scale parameters;

[0060] By running the simulation process, the daily operating cost and wind and solar power abandonment rate of the power system under a set of decision variables are obtained. The decision variables are optimized by the NSGA-II algorithm with the daily operating cost and wind and solar power abandonment rate of the power system as the fitness value, and the optimal scale parameters of each component of the power system are obtained.

[0061] The NSGA-II+ operation simulation integrated algorithm involves three modules, namely scenario design, algorithm design and comparative analysis. The main contents of the three modules and the relationship between the modules are as follows: Figure 2 As shown. The scenario design module provides parameter input for the algorithm. By obtaining one year's meteorological data and load data, cluster analysis is used to design typical daily scenarios representing different seasons, weather conditions and load conditions. These scenario parameters are used as parameter inputs for algorithm design and comparative analysis to reflect the operating status of the system under different conditions. The algorithm design module includes the integration of two key parts. The system simulation part simulates the operation of the power system based on the input of scenario parameters and hydrogen energy storage technology heterogeneity parameters, and obtains the model objectives under different parameters, namely the system daily operating cost and wind and solar abandonment rate. The NSGA-II intelligent optimization algorithm optimizes the simulation results to obtain the optimal component scale and system configuration plan. The two are integrated to complete the algorithm design. The comparative analysis module evaluates the impact of power system resource configuration and hydrogen energy storage technology heterogeneity under different scenarios through sensitivity analysis. The results of the algorithm design will directly affect the results of the comparative analysis and provide an important reference for system optimization and management.

[0062] The system simulation flow chart is as follows: Figure 3 As shown. The simulation method is used to characterize the system operation process. By inputting the heterogeneous parameters of hydrogen energy storage technology, the influence of different hydrogen energy storage technologies on the two objectives of the resource scheduling model is demonstrated. The simulation input data includes typical daily wind speed data, light intensity data, temperature data and load data given by scenario design, as well as the scale parameters of each component of the power system and the heterogeneous parameters of hydrogen energy storage technology optimized by the algorithm;

[0063] The specific steps of step S3 are as follows:

[0064] Step S3.1: Take the meteorological data of a typical day, the local electricity price, the hydrogen price, the load data of the power system, the scale parameters of each component and the heterogeneity parameters of the hydrogen energy storage technology as the input of the NSGA-II+ operation simulation integration algorithm;

[0065] Step S3.2: First, calculate the output power P of wind turbine power generation and solar photovoltaic power generation at time t based on typical daily meteorological data supply,t =P PV,t +P WT,t , and at the same time clearly define the power load demand P at time t load,t ;

[0066] According to the load following strategy, renewable energy output is first used to meet the load demand. If excess power P is generated, supply,t -P load,t , using excess electricity to produce hydrogen in the electrolyzer until the maximum capacity of the hydrogen storage tank is reached Or reach the rated power of the electrolyzer If there is still surplus electricity, wind and solar power will be abandoned. The amount of wind and solar power abandoned at time t is as follows:

[0067]

[0068] If renewable energy generation cannot meet the load demand, the fuel cell uses the hydrogen in the hydrogen storage tank to generate electricity until the minimum capacity of the hydrogen storage tank is reached or the rated power of the fuel cell is reached. If the load demand is still not met, the power system will purchase electricity from the power grid to make up for the shortfall. The amount of electricity purchased from the power grid at time t is as follows:

[0069]

[0070] Step S3.3: After supply and demand are balanced, based on the scale parameters of each component of the power system and the heterogeneity parameters of hydrogen energy storage technology, as well as the dispatch model of the power system constructed in step S1, output the daily operating cost and wind and solar power abandonment rate;

[0071] Step S3.4: Based on the NSGA-II algorithm, the scale parameters of each component of the power system are optimized with the daily operating cost and the wind and solar power abandonment rate as the fitness value, and the cycle is iterated until 24 hours to obtain the optimal scale parameters of the power system components on the typical day.

[0072] Step S4: Conduct a sensitivity analysis of power system resource configuration based on the optimal scale parameters of each component of the power system to complete resource scheduling of the target power system.

[0073] The system resource configuration sensitivity analysis is conducted on typical days in four seasons. By disturbing a parameter in the dispatch model and then performing comparative analysis based on the dispatch results, the impact of the parameter on the power system dispatch is discussed to achieve economical and efficient operation of the power system.

[0074] The results of the heterogeneous scheduling of electrolyzer technologies on a typical day in spring are as follows Figure 4 As shown in the figure, AE electrolyzer and PEM electrolyzer are selected to analyze the impact of electrolyzer technology heterogeneity on the resource dispatch of the new power system, and the impact of electrolyzer technology heterogeneity on the scale of new power system components and the daily operating cost of the system is analyzed under typical days in four seasons. The fuel cell is selected as PEMFC, and the point with zero wind and solar power abandonment rate in the dispatch result is selected. Taking the typical day result in spring as an example, the daily operating cost of the system is significantly lower when the AE electrolyzer is selected. This is because the cost of PEM electrolyzer is higher than that of AE electrolyzer. In addition, the AE electrolyzer shows obvious advantages over the PEM electrolyzer in dispatching objectives, and the same results are shown under different typical day scenarios and fuel cell technologies.

[0075] The results of heterogeneous scheduling of fuel cell technologies on a typical day in spring are as follows: Figure 5 As shown; select the more advantageous AE electrolyzer, analyze the impact of fuel cell technology heterogeneity on the resource scheduling of the new power system, the present invention considers five fuel cell technologies, namely PEMFC (proton exchange membrane fuel cell), AFC (alkaline fuel cell), PAFC (phosphoric acid fuel cell), MCFC (molten carbonate fuel cell) and SOFC (solid oxide fuel cell), analyze the impact of different fuel cell technologies on the daily operating cost of the system and the scale of each component of the system on typical days in four seasons. Select the point where the wind and solar abandonment rate is zero from the scheduling results, and the analysis result is that SOFC has a more advantageous performance in scheduling.

[0076] The dispatch results of the power system with and without hydrogen energy storage system on a typical day in spring are as follows Figure 6 As shown in the figure, the two situations are compared by taking AE electrolyzer and SOFC fuel cell technology as components of the hydrogen energy storage system; the new power system with hydrogen energy storage shows obvious advantages in scheduling targets. After adding the hydrogen energy storage system, the daily operating cost of the system and the wind and solar power abandonment rate can be reduced. When the wind and solar power abandonment rate is close to zero, the new power system with hydrogen energy storage shows more obvious cost advantages; the hydrogen energy storage system completes the consumption of renewable energy during the peak period of renewable energy generation, and as a supplementary power load during the low period of renewable energy generation, it achieves the effect of peak shaving and valley filling. The hydrogen energy storage system plays an important role in the economic operation of the power system and the friendly consumption of renewable energy.

[0077] Analysis of electrolytic cell power sensitivity on a typical spring day Figure 7As shown in the figure, the upper limits of the electrolyzer power are selected as 0kW, 500kW, 1000kW, 1500kW and 2000kW for sensitivity analysis. The power change of the electrolyzer has a greater impact on the dispatch. When the electrolyzer power is 0kW, that is, there is no electrolyzer component in the system, the dispatch result is the worst. When the upper limit of the electrolyzer power is 500kW, it is not enough to absorb all the excess renewable energy. Therefore, when the wind and solar power abandonment rate is low, the system cost is very high. As the wind and solar power abandonment rate gradually increases, the cost also drops rapidly. When the upper limit of the electrolyzer power is 1000kW, it is similar to the situation of 500kW, but the cost drops faster. When the upper limit of the electrolyzer power is 1500kW and 2000kW, the Pareto curves of the dispatch results are basically overlapped. In this implementation, it is more appropriate to set the upper limit of the electrolyzer power to 1500kW.

[0078] On a typical spring day, the dispatch results under different hydrogen storage tank power limits are as follows: Figure 8 As shown; the power of the hydrogen storage tank has the greatest impact on the scheduling results. As the upper limit of the hydrogen storage tank power continues to increase, the scheduling results of the system are getting better and better. The power of the hydrogen storage tank represents the hydrogen generated by the system. The present invention does not set the interaction between the hydrogen generated by the system and the hydrogen market. Considering the actual application, the upper limit of the hydrogen storage tank power is set to 5000kW in this implementation.

[0079] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in this field without departing from the purpose of the present invention.

Claims

1. A new power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology, characterized in that: For a power system with wind and solar power as the main power generation and hydrogen energy storage as the energy storage system, the following steps S1 to S4 are performed to complete the resource scheduling of the power system containing hydrogen energy storage: Step S1: for the power system, based on the daily operation cost, daily investment and construction cost, daily operation and maintenance cost, transaction cost, and wind and solar power abandonment rate of the power system, a dispatch model of the power system under constraints is established, wherein the dispatch model aims to minimize the daily operation cost and wind and solar power abandonment rate of the power system; Step S2: Collect meteorological data and load data of the power system within a preset time period, and select a typical day using a clustering algorithm; Step S3: designing an NSGA-II+ operation simulation integrated algorithm for solving the dispatch model, using typical day meteorological data, local electricity prices, hydrogen prices, load data of the power system, scale parameters of each component and heterogeneity parameters of hydrogen energy storage technology as inputs of the NSGA-II+ operation simulation integrated algorithm, wherein the scale parameters of each component include photovoltaic scale parameters, wind scale parameters, electrolyzer scale parameters, hydrogen storage tank scale parameters and fuel cell scale parameters; By running the simulation process, the daily operating cost and wind and solar power abandonment rate of the power system under a set of decision variables are obtained. The decision variables are optimized by the NSGA-II algorithm with the daily operating cost and wind and solar power abandonment rate of the power system as the fitness value, and the optimal scale parameters of each component of the power system are obtained. Step S4: Conduct a sensitivity analysis of power system resource configuration based on the optimal scale parameters of each component of the power system to complete resource scheduling of the target power system.

2. According to claim 1, the new power system resource scheduling method considering the heterogeneity of hydrogen energy storage technology is characterized in that: The constraints of the power system in step S1 include hydrogen energy storage technology heterogeneity constraints, renewable energy generation constraints, and power balance constraints.

3. The novel power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology according to claim 1 is characterized in that: The dispatch model of the power system described in step S1 is specifically as follows: minC s =C sc +C om +C deal In the formula, C s is the daily operating cost of the system; C sc C is the daily investment and construction cost of the system; om is the daily operation and maintenance cost of the system; C deal The transaction cost of buying electricity from the electricity market or selling hydrogen to the hydrogen market for the system; c b,sc is the unit construction cost of equipment b, is the rated power of equipment b during operation, r is the discount rate, and Y b b is the service life of equipment, c is b,om is the operation and maintenance cost coefficient of equipment b, c e , t is the electricity price at time t, c H is the cost of purchasing hydrogen, P lack,t P is the amount of electricity purchased from the grid when power supply is insufficient at time t, H is the hydrogen produced on that day; θ rec is the wind and solar curtailment rate of the system; when the sum of the electric load and the input power of the electrolytic cell is greater than the sum of the output of renewable energy, θ rec =0;P loss,t is the amount of wind and solar power abandoned at time t, P PV,t is the theoretical output power of the solar photovoltaic system at time t, P WT , t is the theoretical output power of the wind turbine at time t.

4. The novel power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology according to claim 2 is characterized in that: The hydrogen energy storage technology heterogeneity constraints include water electrolysis hydrogen production technology heterogeneity constraints, hydrogen storage tank constraints, and fuel cell power generation technology heterogeneity constraints, which are as follows: The heterogeneity constraint of water electrolysis hydrogen production technology is established based on the electrolyzer scale parameters and hydrogen energy storage technology heterogeneity parameters, as shown in the following formula: P ET,t =P et,t (oh AE or AE +oh PEM or PEM ) Where P ET,t represents the hydrogen produced by the electrolyzer at time t; P et,t represents the power input into the electrolytic cell at time t; η AE , η PEM They represent the efficiency of electrolyzer AE and electrolyzer PEM respectively; Indicates the rated power of the electrolytic cell; the above parameters belong to the scale parameters of the electrolytic cell; δ AE , δ PEM ∈{0, 1}, indicating that one of the electrolyzers AE and PEM is selected as the electrolyzer of the power system, which belongs to the heterogeneity parameter of hydrogen energy storage technology; The hydrogen storage tank constraint is established based on the hydrogen storage tank scale parameter, as shown in the following formula: Where P HYS,t and P HYS,t-1 Respectively represent the energy of hydrogen in the hydrogen storage tank at time t and time t-1; P ET,t It represents the energy of hydrogen produced by the electrolyzer and entering the hydrogen storage tank at time t; η HYS,in Indicates the efficiency of hydrogen produced by the electrolyzer entering the hydrogen storage tank, η HYS,out Indicates the efficiency of releasing hydrogen from the hydrogen storage tank for fuel cell power generation; P hfc,t represents the hydrogen energy released from the hydrogen storage tank to the fuel cell at time t; μ HYS ∈{0, 1}, to control the charging and discharging states of the hydrogen storage tank to be different at the same time; Indicates the rated hydrogen storage capacity of the hydrogen storage tank; The above parameters belong to the scale parameters of hydrogen storage tanks; The heterogeneity constraint of fuel cell power generation technology is established based on the fuel cell scale parameter and the hydrogen energy storage technology heterogeneity parameter, as shown in the following formula: P HFC,t =P hfc,t (oh PEMFC or PEMFC +oh PAFC or PAFC +oh SOFC or SOFC +oh MCFC or MCFC +oh AFC or AFC ) Where P HFC,t represents the electrical energy output by the fuel cell at time t; P hfc,t represents the hydrogen energy input into the fuel cell at time t; η PEMFC , η PAFC , η SOFC , η MCFC , η AFC Respectively represent the power generation efficiency of five types of fuel cells; Indicates the rated power of the fuel cell system; The above parameters belong to fuel cell scale parameters; ω PEMFC ,ω PAFC ,ω SOFC ,ω MCFC ,ω AFC ∈{0, 1}, indicating that one of the five fuel cells is selected to be configured in the power system, which is a heterogeneity parameter of hydrogen energy storage technology.

5. The novel power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology according to claim 2 is characterized in that: The renewable energy generation constraints include wind turbine generation constraints and solar photovoltaic generation constraints, which are as follows: Wind turbine power generation constraints are established based on wind power size parameters as follows: Without considering the time lag effect and wake effect of wind power generation, the theoretical output of the wind turbine is expressed as a function of wind speed: Where P WT,t represents the theoretical output power of the wind turbine at time t; P wt,t represents the actual output power of the wind turbine at time t; Indicates the rated power of the wind turbine; V W ,t is the wind speed of the wind turbine during the period t; V rated is the rated wind speed of the wind turbine; V in is the cut-in wind speed for wind turbines; V out Cut out wind speed for wind turbines; the above parameters belong to wind scale parameters; The solar photovoltaic power generation constraints are established based on the photovoltaic scale parameters, as shown in the following formula: 0≤P pv,t ≤P PV,t Where P PV ,t represents the theoretical output power of the solar photovoltaic system at time t; Ppv,t represents the actual output power of the solar photovoltaic system at time t; Indicates the rated power of the photovoltaic system; f PV Represents the power reduction coefficient of the photovoltaic system; G t represents the light intensity at time t; G ref represents the reference light intensity; α represents the photovoltaic system power temperature coefficient; T t Indicates the actual temperature of the photovoltaic panel at time t; T ref Indicates the operating reference temperature of the photovoltaic panel; the above parameters belong to photovoltaic scale parameters.

6. The novel power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology according to claim 2 is characterized in that: The power balance constraint is specifically as follows: P wt,t +P pv,t +P HFC，t +P lack,t =P PEM,t +P load,t +P loss,t Where P wt,t , P pv,t , P HFC,t They represent the actual output power of wind turbines, solar photovoltaics and hydrogen fuel cells at time t respectively; P PEM,t represents the input power of the electrolytic cell at time t; P load,t represents the electrical load at time t; P lack,t P represents the power shortage of the power system at time t; loss,t Indicates the amount of wind and solar power curtailment at time t.

7. The novel power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology according to claim 1 is characterized in that: The specific method of step S2 is as follows: In step S2, meteorological data and load data of the power system within one year are collected, wherein the meteorological data include wind speed data, light intensity data, and temperature data; based on the meteorological data and load data, a clustering algorithm is used to cluster the scenes, and the number of days corresponding to each type of scene and the probability of occurrence of this type of scene are obtained; according to the date on which each type of scene occurs, each type of scene is counted again into four seasons, and the number of days that each type of scene occurs in each season is obtained, and the scene with the most days is selected as the typical day data of the season, and finally the wind speed data, light intensity data, temperature data, and load data of the power system on typical days of the four seasons are obtained.

8. The novel power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology according to claim 1 is characterized in that: The specific steps of step S3 are as follows: Step S3.1: Take the meteorological data of a typical day, the local electricity price, the hydrogen price, the load data of the power system, the scale parameters of each component and the heterogeneity parameters of the hydrogen energy storage technology as the input of the NSGA-II+ operation simulation integration algorithm; Step S3.2: First, calculate the output power P of wind turbine power generation and solar photovoltaic power generation at time t based on typical daily meteorological data supply,t =P PV,t +P WT,t , and at the same time clearly define the power load demand P at time t load,t ; According to the load following strategy, renewable energy output is first used to meet the load demand. If excess power P is generated, supply,t -P load,t , using excess electricity to produce hydrogen in the electrolyzer until the maximum capacity of the hydrogen storage tank is reached Or reach the rated power of the electrolyzer If there is still surplus electricity, wind and solar power will be abandoned. The amount of wind and solar power abandoned at time t is as follows: If renewable energy generation cannot meet the load demand, the fuel cell uses the hydrogen in the hydrogen storage tank to generate electricity until the minimum capacity of the hydrogen storage tank is reached or the rated power of the fuel cell is reached. If the load demand is still not met, the power system will purchase electricity from the power grid to make up for the shortfall. The amount of electricity purchased from the power grid at time t is as follows: Step S3.3: After supply and demand are balanced, based on the scale parameters of each component of the power system and the heterogeneity parameters of hydrogen energy storage technology, as well as the dispatch model of the power system constructed in step S1, output the daily operating cost and wind and solar power abandonment rate; Step S3.4: Based on the NSGA-II algorithm, the scale parameters of each component of the power system are optimized with the daily operating cost and the wind and solar power abandonment rate as the fitness value, and the cycle is iterated until 24 hours to obtain the optimal scale parameters of the power system components on the typical day.

9. The novel power system resource dispatching method considering the heterogeneity of hydrogen energy storage technology according to claim 1 is characterized in that: The system resource configuration sensitivity analysis described in step S4 is performed by disturbing a parameter in the scheduling model, and then performing comparative analysis based on the scheduling results to discuss the impact of the parameter on the power system scheduling.

Images