A new power system resource scheduling method considering the heterogeneity of hydrogen energy storage technology

Abstract

The present invention discloses a new power system resource scheduling method that takes into account the influence of the heterogeneity of hydrogen energy storage technology. The present invention constructs a new power system with wind and solar power as the main power generation body and hydrogen energy storage as the energy storage system, analyzes the technical status of each component in the system, and characterizes the heterogeneity of hydrogen energy storage technology. When modeling, the characteristics of system resources are analyzed, and with the goal of minimizing the system's daily operating cost and the wind and solar power abandonment rate, a new power system resource scheduling model that takes into account the heterogeneity of hydrogen energy storage technology is constructed, and an "NSGA-II+scenario simulation" integrated method is designed to solve the scheduling model. Finally, case data is introduced for multi-scenario cluster analysis, and a sensitivity analysis of the heterogeneity of hydrogen energy storage technology and power system resource configuration is performed based on typical daily data. The present invention has high theoretical value for resource configuration and the application of hydrogen energy storage in new power systems, and can provide relevant management suggestions to promote the development of hydrogen energy storage.

Description

Technical Field

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

[0002] With the large-scale development and utilization of new energy, new energy development faces multiple challenges, including high-level absorption and ensuring stable power supply. In new power systems, energy storage is a flexible regulatory resource with the dual attributes of power source and load, facilitating the large-scale integration and effective absorption of renewable energy. Hydrogen energy storage, as a new energy storage technology, demonstrates unique advantages in addressing long-term, large-scale energy storage needs and renewable energy integration. However, the technological heterogeneity of hydrogen energy storage limits its development and large-scale application. Currently, the key technologies of hydrogen energy storage systems mainly include hydrogen production, hydrogen storage, and fuel cell power generation. These heterogeneous hydrogen energy storage technologies have a significant impact on the cost, renewable energy absorption rate, and grid stability of resource scheduling in new power systems, hindering the high-quality development and large-scale application of hydrogen energy storage in new power systems. Therefore, the impact of technological heterogeneity of hydrogen energy storage on resource scheduling in new power systems is an issue that urgently needs to be studied and will make a significant 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] To achieve the above functions, the present invention designs a new power system resource scheduling method that considers the impact 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, a dispatch model of the power system under constraints is established based on the daily operating cost, daily investment and construction cost, daily operation and maintenance cost, transaction cost, and wind and solar power curtailment rate of the power system. The dispatch model aims to minimize the daily operating cost and wind and solar power curtailment rate of the power system.

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

[0007] Step S3: Designing an NSGA-II+ operation simulation integrated algorithm for solving the scheduling 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 to 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 curtailment rate of the power system under a set of decision variables are obtained. The decision variables are optimized using the NSGA-II algorithm with the daily operating cost and wind and solar curtailment rate as the fitness value, and the optimal scale parameters of each power system component are obtained.

[0009] Step S4: Conduct a sensitivity analysis of power system resource configuration based on the optimal scale parameters of each power system component 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. This invention considers hydrogen energy storage, a new energy storage with broad development prospects, analyzes the heterogeneity of its key technologies and its characteristics, conducts heterogeneity modeling, and studies the resource scheduling problem after its connection to the new power system. Through the rapid response of charging and discharging functions, it effectively ensures the stable operation of the power grid and realizes the friendly consumption of renewable energy. In addition, it obtains the optimal electrolyzer technology and fuel cell technology in the hydrogen energy storage system during resource scheduling.

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

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

[0017] Figure 5 is a diagram showing heterogeneous scheduling results of hydrogen fuel cell technologies provided in an embodiment of the present invention;

[0018] Figure 6 This is a diagram showing the results of a sensitivity analysis and scheduling of an energy storage system according to 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 3 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 solutions of the present invention and are not intended to limit the scope of protection of the present invention.

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

[0023] Step S1: For the power system, a dispatch model of the power system under constraints is established based on the daily operating cost, daily investment and construction cost, daily operation and maintenance cost, transaction cost, and wind and solar power curtailment rate of the power system. The dispatch model aims to minimize the daily operating cost and wind and solar power curtailment 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 specifically as follows:

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

[0026]

[0027] Where C s is the daily operating cost of the system; C sc is the daily investment and construction cost of the system; C 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 The wind and solar curtailment rate of the system 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 electrolyzer in the total theoretical output of renewable energy. When the sum of the electricity load and the electrolyzer input power is greater than the sum of the renewable energy output, θ rec =0;P loss,t is the amount of wind and solar power curtailment 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 input power of the electrolytic cell at time t; η AE , η PEM 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 is a heterogeneous 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 Represents the energy of hydrogen in the hydrogen storage tank at time t and time t-1 respectively; P′ ET,t represents the hydrogen energy 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 hydrogen released from the hydrogen storage tank for fuel cell power generation; P hfc,t represents the hydrogen energy input to the fuel cell at time t; μ HYS ∈{0, 1}, where μ HYS =0 means the hydrogen storage tank is in the hydrogen release state, μ HYS =1 means the hydrogen storage tank is in the hydrogen filling state; 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 actual output power of the hydrogen fuel cell at time t; P hfc,t represents the hydrogen energy input to the fuel cell at time t; η PEMFC , η PAFC , η SOFC , η MCFC , η AFCRepresent the power generation efficiency of proton exchange membrane fuel cell, phosphoric acid fuel cell, solid oxide fuel cell, molten carbonate fuel cell, and alkaline fuel cell respectively; 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 heterogeneous 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 scale 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 period t; rated is the rated wind speed of the wind turbine; V in is the wind turbine cut-in wind speed; V out Cut out wind speed for wind turbines; the above parameters belong to wind scale parameters;

[0050] Solar photovoltaic power generation constraints are established based on 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 Indicates the power reduction coefficient of the photovoltaic system; G t represents the light intensity at time t; G refrepresents 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.

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

[0054] P wt,t +P pv,t +P HFC,t +P lack,t =P et,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; P et,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 amount of electricity purchased from the grid when power supply is insufficient at time t; loss,t Indicates the amount of wind and solar power curtailment at time t.

[0056] Step S2: collecting meteorological data and power system load data within a preset time period, and selecting 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 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 again counted into four seasons, and the number of days each type of scene occurs in each season is calculated. The scene with the most days is selected as the typical daily 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;

[0058] In one embodiment, a 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 scenarios, and the probability of occurrence of each scenario is greater than 1%.

[0059] Step S3: Designing an NSGA-II+ operation simulation integrated algorithm for solving the scheduling 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 to 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 curtailment rate of the power system under a set of decision variables are obtained. The decision variables are optimized using the NSGA-II algorithm with the daily operating cost and wind and solar curtailment rate as the fitness value, and the optimal scale parameters of each power system component are obtained.

[0061] The NSGA-II+ operation simulation integration algorithm involves three modules, namely scenario design, algorithm design and comparative analysis. The main contents of the three modules and the relationship between them are as follows: Figure 2 As shown in the figure, the scenario design module provides parameter inputs for the algorithm. By acquiring a year's worth of meteorological and load data and using cluster analysis, typical daily scenarios representing different seasons, weather conditions, and load profiles are designed. These scenario parameters are used as inputs for algorithm design and comparative analysis to reflect the system's operating status under different conditions. The algorithm design module integrates two key components. The system simulation component simulates the operation of the power system based on the input scenario parameters and hydrogen energy storage technology heterogeneity parameters, deriving model objectives under different parameters: the system's daily operating cost and wind and solar curtailment rate. The NSGA-II intelligent optimization algorithm optimizes the simulation results to determine the optimal component size and system configuration. The two components are integrated to complete the algorithm design. The comparative analysis module uses sensitivity analysis to evaluate the impact of power system resource allocation and hydrogen energy storage technology heterogeneity under different scenarios. The results of the algorithm design will directly influence the results of the comparative analysis, providing an important reference for system optimization and management.

[0062] The system simulation flow chart is as follows Figure 3 As shown in the figure, a simulation method is used to characterize the system operation process. By inputting the heterogeneous parameters of hydrogen energy storage technology, the impact 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 power system component 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 typical day meteorological data, local electricity prices, hydrogen prices, as well as power system load data, component size parameters, and hydrogen storage technology heterogeneity parameters as inputs to the NSGA-II+ run 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 , P pv,t Represents the actual output power of the solar photovoltaic system at time t, P wt,t Indicates the actual output power of the wind turbine at time t; at the same time, it specifies the electrical load 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 rated storage capacity of the hydrogen storage tank is reached Or reach the rated power of 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] P HYS,t Represents the energy of hydrogen in the hydrogen storage tank at time t; 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 system is reached If the load demand is still not met, the power system will purchase electricity from the grid to make up for the shortfall. The amount of electricity purchased from the 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 power system component and the heterogeneity parameters of hydrogen energy storage technology, as well as the power system dispatch model constructed in step S1, output the daily operating cost and wind and solar power curtailment 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 curtailment rate as the fitness value. The cycle is iterated for 24 hours to obtain the optimal scale parameters of the power system components for the typical day.

[0072] Step S4: Conduct a sensitivity analysis of power system resource configuration based on the optimal scale parameters of each power system component 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 scheduling model and then performing comparative analysis based on the scheduling results, the impact of the parameter on power system scheduling is discussed to achieve economical and efficient operation of the power system.

[0074] The results of electrolyzer technology heterogeneity scheduling on a typical spring day are as follows: Figure 4 As shown in the figure, AE and PEM electrolyzers were selected to analyze the impact of electrolyzer technology heterogeneity on the resource scheduling of the new power system. The impact of electrolyzer technology heterogeneity on the component scale and daily system operating cost of the new power system was analyzed under typical four-season days. The fuel cell was selected as PEMFC, and the point in the scheduling results where the wind and solar curtailment rate was zero was selected. Taking the results of a typical spring day as an example, the daily system operating cost was significantly lower when the AE electrolyzer was selected. This is due to the higher cost of PEM electrolyzers. Furthermore, the AE electrolyzer demonstrated a clear advantage over the PEM electrolyzer in terms of scheduling objectives, and the same results were observed under different typical day scenarios and fuel cell technologies.

[0075] The results of fuel cell technology heterogeneity scheduling on a typical day in spring are as follows: Figure 5 As shown in the figure, the more advantageous AE electrolyzer is selected to analyze the impact of fuel cell technology heterogeneity on the resource scheduling of the new power system. This paper considers five fuel cell technologies: 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). The impact of different fuel cell technologies on the daily operating cost of the system and the scale of each system component are analyzed under typical days in four seasons. From the scheduling results, the point with zero wind and solar curtailment rate is selected, and the analysis result shows 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 6As shown, the two situations are compared with 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 serves as a supplementary power load during the low period of renewable energy generation, achieving 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 7 As shown, a sensitivity analysis was conducted with electrolyzer power caps of 0 kW, 500 kW, 1000 kW, 1500 kW, and 2000 kW. Variations in electrolyzer power significantly impact scheduling. When the electrolyzer power is 0 kW, meaning there are no electrolyzer components in the system, the scheduling results are the worst. When the electrolyzer power cap is 500 kW, it is insufficient to fully absorb the excess renewable energy. Therefore, when the wind and solar power curtailment rates are low, the system costs are very high. As the curtailment rates increase, the costs decrease rapidly. When the electrolyzer power cap is 1000 kW, the situation is similar to the 500 kW case, but the costs decrease more rapidly. The Pareto curves for scheduling results with electrolyzer power caps of 1500 kW and 2000 kW are essentially identical. In this implementation, an electrolyzer power cap of 1500 kW is more appropriate.

[0078] On a typical spring day, the dispatch results under different hydrogen storage tank power limits are as follows: Figure 8 As shown in the figure, the power of the hydrogen storage tank has the greatest impact on the scheduling results. As the upper limit of the power of the hydrogen storage tank 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. Taking into account the actual application, the upper limit of the power of the hydrogen storage tank 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. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in this field without departing from the spirit of the present invention.

Claims

1. A new power system resource scheduling method considering the heterogeneity of hydrogen energy storage technology, characterized by: For a power system with wind and solar power as the main power generation and hydrogen energy storage as the energy storage system, execute the following steps S1 to S4 to complete the resource scheduling of the power system with hydrogen energy storage: Step S1: For the power system, a dispatch model of the power system under constraints is established based on the daily operating cost, daily investment and construction cost, daily operation and maintenance cost, transaction cost, and wind and solar power curtailment rate of the power system. The dispatch model aims to minimize the daily operating cost and wind and solar power curtailment rate of the power system. Power system constraints include hydrogen storage technology heterogeneity constraints, renewable energy generation constraints, and power balance constraints; 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 input power of the electrolytic cell at time t; η AE , η PEM 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; 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 is a heterogeneous 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 Represent the energy of hydrogen in the hydrogen storage tank at time t and time t-1 respectively; P′ ET,t represents the hydrogen energy 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 hydrogen released from the hydrogen storage tank for fuel cell power generation; P hfc,t represents the hydrogen energy input to the fuel cell at time t; μ HYS ∈{0, 1}, where μ HYS =0 means the hydrogen storage tank is in the hydrogen release state, μ HYS =1 means the hydrogen storage tank is in the hydrogen filling state; 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 actual output power of the hydrogen fuel cell at time t; P hfc,t represents the hydrogen energy input to the fuel cell at time t; η PEMFC , η PAFC , η SOFC , η MCFC , η AFC Represent the power generation efficiency of proton exchange membrane fuel cell, phosphoric acid fuel cell, solid oxide fuel cell, molten carbonate fuel cell, and alkaline fuel cell respectively; 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 heterogeneous parameter of hydrogen energy storage technology; Step S2: collecting meteorological data and power system load data within a preset time period, and selecting a typical day using a clustering algorithm; Step S3: Designing an NSGA-II+ operation simulation integrated algorithm for solving the scheduling 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 to 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 curtailment rate of the power system under a set of decision variables are obtained. The decision variables are optimized using the NSGA-II algorithm with the daily operating cost and wind and solar curtailment rate as the fitness value, and the optimal scale parameters of each power system component are obtained. Step S4: Conduct a sensitivity analysis of power system resource configuration based on the optimal scale parameters of each power system component to complete resource scheduling of the target power system.

2. The novel power system resource scheduling 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 Where C s is the daily operating cost of the system; C sc is the daily investment and construction cost of the system; C 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 system's wind and solar curtailment rate; when the sum of the electrical load and the electrolyzer input power is greater than the sum of the renewable energy output, θ rec =0;P loss,t is the amount of wind and solar power curtailment 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.

3. The novel power system resource scheduling method considering the heterogeneity of hydrogen energy storage technology according to claim 1 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 scale 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: 0≤P wt,t ≤P WT,t 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 period t; rated is the rated wind speed of the wind turbine; V in is the wind turbine cut-in wind speed; V out Cut out wind speed for wind turbines; the above parameters belong to wind scale parameters; Solar photovoltaic power generation constraints are established based on photovoltaic scale parameters, as shown in the following formula: 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 Indicates 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.

4. The novel power system resource scheduling method considering the heterogeneity of hydrogen energy storage technology according to claim 1 is characterized in that: The power balance constraint is specifically expressed as follows: P wt,t +P pv,t +P HFC,t +P lack,t =P et,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; P et,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 amount of electricity purchased from the grid when power supply is insufficient at time t; loss,t Indicates the amount of wind and solar power curtailment at time t.

5. The novel power system resource scheduling 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 the scene are obtained. According to the date on which each type of scene occurs, each type of scene is again counted into four seasons, and the number of days that each type of scene occurs in each season is obtained. The scene with the most days is selected as the typical daily 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.

6. The novel power system resource scheduling 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 typical day meteorological data, local electricity prices, hydrogen prices, as well as power system load data, component size parameters, and hydrogen storage technology heterogeneity parameters as inputs to the NSGA-II+ run 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 , P pv,t Represents the actual output power of the solar photovoltaic system at time t, P wt,t Indicates the actual output power of the wind turbine at time t; at the same time, it specifies the electrical load 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 rated storage capacity of the hydrogen storage tank is reached Or reach the rated power of 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: P HYS,t Represents the energy of hydrogen in the hydrogen storage tank at time t; 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 system is reached If the load demand is still not met, the power system will purchase electricity from the grid to make up for the shortfall. The amount of electricity purchased from the grid at time t is as follows: Step S3.3: After supply and demand are balanced, based on the scale parameters of each power system component and the heterogeneity parameters of hydrogen energy storage technology, as well as the power system dispatch model constructed in step S1, output the daily operating cost and wind and solar power curtailment 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 curtailment rate as the fitness value. The cycle is iterated for 24 hours to obtain the optimal scale parameters of the power system components for the typical day.

7. The novel power system resource scheduling 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.

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