Resource allocation method and device of integrated energy base and readable storage medium

Abstract

The invention relates to a resource allocation method and device for a comprehensive energy base and a readable storage medium, and the method comprises the steps: constructing a plurality of objective functions which comprise a power supply stability function, a hydrogen supply stability function and an optimal economy function; constructing an incidence relation function and a fitness function among the plurality of objective functions to form a multi-objective optimization model; setting a plurality of constraint conditions, wherein the constraint conditions comprise a power supply system constraint condition, a hydrogen supply system constraint condition and a hybrid energy storage system constraint condition; and calculating the multi-objective optimization model under the plurality of constraint conditions by adopting a Harlisia eagle optimization algorithm so as to obtain an optimal solution of the resource allocation scheme of the comprehensive energy base. On the basis of ensuring the hydrogen supply stability, the scheme capable of balancing the power supply stability and the investment economy is rapidly screened out from multiple configuration schemes, and therefore the overall economy and reliability of the system are further improved.

Description

Technical Field

[0001] The present application relates to the field of wind, solar and hydrogen storage technology, and specifically to a resource allocation method, device and readable storage medium for an integrated energy base. Background Art

[0002] The power industry connects the energy supply side and the demand side, and plays an important role in achieving the dual carbon goals. In order to implement the dual carbon goals, the central government proposed to build a new power system with new energy as the main body, but new energy has not yet formed a reliable alternative. Although the installed capacity of new energy in my country has reached 30%, the actual reliable capacity accounts for only 5%. Among the elements of the new power system construction, energy storage is a key technology to smooth the randomness, intermittency and volatility of new energy, and has multiple and multi-time scale application scenarios. Before 2030, new energy storage will mainly play a role in intraday regulation, and will mainly develop short-term energy storage at the hourly level. In the long-term transformation and development of the power system, in order to solve the problem of cross-seasonal balance regulation caused by the seasonal mismatch between new energy output and power load, it is necessary to develop long-term energy storage such as hydrogen storage and heat (cold) storage, and promote the organic combination of multi-time scale energy storage.

[0003] Hydrogen energy has the characteristics of low carbon and high energy density. As a long-term, large-scale energy storage method for large-scale new energy bases, it can effectively solve the problem of uncertainty in power generation output. Hydrogen energy has the dual characteristics of source and load, which creates a profitable model for energy suppliers to invest in multi-energy delivery, and will enable large-scale new energy bases to have the characteristics of load following source. At present, many domestic and foreign scholars have conducted research on the configuration of large-scale new energy bases of wind, solar, hydrogen and storage. However, under the concept of load following source, there are few research results on the capacity configuration methods of large-scale wind, solar, hydrogen and storage energy bases that are grid-friendly, and most of the research content is to use the storage capacity of hydrogen energy to improve the output stability of the power generation system, without considering the economic value of electricity-hydrogen dual energy delivery for energy suppliers' investment. Summary of the invention

[0004] To solve the above problems, the present application provides a resource allocation method, device and readable storage medium for a comprehensive energy base. Starting from the "impossible triangle" of the new power system of "safe, stable, clean, low-carbon and economically feasible", it innovatively proposes a capacity optimization allocation strategy that takes into account multi-dimensional goals, and pioneeringly proposes a resource allocation method for a grid-friendly green electricity-green hydrogen dual-delivery energy base that balances power supply stability and investment economy on the basis of ensuring hydrogen supply stability.

[0005] This application adopts the following technical solutions:

[0006] In a first aspect, the present application provides a resource allocation method for a comprehensive energy base, comprising:

[0007] Constructing a plurality of objective functions, wherein the objective functions include: a power supply stability function, a hydrogen supply stability function, and an optimal economy function;

[0008] Constructing a correlation function and a fitness function between the multiple objective functions to form a multi-objective optimization model, wherein the correlation function is a function that takes into account both power supply stability and investment economy on the basis of ensuring hydrogen supply stability;

[0009] Setting a plurality of constraints, wherein the constraints include: power supply system constraints, hydrogen supply system constraints, and hybrid energy storage system constraints;

[0010] The Harris Eagle optimization algorithm is used to calculate the multi-objective optimization model under the multiple constraints to obtain the optimal solution for the resource allocation plan of the comprehensive energy base.

[0011] Optionally, in the above method, calculating the multi-objective optimization model under the multiple constraints to obtain the optimal solution of the resource allocation plan of the integrated energy base includes:

[0012] The first calculation stage: using the Harris Eagle optimization algorithm, taking the sodium battery energy storage system as the calculation unit, to calculate the frequency regulation capability, so as to screen out the first optimal solution of the sodium battery energy storage system configuration scheme from the initial solutions of multiple sodium battery energy storage system configuration schemes randomly generated in the search space, wherein the first optimal solution includes: sodium battery energy storage system configuration parameters, objective function values ​​and algorithm performance indicators;

[0013] The second calculation stage: using the Harris Eagle optimization algorithm, based on the first optimal solution, and taking the hybrid energy storage system working priority as the operation mode, the hybrid energy storage capacity configuration calculation is performed to screen out the second optimal solution of the resource configuration scheme of the hybrid energy storage system from the multiple initial resource configuration solutions randomly generated in the search space. The second optimal solution includes: the installed capacity of the sodium battery energy storage system and the installed capacity of the hydrogen energy storage system.

[0014] Optionally, in the above method, the first calculation stage specifically includes:

[0015] Initialize the parameters required for the first calculation stage;

[0016] Randomly generating initial solutions of multiple energy storage configuration schemes of the sodium battery energy storage system in a search space;

[0017] Calculate the objective function of each of the initial solutions, and based on the fitness function; obtain the optimality of each of the energy storage configuration schemes, and take the one with the largest optimality as the current optimal solution;

[0018] The following steps are executed repeatedly until the iteration requirements are met, and the one with the largest optimum is selected as the first optimal solution:

[0019] A solution is randomly selected as the current benchmark solution, and the current optimal candidate solution is generated based on the benchmark solution. The generation formula is: new =X best -A×|X r -X best |, where X best is the current optimal solution, X r is a randomly selected solution, and A is the control parameter;

[0020] Based on the fitness function, determining whether the current optimal candidate solution is better than the current optimal solution;

[0021] If the current optimal candidate solution is better than the current optimal solution, the current optimal candidate solution is used as the current optimal solution, and the random selection solution is used as the current reference solution, and the current optimal candidate solution is generated according to the reference solution;

[0022] If the current optimal candidate solution is not better than the current optimal solution, then judging whether the current optimal candidate solution satisfies the balanced constraint condition in the association relationship function, and if so, proceeding to judging whether the multiple constraint conditions are satisfied;

[0023] If the current optimal candidate solution satisfies the multiple constraints, the current optimal candidate solution is still retained, and the process returns to the step of taking the randomly selected solution as the current reference solution and generating the current optimal candidate solution according to the reference solution.

[0024] Optionally, in the above method, the first calculation stage specifically further includes:

[0025] If the current optimal candidate solution does not satisfy the balanced constraint conditions of the association function, or if the current optimal candidate solution does not satisfy the multiple constraints; then the current optimal candidate solution is penalized, and the current optimal candidate solution and its degree of superiority are updated, and a randomly selected solution is returned as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution, or the initial solutions of the multiple energy storage configuration schemes are reset.

[0026] Optionally, in the above method, the second calculation stage specifically includes:

[0027] Initialize the parameters required for the second calculation stage;

[0028] Randomly generating initial solutions of multiple hybrid energy storage system configuration schemes of the hybrid energy storage system in a search space;

[0029] Setting at least part of the plurality of constraints based on the first calculation result;

[0030] Calculate the objective function of each of the initial solutions, and based on the fitness function; obtain the optimality of each of the energy storage configuration schemes, and take the one with the largest optimality as the current optimal solution;

[0031] The following steps are executed repeatedly until the iteration requirements are met, and the one with the largest optimum is selected as the first optimal solution:

[0032] A solution is randomly selected as the current benchmark solution, and the current optimal candidate solution is generated based on the benchmark solution. The generation formula is: new =X best -A×|X r -X best | ， Among them, X best is the current optimal solution, X r is a randomly selected solution, and A is the control parameter;

[0033] Based on the fitness function, determining whether the current optimal candidate solution is better than the current optimal solution;

[0034] If the current optimal candidate solution is better than the current optimal solution, the current optimal candidate solution is used as the current optimal solution, and the random selection solution is used as the current reference solution, and the current optimal candidate solution is generated according to the reference solution;

[0035] If the current optimal candidate solution is not better than the current optimal solution, then judging whether the current optimal candidate solution satisfies the balanced constraint condition in the association relationship function, and if so, proceeding to judging whether the multiple constraint conditions are satisfied;

[0036] If the current optimal candidate solution satisfies the multiple constraints, the current optimal candidate solution is still retained, and the process returns to the step of taking the randomly selected solution as the current benchmark solution, and generating the current optimal candidate solution according to the benchmark solution;

[0037] If the current optimal candidate solution does not satisfy the balanced constraint conditions of the association function, or if the current optimal candidate solution does not satisfy the multiple constraints; then the current optimal candidate solution is penalized, and the current optimal candidate solution and its degree of superiority are updated, and a randomly selected solution is returned as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution, or the initial solutions of the multiple energy storage configuration schemes are reset.

[0038] Optionally, in the above method, the power supply stability function is constructed according to the following method:

[0039] Define the target output capacity of the power supply system as P Target (i) The actual output of renewable energy is P Renewable (i), the power supply stability function is expressed as formula (1):

[0040]

[0041] Among them, n is the total number of samples, i is the data sampling point, when P Renewable (i) / P Target (i) When it is greater than 1, the value of the power supply stability function P1 is 1, otherwise, the value of the power supply stability function P1 is the original value;

[0042] The hydrogen supply stability function is constructed according to the following method:

[0043] The probability that the daily output of the hydrogen energy system reaches the annual average daily output is defined as P2, and the hydrogen supply stability function is expressed as formula (2):

[0044]

[0045] Among them, N year is the number of working days of the hydrogen energy system, and the value range of y is [0, 365]; N hour is the number of working hours of the hydrogen energy system per day, and the value range of t is [0, 24]; P Hydr is the working power of the hydrogen energy system; W Hydr_average The average daily power output of the hydrogen energy system;

[0046] Among them, if P Hydr (t)-W Hydr_average ≥1, then The value of is 1; if P Hydr (t)-W Hydr_average <1, then The value of is 0;

[0047] The optimal economic function is expressed as formula (3):

[0048] C total =MIN(C w&s +C h +C Na ) formula (3);

[0049] Among them, C total is the total cost of the system throughout its life cycle, C w&s , C h , C Na They are respectively the power supply system cost, the hydrogen energy system cost, and the sodium ion battery system cost, wherein the sodium ion battery system is at least a part of the hybrid energy storage system.

[0050] Optionally, in the above method, constructing a correlation function and a fitness function between the multiple objective functions to form a multi-objective optimization model includes:

[0051] Construct the correlation function between the multiple objective functions, expressed as formula (4) and formula (5):

[0052] {P1(x) max ,Ctotal(x) min Formula (4);

[0053] P2(x)≥P2(threshold) Formula (5);

[0054] Among them, formula (5) is the balance constraint condition in the correlation function, x represents the resource allocation plan, and P2 (threshold) is the threshold of hydrogen supply stability;

[0055] The original fitness function and the penalty fitness function are constructed to obtain the fitness function, wherein the original fitness function is expressed as formula (6):

[0056] F(x)=ω1×P1-ω3×C total Formula (6);

[0057] The penalty fitness function is expressed as formula (7):

[0058] F(x)=ω1×P1-ω2×[P2(threshold)-P2]-ω3×C total Formula (7);

[0059] Among them, F(x) represents the original fitness function, ω1, ω2, and ω3 are all positive numbers, representing the weights of power supply stability, hydrogen supply stability, and optimal economy, respectively, and x represents the resource allocation plan.

[0060] Optionally, in the above method, the power supply system constraint condition includes: an installed capacity condition of the power supply system, and a power abandonment rate condition of the power supply system;

[0061] The installed capacity condition of the power supply system is expressed as formula (8):

[0062]

[0063] Among them, S w is the multiple of the unit wind turbine, with the upper limit being a1; S s is a multiple of the unit photovoltaic, with an upper limit of a2; S e is the energy storage installed capacity, with an upper limit of a3;

[0064] The power abandonment rate condition of the power supply system is expressed as formula (9):

[0065] Pquit(t)∈[Pquit,min(t),Pquit,max(t)] Formula (9);

[0066] Among them, Pqui t (t) represents the power abandonment rate at time t, Pquit,min ( t ) and Pquit,max ( t ) are the lower and upper limits of the power abandonment rate at that moment respectively;

[0067] The constraints of the hydrogen supply system include: a hydrogen production power condition of the hydrogen supply system, a hydrogen storage working rate condition of the hydrogen supply system, and a hydrogen storage capacity condition of the hydrogen supply system;

[0068] The hydrogen production power condition of the hydrogen supply system is expressed as formula (10):

[0069]

[0070] Among them, P H_work is the working power of hydrogen production equipment, in kg / h; η H_real is the real-time hydrogen production efficiency, in %; P H_real is the real-time available power of the hydrogen production equipment, in kg / h; P rated is the rated power of the hydrogen production equipment, in kg / h;

[0071] The hydrogen storage working rate condition of the hydrogen supply system includes a first hydrogen storage working rate during the inflation process and a second hydrogen storage working rate during the deflation process, wherein the first hydrogen storage working rate is expressed as formula (11):

[0072]

[0073] Among them, M out is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out ;

[0074] The second hydrogen storage working rate is expressed as formula (12):

[0075]

[0076] Among them, Mout is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out ;

[0077] The hydrogen storage capacity condition of the hydrogen supply system includes: the hydrogen storage capacity condition of the hydrogen supply system;

[0078] The hydrogen storage capacity condition of the hydrogen supply system is expressed as formula (13):

[0079] M H =M H_storage ·max[S Hs_in ,S Hs_out ] Formula (13);

[0080] Among them, M H is the minimum capacity of the hydrogen storage system, in kg. Hs_in ,S Hs_out ] is not an integer, its value is rounded up to be used as the number of hydrogen storage tanks;

[0081] The hybrid energy storage system constraint conditions include priority conditions and a utilization rate of the hydrogen energy storage system of the hybrid energy storage system;

[0082] The priority condition is: giving priority to using the sodium battery energy storage system for charging and discharging, and starting the hydrogen fuel cell system when the sodium battery energy storage system is insufficient;

[0083] The utilization rate of the hydrogen energy storage system of the hybrid energy storage system is expressed as formula (14);

[0084]

[0085] Among them, N H_Electric Working hours for hydrogen fuel cell system; N H_Production Working hours for hydrogen production system; N Electric Working hours for wind and solar power generation systems.

[0086] In a second aspect, the present application further provides a resource allocation device for a comprehensive energy base, the device comprising:

[0087] A first construction unit is used to construct a plurality of objective functions, wherein the objective functions include: a power supply stability function, a hydrogen supply stability function, and an optimal economy function;

[0088] A second construction unit is used to construct an association relationship function and a fitness function between the multiple objective functions to form a multi-objective optimization model, wherein the association relationship function is a function that takes into account both power supply stability and investment economy on the basis of ensuring hydrogen supply stability;

[0089] A setting unit, used to set a plurality of constraints, wherein the constraints include: power supply system constraints, hydrogen supply system constraints, and hybrid energy storage system constraints;

[0090] The configuration unit is used to calculate the multi-objective optimization model under the multiple constraints using the Harris Hawk optimization algorithm to obtain the optimal solution for the resource configuration plan of the comprehensive energy base.

[0091] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned resource allocation method for the integrated energy base when executing the computer program.

[0092] In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program, which, when instructed by a processor, implements the steps of the resource allocation method for the above-mentioned integrated energy base.

[0093] At least one of the above technical solutions adopted in this application can achieve the following beneficial effects:

[0094] This application innovatively proposes a capacity optimization configuration strategy that takes into account multi-dimensional goals. On the basis of ensuring the stability of hydrogen supply, it balances the stability of power supply and the economic efficiency of investment. It is a grid-friendly green electricity-green hydrogen dual-delivery energy base capacity optimization configuration method; the introduction of a comprehensive energy storage system in the energy storage link reduces the energy storage cost, optimizes the energy storage efficiency and service life, and effectively improves the frequency regulation response speed and stability of the system, so that large new energy bases can make adjustments more quickly and stably when responding to grid frequency fluctuations; in the resource allocation calculation stage, an advanced Harris Eagle optimization algorithm is used. By simulating the predation behavior of the Harris Eagle in nature, it can quickly find the optimal solution in a complex and multi-dimensional search space, improve the accuracy and efficiency of wind and solar storage capacity calculations, and make the configuration plan of the large energy base more scientific and reasonable. Through the Harris Eagle optimization algorithm, on the basis of ensuring the stability of hydrogen supply, a plan that can balance the stability of power supply and the economic efficiency of investment is quickly screened out from a large number of configuration plans, thereby further improving the overall economy and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0096] Figure 1 A schematic diagram showing a process of a resource configuration method for a comprehensive energy base according to an embodiment of the present application is shown;

[0097] Figure 2 A schematic diagram of a multi-objective optimization model calculation process according to an embodiment of the present application is shown;

[0098] Figure 3 A schematic diagram of the structure of a resource allocation device for a comprehensive energy base according to an embodiment of the present application is shown;

[0099] Figure 4 A schematic structural diagram of a computer device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0101] The idea of ​​this application is to take into account both power supply stability and investment economy on the basis of ensuring the stability of hydrogen supply, improve the frequency response level of large-scale new energy bases with the help of power regulation of hydrogen load, enhance the output stability of large-scale new energy bases by relying on hybrid energy storage systems, and quantify the actual reliable capacity of new energy with the output stability and frequency regulation capability of large-scale wind, solar, and hydrogen storage bases as the goal, and construct a capacity optimization configuration method for large-scale wind, solar, and hydrogen storage energy bases with load-following power delivery of electricity and hydrogen dual energy.

[0102] Figure 1 A schematic diagram of a resource allocation method for an integrated energy base according to an embodiment of the present application is shown. Figure 1 It can be seen that this embodiment includes steps S110 to S140:

[0103] Step S110: construct multiple objective functions, including: a power supply stability function, a hydrogen supply stability function and an optimal economy function.

[0104] This application aims to find the best resource allocation method for large wind, solar, hydrogen and storage energy bases in the "impossible triangle" of the new power system of "safe, stable, clean, low-carbon and economically feasible".

[0105] First, the objective function is set. In this application, there are multiple objective functions, mainly including power supply stability, hydrogen supply stability and optimal economy.

[0106] In this application, the power supply system mainly refers to wind power and photovoltaic power systems. The hydrogen energy system mainly includes a hydrogen supply system and a hydrogen storage system. A hybrid energy storage system refers to a system composed of a variety of energy storage methods. In the present invention, it mainly refers to a combination of sodium ion batteries and hydrogen energy storage. Its advantages are: 1. Complementarity: Sodium ion batteries have a fast response speed and are suitable for short-term frequency modulation; hydrogen energy storage capacity is large and suitable for long-term energy storage; 2. Efficiency improvement: The two energy storage methods can work together to improve overall efficiency; 3. Economy: The cost of sodium ion batteries is relatively low, which can reduce the cost of energy storage. The sodium ion battery system is a component of the hybrid energy storage system, responsible for short-term frequency modulation and peak-shaving tasks, and its advantages are: 1. Fast response speed: Sodium ion batteries have a fast charge and discharge speed and can quickly respond to grid frequency fluctuations; 2. High efficiency: Sodium ion batteries have high energy conversion efficiency and low loss; 3. Long service life: Sodium ion batteries have a long cycle life and long service life. Therefore: The sodium ion battery system is a component of the hybrid energy storage system, responsible for short-term frequency modulation and peak-shaving tasks. The two energy storage methods can work together to improve system efficiency and economy.

[0107] Specifically, in some embodiments, the power supply stability function may be set according to the following method:

[0108] Define the target output capacity of the power supply system as P Target (i) The actual output of renewable energy is P Renewable (i), the power supply stability function is expressed as formula (1):

[0109]

[0110] Among them, n is the total number of samples, i is the data sampling point, when P Renewable (i) / P Target (i) is greater than 1, the value of the power supply stability function P1 is 1, otherwise, the value of the power supply stability function P1 is the original value.

[0111] The hydrogen supply system needs to achieve stable hydrogen delivery on the basis of ensuring the output stability of the power supply system. Therefore, the hydrogen supply stability function can be constructed according to the following method:

[0112] The probability that the daily output of the hydrogen energy system reaches the annual average daily output is defined as P2, and the hydrogen supply stability function is expressed as formula (2):

[0113]

[0114] Among them, N year is the number of working days of the hydrogen energy system, and the value range of y is [0, 365]; N hour is the number of working hours of the hydrogen energy system per day, and the value range of t is [0, 24]; P Hydr is the working power of the hydrogen energy system; W Hydr_average The average daily power output of the hydrogen energy system;

[0115] Among them, if P Hydr (t)-W Hydr_average ≥1, then The value of is 1; if P Hydr (t)-W Hydr_average <1, then The value of is 0; that is, the value of the numerator in P2 is as follows:

[0116]

[0117] There is no unique capacity configuration scheme that meets the stability target of power supply and hydrogen supply system. It is necessary to find the capacity configuration combination with the lowest cost among various schemes. The optimal economic function is expressed as formula (3):

[0118] C total =MIN(C w&s +C h +C Na ) formula (3);

[0119] Among them, C total is the total cost of the system throughout its life cycle, C w&s , C h , C Na They are respectively the cost of the power supply system (wind power and photovoltaic), the cost of the hydrogen energy system (hydrogen production and storage system and hydrogen fuel cell), and the cost of the sodium ion battery system, wherein the sodium ion battery system is at least part of the hybrid energy storage system.

[0120] Step S120: constructing a correlation function and a fitness function among the multiple objective functions to form a multi-objective optimization model, wherein the correlation function is a function that takes into account both power supply stability and investment economy on the basis of ensuring hydrogen supply stability.

[0121] The relationship between the three objective functions of power supply stability, hydrogen supply stability and optimal economy is constructed. On the basis of ensuring the stability of hydrogen supply, the optimal configuration scheme that takes into account both power supply stability and investment economy is selected to achieve the optimal configuration and efficient utilization of resources. This is a multi-objective optimization problem.

[0122] Specifically, in some embodiments, the multi-objective optimization model mainly includes a correlation function between multiple objective functions and a fitness function.

[0123] Among them, the correlation function between multiple objective functions can be expressed as formula (4) and formula (5):

[0124] {P1(x) max ,Ctotal(x) min Formula (4);

[0125] P2(x)≥P2(threshold) Formula (5);

[0126] Among them, formula (5) can be considered as the balanced constraint condition in the association function, x represents the resource allocation plan, and P2 (threshold) is the threshold of hydrogen supply stability.

[0127] The fitness function is used to evaluate the quality of a configuration scheme, and its value can be recorded as the degree of fitness. The higher the degree of fitness of a configuration scheme, the better the scheme is.

[0128] The fitness function includes an original fitness function and a penalty fitness function formed by adding a penalty to the original fitness function, wherein the original fitness function is expressed as formula (6):

[0129] F(x)=ω1×P1-ω3×C total Formula (6);

[0130] The penalty fitness function is expressed as formula (7):

[0131] F(x)=ω1×P1-ω2×[P2(threshold)-P2]-ω3×C total Formula (7);

[0132] Among them, F(x) represents the original fitness function, ω1, ω2, and ω3 are all positive numbers, representing the weights of power supply stability, hydrogen supply stability, and optimal economy, respectively, and x represents the resource allocation plan.

[0133] Step S130: setting a plurality of constraints, wherein the constraints include: power supply system constraints, hydrogen supply system constraints and hybrid energy storage system constraints.

[0134] Set constraints, including power supply system constraints, hydrogen supply system constraints, and hybrid energy storage system constraints.

[0135] The power supply system constraint conditions include: installed capacity conditions of the power supply system, and power abandonment rate conditions of the power supply system.

[0136] The installed capacity of wind power and photovoltaic power will affect the selection of energy storage installed capacity. The installed capacity condition of the power supply system can be expressed as formula (8):

[0137]

[0138] Among them, S w is the multiple of the unit wind turbine, with the upper limit being a1; S s is a multiple of the unit photovoltaic, with an upper limit of a2; S e It is the installed capacity of energy storage, and its upper limit is a3.

[0139] Power abandonment rate in the power supply system: As a source of electric energy, the utilization rate of electric energy must be considered. The present invention sets dynamically changing upper and lower limits of power abandonment rate according to different time periods, seasons or new energy output conditions. During periods of high output of new energy, the upper limit of power abandonment rate is appropriately increased to make full use of new energy; during peak power consumption or when the system regulation capacity is limited, the upper limit of power abandonment rate is lowered to ensure power supply stability. Therefore, the power abandonment rate condition of the power supply system can be expressed as formula (9):

[0140] Pquit(t)∈[Pquit,min(t),Pquit,max(t)] Formula (9);

[0141] Among them, P quit (t) represents the power abandonment rate at time t, P quit,min(t) and P quit,max(t) are the lower and upper limits of the power abandonment rate at that moment respectively.

[0142] The constraints of the hydrogen supply system include: a hydrogen production power condition of the hydrogen supply system, a hydrogen storage working rate condition of the hydrogen supply system, and a hydrogen storage capacity condition of the hydrogen supply system.

[0143] To ensure the safety of hydrogen production, the hydrogen production equipment needs to work in a range above 10% of the rated power. The constraint condition of the influence of power supply system output fluctuation on hydrogen production efficiency, that is, the hydrogen production power condition of the hydrogen supply system can be expressed as formula (10):

[0144]

[0145] Among them, P H_work is the working power of hydrogen production equipment, in kg / h; η H_real is the real-time hydrogen production efficiency, in %; P H_real is the real-time available power of the hydrogen production equipment, in kg / h; P rated It is the rated power of hydrogen production equipment, in kg / h.

[0146] The hydrogen storage working rate condition of the hydrogen supply system includes a first hydrogen storage working rate during the inflation process and a second hydrogen storage working rate during the deflation process. When the hydrogen production rate of the hydrogen storage device is higher than the hydrogen storage intake rate, the problem of hydrogen not being able to be fully stored will occur. The mass flow rate of the hydrogen storage device during the working process refers to a type III 40L hydrogen tank, wherein the first hydrogen storage working rate is expressed as formula (11):

[0147]

[0148] Among them, M out is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out ;

[0149] When the degassing rate cannot meet the hydrogen consumption rate requirement of the fuel cell, the fuel cell still cannot reach the rated power when the hydrogen reserves are sufficient. Therefore, the second hydrogen storage working rate of the degassing process of the hydrogen storage device can be expressed as formula (12):

[0150]

[0151] Among them, M out is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out .

[0152] The hydrogen storage capacity condition of the hydrogen supply system includes: the hydrogen storage capacity condition of the hydrogen supply system.

[0153] The hydrogen storage system should meet the hydrogen production and fuel cell hydrogen flow rate requirements, so the hydrogen storage capacity condition of the hydrogen supply system can be expressed as formula (13):

[0154] M H =M H_storage ·max[S Hs_in ,S Hs_out ] Formula (13);

[0155] Among them, M H is the minimum capacity of the hydrogen storage system, in kg. Hs_in ,S Hs_out] is not an integer, its value is rounded up and used as the number of hydrogen storage tanks.

[0156] The hybrid energy storage system constraint conditions include priority conditions and a hydrogen energy storage system utilization rate of the hybrid energy storage system.

[0157] Among them, the priority condition is: give priority to the use of sodium battery energy storage system charging and discharging, and start the hydrogen fuel cell system when the sodium battery energy storage system is insufficient. PEMFC has poor efficiency when the operating power is low. Considering the energy utilization efficiency, the sodium battery energy storage system is given priority to charge and discharge, participate in peak load and frequency modulation response, and start the hydrogen fuel cell system when its capacity is insufficient.

[0158] The utilization rate of the hydrogen energy storage system in the hybrid energy storage system part of the constraint conditions: The construction capacity of hydrogen energy storage is large, and the system capacity is in a long-term idle state, which will reduce the economic efficiency of investment. Uilization To measure the rationality of its investment scale, some embodiments of this application will Uilization As an adjustable parameter, when the hydrogen energy storage utilization rate is set to be lower than a certain value, such as 60%, the capacity configuration scheme is not selected. Therefore, the hydrogen energy storage system utilization rate of the hybrid energy storage system can be expressed as formula (14);

[0159]

[0160] Among them, N H_Electric Working hours for hydrogen fuel cell system; N H_Production Working hours for hydrogen production system; N Electric Working hours for wind and solar power generation systems.

[0161] Step S140: using the Harris Hawk optimization algorithm to calculate the multi-objective optimization model under the multiple constraints to obtain the optimal solution for the resource allocation plan of the integrated energy base.

[0162] The load-following source dynamic wind, solar, and hydrogen storage large base planning algorithm uses the Harris Eagle algorithm to solve the following multi-objective optimization problem. It is mainly divided into two stages. The first stage completes the frequency regulation capability matching and calculates the wind and solar energy storage capacity; the second stage completes the hybrid energy storage capacity configuration, so as to select the optimal configuration scheme that takes into account both power supply stability and investment economy on the basis of ensuring the stability of hydrogen supply, so as to achieve the optimal configuration and efficient utilization of resources; at the same time, it must meet the constraints, including the power supply system, hydrogen supply system, hybrid energy storage system, etc.

[0163] Specifically, the first calculation stage includes: using the Harris Eagle optimization algorithm, taking the sodium battery energy storage system as a calculation unit, and performing frequency modulation capacity calculation, so as to screen out the first optimal solution of the sodium battery energy storage system configuration scheme from multiple sodium battery energy storage system configuration schemes randomly generated in the search space, wherein the first optimal solution includes: sodium battery energy storage system configuration parameters, objective function values, and algorithm performance indicators;

[0164] The second calculation stage includes: using the Harris Eagle optimization algorithm, based on the first optimal solution, and taking the hybrid energy storage system working priority as the operating mode, to perform hybrid energy storage capacity configuration calculation, so as to screen out the second optimal solution of the resource configuration scheme of the hybrid energy storage system from multiple resource configuration preliminary solutions randomly generated in the search space, and the second optimal solution includes: the installed capacity of the sodium battery energy storage system and the installed capacity of the hydrogen energy storage system.

[0165] Optionally, in the above method, the first calculation stage mainly includes (1) initialization parameters; (2) random initialization, randomly generating multiple sodium battery (wind and solar) energy storage system configuration schemes in the search space; (3) calculating the objective function, and evaluating the power supply stability, hydrogen supply stability and optimal economy of each configuration scheme; (4) iterative optimization, using exploration and utilization strategies to update the Harris Eagle position; ensuring that the hydrogen supply stability constraint is met; calculating the frequency regulation capability, evaluating the objective function; retaining the better configuration scheme, and applying penalties to the schemes that do not meet the constraints; 5. After each iteration, record the current optimal configuration scheme. The final result is a set of optimal configuration parameters, specifically including the optimal sodium battery energy storage system configuration parameters, the objective function value (comprehensive evaluation results of power supply stability, hydrogen supply stability and optimal economy) and the algorithm performance indicators (number of iterations, calculation time, etc.).

[0166] For more details, please refer to Figure 2 , Figure 2 A schematic diagram of the process flow of multi-objective optimization model calculation according to an embodiment of the present application is shown. Figure 2 It can be seen that the use of the Harris Eagle optimization algorithm mainly includes: initialization, where the parameters required for the first calculation stage are specifically initialized; then the initial solutions of multiple energy storage configuration schemes of the sodium battery energy storage system are randomly generated in the search space (the Harris Eagle position is randomly initialized, and each position is a solution); then the objective function value is calculated, and the objective function of each of the initial solutions is specifically calculated, and based on the fitness function, the optimality of each of the energy storage configuration schemes is obtained, and the one with the largest optimality is taken as the current optimal solution; then the main loop is entered, and the main loop ends when the iteration requirements are met. The iteration requirements can be formulated as needed. If the set maximum number of iterations is reached or the quality of the solution is no longer significantly improved, the loop can be ended. The loop process is as follows:

[0167] A solution is randomly selected as the current benchmark solution, and the current optimal candidate solution is generated based on the benchmark solution. The generation formula is: new =X best -A×|X r -X best |, where X best is the current optimal solution, Xr is a randomly selected solution (benchmark solution), and A is the control parameter;

[0168] Based on the fitness function, determine whether the current optimal candidate solution is better than the current optimal solution. The larger the fitness function, the better the solution.

[0169] If the current optimal candidate solution is better than the current optimal solution, the current optimal candidate solution is used as the current optimal solution, and the random selection solution is used as the current reference solution, and the current optimal candidate solution is generated according to the reference solution;

[0170] If the current optimal candidate solution is not better than the current optimal solution, determine whether the current optimal candidate solution satisfies the balanced constraint conditions in the association function. If so, proceed to determine whether the multiple constraints are satisfied. If the current optimal candidate solution does not satisfy the balanced constraint conditions of the association function, penalize the current optimal candidate solution, update the current optimal candidate solution and its quality, return to the randomly selected solution as the current benchmark solution, and generate the current optimal candidate solution based on the benchmark solution. Alternatively, the initial solutions of the multiple energy storage configuration schemes may be reset (this method is not shown in the figure).

[0171] If the current optimal candidate solution satisfies the multiple constraints, the current optimal candidate solution is retained, and the random selection solution is returned to as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution. If the current optimal candidate solution does not meet the multiple constraints, the initial solutions of the multiple energy storage configuration schemes are reset, that is, all Harris Hawk positions are updated, or the current optimal candidate solution is penalized, and the current optimal candidate solution and its superiority are updated, and the random selection solution is returned to as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution (this method is not shown in the figure).

[0172] The second calculation stage is basically the same as the first calculation stage, and the second calculation stage specifically includes: initializing the parameters required for the second calculation stage; randomly generating the initial solutions of multiple hybrid energy storage system configuration schemes of the hybrid energy storage system in the search space; setting at least part of the multiple constraints based on the first calculation result; calculating the objective function of each of the initial solutions, and based on the fitness function; obtaining the optimality of each of the energy storage configuration schemes, and taking the one with the largest optimality as the current optimal solution; looping the following steps until the iteration requirements are met, and selecting the one with the largest optimality as the first optimal solution: randomly selecting a solution as the current benchmark solution, and generating the current optimal candidate solution based on the benchmark solution, and the generation formula is: X new =X best -A×|X r -X best |, where X best is the current optimal solution, Xr is the randomly selected solution, and A is the control parameter; based on the fitness function, determine whether the current optimal candidate solution is better than the current optimal solution; if the current optimal candidate solution is better than the current optimal solution, then use the current optimal candidate solution as the current optimal solution, return to the randomly selected solution as the current benchmark solution, and generate the current optimal candidate solution according to the benchmark solution; if the current optimal candidate solution is not better than the current optimal solution, then determine whether the current optimal candidate solution satisfies the balanced constraint conditions in the association relationship function, and if so, proceed to determine whether the multiple constraints are satisfied; if If the current optimal candidate solution satisfies the multiple constraints, the current optimal candidate solution is retained, and the randomly selected solution is returned to as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution; if the current optimal candidate solution does not satisfy the balanced constraint of the association function, or if the current optimal candidate solution does not satisfy the multiple constraints; the current optimal candidate solution is penalized, the current optimal candidate solution and its priority are updated, and the randomly selected solution is returned to as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution, or the initial solutions of the multiple energy storage configuration schemes are reset.

[0173] Both stages use the Harris Hawk algorithm, which has the following advantages over other algorithms: (1) It relies on simple iterative processes and rules and has lower algorithmic complexity; (2) It does not require an explicit probability model and is more flexible in dealing with non-Gaussian and nonlinear problems; (3) The algorithm is designed with multiple stages and strategies to simulate the hunting behavior of a falcon, has strong local search capabilities, and helps to quickly converge to the optimal solution in the solution space; (4) Through different exploration and utilization strategies, it can maintain the diversity of the population, which helps to avoid the algorithm from falling into local optimality; (5) It is easier to handle constraint problems through the penalty function mechanism; (6) It has fewer parameters and is easier to adjust and optimize; (7) It has strong adaptability and high robustness; (8) It has the potential for parallelization, thereby accelerating the search process.

[0174] After the cycle is completed, the best solution is selected from all the solutions (Harris Hawk positions) as the final solution, that is, the final resource allocation plan, including P1, P2 and C total Etc., that is, the installed capacity of wind power, photovoltaic power, and energy storage.

[0175] Depend on Figure 1 It can be seen from the method shown that the present application has innovatively proposed a capacity optimization configuration strategy that takes into account multi-dimensional objectives. On the basis of ensuring the stability of hydrogen supply, it balances the stability of power supply and the economic efficiency of investment. It is a grid-friendly green electricity-green hydrogen dual-delivery energy base capacity optimization configuration method; the introduction of a comprehensive energy storage system in the energy storage link reduces the energy storage cost, optimizes the energy storage efficiency and service life, and effectively improves the frequency regulation response speed and stability of the system, so that large new energy bases can make adjustments more quickly and stably when responding to grid frequency fluctuations; in the resource allocation calculation stage, an advanced Harris Eagle optimization algorithm is used. By simulating the predation behavior of the Harris Eagle in nature, it can quickly find the optimal solution in a complex and multi-dimensional search space, improve the accuracy and efficiency of wind and solar storage capacity calculation, and make the configuration plan of the energy base more scientific and reasonable. Through the Harris Eagle optimization algorithm, on the basis of ensuring the stability of hydrogen supply, a plan that can balance the stability of power supply and the economic efficiency of investment is quickly screened out from a large number of configuration plans, thereby further improving the overall economy and reliability of the system.

[0176] Figure 3 A schematic diagram of the structure of a resource allocation device for a comprehensive energy base according to an embodiment of the present application is shown. Figure 3 It can be seen that the resource allocation device 300 of the comprehensive energy base includes:

[0177] A first construction unit 310 is used to construct a plurality of objective functions, wherein the objective functions include: a power supply stability function, a hydrogen supply stability function, and an optimal economy function;

[0178] The second construction unit 320 is used to construct a correlation function and a fitness function between the multiple objective functions to form a multi-objective optimization model, wherein the correlation function is a function that takes into account both power supply stability and investment economy on the basis of ensuring hydrogen supply stability;

[0179] A setting unit 330, configured to set a plurality of constraints, wherein the constraints include: power supply system constraints, hydrogen supply system constraints, and hybrid energy storage system constraints;

[0180] The configuration unit 340 is used to adopt the Harris Hawk optimization algorithm to calculate the multi-objective optimization model under the multiple constraints to obtain the optimal solution of the resource configuration plan of the comprehensive energy base.

[0181] In some embodiments, in the above apparatus, under the plurality of constraints, the configuration unit 340 is specifically configured to perform the first calculation phase and the second calculation phase, wherein:

[0182] The first calculation stage: using the Harris Eagle optimization algorithm, taking the sodium battery energy storage system as the calculation unit, to calculate the frequency regulation capability, so as to screen out the first optimal solution of the sodium battery energy storage system configuration scheme from the initial solutions of multiple sodium battery energy storage system configuration schemes randomly generated in the search space, wherein the first optimal solution includes: sodium battery energy storage system configuration parameters, objective function values ​​and algorithm performance indicators;

[0183] The second calculation stage: using the Harris Eagle optimization algorithm, based on the first optimal solution, and taking the hybrid energy storage system working priority as the operation mode, the hybrid energy storage capacity configuration calculation is performed to screen out the second optimal solution of the resource configuration scheme of the hybrid energy storage system from the multiple initial resource configuration solutions randomly generated in the search space. The second optimal solution includes: the installed capacity of the sodium battery energy storage system and the installed capacity of the hydrogen energy storage system.

[0184] In some embodiments, in the above device, the configuration unit 340 is more specifically used to initialize the parameters required for the first calculation stage; randomly generate the initial solutions of multiple energy storage configuration schemes of the sodium battery energy storage system in the search space; calculate the objective function of each of the initial solutions, and based on the fitness function; obtain the superiority of each of the energy storage configuration schemes, and take the one with the largest superiority as the current optimal solution; loop the following steps until the iteration requirements are met, and select the one with the largest superiority as the first optimal solution: randomly select a solution as the current benchmark solution, and generate the current optimal candidate solution according to the benchmark solution, and the generation formula is: X new =X best -A×|X r -X best |, where X bestis the current optimal solution, Xr is the randomly selected solution, and A is the control parameter; based on the fitness function, determine whether the current optimal candidate solution is better than the current optimal solution; if the current optimal candidate solution is better than the current optimal solution, then use the current optimal candidate solution as the current optimal solution, return to the randomly selected solution as the current benchmark solution, and generate the current optimal candidate solution according to the benchmark solution; if the current optimal candidate solution is not better than the current optimal solution, then determine whether the current optimal candidate solution satisfies the balanced constraint conditions in the association relationship function, and if so, proceed to determine whether the multiple constraints are satisfied; if the current optimal candidate solution satisfies the multiple constraints, then retain the current optimal candidate solution, return to the randomly selected solution as the current benchmark solution, and generate the current optimal candidate solution according to the benchmark solution.

[0185] In some embodiments, in the above-mentioned device, the configuration unit 340 is more specifically used to punish the current optimal candidate solution if the current optimal candidate solution does not satisfy the balanced constraint condition of the association function, or if the current optimal candidate solution does not satisfy the multiple constraints; and update the current optimal candidate solution and its priority, and return a randomly selected solution as the current benchmark solution, and generate the current optimal candidate solution according to the benchmark solution, or reset the initial solution of the multiple energy storage configuration schemes.

[0186] In some embodiments, in the above device, the configuration unit 340 is more specifically used to initialize the parameters required for the second calculation stage; randomly generate the initial solutions of multiple hybrid energy storage system configuration schemes of the hybrid energy storage system in the search space; set at least part of the multiple constraints based on the first calculation result; calculate the objective function of each of the initial solutions, and based on the fitness function; obtain the optimality of each of the energy storage configuration schemes, and take the one with the largest optimality as the current optimal solution; loop the following steps until the iteration requirements are met, and select the one with the largest optimality as the first optimal solution: randomly select a solution as the current benchmark solution, and generate the current optimal candidate solution based on the benchmark solution, and the generation formula is: X new =X best -A×|X r -X best |, where X bestis the current optimal solution, Xr is the randomly selected solution, and A is the control parameter; based on the fitness function, determine whether the current optimal candidate solution is better than the current optimal solution; if the current optimal candidate solution is better than the current optimal solution, then use the current optimal candidate solution as the current optimal solution, return to the randomly selected solution as the current benchmark solution, and generate the current optimal candidate solution according to the benchmark solution; if the current optimal candidate solution is not better than the current optimal solution, then determine whether the current optimal candidate solution satisfies the balanced constraint conditions in the association relationship function, and if so, proceed to determine whether the multiple constraints are satisfied; if If the current optimal candidate solution satisfies the multiple constraints, the current optimal candidate solution is retained, and the randomly selected solution is returned to as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution; if the current optimal candidate solution does not satisfy the balanced constraint of the association function, or if the current optimal candidate solution does not satisfy the multiple constraints; the current optimal candidate solution is penalized, the current optimal candidate solution and its priority are updated, and the randomly selected solution is returned to as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution, or the initial solutions of the multiple energy storage configuration schemes are reset.

[0187] In some embodiments, in the above device, the first construction unit 310 is specifically used to define the target output capacity of the power supply system as P Target (i) The actual output of renewable energy is P Renewable (i), the power supply stability function is expressed as formula (1):

[0188]

[0189] Among them, n is the total number of samples, i is the data sampling point, when P Renewable (i) / P Target (i) When it is greater than 1, the value of the power supply stability function P1 is 1, otherwise, the value of the power supply stability function P1 is the original value;

[0190] The first construction unit 310 is further used to define the probability that the daily output of the hydrogen energy system reaches the annual average daily output as P2, and the hydrogen supply stability function is expressed as formula (2):

[0191]

[0192] Among them, N year is the number of working days of the hydrogen energy system, and the value range of y is [0, 365]; N hour is the number of working hours of the hydrogen energy system per day, and the value range of t is [0, 24]; P Hydr is the working power of the hydrogen energy system; W Hydr_averageThe average daily power output of the hydrogen energy system;

[0193] Among them, if P Hydr (t)-W Hydr_average ≥1, then The value of is 1; if P Hydr (t)-W Hydr_average <1, then The value of is 0;

[0194] The first construction unit 310 is further used to construct an optimal economic function, which is expressed as formula (3):

[0195] C total =MIN(C w&s +C h +C Na ) formula (3);

[0196] Among them, C total is the total cost of the system throughout its life cycle, C w&s , C h , C Na They are respectively the power supply system cost, the hydrogen energy system cost, and the sodium ion battery system cost, wherein the sodium ion battery system is at least a part of the hybrid energy storage system.

[0197] In some embodiments, in the above device, the second construction unit 320 is specifically used to construct the correlation relationship function between the multiple objective functions, which is expressed as formula (4) and formula (5):

[0198] {P1(x) max ,Ctotal(x) min Formula (4);

[0199] P2(x)≥P2(threshold) Formula (5);

[0200] Among them, formula (5) is the balance constraint condition in the correlation function, x represents the resource allocation plan, and P2 (threshold) is the threshold of hydrogen supply stability;

[0201] The second construction unit 320 is further configured to construct an original fitness function and a penalty fitness function to obtain the fitness function, wherein the original fitness function is expressed as formula (6):

[0202] F(x)=ω1×P1-ω3×C total Formula (6);

[0203] The penalty fitness function is expressed as formula (7):

[0204] F(x)=ω1×P1-ω2×[P2(threshold)-P2]-ω3×C total Formula (7);

[0205] Among them, F(x) represents the original fitness function, ω1, ω2, and ω3 are all positive numbers, representing the weights of power supply stability, hydrogen supply stability, and optimal economy, respectively, and x represents the resource allocation plan.

[0206] In some embodiments, in the above device, the second construction unit 320 is specifically used to construct the power supply system constraint conditions including: the installed capacity condition of the power supply system, and the power abandonment rate condition of the power supply system;

[0207] The installed capacity condition of the power supply system is expressed as formula (8):

[0208]

[0209] Among them, S w is the multiple of the unit wind turbine, with the upper limit being a1; S s is a multiple of the unit photovoltaic, with an upper limit of a2; S e is the energy storage installed capacity, with an upper limit of a3;

[0210] The power abandonment rate condition of the power supply system is expressed as formula (9):

[0211]

[0212] Among them, Pqui t (t) represents the power abandonment rate at time t, Pquit,min ( t ) and Pquit,max ( t ) are the lower and upper limits of the power abandonment rate at that moment respectively;

[0213] The constraints of the hydrogen supply system include: a hydrogen production power condition of the hydrogen supply system, a hydrogen storage working rate condition of the hydrogen supply system, and a hydrogen storage capacity condition of the hydrogen supply system;

[0214] The hydrogen production power condition of the hydrogen supply system is expressed as formula (10):

[0215]

[0216] Among them, P H_work is the working power of hydrogen production equipment, in kg / h; η H_real is the real-time hydrogen production efficiency, in %; P H_real is the real-time available power of the hydrogen production equipment, in kg / h; P rated is the rated power of the hydrogen production equipment, in kg / h;

[0217] The hydrogen storage working rate condition of the hydrogen supply system includes a first hydrogen storage working rate during the inflation process and a second hydrogen storage working rate during the deflation process, wherein the first hydrogen storage working rate is expressed as formula (11):

[0218]

[0219] Among them, M out is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out ;

[0220] The second hydrogen storage working rate is expressed as formula (12):

[0221]

[0222] Among them, M out is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out ;

[0223] The hydrogen storage capacity condition of the hydrogen supply system includes: the hydrogen storage capacity condition of the hydrogen supply system;

[0224] The hydrogen storage capacity condition of the hydrogen supply system is expressed as formula (13):

[0225] M H =M H_storage ·max[S Hs_in ,S Hs_out ] Formula (13);

[0226] Among them, M H is the minimum capacity of the hydrogen storage system, in kg. Hs_in ,S Hs_out ] is not an integer, its value is rounded up to be used as the number of hydrogen storage tanks;

[0227] The hybrid energy storage system constraint conditions include priority conditions and a utilization rate of the hydrogen energy storage system of the hybrid energy storage system;

[0228] The priority condition is: giving priority to using the sodium battery energy storage system for charging and discharging, and starting the hydrogen fuel cell system when the sodium battery energy storage system is insufficient;

[0229] The utilization rate of the hydrogen energy storage system of the hybrid energy storage system is expressed as formula (14);

[0230]

[0231] Among them, N H_Electric Working hours for hydrogen fuel cell system; N H_Production Working hours for hydrogen production system; N Electric Working hours for wind and solar power generation systems.

[0232] It should be noted that the resource allocation device of the above-mentioned integrated energy base can implement the resource allocation method of the above-mentioned integrated energy base one by one, which will not be repeated here.

[0233] Figure 4 A schematic diagram showing the structure of a computer device according to an embodiment of the present application is shown. Figure 4 As shown, the internal structure of the computer device may include a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, the functions or steps of the resource configuration method of the integrated energy base are realized.

[0234] In one embodiment, the computer device provided by the present application includes a memory and a processor, the memory stores a database and a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the resource configuration method of the integrated energy base of the present application.

[0235] Alternatively, the computer device provided in the present application includes a memory and a processor, the memory stores a database and a computer program that can be run on the processor, and the processor implements the steps of the resource allocation method of the integrated energy base of the present application when executing the computer program.

[0236] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the resource configuration method for the integrated energy base of the present application are implemented.

[0237] Alternatively, a computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the resource configuration method for the integrated energy base of the present application are implemented.

[0238] It should be noted that the above functions or steps that can be implemented by the computer device or computer-readable storage medium can refer to the relevant description in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0239] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0240] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0241] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A resource allocation method for a comprehensive energy base, characterized in that: include: Constructing a plurality of objective functions, wherein the objective functions include: a power supply stability function, a hydrogen supply stability function, and an optimal economy function; Constructing a correlation function and a fitness function between the multiple objective functions to form a multi-objective optimization model, wherein the correlation function is a function that takes into account both power supply stability and investment economy on the basis of ensuring hydrogen supply stability; Setting a plurality of constraints, wherein the constraints include: power supply system constraints, hydrogen supply system constraints, and hybrid energy storage system constraints; The Harris Eagle optimization algorithm is used to calculate the multi-objective optimization model under the multiple constraints to obtain the optimal solution for the resource allocation plan of the comprehensive energy base.

2. The method according to claim 1, characterized in that The step of calculating the multi-objective optimization model under the multiple constraints to obtain an optimal solution for the resource allocation scheme of the comprehensive energy base includes: The first calculation stage: using the Harris Eagle optimization algorithm, taking the sodium battery energy storage system as the calculation unit, to calculate the frequency regulation capability, so as to screen out the first optimal solution of the sodium battery energy storage system configuration scheme from the initial solutions of multiple sodium battery energy storage system configuration schemes randomly generated in the search space, wherein the first optimal solution includes: sodium battery energy storage system configuration parameters, objective function values ​​and algorithm performance indicators; The second calculation stage: using the Harris Eagle optimization algorithm, based on the first optimal solution, and taking the hybrid energy storage system working priority as the operation mode, the hybrid energy storage capacity configuration calculation is performed to screen out the second optimal solution of the resource configuration scheme of the hybrid energy storage system from the multiple initial resource configuration solutions randomly generated in the search space. The second optimal solution includes: the installed capacity of the sodium battery energy storage system and the installed capacity of the hydrogen energy storage system.

3. The method according to claim 2, characterized in that The first calculation stage specifically includes: Initialize the parameters required for the first calculation stage; Randomly generating initial solutions of multiple energy storage configuration schemes of the sodium battery energy storage system in a search space; Calculate the objective function of each of the initial solutions, and based on the fitness function; obtain the optimality of each of the energy storage configuration schemes, and take the one with the largest optimality as the current optimal solution; The following steps are executed repeatedly until the iteration requirement is met, and the one with the largest optimum is selected as the first optimal solution: A solution is randomly selected as the current benchmark solution, and the current optimal candidate solution is generated based on the benchmark solution. The generation formula is: new =X best -A×|X r -X best |, where X best is the current optimal solution, X r is a randomly selected solution, and A is the control parameter; Based on the fitness function, determining whether the current optimal candidate solution is better than the current optimal solution; If the current optimal candidate solution is better than the current optimal solution, the current optimal candidate solution is used as the current optimal solution, and the random selection solution is used as the current reference solution, and the current optimal candidate solution is generated according to the reference solution; If the current optimal candidate solution is not better than the current optimal solution, then judging whether the current optimal candidate solution satisfies the balanced constraint condition in the association relationship function, and if so, proceeding to judging whether the multiple constraint conditions are satisfied; If the current optimal candidate solution satisfies the multiple constraints, the current optimal candidate solution is still retained, and the process returns to the step of taking the randomly selected solution as the current reference solution and generating the current optimal candidate solution according to the reference solution.

4. The method according to claim 3, characterized in that The first calculation stage specifically includes: If the current optimal candidate solution does not satisfy the balanced constraint conditions of the association function, or if the current optimal candidate solution does not satisfy the multiple constraints; then the current optimal candidate solution is penalized, and the current optimal candidate solution and its degree of superiority are updated, and a randomly selected solution is returned as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution, or the initial solutions of the multiple energy storage configuration schemes are reset.

5. The method according to claim 2, characterized in that: The second calculation stage specifically includes: Initialize the parameters required for the second calculation stage; Randomly generating initial solutions of multiple hybrid energy storage system configuration schemes of the hybrid energy storage system in a search space; Setting at least part of the plurality of constraints based on the first calculation result; Calculate the objective function of each of the initial solutions, and based on the fitness function; obtain the optimality of each of the energy storage configuration schemes, and take the one with the largest optimality as the current optimal solution; The following steps are executed repeatedly until the iteration requirement is met, and the one with the largest optimum is selected as the first optimal solution: A solution is randomly selected as the current benchmark solution, and the current optimal candidate solution is generated based on the benchmark solution. The generation formula is: new =X best -A×|X r -X best |, where X best is the current optimal solution, X r is a randomly selected solution, and A is the control parameter; Based on the fitness function, determining whether the current optimal candidate solution is better than the current optimal solution; If the current optimal candidate solution is better than the current optimal solution, the current optimal candidate solution is used as the current optimal solution, and the random selection solution is used as the current reference solution, and the current optimal candidate solution is generated according to the reference solution; If the current optimal candidate solution is not better than the current optimal solution, then judging whether the current optimal candidate solution satisfies the balanced constraint condition in the association relationship function, and if so, proceeding to judging whether the multiple constraint conditions are satisfied; If the current optimal candidate solution satisfies the multiple constraints, the current optimal candidate solution is still retained, and the process returns to the step of taking the randomly selected solution as the current benchmark solution, and generating the current optimal candidate solution according to the benchmark solution; If the current optimal candidate solution does not satisfy the balanced constraint conditions of the association function, or if the current optimal candidate solution does not satisfy the multiple constraints; then the current optimal candidate solution is penalized, and the current optimal candidate solution and its degree of superiority are updated, and a randomly selected solution is returned as the current benchmark solution, and the current optimal candidate solution is generated according to the benchmark solution, or the initial solutions of the multiple energy storage configuration schemes are reset.

6. The method according to any one of claims 1 to 5, characterized in that: The power supply stability function is constructed according to the following method: Define the target output capacity of the power supply system as P Target (i) The actual output of renewable energy is P Renewable (i), the power supply stability function is expressed as formula (1): Among them, n is the total number of samples, i is the data sampling point, when P Renewable (i) / P Target (i) When it is greater than 1, the value of the power supply stability function P1 is 1, otherwise, the value of the power supply stability function P1 is the original value; The hydrogen supply stability function is constructed according to the following method: The probability that the daily output of the hydrogen energy system reaches the annual average daily output is defined as P2, and the hydrogen supply stability function is expressed as formula (2): Among them, N year is the number of working days of the hydrogen energy system, and the value range of y is [0, 365]; N hour is the number of working hours of the hydrogen energy system per day, and the value range of t is [0, 24]; P Hydr is the working power of the hydrogen energy system; W Hydr_average The average daily power output of the hydrogen energy system; Among them, if P Hydr (t)-W Hydr_average ≥1, then The value of is 1; if P Hydr (t)-W Hydr_average <1, then The value of is 0; The optimal economic function is expressed as formula (3): C total =MIN(C w&s +C h +C Na ) Formula (3); Among them, C total is the total cost of the system throughout its life cycle, C w&s , C h , C Na They are respectively the power supply system cost, the hydrogen energy system cost, and the sodium ion battery system cost, wherein the sodium ion battery system is at least a part of the hybrid energy storage system.

7. The method according to any one of claims 1 to 5, characterized in that: The step of constructing a correlation function and a fitness function between the multiple objective functions to form a multi-objective optimization model includes: Construct the correlation function between the multiple objective functions, expressed as formula (4) and formula (5): {P1(x) max , Ctotal(x) min Formula (4); P2(x)≥P2(threshold) Formula (5); Among them, formula (5) is the balance constraint condition in the correlation function, x represents the resource allocation plan, and P2 (threshold) is the threshold of hydrogen supply stability; The original fitness function and the penalty fitness function are constructed to obtain the fitness function, wherein the original fitness function is expressed as formula (6): F(x)=ω1×P1-ω3×C total Formula (6); The penalty fitness function is expressed as formula (7): F(x)=ω1×P1-ω2×[P2(threshold)-P2]-ω3×C total Formula (7); Among them, F(x) represents the original fitness function, ω1, ω2, and ω3 are all positive numbers, representing the weights of power supply stability, hydrogen supply stability, and optimal economy, respectively, and x represents the resource allocation plan.

8. The method according to any one of claims 1 to 5, characterized in that: The power supply system constraint conditions include: installed capacity conditions of the power supply system, and power abandonment rate conditions of the power supply system; The installed capacity condition of the power supply system is expressed as formula (8): Among them, S w is the multiple of the unit wind turbine, with the upper limit being a1; S s is a multiple of the unit photovoltaic, with an upper limit of a2; S e is the energy storage installed capacity, with an upper limit of a3; The power abandonment rate condition of the power supply system is expressed as formula (9): Pquit(t)∈[Pquit,min(t),Pquit,max(t)] Formula (9); Among them, Pqui t (t) represents the power abandonment rate at time t, Pquit,min ( t ) and Pquit,max ( t ) are the lower and upper limits of the power abandonment rate at that moment respectively; The constraints of the hydrogen supply system include: a hydrogen production power condition of the hydrogen supply system, a hydrogen storage working rate condition of the hydrogen supply system, and a hydrogen storage capacity condition of the hydrogen supply system; The hydrogen production power condition of the hydrogen supply system is expressed as formula (10): Among them, P H_work is the working power of hydrogen production equipment, in kg / h; η H_real is the real-time hydrogen production efficiency, in %; P H_real is the real-time available power of the hydrogen production equipment, in kg / h; P rated is the rated power of the hydrogen production equipment, in kg / h; The hydrogen storage working rate condition of the hydrogen supply system includes a first hydrogen storage working rate during the inflation process and a second hydrogen storage working rate during the deflation process, wherein the first hydrogen storage working rate is expressed as formula (11): Among them, M out is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out ; The second hydrogen storage working rate is expressed as formula (12): Among them, M out is the gas discharge mass flow rate of the hydrogen storage system, in kg / s; is the deflation mass flow rate, in kg / s; P PEMFC_rated The amount of hydrogen required for the fuel cell system to meet the rated power, in kg / s; P PEFC_rated / L H_storage_m The number of storage tanks required for deflation, expressed as S Hs_out ; The hydrogen storage capacity condition of the hydrogen supply system includes: the hydrogen storage capacity condition of the hydrogen supply system; The hydrogen storage capacity condition of the hydrogen supply system is expressed as formula (13): M H =M H_storage ·max[S Hs_in ,S Hs_out ] Formula (13); Among them, M H is the minimum capacity of the hydrogen storage system, in kg. Hs_in ,S Hs_out ] is not an integer, its value is rounded up to be used as the number of hydrogen storage tanks; The hybrid energy storage system constraint conditions include priority conditions and a utilization rate of the hydrogen energy storage system of the hybrid energy storage system; The priority condition is: giving priority to using the sodium battery energy storage system for charging and discharging, and starting the hydrogen fuel cell system when the sodium battery energy storage system is insufficient; The utilization rate of the hydrogen energy storage system of the hybrid energy storage system is expressed as formula (14); Among them, N H_Electric Working hours for hydrogen fuel cell system; N H_Production Working hours for hydrogen production system; N Electric Working hours for wind and solar power generation systems.

9. A resource allocation device for a comprehensive energy base, characterized in that: The device comprises: A first construction unit is used to construct a plurality of objective functions, wherein the objective functions include: a power supply stability function, a hydrogen supply stability function, and an optimal economy function; A second construction unit is used to construct an association relationship function and a fitness function between the multiple objective functions to form a multi-objective optimization model, wherein the association relationship function is a function that takes into account both power supply stability and investment economy on the basis of ensuring hydrogen supply stability; A setting unit, used to set a plurality of constraints, wherein the constraints include: power supply system constraints, hydrogen supply system constraints, and hybrid energy storage system constraints; The configuration unit is used to calculate the multi-objective optimization model under the multiple constraints using the Harris Hawk optimization algorithm to obtain the optimal solution for the resource configuration plan of the comprehensive energy base.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is instructed by the processor, it implements the steps of the resource allocation method of the integrated energy base as described in any one of methods 1 to 8.

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