Calculation method for a hydro-wind-solar hybrid power generation combined green electricity consumption and hydrogen production control system

Abstract

This invention belongs to the field of energy regulation technology and relates to a calculation method for a hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production regulation system. The method includes the following steps: establishing a hydro-wind-solar hybrid hydrogen regulation model, improving the traditional particle swarm optimization algorithm, solving the multi-objective function based on the improved particle swarm optimization algorithm, and solving the objective function based on the lower-level model. This invention explores an optimized regulation mode for a hydro-wind-solar hybrid green electricity consumption hydrogen production system. By establishing mathematical models of wind turbine generators, solar power generation equipment, and hydropower station generators, and further introducing the green electricity consumption hydrogen production system, the feasibility of the hydro-wind-solar hybrid green electricity consumption hydrogen production system is analyzed. The mathematical model of the hydro-wind-solar hydrogen storage system is further deepened, making the system operation mode more flexibly responding to output, and effectively solving the multi-dimensional and constrained difficulties in solving the hydro-wind-solar hydrogen problem.

Description

Technical Field

[0001] This invention relates to the field of energy regulation technology, and in particular to a calculation method, apparatus, computer equipment, and storage medium for a hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production regulation system. Background Technology

[0002] With the continuous development of society and the economy, promoting the application of high-quality clean energy has become a major trend in the current energy transition. Therefore, exploring clean energy sources capable of regulating the volatility of renewable energy has become crucial for mitigating fluctuations in wind and solar power generation, reducing wasted power, and meeting transmission capacity requirements. Simultaneously, ensuring the stable operation of the power system and conducting research on the optimized scheduling of multiple clean energy sources in a complementary manner are essential for alleviating environmental pressures and energy shortages. Hydrogen, as a clean and pollution-free energy carrier, has become one of the important solutions for the development and storage of renewable energy sources such as wind, solar, and hydropower. Hydrogen production through water electrolysis converts electrical energy into chemical energy for storage, enabling hydrogen to play a role in various application scenarios.

[0003] Therefore, in order to realize the short-term multi-objective scheduling of multi-energy complementary systems of hydro, wind, solar, hydrogen and storage, promote the large-scale development and consumption of new energy sources, and promote the construction of new power systems, it is urgent to study an efficient calculation method for multi-energy complementary energy regulation to solve the short-term multi-objective scheduling model of hydro, wind, solar and hydrogen. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a calculation method for a combined hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production and control system, employing the following technical solution, including the following steps:

[0005] S1. Establish a power generation model that meets the constraints of hydro-wind-solar-hydrogen operation. Select the minimum standard deviation of the power output fluctuation of the hydro-wind-solar system and the minimum abandoned power of the joint system as the upper objective function, and the minimum difference between the power generation of the joint system and the capacity of the transmission channel as the lower objective function to establish a hydro-wind-solar complementary joint hydrogen regulation model.

[0006] S2, based on the traditional particle swarm optimization algorithm, establishes a generation strategy based on the initial power of water, wind and solar energy, an inertia weight, and a learning factor that adaptively changes with the number of iterations. Combined with the NSGA-II algorithm, it improves the traditional particle swarm optimization algorithm by using the frontier level and crowding distance as the fitness function in the particle swarm optimization algorithm.

[0007] S3, use the improved particle swarm optimization algorithm to optimize the upper objective function and obtain the Pareto optimal solution set;

[0008] S4 uses the Pareto optimal solution set as the boundary condition for the output power and employs mixed linear integer programming to optimize the lower-level objective function.

[0009] Preferably, the power generation model includes:

[0010] Wind power generation model:

[0011] In the formula, P W,maxt P represents the maximum output power of the wind turbine during time period t. W r V is the rated output power of the fan. in To cut off the wind speed, V out To cut off the wind speed, V r For the rated wind speed, V t The actual wind speed during time period t;

[0012] Photovoltaic power generation model:

[0013] In the formula, P S,maxt P represents the maximum output power of the photovoltaic cell during time period t. S F is the rated output power of photovoltaic power generation. S GTI is the derating factor, GTI is the solar radiation intensity at time t, and α is the derating factor. p T is the temperature correction factor. c,t Let T be the temperature of the photovoltaic cell at time t. c,STC The surface temperature of the photovoltaic array under standard test conditions;

[0014] Hydropower Model:

[0015] In the formula, η Hg Let ρ be the efficiency of the water turbine in converting water energy into electrical energy, ρ be the density of water, g be the acceleration due to gravity, and h be the acceleration due to gravity. Ht Let t be the net head of the hydropower station. Let t be the power generation flow rate of the hydropower station at time t.

[0016] Preferably, S2, based on the traditional particle swarm optimization algorithm, establishes a generation strategy based on the initial power of water, wind, and solar energy, an inertia weight, and a learning factor that adaptively changes with the number of iterations. Combined with the NSGA-II algorithm, the steps to improve the traditional particle swarm optimization algorithm by using the frontier level and crowding distance as the fitness function in the particle swarm optimization algorithm specifically include:

[0017] S21, taking the upstream and downstream water levels of the reservoir as the boundary, the maximum output power of wind power and solar power as the boundary, and the power generation of water, wind and solar power in each time period as the decision variable, introduces the first improvement strategy to obtain the initial population;

[0018] S22 introduces a second improvement strategy based on two parameters: inertia weight and empirical coefficient, to improve the particle swarm optimization algorithm.

[0019] S23, based on the NSGA-II algorithm, uses the leading edge level and crowding distance in the Pareto optimal solution set as the fitness update coefficients in particle swarm optimization, and combines the electric transport channel capacity as the fitness function to update and iterate the individual best position, historical best position and Pareto solution set of the particles.

[0020] Preferably, step S3, which involves optimizing the upper-level objective function using an improved particle swarm optimization algorithm to obtain the Pareto optimal solution set, specifically includes:

[0021] S31, configure the parameters;

[0022] S32 initializes the particles according to the set parameters;

[0023] S33, solve for the fitness of all particles at each time period;

[0024] S34. Based on the initial inertia weight data and initial learning factor data, the velocity, position, and fitness of the particles are calculated for each iteration using the velocity and position formulas of the particle swarm.

[0025] S35, extract the leading edge level and crowding distance of each particle, perform non-dominated sorting, and record the position of the particle with a leading edge level of 1;

[0026] S36, update the inertia weight w and learning factors c1 and c2 using the adaptive inertia weight and learning factor formula, and continue the iterative process;

[0027] S37. Proceed to the next step based on whether the number of iterations meets the termination condition. If it does, output the Pareto optimal solution set; otherwise, continue looping from step S33 to step S36 until the requirement is met.

[0028] Preferably, S4, using the Pareto optimal solution set as the boundary condition for the output power, specifically includes the following steps for optimizing the lower-level objective function using mixed linear integer programming:

[0029] S41, Introduce the hydrogen storage constraints from step S1 to construct a hydro-wind-solar-hydrogen storage system;

[0030] S42, Select the position of a particle in the Pareto solution set obtained in step S3 as the output power of water, wind and light in the lower layer model;

[0031] S43, taking the minimum difference between the combined system power generation and the transmission channel capacity in step S1 as the objective function of the lower-level model, and solving it through the Cplex solver in MATLAB to obtain the operating power of hydropower, wind power, photovoltaic power, electrolyzer, fuel cell and compressor in each time period.

[0032] Preferably, the constraints include:

[0033] Wind and solar power output constraints: 0 ≤ P t W ≤P t W,max , 0≤P t S ≤P t S,max In the formula, P W t P represents the actual output power of the wind turbine during time period t. S t This represents the actual output power of the photovoltaic cell during time period t.

[0034] Water balance constraints: In the formula, V H t , Let Q be the water volume stored in the reservoir at time t. H t and q Ht Let t be the inflow and outflow of the reservoir.

[0035] Outbound flow constraints: In the formula, q Hmin This represents the minimum outflow from the reservoir. This represents the maximum outflow from the reservoir.

[0036] Power generation flow constraints: In the formula, QF Hmin This represents the minimum power generation flow rate of the reservoir. This represents the maximum power generation flow rate of the reservoir.

[0037] Water level constraints: In the formula, Z Hmin This is the minimum operating water level of the reservoir. This is the maximum operating water level of the reservoir;

[0038] Hydropower station output constraints: In the formula, This represents the maximum output power of hydropower.

[0039] Power balance constraints:

[0040] In the formula, These represent the power generation of water, wind, solar, and hydrogen storage fuel cells under hydrogen storage regulation during time period t. Let P be the input power of the electrolyzer during time period t. outt This refers to the system's net output power.

[0041] Electrolytic cell power constraints:

[0042] In the formula, Let λ be the hydrogen input power during time period t. H The efficiency of hydrogen production in an electrolyzer. P YS t These represent the hydrogen storage power, hydrogen storage power generation power, and hydrogen compression power during time period t, respectively; P ELC,mint and P ELC,maxt Let ΔP be the minimum and maximum input power of the electrolyzer during time period t. ELC,min and ΔP ELC,max These represent the lower and upper limits of the electrolytic cell power ramp-up, respectively.

[0043] Compressor power constraints: In the formula, η ELC and η YS These are the energy consumption for hydrogen production via electrolyzer and the energy consumption for hydrogen storage via compressor;

[0044] Fuel cell power constraints:

[0045] In the formula, Let λ be the output power of the fuel cell during time period t. FC For fuel cell power generation efficiency, and ΔP represents the minimum and maximum output power of the fuel cell during time period t. ELC,min and ΔP ELC,max These represent the lower and upper limits of the fuel cell power generation ramp-up;

[0046] Constraints on the regulation of hydrogen production through green electricity consumption:

[0047] Preferably, the objective function includes:

[0048] Objective function for minimizing the power output fluctuation of hydro-wind-solar hybrid systems:

[0049]

[0050] In the formula, N is the standard deviation of the power output fluctuation of the hydro-wind-solar hybrid system per unit period, and P H iter,t P W iter,t P S iter,t P represents the hydropower, wind power, and solar power output during the t-th time interval of the iterth iteration. av This represents the average output of the system within a unit period.

[0051] The objective function for minimizing the amount of power wasted in the joint system is:

[0052] In the formula, M is the total amount of abandoned electricity from water, wind and solar power within a unit period;

[0053] Objective function for minimizing the difference between power generation and transmission channel capacity in the combined system:

[0054] In the formula, Z represents the sum of the absolute values ​​of the difference between the combined hydropower, wind power, and solar power output and the transmission channel capacity at the iterth iteration in the kth population, and P... transport This refers to the capacity of the power grid transmission channels.

[0055] Preferably, the first improvement strategy includes:

[0056] Strategies for improving minimum water levels in different time periods:

[0057] In the formula, H mink,t Let qave be the lowest water level in the reservoir during time period t for the k-th population, and qave be the historical average flow rate of the reservoir. This represents the lowest water level in the reservoir during time period t-1 for the k-th population.

[0058] Improvement strategies for maximum water level in different time periods:

[0059] In the formula, H maxk,t This represents the highest water level in the reservoir during time period t for the k-th population. This represents the highest water level in the reservoir during time period t-1 for the k-th population.

[0060] Improved water level initialization strategies for different time periods:

[0061] In the formula, H k,t H represents the initial water level of the reservoir during time period t for the k-th population. k,t Let be the initial water level of the reservoir during time period t-1 for the kth population, rand be a random function, and population be the population size of the particle swarm.

[0062] Improvement strategies for initializing water outflow from the reservoir at different time periods:

[0063] In the formula, q Hk,t Let `repmat` be the initial outflow rate of the kth population during time period `t`, and `repmat` be a function that repeats an array.

[0064] Improved strategies for initializing hydropower generation flow in different time periods:

[0065] In the formula, QF H k,t Let b1 be the initial power generation flow rate of the kth population during time period t, where b1 is a constant.

[0066] Improvement strategies for initializing water discharge current in different time periods: In the formula, Nq Hk,t This represents the initial current wastage during time period t for the k-th population.

[0067] Wind power generation initialization improvement strategies for different time periods:

[0068] In the formula, P W k,t Let b2 be the initial wind power generation capacity of the kth population in time period t, b2 be a constant, and .* be an array multiplication;

[0069] Initialization improvement strategies for photovoltaic power generation in different time periods:

[0070] In the formula, Let b3 be the initial photovoltaic power generation in the k-th population during time period t, b3 be a constant, and .* be an array multiplication.

[0071] Preferably, the second improvement strategy includes:

[0072] Adaptive inertia weighting improvement strategy:

[0073] In the formula, W is the inertial weight, W min and W max The preset minimum and maximum inertia weights are 0.4 and 0.9 respectively, and iter is the current iteration number. max The maximum number of iterations is 1500.

[0074] Adaptive learning factor improvement strategy:

[0075] In the formula, C1 is the individual learning experience coefficient, C 1s and C 1e The preset initial and stopping values ​​are 1.5 and 0.5 respectively, and C2 is the group learning experience coefficient. 2s and C 2e The preset initial and stop values ​​are 0.5 and 1.5, respectively.

[0076] Preferably, fitness includes:

[0077] In the formula, F k,iter Let Rank be the fitness of the k-th population at the iteration number iter. k,iter Crowd represents the Pareto front rank at the iteration number of the k-th population. k,iter Dp represents the crowding distance in the k-th population at the iter-th iteration. k,iter,outP is the sum of the absolute values ​​of the difference between the combined hydropower, wind power, and solar power output and the transport channel capacity at the iterth iteration in the k-th population. transport This refers to the capacity of the power grid transmission channels.

[0078] Compared with the prior art, the present invention has the following advantages: This embodiment explores an optimized control mode for a hydro-wind-solar hybrid combined green electricity consumption and hydrogen production system. First, by establishing a mathematical model of wind turbine generator set, solar power generation equipment, and hydropower station generator set, a green electricity consumption and hydrogen production system is further introduced, thereby analyzing the feasibility of the hydro-wind-solar hybrid combined green electricity consumption and hydrogen production system. The NSPSO algorithm provided by this invention uses the minimum standard deviation of the combined output of hydro-wind-solar power and the minimum amount of abandoned power as dual objective functions. It considers the stability and power balance of the system's operation, combining the basic constraints of wind, solar, and hydropower, the predictive consistency of typical days in specific regions, and the output constraints of each energy source. It establishes a combined hydro-wind-solar complementary power generation system and performs simulations and further analysis of optimized scheduling. Secondly, it further deepens the mathematical model of the hydro-wind-solar-hydrogen-storage system, establishing a multi-objective, two-layer optimization model that minimizes the standard deviation of the combined output of the hydro-wind-solar system, the amount of abandoned power, and the difference between the combined output and the transmission channel capacity. The upper-layer model optimizes to obtain the solution set with the minimum standard deviation of different fluctuations and the minimum amount of abandoned power, while the lower-layer model also minimizes the difference in the transmission channel capacity, making the system operation mode more flexible in response to output, effectively solving the multi-dimensional and constrained difficulties in solving the hydro-wind-solar-hydrogen system problem. Attached Figure Description

[0079] To more clearly illustrate the solutions in this invention, the accompanying drawings used in the description of the embodiments of this invention will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0080] Figure 1 This is a flowchart of an embodiment of the calculation method for the hydro-wind-solar hybrid power generation combined with green electricity consumption and hydrogen production control system of the present invention;

[0081] Figure 2 This is a flowchart of an embodiment of a two-layer optimization control method using the calculation method of the hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production regulation system of the present invention;

[0082] Figure 3 for Figure 2 Pareto solution set graph in the middle and upper levels of optimization;

[0083] Figure 4 for Figure 2 Figure 1 shows the first operational status of the Pareto frontal hydropower-wind-solar system in the middle and upper layers of the optimization process.

[0084] Figure 5 for Figure 2 The second operational status diagram of the concentrated water, wind and solar power system in the middle and upper layers of the optimization Pareto solution;

[0085] Figure 6 for Figure 2 Results of the middle and lower layer optimization. Detailed Implementation

[0086] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings are used to distinguish different objects and not to describe a particular order.

[0087] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0088] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0089] It should be noted that the calculation method of the hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production regulation system provided in the embodiments of the present invention is generally executed by a server / terminal device. Correspondingly, the calculation device of the hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production regulation system is generally set in the server / terminal device.

[0090] It should be understood that the number of terminal devices, networks, and servers is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be used.

[0091] Example 1

[0092] Please refer to Figure 1 This document illustrates a flowchart of an embodiment of the calculation method for the hydro-wind-solar hybrid power generation and green energy consumption hydrogen production regulation system of the present invention. The calculation method for the hydro-wind-solar hybrid power generation and green energy consumption hydrogen production regulation system includes the following steps:

[0093] Step S1: Establish a power generation model that meets the operational constraints of hydro-wind-solar-hydrogen, and select the minimum standard deviation of the power output fluctuation of the hydro-wind-solar system and the minimum abandoned power of the joint system as the upper-level objective function, and the minimum difference between the power generation of the joint system and the capacity of the transmission channel as the lower-level objective function, to establish a hydro-wind-solar complementary joint hydrogen regulation model.

[0094] In this embodiment, the electronic equipment (e.g., server / terminal equipment) on which the calculation method of the hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production control system runs can receive calculation requests from the hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production control system via wired or wireless connections. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, Wi-Fi connections, Zigbee connections, UWB (ultra-wide wireless) connections, and other currently known or future-developed wireless connection methods.

[0095] In specific implementation, the power generation model in step S1 includes, but is not limited to, wind power generation model, photovoltaic power generation model, and hydropower generation model.

[0096] The wind power generation model is as follows:

[0097] In the formula, P W,maxt P represents the maximum output power of the wind turbine during time period t. W r V is the rated output power of the fan. in To cut off the wind speed, V out To cut off the wind speed, V r For the rated wind speed, V t The actual wind speed during time period t.

[0098] The photovoltaic power generation model is as follows: In the formula P S,maxt P represents the maximum output power of the photovoltaic cell during time period t. S F is the rated output power of photovoltaic power generation. S GTI is the derating factor, GTI is the solar radiation intensity at time t, and α is the derating factor. p T is the temperature correction factor. c,t Let T be the temperature of the photovoltaic cell at time t. c,STC This represents the surface temperature of the photovoltaic array under standard test conditions.

[0099] The hydroelectric power generation model is as follows: In the formula η Hg Let ρ be the efficiency of the water turbine in converting water energy into electrical energy, ρ be the density of water, g be the acceleration due to gravity, and h be the acceleration due to gravity. Ht Let t be the net head of the hydropower station. Let t be the power generation flow rate of the hydropower station at time t.

[0100] In some optional implementation methods, the constraints include, but are not limited to, wind and solar power output constraints, water balance constraints, outflow constraints, power generation flow constraints, water level constraints, hydropower station output constraints, power balance constraints, electrolyzer power constraints, compressor power constraints, fuel cell power constraints, and green electricity consumption and hydrogen production regulation constraints.

[0101] The power output constraint for wind and solar power is: 0 ≤ P t W ≤P t W,max , 0≤P t S ≤P t S,max In the formula P W t P represents the actual output power of the wind turbine during time period t. S t This represents the actual output power of the photovoltaic cell during time period t.

[0102] The water balance constraints are: In the formula V H t , Let Q be the water volume stored in the reservoir at time t. H t and q Ht Let t be the inflow and outflow of the reservoir.

[0103] The outbound flow constraint is: In the formula q Hmin This represents the minimum outflow from the reservoir. This represents the maximum outflow from the reservoir.

[0104] The power generation flow constraint is: In the formula QF Hmin This represents the minimum power generation flow rate of the reservoir. This represents the maximum power generation flow of the reservoir.

[0105] The water level constraint is: In the formula Z Hmin This is the minimum operating water level of the reservoir. This is the maximum operating water level of the reservoir.

[0106] The power output constraints of the hydropower station are: In the formula, This represents the maximum output power of hydropower.

[0107] The power balance constraint is:

[0108] In the formula These represent the power generation of water, wind, solar, and hydrogen storage fuel cells under hydrogen storage regulation during time period t. Let P be the input power of the electrolyzer during time period t. outt This represents the system's net output power.

[0109] The power constraint conditions for the electrolytic cell are:

[0110] In the formula Let λ be the hydrogen input power during time period t. H The efficiency of hydrogen production in an electrolyzer. P represents the hydrogen storage power, hydrogen storage power generation power, and compressed hydrogen power during time period t, respectively. ELC,mint and P ELC,maxt Let ΔP be the minimum and maximum input power of the electrolyzer during time period t. ELC,min and ΔP ELC,max These represent the lower and upper limits of the electrolytic cell power ramp-up, respectively.

[0111] The compressor power constraint is: In the formula η ELC and η YS These are the energy consumption for hydrogen production via electrolyzer and the energy consumption for hydrogen storage via compressor.

[0112] The power constraints for fuel cells are:

[0113] In the formula Let λ be the output power of the fuel cell during time period t. FC For fuel cell power generation efficiency, and ΔP represents the minimum and maximum output power of the fuel cell during time period t. ELC,min and ΔP ELC,max These represent the lower and upper limits of the fuel cell power generation ramp-up, respectively.

[0114] The constraints for regulating hydrogen production through green electricity consumption are:

[0115] In some optional implementations, the objective function includes, but is not limited to, the objective function for minimizing the output fluctuation of hydro-wind-solar hybrid systems, the objective function for minimizing the curtailment of the combined system's power generation, and the objective function for minimizing the difference between the combined system's power generation and transmission channel capacity.

[0116] The objective function for minimizing the power output fluctuation of the hydro-wind-solar hybrid system is:

[0117] In the formula, N is the standard deviation of the power output fluctuation of the hydro-wind-solar hybrid system per unit period, and P Hiter,t P W iter,t P S iter,t P represents the hydropower, wind power, and solar power output during the t-th time interval of the iterth iteration. av This represents the average output of the system within a unit period.

[0118] The objective function for minimizing the amount of power wasted in the joint system is:

[0119] In the formula, M represents the total amount of abandoned electricity generated by water, wind, and solar power within a unit period.

[0120] The objective function for minimizing the difference between the combined system's power generation and transmission channel capacity is:

[0121] In the formula, Z represents the sum of the absolute values ​​of the difference between the combined hydropower, wind power, and solar power output and the transmission channel capacity at the iterth iteration in the kth population, and P... transport This refers to the capacity of the power grid transmission channels.

[0122] Step S2: Based on the traditional particle swarm optimization algorithm, a generation strategy based on the initial power of water, wind and light, an inertia weight, and a learning factor adaptively changing with the number of iterations are established. Combined with the NSGA-II algorithm, the traditional particle swarm optimization algorithm is improved by using the frontier level and crowding distance as the fitness function in the particle swarm optimization algorithm.

[0123] In this embodiment, step S2 may further include the following steps:

[0124] S21, taking the upstream and downstream water levels of the reservoir as boundaries, the maximum output power of wind power and solar power as boundaries, and the power generation of water, wind and solar power in each time period as decision variables, introduces the first improvement strategy to obtain the initial population.

[0125] In some optional implementations, the first improvement strategy includes, but is not limited to, the minimum water level improvement strategy for each time period, the maximum water level improvement strategy for each time period, the water level initialization improvement strategy for each time period, the water outflow initialization improvement strategy for each time period, the hydropower generation flow initialization improvement strategy for each time period, the hydropower curtailment initialization improvement strategy for each time period, the wind power generation initialization improvement strategy for each time period, and the photovoltaic power generation initialization improvement strategy for each time period.

[0126] The improvement strategy for the minimum water level in each time period is as follows:

[0127] In the formula H mink,t Let qave be the lowest water level in the reservoir during time period t for the k-th population, and qave be the historical average flow rate of the reservoir. This represents the lowest water level in the reservoir during time period t-1 for the k-th population.

[0128] The strategy for improving the maximum water level for each time period is as follows:

[0129] In the formula H maxk,t This represents the highest water level in the reservoir during time period t for the k-th population. This represents the highest water level in the reservoir during time period t-1 for the k-th population.

[0130] The water level initialization improvement strategy for each time period is as follows:

[0131] In the formula H k,t H represents the initial water level of the reservoir during time period t for the k-th population. k,t Let be the initial water level of the reservoir during time period t-1 for the k-th population, rand be a random function, and population be the number of particles in the swarm.

[0132] The initialization improvement strategy for water outflow from the reservoir at different time periods is as follows:

[0133] In the formula q Hk,t Let `repmat` be the initial outflow rate in time period `t` for the `k`th population, and `repmat` be a function that repeats an array.

[0134] The initialization improvement strategy for hydropower generation flow in each time period is as follows:

[0135] In the formula QF H k,t Let b1 be the initial power generation flow rate of the k-th population during time period t, and b1 be a constant.

[0136] The initialization improvement strategy for water discharge current in each time period is as follows:

[0137] In the formula Nq Hk,t Let be the initial current discharge amount in time period t for the k-th population.

[0138] The initialization improvement strategies for wind power generation in different time periods are as follows:

[0139] In the formula P W k,t Let b2 be the initial wind power generation in the k-th population during time period t, b2 be a constant, and .* be an array multiplication.

[0140] The initialization improvement strategies for photovoltaic power generation in different time periods are as follows:

[0141] In the formula Let b3 be the initial photovoltaic power generation in the k-th population during time period t, b3 be a constant, and .* be an array multiplication.

[0142] S22 introduces a second improvement strategy based on two parameters: inertia weight and empirical coefficient, to improve the particle swarm optimization algorithm.

[0143] In some alternative implementations, the second improvement strategy includes adaptive inertia weight improvement strategy and adaptive learning factor improvement strategy, etc.

[0144] The adaptive inertia weight improvement strategy is as follows:

[0145] In the formula, W is the inertial weight. min and W max The preset minimum and maximum inertia weights are 0.4 and 0.9 respectively, and iter is the current iteration number. max The maximum number of iterations is 1500.

[0146] The adaptive learning factor improvement strategy is as follows:

[0147] In the formula, C1 is the individual learning experience coefficient, C 1s and C 1e The preset initial and stopping values ​​are 1.5 and 0.5 respectively, and C2 is the group learning experience coefficient. 2s and C 2e The preset initial and stop values ​​are 0.5 and 1.5, respectively.

[0148] S23, based on the NSGA-II algorithm, uses the leading edge level and crowding distance in the Pareto optimal solution set as the fitness update coefficients in particle swarm optimization, and combines the electric transport channel capacity as the fitness function to update and iterate the individual best position, historical best position and Pareto solution set of the particles.

[0149] In some alternative implementations, fitness includes:

[0150] In the formula F k,iter Let Rank be the fitness of the k-th population at the iteration number iter. k,iter Crowd represents the Pareto front rank at the iteration number of the k-th population. k,iter Dp represents the crowding distance in the k-th population at the iter-th iteration. k,iter,out P is the sum of the absolute values ​​of the difference between the combined hydropower, wind power, and solar power output and the transport channel capacity at the iterth iteration in the k-th population. transport This refers to the capacity of the power grid transmission channels.

[0151] The specific process for solving the NSPSO algorithm is as follows:

[0152] Suppose a population exists in a D-dimensional space containing K particles, defined as S = {X1, X2, ..., X}. K}, where S is the set of spaces, X i ={X i1 ,X i2 ,…X id}, i = 1, 2, ..., K, the solution containing information about the position and velocity of the i-th particle in this space;

[0153] The position formula is: Where U1 and L1 represent the upper and lower limits of the position change of the particle during its motion;

[0154] The velocity formula is: Where U2 and L2 represent the upper and lower limits of the velocity change of the particle during its motion;

[0155] Global optimal position:

[0156] Individual optimal position:

[0157] The particle velocity update formula is:

[0158] The particle position update formula is:

[0159] Frontier ranking formula: F = {F1, F2, ..., F} n}where F i ={X i1 ,X i2 ,…,X im}

[0160] Crowded distance formula:

[0161] In the formula: W is the inertia weight, inheriting the velocity of past particles and representing the particle's inertial characteristics. Through adaptive inertia weighting, a larger W in the early iterations prevents the algorithm from getting trapped in local minima, facilitating global search. In the later iterations, a smaller W is beneficial for local search and algorithm convergence. C1 and C2 are learning factors. C1 represents individual cognition in the particle swarm optimization algorithm, indicating that during iteration, a particle needs to compare its previous value with its own to achieve positional changes. C2 reflects common cognition, indicating that during iteration, each particle needs to compare its current position with the currently recognized best position. r1 and r2 are random numbers, assigned initial values ​​between 0 and 1 using a random function. The assignment of r1 and r2 is completely random and independent of each other during each calculation. Through adaptive learning factors, in the early iterations, a large C1 and a small C2 give particles better self-learning ability and poorer social learning ability, which is beneficial for global search. In the later iterations, a small C1 and a large C2 give particles stronger social learning ability and poorer self-learning ability, which is beneficial for algorithm convergence. F i Let be the i-th frontier level, indicating that the frontier level is updated after each iteration based on the dominance relationship of the solutions. Each solution is assigned a frontier level, representing its relative superiority. and Is the solution X i The objective values ​​of the previous and next solutions on objective m. and These are the maximum and minimum values ​​of all solutions to the objective. This indicates that for solutions at the same frontier level, the optimal solution will be further selected based on the crowding distance. Solutions with larger crowding distances indicate higher diversity in the objective space, which helps avoid over-concentration of the solution set.

[0162] Step S3: Use the improved particle swarm optimization algorithm to optimize the upper objective function and obtain the Pareto optimal solution set.

[0163] In this embodiment, step S3 may further include the following steps:

[0164] S31, configure the parameters.

[0165] It involves wind power, photovoltaic power, hydropower generation flow and water discharge, so the dimension is 4, the number of particles is 100, the particle movement range is the upper and lower limits of the actual operating output of each generator unit, and the number of iterations is 1500.

[0166] S32 initializes the particles according to the set parameters.

[0167] Based on the generation strategy, the initial position and velocity of the particles are initialized.

[0168] S33, solve for the fitness of all particles at each time period.

[0169] Determine the individual optimal value p for each particle. best And the optimal value G of the entire population best。

[0170] S34, based on the initial inertia weight data and initial learning factor data, uses the velocity and position formulas of the particle swarm to calculate the updated velocity, position, and fitness of the particles in each iteration.

[0171] S35, extract the leading edge level and crowding distance of each particle, perform non-dominated sorting, and record the position of the particle with leading edge level 1.

[0172] S36, update the inertia weight w and learning factors c1 and c2 using the adaptive inertia weight and learning factor formulas, and continue the iterative process.

[0173] S37. Proceed to the next step based on whether the number of iterations meets the termination condition. If it does, output the Pareto optimal solution set; otherwise, continue looping from step S33 to step S36 until the requirement is met.

[0174] Step S4: Using the Pareto optimal solution set as the boundary condition for the output power, the lower-level objective function is optimized using mixed linear integer programming.

[0175] In practice, step S4 may further include the following steps:

[0176] S41, Introduce the hydrogen storage constraints from step S1 to construct a hydro-wind-solar-hydrogen storage system;

[0177] S42, Select the position of a particle in the Pareto solution set obtained in step S3 as the output power of water, wind and light in the lower layer model;

[0178] S43, taking the minimum difference between the combined system power generation and the transmission channel capacity in step S1 as the objective function of the lower-level model, and solving it through the Cplex solver in MATLAB to obtain the operating power of hydropower, wind power, photovoltaic power, electrolyzer, fuel cell and compressor in each time period.

[0179] This embodiment analyzes the power output characteristics of hydro-wind-solar systems and further studies the hydro-wind-solar power complementarity strategy. Based on the traditional particle swarm optimization (PSO) algorithm, this invention improves the initial population generation strategy and proposes a strategy that adaptively changes the inertia weight and learning factor with the number of iterations. Simultaneously, using the leading edge level and congestion distance from the NSGA-II algorithm combined with the power transmission channel capacity as fitness, a multi-objective particle swarm optimization algorithm (NSPSO) is constructed. A dual-objective model is established, aiming to minimize the standard deviation of the power output fluctuation and the amount of power wasted, to optimize the hydro-wind-solar complementary system, obtaining a Pareto optimal solution set that satisfies both the minimum total power output fluctuation and the minimum total power wasted. Furthermore, a hydrogen storage model is introduced to establish an NSPSO-MILP two-level optimization model, which will be used to optimize the hydro-wind-solar hydrogen co-generation system using the obtained Pareto optimal solution set.

[0180] This embodiment uses combined power generation as the basic data and comprehensively considers various factors, including power output fluctuations and abandoned power, to establish a mathematical model for the regulation and control of combined hydro-wind-solar hybrid power generation and hydrogen production. In hydro-wind-solar hybrid systems, peak shaving is a key concern during the combined grid connection and transmission of these systems. In this energy combination based on hydrogen production from green electricity generation, the rapid regulation capability of hydrogen storage is utilized to coordinate the joint operation of the hydro-wind-solar systems.

[0181] The process of leveraging the control capabilities of green electricity for hydrogen production is often affected by fluctuations in the grid connection of hydropower, wind power, and solar power. Therefore, a comprehensive model can be constructed to effectively solve the grid connection and consumption problem during the coordinated operation of hydropower, wind power, and solar power. Based on the above theoretical foundation, this invention selects minimizing the standard deviation of the power output fluctuation of the hydropower, wind power, and solar power system, minimizing the amount of power wasted, and minimizing the difference between the power generation and transmission channel capacity of the combined system as objective functions, and establishes an optimized control model accordingly. Using the comprehensive output of the combined system as the data basis, the control capabilities of green electricity for hydrogen production are fully utilized.

[0182] The beneficial effects of implementing this embodiment are as follows: This embodiment explores an optimized control mode for a hydro-wind-solar hybrid combined green electricity consumption and hydrogen production system. First, by establishing mathematical models of wind turbine generators, solar power generation equipment, and hydropower station generators, a green electricity consumption and hydrogen production system is further introduced, thereby analyzing the feasibility of the hydro-wind-solar hybrid combined green electricity consumption and hydrogen production system. The NSPSO algorithm provided by this invention uses the minimum standard deviation of the combined output of hydro-wind-solar power and the minimum amount of abandoned power as dual objective functions. It considers the stability and power balance of the system's operation, combining the basic constraints of wind, solar, and hydropower, the predictive consistency of typical days in specific regions, and the output constraints of each energy source. It establishes a combined hydro-wind-solar complementary power generation system and performs simulations and further analysis of optimized scheduling. Secondly, it further deepens the mathematical model of the hydro-wind-solar-hydrogen-storage system, establishing a multi-objective, two-layer optimization model that minimizes the standard deviation of the combined output of the hydro-wind-solar system, the amount of abandoned power, and the difference between the combined output and the transmission channel capacity. The upper-layer model optimizes to obtain the solution set with the minimum standard deviation of different fluctuations and the minimum amount of abandoned power, while the lower-layer model also minimizes the difference in the transmission channel capacity, making the system operation mode more flexible in response to output, effectively solving the multi-dimensional and constrained difficulties in solving the hydro-wind-solar-hydrogen system problem.

[0183] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0184] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0185] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0186] Example 2

[0187] Figure 2 This is a flowchart of an embodiment of a two-layer optimization control method using the calculation method of the hydro-wind-solar hybrid power generation and green electricity consumption hydrogen production regulation system of the present invention. Figure 2 As shown, the upper layer is a flow chart for minimizing power generation volatility and curtailment, including the following steps:

[0188] S111, Input power grid transmission channel capacity.

[0189] S112, input the maximum output curve of wind and solar power.

[0190] S113, Input hydropower remaining load task curve.

[0191] S114. An initial population is generated based on the power grid transmission channel capacity, the maximum output curves of wind and solar power, and the remaining load task curve of hydropower.

[0192] S115, calculate fitness based on the initial population.

[0193] S116, update the velocity and position of each particle, update the optimal information, and update the Pareto optimal solution set.

[0194] S117. Determine whether the iteration termination condition is met. If yes, proceed to step S118; otherwise, proceed to step S113.

[0195] S118, output the Pareto solution set of the short-term multi-objective regulation model for hydropower, wind power, and solar power. Proceed to the next step, minimizing the difference between the combined power generation and transmission channel capacity, and proceed to step S211.

[0196] S211, Input the actual power output curve of water, wind and solar power.

[0197] S212, Input hydropower, wind power, and solar power curtailment curves.

[0198] S213, Input hydrogen to regulate the remaining load task curve.

[0199] S214. Based on the actual power output curves of hydropower, wind power, and solar power, the curtailment curves of hydropower, wind power, and solar power, and the remaining load task curves for hydrogen regulation, Matlab+Cplex is used for solving.

[0200] S215, update the capacity of the power grid transmission channel.

[0201] S216, Calculate the optimal value.

[0202] S217, determine whether the constraint conditions are met. If yes, proceed to step S218; otherwise, proceed to step S215.

[0203] S218 outputs the calculation results of the short-term multi-objective regulation model of water, wind, solar and hydrogen.

[0204] To verify the applicability of the NSPSO-MI LP proposed in this invention for the optimization and control calculation of a hydropower-wind-solar hybrid combined green electricity consumption and hydrogen production system, the following example was used for verification. The model in this embodiment selected a combined power generation system including one river basin hydropower station, one wind farm, and one photovoltaic power station, with a daily scheduling cycle at hourly intervals. The data used was basic data from a typical day in the region. Optimization scheduling was solved using a two-level NSPSO-MI LP model based on the Matlab program to verify the effectiveness and applicability of the improved optimization method.

[0205] Analysis of typical results for hydro-wind-solar hybrid days: Figure 3 for Figure 2 Pareto solution set graph in the middle and upper layers of optimization. Figure 3 This paper presents the Pareto front between the curtailment and volatility of a hydro-wind-solar hybrid system under different output conditions. The front exhibits a uniform distribution, indicating a significant trade-off between curtailment and volatility. As volatility decreases, the curtailment increases, suggesting that reducing volatility to improve the system's smoothing capability comes at the cost of some energy loss. Therefore, optimizing the balancing strategy of a hydro-wind-solar hybrid system requires comprehensively considering the interaction between volatility and curtailment to find the optimal compromise.

[0206] Figure 4 for Figure 2 The first operational status diagram of the Pareto front in the middle and upper layers of the hydropower-wind-solar system. Figure 4 This study demonstrates the hydro-wind-solar power output state with the lowest volatility and highest curtailment in the Pareto front. It shows that hydropower output remains high when wind and solar output is low, and low when wind and solar output is high, effectively improving the stability of the hydro-wind-solar hybrid power generation system. This adjustment strategy ensures the smooth operation of the system under different wind and solar output conditions. However, because the adjustment of hydropower output leads to an increase in water curtailment, the overall curtailment also increases, exhibiting a certain contradictory relationship.

[0207] Figure 5 for Figure 2 The second operational status diagram of the water, wind and solar power system in the Pareto solution of the middle and upper layers of optimization. Figure 5 This demonstrates the power output patterns of hydropower, wind power, and solar power, exhibiting the highest volatility and lowest curtailment among the Pareto fronts. It shows that hydropower maintains higher output when wind and solar power output is low, and lower output when wind and solar power output is high, effectively reducing curtailment. However, to minimize curtailment, hydropower regulation exhibits varying degrees of increase and decrease during peak and off-peak wind and solar power generation. While successfully reducing curtailment, this also leads to increased volatility, resulting in overall high volatility.

[0208] Analysis of the results of combined hydropower, wind power, and solar power integration for hydrogen production and regulation: Figure 6 for Figure 2 The results of the middle and lower level optimization. Figure 6 This demonstrates the operation of a hydro-wind-solar hybrid combined green electricity consumption and hydrogen production control system under different output conditions. Under the joint control of the green electricity consumption and hydrogen production system, the system can effectively utilize surplus electricity. This is combined with the NSPSO algorithm... Figure 4 The obtained hydropower, wind power, and solar power output results were compared with the MILP algorithm. The results show that when the total output of hydropower, wind power, and solar power exceeds the capacity of the transmission channel, the hydrogen storage system converts redundant electrical energy into hydrogen storage; while when the output of hydropower, wind power, and solar power is less than the capacity of the transmission channel, the hydrogen fuel cell uses the stored hydrogen to generate electricity, thereby maximizing the utilization and stable operation of hydropower, wind power, and solar power.

[0209] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0210] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A computing method of a water-wind-solar complementary power generation combined green power consumption hydrogen production regulation system, characterized in that, The method comprises the following steps: S1, a power generation model meeting water, wind, light and hydrogen operation constraint conditions is established, and a water, wind, light and hydrogen complementary joint hydrogen regulation model is established by taking the minimum standard deviation of water, wind, light system output fluctuation and the minimum joint system power abandonment as upper layer objective functions, and taking the minimum absolute value of joint system power generation and power transmission channel capacity difference as a lower layer objective function; S2, based on a traditional particle swarm algorithm, a generation strategy based on water, wind and light initial power, an inertia weight, and a learning factor adaptive change strategy with iteration number are established, and the traditional particle swarm optimization algorithm is improved by combining an NSGA-II algorithm and taking a front level and a crowded distance as an adaptive function in the particle swarm algorithm; S3, the upper layer objective function is optimized and solved by using the improved particle swarm algorithm to obtain a Pareto optimal solution set; S4, the Pareto optimal solution set is taken as a boundary condition of output power, and the lower layer objective function is optimized and solved by using a mixed linear integer programming.

2. The computing method of the water, wind, light, and complementary power generation combined green power consumption and hydrogen production regulation system according to claim 1, characterized in that, The power generation model comprises: Wind power generation model: wherein Pmax is the maximum output power of the fan at time t, Pnom is the rated output power of the fan, V in Vi is the cut-in wind speed, V out Vo is the cut-out wind speed, V r Vnom is the rated wind speed, V t Vt is the actual wind speed at time t; Photovoltaic power generation model: In the formula, Pmaxis the maximum output power of the photovoltaic cell at time t, P S F is the rated output power of the photovoltaic power generation, F S GTI is the derating factor, GTI t α is the solar radiation intensity at time t, α p T is the temperature correction coefficient, T c,t T is the temperature of the photovoltaic cell at time t, T c,STC T is the surface temperature of the photovoltaic array under standard test conditions; Hydroelectric power generation model: In the formula, is the efficiency of the water turbine to convert water energy into electric energy, p is the density of water, g is the acceleration of gravity, is the net water head of the hydropower station at time t, is the power generation flow of the hydropower station at time t.

3. The computing method of the water, wind, light, and complementary power generation combined green power consumption and hydrogen production regulation system according to claim 1, characterized in that, The step of S2, based on a traditional particle swarm algorithm, establishing a generation strategy based on water, wind and light initial power, an inertia weight, and a learning factor adaptive change strategy with iteration number, and improving the traditional particle swarm optimization algorithm by combining an NSGA-II algorithm and taking a front level and a crowded distance as an adaptive function in the particle swarm algorithm, specifically comprises: S21, taking reservoir uplink and downlink water levels as boundaries, maximum wind power and maximum light power as boundaries, and water, wind and light power generation power in each period as decision variables, a first improvement strategy is introduced to obtain an initial population; S22, a second improvement strategy is introduced from inertia weight and experience coefficient two parameters to improve the particle swarm optimization algorithm; S23, based on the NSGA-II algorithm, a front level and a crowded distance in the Pareto optimal solution set are taken as an adaptive update coefficient in the particle swarm optimization, and the individual best position, the historical best position and the Pareto solution set of the particle are updated and iterated by combining the power transmission channel capacity as the adaptive function.

4. The computing method of the water, wind, light, and complementary power generation combined green power consumption and hydrogen production regulation system according to claim 1, characterized in that, The step of S3, using the improved particle swarm algorithm to optimize and solve the upper layer objective function to obtain the Pareto optimal solution set, specifically comprises: S31, parameter setting is performed; S32, the particles are initialized according to the set parameters; S33, the adaptability of all particles in each period is solved; S34, according to initial inertia weight data and initial learning factor data, the velocity and position formula of the particle swarm is used to calculate the updated velocity, position and adaptability of the particle in each iteration; S35, the front level and the crowded distance of each particle are taken out, non-dominated sorting is performed, and the particle position with the front level of 1 is recorded; S36, the inertia weight w and the learning factors c1 and c2 are updated by using the adaptive inertia weight and learning factor formula to continue the iteration process; S37, whether the iteration number meets the termination condition is determined to perform the next operation; if yes, the Pareto optimal solution set is output, otherwise the steps S33 to S36 are continuously cycled until the requirement is met.

5. The computing method of the water, wind, light, and complementary power generation combined green power consumption and hydrogen production regulation system according to claim 1, characterized in that, The S4 takes the Pareto optimal solution set as the boundary condition of output power, and uses mixed linear integer programming to optimize and solve the lower target function, and the specific steps include: S41, introducing the hydrogen storage constraint condition in step S1, constructing a water, wind, light and hydrogen storage system; S42, selecting the position of a particle in the Pareto solution set obtained in step S3 as the output power of water, wind and light in the lower model; S43, taking the absolute value of the difference between the joint system power generation and the electric transmission channel capacity in step S1 as the minimum as the lower model target function, and solving it through the Cplex solver in matlab to obtain the operation power of water, wind, light, electrolytic tank, fuel cell and compressor in each period.

6. The computing method of claim 2, wherein, The constraint conditions include: Wind and solar power output constraint condition: , , wherein, is the actual output power of the wind turbine at the t period, is the actual output power of the photovoltaic cell at the t period; Water balance constraint condition: , wherein, , respectively represent the water storage of the reservoir at time t and time t+1, and respectively represent the inflow and outflow of the reservoir at time t. Outlet flow constraint condition: wherein is the minimum outlet flow of the reservoir, is the maximum outlet flow of the reservoir; Generation flow rate constraint condition: wherein, Qminis the minimum generation flow rate of the reservoir, Qmaxis the maximum generation flow rate of the reservoir, Qtis the generation flow rate of the reservoir at time t. Water level constraint condition: wherein, is the minimum operating water level of the reservoir, is the maximum operating water level of the reservoir, is the operating water level of the reservoir at time period t; Hydropower plant output constraint condition: , where is the maximum output power of the hydropower. Power balance constraint condition: wherein, , , , are the power generation of water, wind, solar and hydrogen storage fuel cell at time t under hydrogen storage regulation respectively, is the power of water power curtailment at time t, is the power input of electrolyzer at time t, is the net output power of the system; Electrolytic cell power constraint condition: wherein, P is the hydrogen input power for the time period t, λ H is the electrolyzer hydrogen production efficiency, , , Psh, Ph, and Pcomp are the hydrogen storage power, hydrogen storage and generation power, and compressed hydrogen power, respectively, for the time period t; and Pmin and Pmax are the minimum and maximum input power, respectively, for the electrolyzer for the time period t, ΔP ELC,min and ΔP ELC,max are the lower and upper limits, respectively, for the electrolyzer power ramp. Compressor power constraint condition: , wherein ELC and YS are the energy consumption of hydrogen production by electrolysis and the energy consumption of hydrogen storage by compressor, respectively. Fuel cell power constraints: In the formula, Let λ be the output power of the fuel cell during time period t. FC For fuel cell power generation efficiency, and ΔP represents the minimum and maximum output power of the fuel cell during time period t. FC,min and ΔP FC,max These represent the lower and upper limits of the fuel cell power generation ramp-up; Green electricity consumption hydrogen production regulation constraints: .

7. The computing method of claim 2, wherein, The target function includes: The minimum fluctuation target function of water, wind and light complementary output: In the formula, N is the standard deviation of the output fluctuation of the water-wind-solar complementary system in a unit period, , , are the water-wind-solar output in the t period of the iter-th iteration, respectively; av is the average output value of the system in a unit period. The minimum joint system power abandonment target function: , wherein M is the total amount of abandoned water and wind power in a unit period, is the abandoned water and wind power in the t period of the iter iteration. The absolute value of the difference between the joint system power generation and the electric transmission channel capacity as the lower target function: In the formula, Z is the absolute value sum of the difference between the water, wind, and light comprehensive output and the electric transmission channel capacity in the kth population at the iterth iteration, P transport is the electric transmission channel capacity.

8. The computing method of claim 3, wherein, The first improvement strategy includes: The minimum water level in each period improvement strategy: ； wherein is the minimum water level of the reservoir operation in the t period of the kth population, q ave is the historical average flow of the reservoir, is the minimum water level of the reservoir operation in the t-1 period of the kth population; The maximum water level in each period improvement strategy: wherein, is the highest water level of the reservoir operation in the t period of the kth population, is the highest water level of the reservoir operation in the t-1 period of the kth population; is the minimum outflow of the reservoir, is the maximum outflow of the reservoir; is the minimum operating water level of the reservoir, is the maximum operating water level of the reservoir; The water level initialization improvement strategy in each period: In the formula, H k,t Hk(t) is the initial water level of the reservoir in the t period of the kth population k,t-1 Hk(t-1) is the initial water level of the reservoir in the t-1 period of the kth population, and rand is a random function, and population is the population number of the particle swarm The water outflow initialization improvement strategy in each period: In the formula, is the initial outflow of the kth population at time t, and repmat is a repeated array function. The water power generation flow initialization improvement strategy in each period: In the formula, is the initial power generation flow in the t period of the kth population, and b1 is a constant; is the maximum power generation flow of the reservoir; The water power abandonment flow initialization improvement strategy in each period: In the formula, is the initial amount of power discarded in the kth population at time t; The wind power generation initialization improvement strategy in each period: In the formula, is the initial wind power of the kth population at time t, and b2 is a constant. is an array multiplication; The photovoltaic power generation initialization improvement strategy in each period: In the formula, is the initial photovoltaic power generation power in the t period of the kth population, b3 is a constant. is an array multiplication.

9. The computing method of claim 3, wherein, The second improvement strategy includes: The adaptive inertia weight improvement strategy: In the formula, W is an inertial weight, W min and W max are preset minimum and maximum inertial weights, respectively 0.4 and 0.9, iter is the current iteration number, iter max is the maximum iteration number, which is 1500 times. The adaptive learning factor improvement strategy: ， wherein C1 is an individual learning experience coefficient, C 1s and C 1e are preset initial and stop values, respectively 1.5 and 0.5, C2 is a group learning experience coefficient, C 2s and C 2e are preset initial and stop values, respectively 0.5 and 1.

5.

10. The computing method of claim 3, wherein, The fitness includes: In the formula, F k,iter is the fitness of the kth population at the iter iteration, Rank k,iter is the Pareto front rank of the kth population at the iter iteration, Crowd k,iter is the crowding distance of the kth population at the iter iteration; Dp k,iter,out is the absolute value sum of the difference between the water, wind and solar power output and the electric transmission channel capacity of the kth population at the iter iteration, P transport is the electric transmission channel capacity; 、 、 are the water, wind and solar power output in the tth period of the iter iteration, respectively.

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