Coordinated operation optimization scheduling method and system based on wind-solar-storage complementary combined hydrogen production

Abstract

The invention discloses a coordinated operation optimization scheduling method and system based on wind-solar-storage complementary combined hydrogen production. Acquiring and processing operation data of the new energy power system to obtain wind and light output characteristics; establishing a source-load coordinated operation scheduling model of the electro-hydrogen system based on complementary characteristics of electro-hydrogen loads by combining wind and light output characteristics and an electrolytic cell adjusting range; a particle swarm optimization scheduling method is adopted for the source-load coordinated operation scheduling model of the electro-hydrogen system to find an optimal scheduling scheme; and calculating the new energy consumption rate and the equipment utilization rate of the optimal scheduling scheme, and completing the power system optimization scheduling based on the demand side response. Supply and demand balance of the electro-hydrogen system can be achieved, abandoned wind and abandoned light are reduced, the new energy consumption rate and the equipment utilization rate are improved, efficient scheduling is achieved, and the method has wide application prospects and use value.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric hydrogen systems, and specifically relates to a coordinated operation optimization scheduling method and system based on wind, solar, and storage complementary combined hydrogen production. Background Art

[0002] Increasing the proportion of new energy in the power system by incorporating renewable energy such as wind power and photovoltaics is an effective way to reduce carbon emissions. However, due to the randomness, volatility and intermittency of wind and solar power output, it not only poses acceptance difficulties for the stable operation of the power system, but also brings huge challenges to the consumption of renewable energy, leading to the emergence of wind and solar power abandonment to varying degrees. For this reason, a model of directly using new energy power generation for production and application in various industries has emerged. Among them, the water electrolysis hydrogen production equipment consumes a lot of electricity, which provides a solution to the problem of new energy consumption, and ensures the green attributes of the product, making a great contribution to energy conservation and carbon reduction. The production of green hydrogen from green electricity has developed rapidly, so the application of optimized scheduling methods is of great significance.

[0003] Current technologies mainly focus on the optimization dispatching and control methods of power systems, covering integrated management of sources, grids, loads and storage, flexible resource utilization, carbon emission control and other aspects. It aims to improve the reliability, economy and sustainability of power systems. Intelligent algorithms and optimization models are applied to achieve efficient dispatching of wind, solar and storage systems, optimized control of distribution network voltage, low-carbon dispatching of demand response, and coordinated dispatching of multi-source and multi-load systems.

[0004] From the perspective of the "source-grid-load-storage" model, this method belongs to the "source-load-storage" model, which means that under the premise of ensuring the minimum hydrogen consumption, the optimal stable operation of "source-load-storage" and the scheduling scheme to improve utilization are achieved by optimizing the wind and solar output, energy storage complementarity and load regulation operation of hydrogen production equipment. In the current commonly used system scheduling, energy storage is mostly used to adjust the supply and demand balance of the system, and the load side is usually set to a fixed value, which makes it difficult to achieve flexible and efficient scheduling. In the off-grid hydrogen production system, intelligent scheduling of energy storage and load through demand response can effectively improve the utilization rate of new energy and provide a guarantee for stable hydrogen supply. At present, the optimization demand of the power system with new energy as the main power source still has the problem that the fluctuation of new energy and load regulation cannot be intelligently coordinated. Summary of the invention

[0005] The purpose of the present invention is to overcome the problem that new energy fluctuations and load regulation cannot achieve intelligent coordination in the optimization needs of power systems with new energy as the main power source, and proposes a coordinated operation optimization scheduling method and system based on wind, solar and storage complementary combined hydrogen production.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: The coordinated operation optimization scheduling method based on wind, solar and storage complementary combined hydrogen production includes the following steps: Collect and process the operating data of new energy power systems to obtain wind and solar output characteristics; Combining the wind and solar power output characteristics and the electrolyzer regulation range, and based on the complementary characteristics of electric and hydrogen loads, a source-load coordinated operation and dispatching model for the electric and hydrogen system is established; The particle swarm optimization scheduling method is used to find the optimal scheduling scheme for the source-load coordinated operation scheduling model of the electric-hydrogen system; Calculate the new energy consumption rate and equipment utilization rate of the optimal dispatching plan, and complete the optimal dispatching of the power system based on demand-side response.

[0007] Furthermore, the wind and solar power output characteristics are obtained by collecting and processing the operation data of the new energy power system, specifically: Obtain wind power data of the wind farm and light intensity data of the photovoltaic area, and calculate the maximum wind power and maximum photovoltaic power according to wind speed and light intensity respectively; The calculation of the maximum wind power is shown in the following formula:

[0008] in, is the maximum wind power at time t, is the wind speed at time t, For the cut-in wind speed, is the rated wind speed, To cut out the wind speed, is the rated power; The calculation of the maximum photovoltaic power is shown in the following formula:

[0009]

[0010] in, is the maximum photovoltaic power at time t, A is the area of ​​the photovoltaic module, is the light intensity at time t, is the efficiency of the photovoltaic module at time t, is the temperature at time t.

[0011] Furthermore, the above-mentioned combination of wind and solar power output characteristics and electrolyzer adjustment range, based on the complementary characteristics of electric and hydrogen loads, establishes a source-load coordinated operation and dispatching model for the electric and hydrogen system, specifically: Establish a mathematical model of the start-stop characteristics of the electrolytic cell, including the start-up and shutdown actions, start-up delay, and the limit of the number of start-up and shutdown times; set the upper and lower limits of the operating power of the electrolytic cell in the start-up state, as well as the electrical power consumed during the startup process; Establish mathematical models and constraints for energy storage charging and discharging, including battery capacity, charging and discharging power, charging and discharging efficiency, maximum charging and discharging power limit, upper and lower limits of battery capacity, and charging and discharging status; According to the real-time energy balance principle, set the system balance power; Calculate the total load demand on the load side, including fixed loads, adjustable loads, interruptible loads, and transferable loads.

[0012] Furthermore, the mathematical model of the start-stop characteristics of the electrolyzer is as follows:

[0013]

[0014]

[0015]

[0016]

[0017] Wherein, M is the electrolytic cell; k is the electrolytic cell number; t is the unit operation time period within the day, and T is the total optimized operation time period; is the switch status of the electrolytic cell; , They are respectively the start-up action and the start-up and shutdown action of the electrolytic cell; Delay the start of the electrolytic cell; , They are the upper limits of the number of startup and shutdown times of M electrolytic cell in a day; The upper limit power constraint condition of the electrolyzer operation is as follows:

[0018] in, It is the upper limit of the operating power of the electrolyzer when it is turned on. It is the electric power consumed during the startup of the electrolyzer; The lower limit power constraint condition of the electrolyzer operation is as follows:

[0019] in, It is the lower limit of the operating power of the electrolyzer when it is turned on. It is the electric power consumed during the startup of the electrolyzer; The energy storage charging and discharging mathematical model and constraints are as follows:

[0020] in, Store electricity in the battery; and are charging and discharging power respectively; and are the charge and discharge efficiency respectively; and They are the maximum power limits for battery charging and discharging respectively; and They are the upper and lower limits of the battery power respectively; and They are charge and discharge states respectively; The system balanced power is as follows:

[0021] in, is the wind power at time t, is the photovoltaic power at time t, is the energy storage charging power at time t, is the total load demand on the load side at time t, is the energy storage discharge power at time t, is the other adjusted power at time t; The total load demand on the load side is as follows:

[0022] in, is the total load demand on the load side at time t, , , , They are fixed load, adjustable load, interruptible load and transferable load at time t respectively.

[0023] Furthermore, the particle speed and position iteration rules of the particle swarm optimization scheduling method are as follows:

[0024]

[0025]

[0026]

[0027] in, represents the velocity vector of the nth particle in the i-th generation of wind, light, storage output and load, The velocity vector representing the energy storage charge state of the nth particle, Represents the position vector of wind, solar, storage output and load, represents the position vector of the energy storage charge state of the nth particle, and represents the optimal position searched by the nth particle in the i-th generation, and represents the optimal value searched by the entire population in the i-th generation, and Represents a random number vector between 0 and 1 with a shape of (500, 5, 24). Since the energy storage has an initial charge state, there are 25 energy storage charge states within 24 hours. and is a random number vector between 0 and 1 with a shape of (500, 5, 24); The energy storage, wind power, photovoltaic output and load coordination scheduling rules of the particle swarm optimization scheduling method include: When the current load demand is greater than the total output power of wind power and photovoltaic power, , analyze whether the energy storage discharge can meet the remaining power demand; if the energy storage discharge meets the remaining power demand, set the wind power and photovoltaic power to output at the maximum power, and the energy storage discharge will supplement the power required by the remaining load; if the energy storage discharge cannot meet the remaining power demand, set the wind power and photovoltaic power to output at the maximum power, and after the energy storage is discharged to the current discharge power upper limit, meet the remaining demand by adjusting part of the load; When the total output power of wind power and photovoltaic power is greater than the current load demand, , determine whether the energy storage system can fully absorb the excess power output; if the energy storage can fully store the excess power, set the wind power and photovoltaic power to output at the maximum power, and at the same time, the energy storage is charged to absorb all the excess power; if the energy storage cannot fully store the excess power, after charging the energy storage to the current charging power limit, only the new energy power abandonment is processed. At this time, wind power is prioritized to output at the maximum power and the excess photovoltaic power is abandoned; When the output power of wind power fully meets the current load demand, , setting the wind power output alone to meet the load demand, discarding the excess wind power, and abandoning all photovoltaic output power; The load scheduling rules of the particle swarm optimization scheduling method include: When the system load is greater than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, , take measures to transfer out adjustable or transferable loads; When the system load is less than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, , energy storage meets load demand with maximum output and provides power for loads outside the system as emergency power supply.

[0028] Furthermore, the particle swarm optimization scheduling method comprises the following steps: S1, initializing particle swarm parameters, wherein the particle swarm parameters include population size, maximum number of iterations, speed range, optimization target and optimization variable; S2, for the first generation of particles at time t, randomly generate the values ​​of wind, light, storage, and input and output loads as the initial positions of the particles; S3. Calculate the objective function value of each particle according to the wind and solar output, energy storage configuration, and the value of the input and output loads, and evaluate the solution represented by the particle; S4. Calculate the optimal historical position of the i-th particle , the personal historical optimal objective function value found by the i-th particle in the iteration process , the global optimal position found by the entire population during the iteration process and the global optimal objective function value found by the entire population during the iteration process ; S5, using the particle speed and position iteration rules of the particle swarm algorithm to update the speed and position of each particle, and iteratively update the particle swarm; S6. After updating the particle position, adjust the variable value according to the scheduling rule to obtain the value of the optimized variable; S7, calculating the power of each particle according to the value of the optimization variable; S8, compare the current particle with the previous personal historical optimal position and the global optimal position, if the current particle is better, update the corresponding optimal position; S9, detect whether the iteration process reaches the stop condition, if the iteration number reaches the maximum iteration number, terminate the iteration process; otherwise, increase the iteration number by 1, and return to S3 to continue iteration; S10. After the iteration is completed, the output is stable and coordinated to operate the wind power, photovoltaic output, energy storage discharge and load value in each period.

[0029] Furthermore, the calculation of the new energy consumption rate is shown in the following formula:

[0030] in, CR is the new energy consumption rate, for, for, is the maximum possible output power of wind power at time t, is the maximum possible output power of the photovoltaic at time t; The equipment utilization rate is calculated as follows:

[0031] in, LF is the equipment utilization rate, is the actual wind power output at time t, is the actual photovoltaic output power at time t, is the power obtained from the grid at time t, is the charging and discharging power of energy storage at time t, is the load demand at time t.

[0032] The coordinated operation optimization dispatching system based on wind, solar and storage complementary hydrogen production includes: The module for collecting wind and solar power output characteristics is used to collect and process the operation data of the new energy power system to obtain wind and solar power output characteristics; Establish a coordinated operation model module to combine the wind and solar power output characteristics and the electrolyzer adjustment range, and establish a source-load coordinated operation scheduling model for the electric-hydrogen system based on the complementary characteristics of the electric-hydrogen load; The particle swarm optimization module is used to find the optimal scheduling scheme for the source-load coordinated operation scheduling model of the electric hydrogen system using the particle swarm optimization scheduling method; Complete the optimization scheduling module, which is used to calculate the new energy consumption rate and equipment utilization rate of the optimal scheduling plan, and complete the optimization scheduling of the power system based on demand-side response.

[0033] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the coordinated operation optimization scheduling method based on wind, solar and storage complementary combined hydrogen production is implemented.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the coordinated operation optimization scheduling method based on wind, solar, and storage complementary combined hydrogen production.

[0035] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention proposes a coordinated operation optimization scheduling method based on the complementary joint hydrogen production of wind, solar and storage, which realizes stable and intelligent scheduling of the electric hydrogen system. First, data collection and analysis are performed on the power source mainly based on renewable energy such as wind and solar, and then the energy storage is charged and discharged based on the demand side response. The established source-load coordinated operation scheduling model of the electric hydrogen system is based on the demand side response. Finally, an optimization scheduling method based on particle swarm optimization is designed according to the actual operating conditions of the electric hydrogen system to realize the scheduling of optimized, stable and coordinated operation of the electric hydrogen system. This method can achieve supply and demand balance of the electric hydrogen system, reduce wind and solar power abandonment, improve new energy consumption rate and equipment utilization rate, and achieve efficient scheduling. The present invention has broad application prospects and use value.

[0036] Furthermore, the particle swarm optimization scheduling method provided by the present invention is based on demand-side response, and uses the principle of demand response to effectively realize the intelligent regulation of wind power, photovoltaics, energy storage, power grids and loads transferred in and out. In the application of actual power system scheduling, it can minimize costs while ensuring the stable operation of the system, while effectively reducing the wind and solar power abandonment rate.

[0037] Furthermore, the present invention aims to solve the problem that the fluctuation of new energy and load regulation cannot achieve intelligent coordination for the optimization demand of the power system with new energy as the main power source. The intelligent regulation of energy storage and load is carried out through the demand response mechanism, and a scheduling method based on particle swarm optimization (PSO) is designed according to the actual operating conditions of the power system. Compared with the prior art, the present invention has made significant progress in the optimization of scheduling rules, demand response mechanism, energy storage scheduling rules, model complexity and intelligent optimization algorithm design. First, the optimization scheduling rules are more flexible and can be dynamically adjusted to adapt to the actual working conditions. Secondly, the introduction of the demand response mechanism makes load regulation and energy storage scheduling more accurate, effectively completing the real-time supply and demand balance of energy, and reducing the phenomenon of wind and solar abandonment. The energy storage scheduling rules are more refined and intelligent, which improves the new energy consumption rate and the overall stability of the system. In terms of model complexity, the calculation efficiency is improved through reasonable optimization and comprehensive consideration. Finally, in the design of the intelligent optimization algorithm, the optimization rules are formulated in combination with a variety of actual new energy output conditions, which further improves the flexibility and stability of scheduling and realizes the safe and efficient scheduling of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are for explanation purposes only and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only for illustration purposes to help understand the present invention, and are not intended to specifically limit the shapes and proportional dimensions of the components of the present invention. In the drawings: Figure 1 This is a flow chart of the coordinated operation optimization scheduling method based on wind, solar and storage complementary combined hydrogen production of the present invention.

[0039] Figure 2 This is a structural diagram of the coordinated operation optimization scheduling system based on the wind, solar and storage complementary combined hydrogen production of the present invention.

[0040] Figure 3 This is a schematic diagram of an electronic device for the coordinated operation optimization scheduling method based on wind, solar and storage complementary combined hydrogen production of the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0042] Embodiment 1 See also Figure 1 The coordinated operation optimization scheduling method based on wind, solar and storage complementary combined hydrogen production includes the following steps: Collect and process the operating data of new energy power systems to obtain wind and solar output characteristics; Combining the wind and solar power output characteristics and the electrolyzer regulation range, and based on the complementary characteristics of electric and hydrogen loads, a source-load coordinated operation and dispatching model for the electric and hydrogen system is established; The particle swarm optimization scheduling method is used to find the optimal scheduling scheme for the source-load coordinated operation scheduling model of the electric-hydrogen system; Calculate the new energy consumption rate and equipment utilization rate of the optimal dispatching plan, and complete the optimal dispatching of the power system based on demand-side response.

[0043] This embodiment aims at the coordinated and stable operation optimization needs of new energy as the main power source and hydrogen production equipment as the load, as well as the conventional solution to the problem of poor sub-optimal scheduling. A particle swarm optimization scheduling method for the electric hydrogen system based on demand-side response is proposed. The actual working conditions of the system are comprehensively considered and the corresponding scheduling strategy is designed. In the actual system scheduling, the system supply and demand are effectively balanced, the wind and solar power abandonment rate is reduced, and the new energy consumption rate is increased, so as to achieve coordinated and stable operation of the electric hydrogen system, and provide technical support for the coordinated operation and optimized scheduling of wind, solar and storage complementary joint hydrogen production.

[0044] Collect and process the operation data of the new energy power system to obtain the wind and solar output characteristics, specifically: Obtain wind power data of the wind farm and light intensity data of the photovoltaic area, and calculate the maximum wind power and maximum photovoltaic power according to wind speed and light intensity respectively; The calculation of the maximum wind power is shown in the following formula:

[0045] in, is the maximum wind power at time t, is the wind speed at time t, For the cut-in wind speed, is the rated wind speed, To cut out the wind speed, is the rated power; The calculation of the maximum photovoltaic power is shown as follows:

[0046]

[0047] in, is the maximum photovoltaic power at time t, A is the area of ​​the photovoltaic module, is the light intensity at time t, is the efficiency of the photovoltaic module at time t, is the temperature at time t.

[0048] Combining the wind and solar power output characteristics and the adjustment range of the electrolyzer, based on the complementary characteristics of the electric and hydrogen loads, a source-load coordinated operation and dispatching model for the electric and hydrogen system is established. Specifically: Establish a mathematical model of the start-stop characteristics of the electrolytic cell, including the start-up and shutdown actions, start-up delay, and the limit of the number of start-up and shutdown times; set the upper and lower limits of the operating power of the electrolytic cell in the start-up state, as well as the electrical power consumed during the startup process; Establish mathematical models and constraints for energy storage charging and discharging, including battery capacity, charging and discharging power, charging and discharging efficiency, maximum charging and discharging power limit, upper and lower limits of battery capacity, and charging and discharging status; According to the real-time energy balance principle, set the system balance power; Calculate the total load demand on the load side, including fixed loads, adjustable loads, interruptible loads, and transferable loads.

[0049] The mathematical model of the start-stop characteristics of the electrolyzer is as follows:

[0050]

[0051]

[0052]

[0053]

[0054] Wherein, M is the electrolytic cell; k is the electrolytic cell number; t is the unit operation time period within the day, and T is the total optimized operation time period; is the switch status of the electrolytic cell; , They are respectively the start-up action and the start-up and shutdown action of the electrolytic cell; Delay the start of the electrolytic cell; , They are the upper limits of the number of startup and shutdown times of M electrolytic cell in a day; The upper limit power constraint of the electrolyzer operation is as follows:

[0055] in, It is the upper limit of the operating power of the electrolyzer when it is turned on. It is the electric power consumed during the startup of the electrolyzer; The lower limit power constraint of the electrolyzer operation is as follows:

[0056] in, It is the lower limit of the operating power of the electrolyzer when it is turned on. It is the electric power consumed during the startup of the electrolyzer; The energy storage charging and discharging mathematical model and constraints are as follows:

[0057] in, Store electricity in the battery; and are charging and discharging power respectively; and are the charge and discharge efficiency respectively; and They are the maximum power limits for battery charging and discharging respectively; and They are the upper and lower limits of the battery power respectively; and They are charge and discharge states respectively; The system balanced power is as follows:

[0058] in, is the wind power at time t, is the photovoltaic power at time t, is the energy storage charging power at time t, is the total load demand on the load side at time t, is the energy storage discharge power at time t, is the other adjusted power at time t; The total load demand on the load side is as follows:

[0059] in, is the total load demand on the load side at time t, , , , They are fixed load, adjustable load, interruptible load and transferable load at time t respectively.

[0060] The particle speed and position iteration rules of the particle swarm optimization scheduling method are as follows:

[0061]

[0062]

[0063]

[0064] in, represents the velocity vector of the nth particle in the i-th generation of wind, light, storage output and load, The velocity vector representing the energy storage charge state of the nth particle, Represents the position vector of wind, solar, storage output and load, represents the position vector of the energy storage charge state of the nth particle, and represents the optimal position searched by the nth particle in the i-th generation, and represents the optimal value searched by the entire population in the i-th generation, and Represents a random number vector between 0 and 1 with a shape of (500, 5, 24). Since the energy storage has an initial charge state, there are 25 energy storage charge states within 24 hours. and is a random number vector between 0 and 1 with a shape of (500, 5, 24); The energy storage, wind power, photovoltaic output and load coordination scheduling rules of the particle swarm optimization scheduling method include: When the current load demand is greater than the total output power of wind power and photovoltaic power, , analyze whether the energy storage discharge can meet the remaining power demand; if the energy storage discharge meets the remaining power demand, set the wind power and photovoltaic power to output at the maximum power, and the energy storage discharge will supplement the power required by the remaining load; if the energy storage discharge cannot meet the remaining power demand, set the wind power and photovoltaic power to output at the maximum power, and after the energy storage is discharged to the current discharge power upper limit, meet the remaining demand by adjusting part of the load; When the total output power of wind power and photovoltaic power is greater than the current load demand, , determine whether the energy storage system can fully absorb the excess power output; if the energy storage can fully store the excess power, set the wind power and photovoltaic power to output at the maximum power, and at the same time, the energy storage is charged to absorb all the excess power; if the energy storage cannot fully store the excess power, after charging the energy storage to the current charging power limit, only the new energy power abandonment is processed. At this time, wind power is prioritized to output at the maximum power and the excess photovoltaic power is abandoned; When the output power of wind power fully meets the current load demand, , setting the wind power output alone to meet the load demand, discarding the excess wind power, and abandoning all photovoltaic output power; The load scheduling rules of the particle swarm optimization scheduling method include: When the system load is greater than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, , take measures to transfer out adjustable or transferable loads; When the system load is less than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, , energy storage meets load demand with maximum output and provides power for loads outside the system as emergency power supply.

[0065] The particle swarm optimization scheduling method includes the following steps: S1, initialize the particle swarm parameters, which include population size, maximum number of iterations, speed range, optimization target and optimization variable; S2, for the first generation of particles at time t, randomly generate the values ​​of wind, light, storage, and input and output loads as the initial positions of the particles; S3. Calculate the objective function value of each particle according to the wind and solar output, energy storage configuration, and the value of the input and output loads, and evaluate the solution represented by the particle; S4. Calculate the optimal historical position of the i-th particle , the personal historical optimal objective function value found by the i-th particle in the iteration process , the global optimal position found by the entire population during the iteration process and the global optimal objective function value found by the entire population during the iteration process ; S5, using the particle speed and position iteration rules of the particle swarm algorithm to update the speed and position of each particle, and iteratively update the particle swarm; S6. After updating the particle position, adjust the variable value according to the scheduling rule to obtain the value of the optimized variable; S7, calculating the power of each particle according to the value of the optimization variable; S8, compare the current particle with the previous personal historical optimal position and the global optimal position, if the current particle is better, update the corresponding optimal position; S9, detect whether the iteration process reaches the stop condition, if the iteration number reaches the maximum iteration number, terminate the iteration process; otherwise, increase the iteration number by 1, and return to S3 to continue iteration; S10. After the iteration is completed, the output is stable and coordinated to operate the wind power, photovoltaic output, energy storage discharge and load value in each period.

[0066] The calculation of new energy consumption rate is as follows:

[0067] in, CR is the new energy consumption rate, for, for, is the maximum possible output power of wind power at time t, is the maximum possible output power of the photovoltaic at time t; The calculation of equipment utilization is shown in the following formula:

[0068] in, LF is the equipment utilization rate, is the actual wind power output at time t, is the actual photovoltaic output power at time t, is the power obtained from the grid at time t, is the charging and discharging power of energy storage at time t, is the load demand at time t.

[0069] This embodiment is based on the coordinated operation optimization scheduling method of wind, solar and storage complementary joint hydrogen production. It comprehensively processes the data of coordinated scheduling operation with new energy as the main power source, energy storage as the auxiliary power source, and hydrogen production equipment as the load, and establishes a corresponding electric hydrogen system scheduling model. It designs an optimization scheduling method based on the particle swarm algorithm. Under the premise of ensuring the minimum hydrogen consumption, the actual operating conditions are comprehensively considered to improve the energy utilization rate and equipment utilization rate, reduce the abandonment of wind and solar power, ensure the safe and stable operation of the hydrogen supply and the hydrogen consumption end, and fully coordinate all links of the system to achieve the best optimization scheduling plan.

[0070] The optimization dispatching strategy of this embodiment takes the output and transfer-in and transfer-out load values ​​of wind, light, and storage as optimization variables, and safe, stable and efficient utilization as optimization goals. Based on real-time energy balance and energy storage charging and discharging, the optimal dispatching strategy is obtained through particle swarm optimization. Finally, the equipment utilization rate and new energy utilization rate are used as the direct evaluation basis of the solution to complete the optimization dispatching of the power system based on demand-side response.

[0071] Embodiment 2 See also Figure 2 , a coordinated operation optimization dispatching system based on wind, solar and storage complementary combined hydrogen production. Including: The module for collecting wind and solar power output characteristics is used to collect and process the operation data of the new energy power system to obtain wind and solar power output characteristics; Establish a coordinated operation model module to combine the wind and solar power output characteristics and the electrolyzer adjustment range, and establish a source-load coordinated operation scheduling model for the electric-hydrogen system based on the complementary characteristics of the electric-hydrogen load; The particle swarm optimization module is used to find the optimal scheduling scheme for the source-load coordinated operation scheduling model of the electric hydrogen system using the particle swarm optimization scheduling method; Complete the optimization scheduling module, which is used to calculate the new energy consumption rate and equipment utilization rate of the optimal scheduling plan, and complete the optimization scheduling of the power system based on demand-side response.

[0072] Embodiment 3 See also Figure 3 , an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the coordinated operation optimization scheduling method based on wind, solar, and storage complementary combined hydrogen production is implemented.

[0073] Embodiment 4 A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the coordinated operation optimization scheduling method based on wind, solar, and storage complementary combined hydrogen production.

[0074] Embodiment 5 According to the constraints, design principles and judgment basis, the implementation of the overall electric hydrogen system optimization dispatching operation method mainly includes the following steps: Based on the coordinated operation optimization scheduling method of wind, solar and storage complementary joint hydrogen production, according to the hydrogen consumption of green ammonia and green alcohol at the back end, under the premise of ensuring the minimum demand, combined with the wind and solar output characteristics and the adjustable range of the electrolyzer, a complementary characteristic based on electric and hydrogen load is constructed to realize the coordinated operation scheduling model of source-load.

[0075] 1. Collect and process the operation data of the power system with renewable energy as the main power source, specifically: Obtain wind power data from the wind farm and calculate the maximum wind power at time t : (1) In the formula, is the wind speed at time t, in meters per second, is the cut-in wind speed in meters per second, is the rated wind speed in meters per second, is the cut-out wind speed, in meters per second, is the rated power in kilowatts.

[0076] Obtain the light intensity data of the photovoltaic field and calculate the maximum photovoltaic power at time t : (2) (3) Where A is the area of ​​the photovoltaic module, in square meters. is the light intensity at time t, in kilowatts per square meter, is the efficiency of the photovoltaic module at time t, in percentage, is the temperature at time t, in degrees Celsius. two, The mathematical model of the start-stop characteristics of the electrolyzer is as follows: (4) (5) (6) (7) (8) Where: M is the electrolytic cell; k is the electrolytic cell number; t is the unit operation time period within the day, and T is the total optimized operation time period; is the switch status of the electrolytic cell; , They are respectively the start-up action and the start-up and shutdown action of the electrolytic cell; Delay the start of the electrolytic cell; , They are the upper limits of the number of startups and shutdowns of M electrolytic cell within a day.

[0078] The upper limit power constraint of electrolyzer operation is: (9) The lower limit power constraint of the electrolyzer operation is: (10) in, and They are the upper and lower limits of the operating power of the electrolyzer when it is turned on; It is the electric power consumed during the startup of the electrolytic cell.

[0079] Mathematical model and constraints of energy storage charging and discharging: (11) Where: Store electricity in the battery; and are charging and discharging power respectively; and are the charge and discharge efficiency respectively; and They are the maximum power limits for battery charging and discharging respectively; and They are the upper and lower limits of the battery power respectively; and They are charge and discharge states respectively.

[0080] In the power system, the principle of real-time energy balance is the basis for ensuring the stable operation of the system. This means that at any time, the system's power generation must be equal to the load power plus the possible energy storage charging and discharging power and any other power adjustments (such as backup power). According to the principle of real-time energy balance, in order to ensure the stable operation of the system, it is necessary to ensure that the power balance is met at all times: (12) The total load demand value at the load side at time t is: (13) is the wind power output power at time t, in kilowatts, is the photovoltaic output power at time t, in kilowatts, is the energy storage charging power at time t, in kilowatts, is the energy storage discharge power at time t, in kilowatts, , , , They are fixed load, adjustable load, interruptible load and transferable load at time t, in kilowatts. is other adjustment power at time t, such as backup power supply, reactive power compensation, etc.; 3. Coordination scheduling method based on particle swarm optimization: According to the actual operating conditions of the system, the particle swarm optimization scheduling method is used to find the optimal output and energy storage charge state, and the priority rules of energy storage and wind and solar processing are used to further optimize the power system scheduling target based on demand-side response.

[0081] (a) Design of particle velocity and position iteration rules in particle swarm optimization (14) (15) (16) (17) In the formula, represents the velocity vector of the nth particle in the i-th generation of wind, light, storage output and load, The velocity vector representing the energy storage charge state of the nth particle, Represents the position vector of wind, solar, storage output and load, represents the position vector of the energy storage charge state of the nth particle, and represents the optimal position searched by the nth particle in the i-th generation, and represents the optimal value searched by the entire population in the i-th generation, and Represents a random number vector between 0 and 1 with a shape of (500, 5, 24). Since the energy storage has an initial charge state, there are 25 energy storage charge states within 24 hours. and is a random number vector between 0 and 1 with a shape of (500, 5, 25); (b) Design principles for energy storage, wind power, photovoltaic output and load coordination dispatch rules It aims to efficiently utilize renewable energy (such as wind power and photovoltaics) and meet load demand through the regulation of energy storage systems while minimizing energy waste. The following is a detailed explanation of the method: when When the energy storage discharge meets the remaining power demand, the system detects that the current load demand is greater than the total output power of wind power and photovoltaics. First, it determines whether the energy storage system discharge can meet the remaining power demand, and analyzes whether the energy storage discharge can meet the remaining power demand; if it can be fully satisfied, the maximum power output of wind power and photovoltaics is set, and the energy storage discharge provides the power required by the remaining load. Otherwise, the maximum power output of wind power and photovoltaics is set, and the remaining load can only adjust part of the load after the energy storage discharges to the current discharge power upper limit to reduce power consumption; when When the total output power of wind power and photovoltaic power is greater than the current load demand, the system determines whether the energy storage system can fully absorb this excess power output and analyzes whether charging the energy storage system can fully absorb the excess power output; if it can be completely stored in the energy storage, the maximum power output of wind power and photovoltaic power is set, and the energy storage is charged to absorb all the remaining power. Otherwise, when the energy storage is charged to the current charging power upper limit, it cannot continue to absorb excess electricity and can only abandon new energy power. If wind power output is given priority, the maximum power of wind power is output and the excess photovoltaic power is abandoned; when In some special cases, when the system determines that the output power of wind power is sufficient to meet the current load demand, the system will set the output power of wind power alone to meet the load demand, set the output power of wind power alone to meet the load demand, discard the excess wind power, and abandon all photovoltaic output power.

[0082] (c) Design of various load dispatching rules Depending on the system load ( )、Maximum output power of wind power( )、PV maximum output power( ) and the maximum charge and discharge power of the energy storage system ( ) to formulate the power dispatch strategy; the following is a detailed explanation of these two situations: when When the system load is greater than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, this condition means that even if wind power and photovoltaic power are both output at maximum power, and the energy storage system is also discharged at its adjustable maximum power (here it is assumed to be the discharge power), it still cannot meet the current load demand. In view of the system stability requirements, measures are taken to transfer adjustable and transferable loads; transferring adjustable loads means adjusting the power consumption of some non-critical equipment, or temporarily shutting down some non-essential power equipment; transferring transferable loads means transferring some loads to other time periods, and balancing supply and demand through demand-side management.

[0083] when When the system load is less than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, this condition indicates that the total output power of wind power and photovoltaic power has exceeded the current load demand even without considering the charging of the energy storage system (that is, the energy storage system does not participate in the discharge). At the same time, since the energy storage system still has sufficient adjustable power (here it is assumed to be the power that can be used for charging), the system has sufficient power reserves. The maximum output of energy storage meets the load demand and can be used as an emergency power supply to provide power for loads outside the system; at this time, the energy storage system is used to discharge to meet the current load demand and ensure the stable operation of the system. At the same time, since there is excess power, the energy storage system can also be charged for emergency use. In addition, if there is demand for loads outside the system and it does not affect the stability and safety of the system, power supply to the outside of the system can also be considered.

[0084] 4. Implementation of optimal dispatch of electric hydrogen system based on demand-side response, specifically: Step 1, initialize the particle swarm parameters, the population size is 500, the maximum number of iterations is 500, the speed range is [-0.5, 0.5], the coordinated and stable operation of the system is taken as the optimization goal, and the wind and solar output, energy storage configuration, and the load transfer are taken as the optimization variables; Step 2: Randomly generate wind, solar, storage, and in / out load values ​​as t The first generation of particles is input at this moment; Step 3, calculate the objective function value of the particle; Step 4: Calculate , , and ; is the personal historical optimal position of the ith particle (at a point in n-dimensional space, where n is the number of optimization variables); is the personal historical optimal objective function value found by the i-th particle during the iteration process; is the global optimal position found by the entire population during the iteration process; is the global optimal objective function value found by the entire population during the iteration process; Step 5: Use the speed and position update formula to iteratively update the particle swarm; Step 6: Adjust wind, light, storage and load values ​​according to the dispatching rules; Step 7: Calculate the power corresponding to each particle; Step 8: Update the historical optimal position of the population and the optimal position of the group; Step 9: Check whether the iteration process reaches the stop condition (the number of iterations reaches 500): If it reaches the stop condition, terminate the iteration process and jump to step 10; otherwise, increase the number of iterations by 1 and return to step 3; Step 10: Output the wind power and solar power output, energy storage discharge and load adjustment values ​​for each period of stable coordinated operation. These values ​​correspond to the solution represented by the global optimal position and are the optimal solutions found by the algorithm.

[0085] 5. Calculate the new energy consumption rate and equipment utilization rate, and use them as evaluation indicators for optimizing the scheduling scheme performance.

[0086] The new energy consumption rate is: (18) CR (Capacity Ratio) is defined as the ratio of the actual output power of wind power and photovoltaic power to the sum of their maximum output power; CR is the new energy consumption rate, for, for, is the maximum possible output power of wind power at time t (note that this is usually independent of time, because the maximum output power is mainly determined by the physical characteristics of the wind farm, but may vary due to changes in wind speed. Here it is simplified to a constant or an average value for a given time period), is the maximum possible output power of the photovoltaic at time t (again, this may vary due to factors such as solar radiation intensity and temperature, but to simplify the analysis, it can be regarded as a constant or an average value for a given time period); The equipment utilization rate is: (19) in, LF is the equipment utilization rate, is the actual wind power output at time t, is the actual photovoltaic output power at time t, is the power drawn from the grid at time t (note that the sign here may represent the power input or output from the grid, depending on the configuration and definition of the system. If the system draws power from the grid, this value is positive; if the system outputs power to the grid, this value may be negative, but it is assumed to be positive in this formula, indicating that the grid is used as a backup or supplementary power source), is the charge and discharge power of the energy storage battery (or energy storage system) at time t. If the battery is charging, this value is negative; if the battery is discharging, this value is positive. But in this formula, since it is subtracted from the denominator, when the battery is discharging (that is, providing power to the system), it increases the value of the denominator, and vice versa. is the load demand at time t.

[0087] In practical applications, the charging and discharging state of the energy storage system (i.e. The positive and negative of the power) and the direction of power exchange between the grid and the system (i.e. Both positive and negative values ​​are dynamic and are affected by a variety of factors. Therefore, when calculating this or any similar metric, you need to carefully consider these factors and their impact on the overall performance of the system.

[0088] Output the new energy consumption rate and equipment utilization rate under the optimal scheduling plan to achieve optimal scheduling of the power system based on demand-side response.

[0089] Compared with the traditional method, the optimization scheduling rules of this embodiment are more flexible, can be dynamically adjusted to adapt to changes in actual working conditions, and have higher adaptability. Secondly, the demand response mechanism is more sophisticated in design, fully analyzing the actual working conditions, and realizing precise regulation of the demand side. In terms of energy storage scheduling rules, this embodiment introduces a more complex model, fully considering the state of charge of energy storage and its different scheduling strategies, so that the energy storage utilization efficiency is higher. In terms of model complexity, although the model of this embodiment is more complex, the calculation efficiency is significantly improved through reasonable optimization and comprehensive consideration. Finally, in the design of intelligent optimization control strategy, this embodiment combines new energy optimization rules and particle swarm optimization algorithm to further improve the coordination and stability of scheduling, and achieve the effect of balancing supply and demand, reducing wind and solar power abandonment rates, and improving new energy consumption rate and equipment utilization rate in actual system scheduling.

[0090] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not deviate from the spirit and scope of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A coordinated operation optimization scheduling method based on wind, solar and storage complementary hydrogen production, characterized in that: The following steps are involved: Collect and process the operating data of new energy power systems to obtain wind and solar output characteristics; Combining the wind and solar power output characteristics and the electrolyzer regulation range, and based on the complementary characteristics of electric and hydrogen loads, a source-load coordinated operation and dispatching model for the electric and hydrogen system is established; The particle swarm optimization scheduling method is used to find the optimal scheduling scheme for the source-load coordinated operation scheduling model of the electric-hydrogen system; Calculate the new energy consumption rate and equipment utilization rate of the optimal scheduling plan, and complete the optimal scheduling of the power system based on demand-side response.

2. The coordinated operation optimization scheduling method based on wind, solar and storage complementary hydrogen production according to claim 1 is characterized in that: The wind and solar power output characteristics are obtained by collecting and processing the operation data of the new energy power system, specifically: Obtain wind power data of the wind farm and light intensity data of the photovoltaic area, and calculate the maximum wind power and maximum photovoltaic power according to wind speed and light intensity respectively; The calculation of the maximum wind power is shown in the following formula: in, is the maximum wind power at time t, is the wind speed at time t, For the cut-in wind speed, is the rated wind speed, To cut out the wind speed, is the rated power; The calculation of the maximum photovoltaic power is shown in the following formula: in, is the maximum photovoltaic power at time t, A is the area of ​​the photovoltaic module, is the light intensity at time t, is the efficiency of the photovoltaic module at time t, is the temperature at time t.

3. The coordinated operation optimization scheduling method based on wind, solar and storage complementary hydrogen production according to claim 1 is characterized in that: The above-mentioned combination of wind and solar power output characteristics and electrolyzer adjustment range, based on the complementary characteristics of electric and hydrogen loads, establishes a source-load coordinated operation and dispatching model for the electric and hydrogen system, specifically: Establish a mathematical model of the start-stop characteristics of the electrolytic cell, including the start-up and shutdown actions, start-up delay, and the limit of the number of start-up and shutdown times; set the upper and lower limits of the operating power of the electrolytic cell in the start-up state, as well as the electrical power consumed during the startup process; Establish mathematical models and constraints for energy storage charging and discharging, including battery capacity, charging and discharging power, charging and discharging efficiency, maximum charging and discharging power limit, upper and lower limits of battery capacity, and charging and discharging status; According to the real-time energy balance principle, set the system balance power; Calculate the total load demand on the load side, including fixed loads, adjustable loads, interruptible loads, and transferable loads.

4. The coordinated operation optimization scheduling method based on wind, solar and storage complementary hydrogen production according to claim 3 is characterized in that: The mathematical model of the start-stop characteristics of the electrolyzer is as follows: Wherein, M is the electrolytic cell; k is the electrolytic cell number; t is the unit operation time period within the day, and T is the total optimized operation time period; is the switch status of the electrolytic cell; , They are respectively the start-up action and the start-up and shutdown action of the electrolytic cell; Delay the start of the electrolytic cell; , They are the upper limits of the number of startup and shutdown times of M electrolytic cell in a day; The upper limit power constraint condition of the electrolyzer operation is as follows: in, It is the upper limit of the operating power of the electrolyzer when it is turned on. The electric power consumed during the startup of the electrolyzer; The lower limit power constraint condition of the electrolyzer operation is as follows: in, It is the lower limit of the operating power of the electrolyzer when it is turned on. The electric power consumed during the startup of the electrolyzer; The energy storage charging and discharging mathematical model and constraints are as follows: in, Store electricity in the battery; and are charging and discharging power respectively; and are the charge and discharge efficiency respectively; and They are the maximum power limits for battery charging and discharging respectively; and They are the upper and lower limits of the battery power respectively; and They are charge and discharge states respectively; The system balanced power is as follows: in, is the wind power at time t, is the photovoltaic power at time t, is the energy storage charging power at time t, is the total load demand on the load side at time t, is the energy storage discharge power at time t, is the other adjusted power at time t; The total load demand on the load side is as follows: in, is the total load demand on the load side at time t, , , , They are fixed load, adjustable load, interruptible load and transferable load at time t respectively.

5. The coordinated operation optimization scheduling method based on wind, solar and storage complementary hydrogen production according to claim 1 is characterized in that: The particle speed and position iteration rules of the particle swarm optimization scheduling method are as follows: in, represents the velocity vector of the nth particle in the i-th generation of wind, light, storage output and load, The velocity vector representing the energy storage charge state of the nth particle, Represents the position vector of wind, solar, storage output and load, represents the position vector of the energy storage charge state of the nth particle, and represents the optimal position searched by the nth particle in the i-th generation, and represents the optimal value searched by the entire population in the i-th generation, and Represents a random number vector between 0 and 1 with a shape of (500, 5, 24). Since the energy storage has an initial charge state, there are 25 energy storage charge states within 24 hours. and is a random number vector between 0 and 1 with a shape of (500, 5, 24); The energy storage, wind power, photovoltaic output and load coordination scheduling rules of the particle swarm optimization scheduling method include: When the current load demand is greater than the total output power of wind power and photovoltaic power, , analyze whether the energy storage discharge can meet the remaining power demand; if the energy storage discharge meets the remaining power demand, set the wind power and photovoltaic power to output at the maximum power, and the energy storage discharge will supplement the power required by the remaining load; if the energy storage discharge cannot meet the remaining power demand, set the wind power and photovoltaic power to output at the maximum power, and after the energy storage is discharged to the current discharge power upper limit, meet the remaining demand by adjusting part of the load; When the total output power of wind power and photovoltaic power is greater than the current load demand, , determine whether the energy storage system can fully absorb the excess power output; if the energy storage can fully store the excess power, set the wind power and photovoltaic power to output at the maximum power, and at the same time, the energy storage is charged to absorb all the excess power; if the energy storage cannot fully store the excess power, after charging the energy storage to the current charging power limit, only the new energy power abandonment is processed. At this time, wind power is prioritized to output at the maximum power and the excess photovoltaic power is abandoned; When the output power of wind power fully meets the current load demand, , setting the wind power output alone to meet the load demand, discarding the excess wind power, and abandoning all photovoltaic output power; The load scheduling rules of the particle swarm optimization scheduling method include: When the system load is greater than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, , take measures to transfer out adjustable or transferable loads; When the system load is less than the difference between the maximum output power of wind power and photovoltaic power and the adjustable power of energy storage, , energy storage meets load demand with maximum output and provides power for loads outside the system as emergency power supply.

6. The coordinated operation optimization scheduling method based on wind, solar and storage complementary hydrogen production according to claim 5 is characterized in that: The particle swarm optimization scheduling method comprises the following steps: S1, initializing particle swarm parameters, wherein the particle swarm parameters include population size, maximum number of iterations, speed range, optimization target and optimization variable; S2, for the first generation of particles at time t, randomly generate the values ​​of wind, light, storage, and input and output loads as the initial positions of the particles; S3. Calculate the objective function value of each particle according to the wind and solar output, energy storage configuration, and the value of the input and output loads, and evaluate the solution represented by the particle; S4. Calculate the optimal historical position of the i-th particle , the personal historical optimal objective function value found by the i-th particle in the iteration process , the global optimal position found by the entire population during the iteration process and the global optimal objective function value found by the entire population during the iteration process ; S5, using the particle speed and position iteration rules of the particle swarm algorithm to update the speed and position of each particle, and iteratively update the particle swarm; S6. After updating the particle position, adjust the variable value according to the scheduling rule to obtain the value of the optimized variable; S7, calculating the power of each particle according to the value of the optimization variable; S8, compare the current particle with the previous personal historical optimal position and the global optimal position, if the current particle is better, update the corresponding optimal position; S9, detecting whether the iteration process reaches the stop condition, and terminating the iteration process if the number of iterations reaches the maximum number of iterations; Otherwise, the number of iterations increases by 1, and the process returns to S3 to continue iterating; S10. After the iteration is completed, the output is stable and coordinated to operate the wind power, photovoltaic output, energy storage discharge and load value in each period.

7. The coordinated operation optimization scheduling method based on wind, solar and storage complementary hydrogen production according to claim 1 is characterized in that: The calculation of the new energy consumption rate is shown in the following formula: in, CR is the new energy consumption rate, for, for, is the maximum possible output power of wind power at time t, is the maximum possible output power of the photovoltaic at time t; The equipment utilization rate is calculated as follows: in, LF is the equipment utilization rate, is the actual wind power output at time t, is the actual photovoltaic output power at time t, is the power obtained from the grid at time t, is the charging and discharging power of energy storage at time t, is the load demand at time t.

8. A coordinated operation optimization dispatching system based on wind, solar and storage complementary hydrogen production is characterized by: include: The module for collecting wind and solar power output characteristics is used to collect and process the operation data of the new energy power system to obtain wind and solar power output characteristics; Establish a coordinated operation model module to combine the wind and solar power output characteristics and the electrolyzer adjustment range, and establish a source-load coordinated operation scheduling model for the electric-hydrogen system based on the complementary characteristics of the electric-hydrogen load; The particle swarm optimization module is used to find the optimal scheduling scheme for the source-load coordinated operation scheduling model of the electric hydrogen system using the particle swarm optimization scheduling method; Complete the optimization scheduling module, which is used to calculate the new energy consumption rate and equipment utilization rate of the optimal scheduling plan, and complete the optimization scheduling of the power system based on demand-side response.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for optimizing the coordinated operation and scheduling based on wind, solar, and storage complementary combined hydrogen production as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the coordinated operation optimization scheduling method based on wind, solar and storage complementary combined hydrogen production as described in any one of claims 1 to 7.

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