A day-ahead scheduling method for wind-solar-hydrogen storage system based on simulated annealing algorithm

Abstract

The present invention belongs to the technical field of optimized scheduling of new energy storage and production of hydrogen, and in particular, relates to a method for day-ahead scheduling of a wind-solar storage and production of hydrogen system based on a simulated annealing algorithm. The specific steps of the method are: according to the peak-valley time-of-use electricity price policy of the power grid, the hydrogen demand at the load end, and the intermittent nature of new energy power generation, a day-ahead processing model of the hydrogen production system with the lowest day-ahead operating cost of the system is established; the day-ahead processing model of the hydrogen production system is solved by an adaptive simulated annealing particle swarm algorithm to obtain a day-ahead optimization scheduling method for the wind-solar storage and production of hydrogen system; according to the obtained day-ahead optimization scheduling method for the wind-solar storage and production of hydrogen system, the battery output power, the power purchased by the power grid, and the operating power of the electrolyzer are set for each period of the wind-solar storage and production of hydrogen system, thereby scheduling the wind-solar storage and production of hydrogen system. The present invention improves the traditional PSO algorithm by using the variation characteristics of the simulated annealing algorithm, and then solves the day-ahead processing model of the hydrogen production system, thereby obtaining a day-ahead scheduling plan for the hydrogen production system with the lowest daily operating cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimized scheduling of new energy hydrogen storage and production, and in particular relates to a day-ahead scheduling method for a wind-solar hydrogen storage and production system based on a simulated annealing algorithm. Background Art

[0002] The pollution and energy depletion problems of conventional energy have promoted the vigorous development of new energy power generation technologies such as photovoltaics and wind power, but the randomness and volatility of new energy such as wind power and photovoltaics make their share in electricity production relatively small. Large-scale electrolysis of new energy hydrogen can effectively prevent economic losses and idle green resources caused by energy abandonment. Water electrolysis hydrogen production technology is the core technology in large-scale new energy hydrogen production. The existing wind and solar hydrogen storage system is mainly composed of wind turbines, solar photovoltaic battery groups, control / conversion units, energy storage devices, inverters, electrolyzers and other parts. The specific process is that the DC power supply from the photovoltaic array and wind turbine stores the excess energy in the energy storage device through the controller, and then converts it into AC power through the inverter for water electrolysis hydrogen production. Electric energy is converted into hydrogen through water electrolysis hydrogen production equipment, and the hydrogen is transported to the hydrogen application terminal or connected to the power grid through fuel cells to complete the conversion from renewable energy to hydrogen energy.

[0003] Domestic and foreign scholars have done a lot of research on improving the efficiency of water electrolysis equipment and optimizing the dispatching methods of wind-solar hydrogen storage and production systems. The research on optimized dispatching mainly includes two aspects: optimization of the structure of wind-solar hydrogen storage and production systems and improvement of optimization algorithms. Most of the improvements in optimization algorithms are optimization of convergence speed and accuracy. Particle swarm optimization algorithm (PSO) has achieved good results in the optimization dispatching of wind-solar hydrogen storage and production systems. A particle is used to simulate individual birds. Each particle can be regarded as a search individual in the N-dimensional search space. The current position of the particle is a candidate solution to the corresponding optimization problem, and the flight process of the particle is the search process of the individual. The flight speed of the particle can be dynamically adjusted according to the historical optimal position of the particle and the historical optimal position of the population. Particles have only two attributes: speed and position. The speed represents the speed of movement, and the position represents the direction of movement. The optimal solution searched by each particle alone is called the individual extreme value, and the optimal individual extreme value in the particle swarm is the current global optimal solution. Continuously iterate, update the speed and position, and finally obtain the optimal solution that meets the termination conditions. The PSO algorithm has been applied to the optimization and scheduling of wind, solar, storage and hydrogen production systems with good results. However, when the traditional particle swarm algorithm is used to solve the model, it will fall into the local optimal solution during the iterative calculation process and cannot obtain the global optimal solution. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a day-ahead scheduling method for a wind, solar, storage and hydrogen production system based on a simulated annealing algorithm, establish a day-ahead processing model for a hydrogen production system with the lowest day-ahead operating cost of the system, improve the traditional PSO algorithm by utilizing the variation characteristics of the simulated annealing algorithm, and use the improved adaptive simulated annealing particle swarm algorithm to solve it, thereby obtaining a day-ahead scheduling plan for a hydrogen production system with the lowest daily operating cost.

[0005] The technical solution of the present invention is: a day-ahead scheduling method for a wind-solar-hydrogen storage system based on a simulated annealing algorithm, comprising the following steps:

[0006] S1: Based on the cost of using alkaline electrolyzers, the price of electricity purchased by the system from the power grid, the maintenance cost of the battery device per unit of electricity, and the operating cost of the compressor, a day-ahead processing model of the hydrogen production system with the lowest day-ahead operating cost is established;

[0007] S2: Use the adaptive simulated annealing particle swarm algorithm to solve the day-ahead processing model of the hydrogen production system and obtain the day-ahead optimization scheduling method of the wind-solar-storage hydrogen production system;

[0008] S3: According to the day-ahead optimization dispatching method of the wind-solar-hydrogen storage and production system obtained in S2, the battery output power, grid-purchased power and electrolyzer operating power of the wind-solar-hydrogen storage and production system in each period are set, thereby realizing the dispatching of the wind-solar-hydrogen storage and production system.

[0009] In step S1, the process of establishing a day-ahead processing model for a wind-solar-hydrogen storage system with the lowest day-ahead operating cost of the system includes the following steps:

[0010] S11: Establish the objective function, the system day-ahead operating cost C day The following formula is used to characterize it:

[0011]

[0012] Where: C day is the system's day-ahead operating cost, yuan; C ele is the cost of using alkaline electrolytic cell, RMB / (kW·h); is the operating power of the electrolytic cell in time period t, kW; C grid The price of electricity purchased by the system from the power grid, RMB / (kW·h); is the interactive power when purchasing electricity from the grid, kW; C sto is the maintenance cost per unit of electricity of the battery device, yuan / (kW·h); is the actual output of the battery, kW; C com is the operating cost of the compressor, RMB / (N·m 3) ; is the amount of hydrogen to be compressed in time period t, N·m 3; Δt is the time interval, 1h;

[0013] S12: Obtain constraints, including:

[0014] S121: System power balance constraint:

[0015]

[0016] Where: is the output of renewable energy, i.e. the maximum output power of wind-solar hybrid power generation in period t, kW; is the charge / discharge power of the battery, where the battery discharge power is a positive value and the charging power is a negative value, kW;

[0017] S122: Battery energy storage system constraints:

[0018] The capacity of the battery is related to the capacity of the previous period, the charge and discharge power and the self-discharge amount in the previous period. The state of charge of the battery in period t is:

[0019]

[0020] Where: is the state of charge of the battery during period t; σ is the self-discharge rate of the battery; η c E is the battery charge / discharge efficiency; bat is the total capacity of the battery, kW·h;

[0021] S123: Grid interaction power constraint:

[0022]

[0023] Where: and are the minimum and maximum power that the system purchases from the grid, kW;

[0024] S124: Electrolyzer operating power constraints:

[0025]

[0026] Where: and are the minimum operating power and maximum operating power of the alkaline electrolyzer in production state, kW,

[0027] S125: Hydrogen production constraints

[0028]

[0029] Where: is the remaining hydrogen storage capacity of the system during period t, Nm 3 ; is the hydrogen production rate of the alkaline electrolyzer during period t, Nm 3 / h; is the hydrogen load on the demand side during period t, Nm 3 ; is the hydrogen surplus of the system at the end of the day-ahead operation plan, Nm 3 .

[0030] In step S122, the battery energy storage system constraints, in addition to satisfying the capacity change constraints, also need to satisfy the battery safety constraints, including the upper and lower limits of charge and discharge power constraints, the upper and lower limits of capacity constraints, and the constraint that the battery daily internal capacity is equal from beginning to end, which are as follows:

[0031] The upper and lower limits of battery charging and discharging power are:

[0032]

[0033] Where: and They are the battery charging power limit and discharging power limit, kW;

[0034] The upper and lower limits of the battery state of charge are:

[0035]

[0036] Where: and are the minimum state of charge value and the maximum state of charge value of the battery respectively;

[0037] The battery daily capacity is always equal to the constraint:

[0038]

[0039] Where: and They are the charge states of the battery at the start and end of the scheduling period, respectively.

[0040] In step S2, the adaptive simulated annealing particle swarm algorithm adopts the following speed update formula:

[0041]

[0042]

[0043] Where: and Respectively represent the speed and position of the i-th particle at the k-th iteration; r1 and r2 are random numbers; p ibest is the individual optimal position of the i-th particle; G best The best position for the population.

[0044] The process of solving the hydrogen production system day-ahead processing model using the adaptive simulated annealing particle swarm algorithm in step S2 is as follows:

[0045] S21: Set the population size, number of iterations, and search space dimension of the annealing particle swarm;

[0046] S22: Initialize annealing particle swarm population parameters;

[0047] S23: Determine penalty function and constraint conditions;

[0048] S24: Calculate the fitness values ​​of all particles;

[0049] S25: Solve the particles that are smaller than the average value of the objective function;

[0050] S26: Calculate particle speed and select the optimal individual particle and the global optimal particle;

[0051] S27: When the particle velocity is within the definition domain, determine whether the number of iterations has reached the maximum. If so, the optimized result is obtained, and the optimal scheduling plan is output, including the battery output power, the power purchased by the power grid, and the operating power of the electrolyzer in each period; if not, jump to step S24 to continue the iterative calculation.

[0052] In step S26, the individual optimum and the global optimum of the particles are selected, and the formula for calculating the inertia weight used for the particle velocity and the two learning factors that change dynamically according to the number of iterations is:

[0053] ω=(ω max +ω min ) / 2+tanh(-4+8×(k max -k) / k max )(ω max -ω min ) / 2 (11)

[0054]

[0055]

[0056] Where: max and ω min are the maximum and minimum values ​​of the inertia weight coefficient, which are usually 0.95 and 0.4 respectively; k is the current iteration number, k max is the maximum number of iterations.

[0057] The population parameters of the annealing particle swarm initialized in step S22 include particle position, particle velocity, battery charge state, grid dispatching power and electrolytic cell operating power.

[0058] The technical effect of the present invention is that compared with the prior art, the present invention establishes a day-ahead processing model for a hydrogen production system with the lowest day-ahead operating cost of the system based on the use cost of the alkaline electrolyzer, the price of electricity purchased by the system from the power grid, the maintenance cost per unit of electricity of the battery device and the operating cost of the compressor, and takes into account the impact of demand-side responses such as the peak-valley time-of-use electricity price policy of the power grid, the hydrogen demand at the load end and the intermittent nature of new energy power generation on the day-ahead scheduling and operation of the wind, solar, storage and hydrogen production system; at the same time, a parameter adaptive algorithm is applied, the traditional PSO algorithm is improved by using the simulated annealing algorithm, and the improved adaptive simulated annealing particle swarm algorithm is used to solve the problem, thereby obtaining a day-ahead scheduling plan for the hydrogen production system with the lowest daily operating cost.

[0059] The following is a further description with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of the adaptive simulated annealing particle swarm algorithm according to an embodiment of the present invention.

[0061] Figure 2 This is a prediction curve of wind-solar combined power generation output on a typical day in a certain place in the northwest of the United States according to an embodiment of the present invention.

[0062] Figure 3 This is the hydrogen demand load curve in one day according to the embodiment of the present invention.

[0063] Figure 4 This is a schematic diagram of the scheduling and operating status of each unit of the wind-solar-storage coupled hydrogen production system in a day under the demand of the traffic hydrogen load scenario according to an embodiment of the present invention.

[0064] Figure 5 This is a schematic diagram of the charge state of a battery energy storage system of a wind-solar-storage coupled hydrogen production system under the demand of a traffic hydrogen load scenario in an embodiment of the present invention.

[0065] Figure 6 This is a schematic diagram of the system hydrogen production rate and hydrogen storage surplus of a wind-solar-storage coupled hydrogen production system under the demand of a traffic hydrogen load scenario in an embodiment of the present invention.

[0066] Figure 7 This is a schematic diagram of the scheduling and operating status of each unit of the wind-solar-storage coupled hydrogen production system during a day under the gas-hydrogen load scenario requirements in an embodiment of the present invention.

[0067] Figure 8 This is a schematic diagram of the charge state of a battery energy storage system of a wind-solar-storage coupled hydrogen production system under the requirements of a gas-hydrogen load scenario in an embodiment of the present invention.

[0068] Fig. 9 This is a schematic diagram of the system hydrogen production rate and hydrogen storage surplus of a wind-solar-storage coupled hydrogen production system under the gas-hydrogen load scenario requirements in an embodiment of the present invention.

[0069] Fig.10 This is a fitness value curve diagram of the adaptive simulated annealing particle swarm algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] Example 1

[0071] The present invention provides a method for day-ahead scheduling of a wind-solar-hydrogen storage system based on a simulated annealing algorithm, comprising the following steps:

[0072] S1: Based on the cost of using alkaline electrolyzers, the price of electricity purchased by the system from the power grid, the maintenance cost of the battery device per unit of electricity, and the operating cost of the compressor, a day-ahead processing model of the hydrogen production system with the lowest day-ahead operating cost is established;

[0073] S2: Use the adaptive simulated annealing particle swarm algorithm to solve the day-ahead processing model of the hydrogen production system and obtain the day-ahead optimization scheduling method of the wind-solar-storage hydrogen production system;

[0074] S3: According to the day-ahead optimization dispatching method of the wind-solar-hydrogen storage and production system obtained in S2, the battery output power, grid-purchased power and electrolyzer operating power of the wind-solar-hydrogen storage and production system in each period are set, thereby realizing the dispatching of the wind-solar-hydrogen storage and production system.

[0075] In step S1, the process of establishing a day-ahead processing model for a wind-solar-hydrogen storage system with the lowest day-ahead operating cost of the system includes the following steps:

[0076] S11: Establish the objective function, the system day-ahead operating cost C day The following formula is used to characterize it:

[0077]

[0078] Where: C day is the system's day-ahead operating cost, yuan; C ele is the cost of using alkaline electrolytic cell, RMB / (kW·h); is the operating power of the electrolytic cell in time period t, kW; C grid The price of electricity purchased by the system from the power grid, RMB / (kW·h); is the interactive power when purchasing electricity from the grid, kW; C sto is the maintenance cost per unit of electricity of the battery device, yuan / (kW·h); is the actual output of the battery, kW; C com is the operating cost of the compressor, RMB / (N·m 3) ; is the amount of hydrogen to be compressed in time period t, N·m 3 ; Δt is the time interval, 1h;

[0079] S12: Obtaining constraints, including:

[0080] S121: System power balance constraint:

[0081]

[0082] Where: is the output of renewable energy, i.e. the maximum output power of wind-solar hybrid power generation in period t, kW; is the charge / discharge power of the battery, where the battery discharge power is a positive value and the charging power is a negative value, kW;

[0083] S122: Battery energy storage system constraints:

[0084] The capacity of the battery is related to the capacity of the previous period, the charge and discharge power and the self-discharge amount in the previous period. The state of charge of the battery in period t is:

[0085]

[0086] Where: is the state of charge of the battery during period t; σ is the self-discharge rate of the battery; η c E is the battery charge / discharge efficiency; bat is the total capacity of the battery, kW·h;

[0087] S123: Grid interaction power constraint:

[0088]

[0089] Where: and are the minimum and maximum power that the system purchases from the grid, kW;

[0090] S124: Electrolyzer operating power constraints:

[0091]

[0092] Where: and are the minimum operating power and maximum operating power of the alkaline electrolyzer in production state, kW,

[0093] S125: Hydrogen production constraints

[0094]

[0095] Where: is the remaining hydrogen storage capacity of the system during period t, Nm 3 ; is the hydrogen production rate of the alkaline electrolyzer during period t, Nm 3 / h; is the hydrogen load on the demand side during period t, Nm 3 ; is the hydrogen surplus of the system at the end of the day-ahead operation plan, Nm 3 .

[0096] In order to match the hydrogen load on the demand side and achieve the goal of producing hydrogen while consuming it, the system does not store a large amount of hydrogen after a scheduling cycle. Set the value of to 0.

[0097] In step S122, the battery energy storage system constraints, in addition to satisfying the capacity change constraints, also need to satisfy the battery safety constraints, including the upper and lower limits of charge and discharge power constraints, the upper and lower limits of capacity constraints, and the constraint that the battery daily internal capacity is equal from beginning to end, which are as follows:

[0098] The upper and lower limits of battery charging and discharging power are:

[0099]

[0100] Where: and They are the battery charging power limit and discharging power limit, kW;

[0101] The upper and lower limits of the battery state of charge are:

[0102]

[0103] Where: and are the minimum state of charge value and the maximum state of charge value of the battery respectively;

[0104] The battery daily capacity is always equal to the constraint:

[0105]

[0106] Where: and They are the charge states of the battery at the start and end of the scheduling period, respectively.

[0107] In step S2, the adaptive simulated annealing particle swarm algorithm adopts the following speed update formula:

[0108]

[0109]

[0110] Where: and Respectively represent the speed and position of the i-th particle at the k-th iteration; r1 and r2 are random numbers; pibest is the individual optimal position of the i-th particle; G best The best position for the population.

[0111] Traditional particle swarm optimization is prone to fall into local optimality, while in the process of searching for the optimal solution, each new solution of the simulated annealing algorithm (SA) has the possibility of being accepted and has a sudden jump probability to replace the current optimal solution, which can reasonably prevent falling into local extreme values.

[0112] like Figure 1 As shown, the process of solving the hydrogen production system day-ahead processing model using the adaptive simulated annealing particle swarm algorithm in step S2 is as follows:

[0113] S21: Set the population size, number of iterations, and search space dimension of the annealing particle swarm;

[0114] S22: Initialize annealing particle swarm population parameters;

[0115] S23: Determine penalty function and constraint conditions;

[0116] S24: Calculate the fitness values ​​of all particles;

[0117] S25: Solve the particles that are smaller than the average value of the objective function;

[0118] S26: Calculate particle speed and select the optimal individual particle and the global optimal particle;

[0119] S27: When the particle velocity is within the definition domain, determine whether the number of iterations has reached the maximum. If so, the optimized result is obtained, and the optimal scheduling plan is output, including the battery output power, the power purchased by the power grid, and the operating power of the electrolyzer in each period; if not, jump to step S24 to continue the iterative calculation.

[0120] In step S26, the individual optimum and the global optimum of the particles are selected, and the formula for calculating the inertia weight used for the particle velocity and the two learning factors that change dynamically according to the number of iterations is:

[0121] ω=(ω max +ω min ) / 2+tanh(-4+8×(k max -k) / k max )(ω max -ω min ) / 2 (11)

[0122]

[0123]

[0124] Where: max and ωmin are the maximum and minimum values ​​of the inertia weight coefficient, which are usually 0.95 and 0.4 respectively; k is the current iteration number, k max is the maximum number of iterations.

[0125] The population parameters of the annealing particle swarm initialized in step S22 include particle position, particle velocity, battery charge state, grid dispatching power and electrolytic cell operating power.

[0126] Example 2

[0127] This embodiment adopts the day-ahead scheduling method of a wind-solar-storage-hydrogen-production system based on a simulated annealing algorithm described in Embodiment 1 to optimize the day-ahead scheduling of a wind-solar-storage-coupled hydrogen-production system in a certain place in the northwest. The wind-solar-storage-coupled hydrogen-production system includes a wind-solar complementary power generation subsystem, an energy storage battery, an alkaline hydrolysis tank and its supporting devices, with a scheduling period of 24 hours. The day-ahead processing model of the hydrogen-production system with the lowest day-ahead operating cost of the system is used as the Simulink model, and a typical day in a certain place in the northwest is selected for research. The irradiation intensity, wind speed, and temperature meteorological data in a typical day are used as inputs to the Simulink model, and the simulation operation is performed to obtain the forecast of the wind-solar combined power generation output. The wind-solar combined output forecast curve in a typical day is as follows: Figure 2 The two assumed hydrogen load scenarios are shown in Figure 3 shown.

[0128] The wind-solar-storage coupled hydrogen production system purchases electricity from the large power grid on a time-of-day basis. The electricity prices during the peak, normal and valley periods of the power grid are 0.88, 0.594 and 0.308 yuan / (kW·h) respectively. See Table 1 for details.

[0129] Table 1 Grid time-of-use electricity price data

[0130] Period Type Specific time period Electricity purchase price Lows 0:00-07:00 0.308 usually 07:00-09:00，11:00-19:00 0.594 peak 09:00-11:00，19:00-24:00 0.88

[0131] The rated power of the electrolyzer is 800kW, and the minimum operating power is 120kW. The maximum charge and discharge power of the battery is 240kW. Other detailed parameters in the example are shown in Table 2

[0132] Table 2 Output plan example parameters

[0133]

[0134] (1) Transportation hydrogen load scenario requirements

[0135] Figure 4 This is a schematic diagram of the dispatching and operating status of each unit of the wind-solar-storage coupled hydrogen production system during the day under the traffic hydrogen load scenario demand. Figure 5 This is a schematic diagram of the charge state of the battery energy storage system of the wind-solar-storage coupled hydrogen production system under the demand of the traffic hydrogen load scenario. Figure 6This is a schematic diagram of the system hydrogen production rate and hydrogen storage surplus of the wind-solar storage-coupled hydrogen production system under the demand of traffic hydrogen load scenario. Figure 4 It can be seen that the actual wind and solar output value P real Always less than the maximum theoretical output value P the , the system is always under power balance constraints. In this scenario, wind and solar power generation and battery energy storage are sufficient to maintain the electrolyzer to produce hydrogen and meet the daily supply of hydrogen for gas, without the need to dispatch electricity from the grid. When the wind and solar power output is excessive, the battery is charged and stored, and the stored energy is used to supply energy for hydrogen production in the electrolyzer when the hydrogen load demand increases and the wind and solar power output is insufficient.

[0136] from Figure 5 It can be seen that the battery plays a role of "peak shaving and valley filling". When the wind and solar power generation is insufficient, the battery device supplements energy to maintain the normal operation of the electrolyzer, ensuring that the system's hydrogen production meets the hydrogen supply under the gas hydrogen load scenario. When the wind and solar power output is sufficient to maintain the electrolysis power required for the operation of the electrolyzer, the optimization model tends to keep the battery inactive, thereby reducing the daily operating cost of the system.

[0137] from Figure 6 It can be seen that the optimization model tends to produce and store a large amount of hydrogen when the wind and solar power output is large, in order to ensure the supply of hydrogen when the hydrogen load demand increases in the future. When the hydrogen load demand decreases, the hydrogen production rate of the electrolyzer decreases, and the electrolyzer does not over-operate to produce hydrogen, ensuring that the hydrogen production of the day is consumed on the same day, which matches the hydrogen load demand very well and also reduces the daily operating cost of the hydrogen production system, further enhancing the economy of the system.

[0138] (2) Gas-hydrogen load scenario requirements

[0139] Figure 7 This is a schematic diagram of the dispatching and operating status of each unit of the wind-solar-storage coupled hydrogen production system during the day under the gas-hydrogen load scenario demand. Figure 8 This is a schematic diagram of the charge state of the battery energy storage system of the wind-solar-storage coupled hydrogen production system under the gas-hydrogen load scenario requirements. Fig. 9 This is a schematic diagram of the system hydrogen production rate and hydrogen storage surplus of the wind-solar-storage coupled hydrogen production system under the gas-hydrogen load scenario requirements.

[0140] Depend on Figure 7 It can be seen that in the gas hydrogen load scenario, the system tends to purchase electricity from the grid during the valley or normal times of electricity prices. For example, during the time periods of 01:00-02:00, 03:00-04:00, 10:00-12:00, and 17:00-19:00, the electrolyzer produces a large amount of hydrogen to ensure hydrogen reserves. Fig. 9 , Fig.10The system tries to avoid purchasing electricity from the grid when electricity prices are peak. When the wind and solar power output is insufficient, it ensures hydrogen production by obtaining low-priced electricity from the grid. The battery device charges and stores energy to ensure that when the wind and solar power output is insufficient or the hydrogen load demand increases in the future, the system can produce hydrogen through the electrolyzer and the remaining hydrogen in the hydrogen storage tank to meet the demand.

[0141] When this embodiment uses the traditional particle swarm algorithm to solve the model, it will fall into the local optimal solution during the iterative calculation process and cannot obtain the global optimal solution. The simulated annealing operator and adaptive inertia weight are added to the algorithm of this patent. The simulated annealing algorithm itself has the characteristics of probabilistic jump, which can help to jump out of the local optimal solution. The adaptive change of the key parameters of the particle swarm algorithm enhances the global search ability of the algorithm. Combining the two can better enrich the population diversity and help find the global optimal solution of the model. Taking the model results in the traffic hydrogen load scenario as an example, the traditional particle swarm algorithm often has difficulty converging or falling into the local optimal solution when solving the model. The calculated daily operating cost of the wind-solar-storage coupled hydrogen production system is often 6.8×10 3 The daily operating cost of the system calculated by the adaptive simulated annealing particle swarm algorithm is only about 5.2×10 3 Yuan, saving about 24% in cost. It can be seen that it is effective to apply the optimization algorithm mentioned in this patent to the day-ahead scheduling of economic optimization of wind-solar-storage coupled hydrogen production system.

[0142] like Fig.10 The figure shows the fitness value curve of the adaptive simulated annealing particle swarm algorithm mentioned in this patent during the search process. It can be found that the algorithm of this patent jumps out of the local optimal solution during the search process and then continues to search, and finally finds the global optimal solution, thereby obtaining the day-ahead scheduling plan for the hydrogen production system with the lowest daily operating cost.

[0143] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A day-ahead scheduling method for a wind-solar-solar hydrogen storage system based on a simulated annealing algorithm, characterized in that: The following steps are involved: S1: Based on the cost of using alkaline electrolyzers, the price of electricity purchased by the system from the power grid, the maintenance cost of the battery device per unit of electricity, and the operating cost of the compressor, a day-ahead processing model for the hydrogen production system with the lowest day-ahead operating cost is established, which specifically includes the following steps: S11: Establish objective function, system day-ahead operating cost The following formula is used to characterize it: （1） Where: is the system day-ahead operating cost, RMB; is the cost of using alkaline electrolyzer, RMB / (kW•h); is the operating power of the electrolyzer in time period t, kW; The price of electricity purchased by the system from the power grid, RMB / (kW•h); is the interactive power when purchasing electricity from the grid, kW; is the maintenance cost per unit of electricity of the battery device, RMB / (kW•h); is the actual output of the battery, kW; is the operating cost of the compressor, RMB / (N•m 3) ; is the amount of hydrogen to be compressed in time period t, N·m 3 ; is the time interval, 1h; S12: Obtaining constraints, including: S121: System power balance constraint: （2） Where: is the output of renewable energy, i.e. the maximum output power of wind-solar hybrid power generation in period t, kW; is the charge / discharge power of the battery, where the battery discharge power is a positive value and the charging power is a negative value, kW; S122: Battery energy storage system constraints: The capacity of the battery is related to the capacity of the previous period, the charge and discharge power and the self-discharge amount in the previous period. The state of charge of the battery in period t is: （3） Where: is the state of charge of the battery during period t; σ is the self-discharge rate of the battery; is the battery charge / discharge efficiency; is the total capacity of the battery, kW•h; In step S122, the battery energy storage system constraints, in addition to satisfying the capacity change constraints, also need to satisfy the battery safety constraints, including the upper and lower limits of charge and discharge power constraints, the upper and lower limits of capacity constraints, and the constraint that the battery daily internal capacity is equal from beginning to end, which are as follows: The upper and lower limits of battery charging and discharging power are: （4） Where: and They are the battery charging power limit and discharging power limit, kW; The upper and lower limits of the battery state of charge are: （5） Where: and are the minimum state of charge value and the maximum state of charge value of the battery respectively; The battery daily capacity is always equal to the constraint: （6） Where: and are the charge states of the batteries at the start and end of the scheduling period, respectively; S123: Grid interaction power constraint: （7） Where: and are the minimum and maximum power that the system purchases from the grid, kW; S124: Electrolyzer operating power constraints: （8） Where: and are the minimum operating power and maximum operating power of the alkaline electrolyzer in production state, kW, S125: Hydrogen production constraints （9） Where: is the remaining hydrogen storage capacity of the system during period t, Nm3; is the hydrogen production rate of the alkaline electrolyzer during period t, Nm3 / h; is the hydrogen load on the demand side during period t, Nm3; The hydrogen surplus of the system at the end of the day-ahead operation plan, Nm3; S2: Using the adaptive simulated annealing particle swarm algorithm to solve the day-ahead processing model of the hydrogen production system, and obtain the day-ahead optimization scheduling method of the wind-solar-storage hydrogen production system; the process of using the adaptive simulated annealing particle swarm algorithm to solve the day-ahead processing model of the hydrogen production system in step S2 is as follows: S21: Set the population size, number of iterations, and search space dimension of the annealing particle swarm; S22: Initialize annealing particle swarm population parameters; S23: Determine penalty function and constraint conditions; S24: Calculate the fitness values ​​of all particles; S25: Solve the particles that are smaller than the average value of the objective function; S26: Calculate particle speed and select the optimal individual particle and the global optimal particle; S27: The particle velocity is within the definition domain, and it is determined whether the number of iterations reaches the maximum. If the number of iterations reaches the maximum, the optimized result is obtained, and the optimal scheduling plan is output, including the battery output power, the power purchased by the power grid, and the operating power of the electrolyzer in each period; if not, jump to step S24 to continue iterative calculation; The population parameters of the annealing particle swarm initialized in step S22 include particle position, particle velocity, battery charge state, grid dispatching power and electrolytic cell operating power; S3: According to the day-ahead optimization dispatching method of the wind-solar-hydrogen storage and production system obtained in S2, the battery output power, grid-purchased power and electrolyzer operating power of the wind-solar-hydrogen storage and production system in each period are set, thereby realizing the dispatching of the wind-solar-hydrogen storage and production system.

2. According to claim 1, a method for day-ahead scheduling of a wind-solar-hydrogen storage system based on a simulated annealing algorithm is characterized by: In step S2, the adaptive simulated annealing particle swarm algorithm adopts the following speed update formula: （10） Where: and Respectively represent the velocity and position of the i-th particle at the k-th iteration; and is a random number; is the individual optimal position of the i-th particle; The best position for the population.

3. According to claim 1, a method for day-ahead scheduling of a wind-solar-hydrogen storage system based on a simulated annealing algorithm is characterized by: In step S26, the individual optimum and the global optimum of the particles are selected, and the formula for calculating the inertia weight used for the particle velocity and the two learning factors that change dynamically according to the number of iterations is: （11） （12） （13） Where: and are the maximum and minimum values ​​of the inertia weight coefficient, which are often 0.95 and 0.4 respectively; is the current iteration number, is the maximum number of iterations.

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