Calculation method of regulation and control system for hydrogen production through combination of water-wind-light complementary power generation and green power consumption

Abstract

The invention belongs to the technical field of energy regulation and control, and relates to a calculation method of a water-wind-light complementary power generation and green power consumption combined hydrogen production regulation and control system, which comprises the following steps: establishing a water-wind-light complementary combined hydrogen regulation and control model, improving a traditional particle swarm optimization algorithm, and solving based on a multi-objective function of the improved particle swarm optimization algorithm. And solving an objective function based on the lower-layer model. In order to explore an optimized regulation and control mode of the water-wind-light complementary combined green power consumption hydrogen production system, mathematical models of a wind generating set, solar power generation equipment and a hydropower station generator set are established, and the green power consumption hydrogen production system is further introduced, so that the feasibility of the water-wind-light complementary combined green power consumption hydrogen production system is analyzed; the mathematical model of the water-wind-light-hydrogen storage system is further deepened, so that the operation mode of the system more flexibly responds to output, and the multi-dimensional and constraint difficulties of water-wind-light-hydrogen solution are effectively solved.

Description

Technical Field

[0001] The present invention relates to the field of energy regulation technology, and in particular to a calculation method, device, computer equipment and storage medium for a water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production regulation system. Background Art

[0002] With the continuous development of social economy, promoting the application of high-quality clean energy has become the main trend of current energy transformation. Therefore, exploring clean energy that can regulate the volatility of renewable energy has become the key to smoothing the fluctuations of wind power and photovoltaic power generation, reducing the amount of abandoned electricity, and meeting the capacity requirements of transmission channels. At the same time, ensuring the stable operation of the power system and conducting research on the optimal scheduling of multiple clean energy sources for joint complementarity are crucial to alleviating environmental pressure and energy shortage problems. As a clean and pollution-free energy carrier, hydrogen has become one of the important solutions for the development and storage of renewable energy such as wind power, photovoltaics and hydropower. Hydrogen production by water electrolysis converts electrical energy into chemical energy for storage, allowing hydrogen to play a role in a variety of application scenarios.

[0003] Therefore, in order to achieve short-term multi-objective scheduling of water, wind, solar, hydrogen and storage multi-energy complementary systems, promote the large-scale development and consumption of new energy, 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 water, wind, solar and hydrogen. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a calculation method for a water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system, which adopts the following technical solution, including the following steps:

[0005] S1. Establish a power generation model that meets the operating constraints of water, wind, solar and hydrogen, and select the minimum standard deviation of the output fluctuation of the water, wind and solar system and the minimum power abandonment of the combined system as the upper objective function, and the minimum difference between the power generation of the combined system and the capacity of the transmission channel as the lower objective function, and establish a water, wind and solar complementary combined hydrogen regulation model;

[0006] S2, based on the traditional particle swarm algorithm, establishes the generation strategy based on the initial power of water, wind and solar power, the inertia weight, and the adaptive change strategy of the learning factor with the number of iterations. Combined with the NSGA-II algorithm, the frontier level and crowding distance are used as the fitness function in the particle swarm algorithm to improve the traditional particle swarm optimization algorithm;

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

[0008] S4, taking the Pareto optimal solution set as the boundary condition of the output power, the lower-level objective function is optimized and solved using mixed linear integer programming.

[0009] Preferably, the power generation model includes:

[0010] Wind power generation model:

[0011] Where P W,maxt is the maximum output power of the fan in period t, P W r is the rated output power of the fan, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V t is the actual wind speed during period t;

[0012] Photovoltaic power generation model:

[0013] Where P S,maxt is the maximum output power of the photovoltaic cell in period t, P S is the rated output power of photovoltaic power generation, F S is the derating factor, GTI is the solar radiation intensity at time t, α p is the temperature correction factor, T c,t is the temperature of the photovoltaic cell at time t, T c,STC is the surface temperature of the photovoltaic array under standard test conditions;

[0014] Hydroelectric power generation model:

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

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

[0017] S21, taking the upper and lower water levels of the reservoir as the boundary, the maximum output power of wind power and photovoltaic power as the boundary, and the wind, solar and water power generation power in each period as the decision variable, introduce the first improved strategy to obtain the initial population;

[0018] S22, introduce the second improvement strategy from the two parameters of inertia weight and empirical coefficient to improve the particle swarm optimization algorithm;

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

[0020] Preferably, S3, the step of optimizing and solving the upper layer objective function using the improved particle swarm algorithm to obtain the Pareto optimal solution set specifically includes:

[0021] S31, setting parameters;

[0022] S32, initializing the particles according to the set parameters;

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

[0024] S34, according to the initial inertia weight data, the initial learning factor data, and the speed and position formula of the particle group, calculate the updated speed, position and fitness of the particle in each iteration;

[0025] S35, taking out the frontier level and crowding distance of each particle, performing non-dominated sorting, and recording the position of the particle with a frontier level of 1;

[0026] S36, using the adaptive inertia weight and learning factor formula to adjust the inertia weight w and learning factor c 1 、c 2 Make updates and continue the iterative process;

[0027] S37, proceed to the next step according to whether the number of iterations meets the termination condition; if so, output the Pareto optimal solution set, otherwise continue to loop steps S33 to S36 until the requirement is met.

[0028] Preferably, S4, the step of using the Pareto optimal solution set as the boundary condition of the output power and optimizing the lower layer objective function using mixed linear integer programming specifically includes:

[0029] S41, introducing the hydrogen storage constraint conditions in step S1 to construct a water-wind-solar hydrogen storage system;

[0030] S42, selecting the position of a particle in the Pareto solution set obtained in step S3 as the output power of the water-wind-solar in the lower model;

[0031] S43, taking the minimum difference between the power generation of the combined system and the capacity of the transmission channel in step S1 as the objective function of the lower model, solving it through the Cplex solver in Matlab, and obtaining 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 constraint: 0≤P t W ≤P t W,max , 0≤P t S ≤P t S,max , where P W t is the actual output power of the fan in period t, P S t is the actual output power of the photovoltaic cell in period t;

[0034] Water balance constraints: Where V H t , is the water storage capacity of the reservoir at time t, Q H t and q Ht is the inflow and outflow of the reservoir at time t;

[0035] Outbound flow constraints: In the formula, q Hmin is the minimum outflow of the reservoir, is the maximum outflow of the reservoir;

[0036] Power generation flow constraints: Where, QF Hmin is the minimum power generation flow of the reservoir, is the maximum power generation flow of the reservoir;

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

[0038] Output constraints of hydropower station: In the formula, is the maximum output power of hydropower;

[0039] Power balance constraints:

[0040] In the formula, are the power generation of water, wind, light and hydrogen storage fuel cell under hydrogen storage regulation in period t, is the input power of the electrolyzer during period t, P outt is the net output power of the system;

[0041] Electrolyzer power constraints:

[0042] In the formula, is the hydrogen input power in period t, λ H is the hydrogen production efficiency of the electrolyzer, P YS t are the hydrogen storage power, hydrogen storage power generation power, and hydrogen compression power in period t respectively; P ELC,mint and P ELC,maxt are the minimum and maximum input power of the electrolyzer during period t, ΔP ELC,min and ΔP ELC,max They are the lower and upper limits of the electrolyzer power ramp, respectively;

[0043] Compressor power constraints: Where η ELC and η YS They are the energy consumption of hydrogen production by electrolyzer and hydrogen storage by compressor;

[0044] Fuel cell power constraints:

[0045] In the formula, is the fuel cell output power during period t, λ FC For the efficiency of fuel cell power generation, and are the minimum and maximum output power of the fuel cell during period t, ΔP ELC,min and ΔP ELC,max They are the lower and upper limits of the fuel cell power generation ramp, respectively;

[0046] Green electricity consumption and hydrogen production control constraints:

[0047] Preferably, the objective function includes:

[0048] Minimum objective function for the output volatility of hydro-wind-solar hybrid system:

[0049]

[0050] Where N is the standard deviation of the output fluctuation of the hydro-wind-solar hybrid system within a unit period, P H iter,t , P W iter,t , P S iter,t are the water, wind and solar power outputs in the tth period of the iterth iteration respectively. av is the average output value of the system within a unit period;

[0051] Minimum objective function of joint system power abandonment:

[0052] Where M is the total amount of abandoned electricity from hydropower, wind power and solar power in a unit period;

[0053] The minimum objective function of the combined system power generation-transmission channel capacity difference is:

[0054] Where Z is the absolute value of the difference between the comprehensive output of water, wind and solar power and the capacity of the transmission channel at the iter iteration in the kth population, P transport Transmission channel capacity for the power grid.

[0055] Preferably, the first improvement strategy includes:

[0056] Improvement strategy for minimum water level in each period:

[0057] In the formula, H mink,t is the lowest water level of the reservoir in the kth population during the t period, qave is the historical average flow of the reservoir, is the lowest water level of the reservoir during the kth population t-1 period;

[0058] Improvement strategy for maximum water level in each period:

[0059] In the formula, H maxk,t is the highest water level of the reservoir in the kth population during period t, is the highest water level of the reservoir during the kth population t-1 period;

[0060] Improved strategy for water level initialization in each period:

[0061] In the formula, H k,t is the initial water level of the reservoir in the kth population in period t, H k,t is the initial water level of the reservoir in the kth population in the t-1 period, rand is a random function, and population is the number of particle swarms;

[0062] Improved strategy for initializing water outflow flow in each period:

[0063] In the formula, q Hk,t is the initial outbound flow of the kth population in period t, repmat is the repeated array function;

[0064] Improved strategy for initialization of hydropower flow in each period:

[0065] Where, QF H k,t is the initial power generation flow of the kth population in period t, b 1is a constant;

[0066] Improved strategy for initializing abandoned hydropower flow in each period: In the formula, Nq Hk,t is the initial current abandonment amount of the kth population in period t;

[0067] Improved strategies for wind power generation initialization in different time periods:

[0068] Where P W k,t is the initial wind power generation power of the kth population in period t, b 2 is a constant, .* is array multiplication;

[0069] Improvement strategy for photovoltaic power generation initialization in different time periods:

[0070] In the formula, is the initial photovoltaic power generation power of the kth population in period t, b 3 is a constant, and .* is array multiplication.

[0071] Preferably, the second improvement strategy includes:

[0072] Adaptive inertia weight improvement strategy:

[0073] Where W is the inertia weight, W min and W max are the preset minimum and maximum inertia weights, which are 0.4 and 0.9 respectively, iter is the current iteration number, iter max is the maximum number of iterations, which is 1500;

[0074] Adaptive learning factor improvement strategy:

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

[0076] Preferably, the fitness includes:

[0077] In the formula, F k,iter is the fitness of the kth population at the iterth iteration, Rank k,iteris the Pareto frontier level at the iter iteration in the kth population, Crowd k,iter Dp is the crowding distance at the iter iteration in the kth population. k,iter,out is the absolute value of the difference between the comprehensive output of water, wind and solar power and the capacity of the transmission channel at the iter iteration in the kth population, P transport Transmission channel capacity for the power grid.

[0078] Compared with the prior art, the present invention has the following beneficial effects: This embodiment explores an optimization control mode for the water-wind-solar complementary combined green electricity consumption and hydrogen production system. First, by establishing a mathematical model of a wind turbine, a solar power generation equipment, and a hydropower station generator set, a green electricity consumption and hydrogen production system is further introduced, thereby analyzing the feasibility of the water-wind-solar complementary combined green electricity consumption and hydrogen production system. The NSPSO algorithm provided by the present invention takes the minimization of the standard deviation of volatility of the combined output of the water, wind and solar systems and the amount of abandoned electricity as dual objective functions, and considers the stability of its operation and power balance, combines the basic constraints of wind power, photovoltaics and hydropower, and the forecast of typical days in specific areas and the output constraints of each energy source, establishes a combined power generation system of water, wind and solar complementarity, and simulates and further analyzes the optimal scheduling; secondly, the mathematical model of the water, wind, solar and hydrogen storage system is further deepened, and a multi-objective two-layer optimization model with the minimum standard deviation of volatility of the combined output of the water, wind and solar systems, the amount of abandoned electricity and the difference between the combined output and the capacity of the transmission channel is established. The upper model is optimized to obtain a solution set with the minimum standard deviation of volatility and the amount of abandoned electricity. At the same time, the difference in the capacity of the transmission channels in the lower model is also minimized, so that the system operation mode can respond to the output more flexibly, effectively solving the multi-dimensional and constrained difficulties in solving the water, wind, solar and hydrogen. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the scheme in the present invention, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0080] Figure 1 It is a flow chart of an embodiment of a calculation method of a water-wind-solar complementary power generation combined with a green electricity consumption and hydrogen production control system of the present invention;

[0081] Figure 2 It is a flow chart of an embodiment of a double-layer optimization control method using the calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system of the present invention;

[0082] Figure 3 for Figure 2 Pareto solution set diagram in middle and upper level optimization;

[0083] Figure 4 for Figure 2 The first operation status diagram of water and wind power in the Pareto frontier of the middle and upper optimization;

[0084] Figure 5 for Figure 2 The second operation status diagram of concentrated hydropower and wind power in the Pareto solution of the middle and upper level optimization;

[0085] Figure 6 for Figure 2 The operation results in the middle and lower layer optimization. DETAILED DESCRIPTION

[0086] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present invention or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

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

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

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

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

[0091] Embodiment 1

[0092] Please refer to Figure 1, showing a flow chart of an embodiment of a calculation method of a water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system of the present invention. The calculation method of a water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system comprises the following steps:

[0093] Step S1, establish a power generation model that meets the operating constraints of water, wind, solar and hydrogen, and select the minimum standard deviation of the output volatility of the water, wind and solar systems and the minimum power abandonment of the combined system as the upper objective function, and the minimum difference between the power generation of the combined system and the capacity of the transmission channel as the lower objective function, and establish a water, wind and solar complementary combined hydrogen regulation model.

[0094] In this embodiment, the electronic device (such as a server / terminal device) on which the calculation method of the water-wind-solar complementary power generation combined with green electricity consumption hydrogen production control system runs can receive the calculation request of the water-wind-solar complementary power generation combined with green electricity consumption hydrogen production control system through a wired connection or a wireless connection. It should be noted that the above-mentioned wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, Wi MAXX connection, Zigbee connection, UWB (ultra wi deband) connection, and other wireless connection methods currently known or to be developed in the future.

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

[0096] The wind power generation model is:

[0097] Where P W,maxt is the maximum output power of the fan in period t, P W r is the rated output power of the fan, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V t is the actual wind speed during period t.

[0098] The photovoltaic power generation model is: Where P S,maxt is the maximum output power of the photovoltaic cell in period t, P S is the rated output power of photovoltaic power generation, F S is the derating factor, GTI is the solar radiation intensity at time t, α p is the temperature correction factor, T c,t is the temperature of the photovoltaic cell at time t, T c,STC is the surface temperature of the photovoltaic array under standard test conditions.

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

[0100] In some optional implementations, the constraints include but are not limited to wind and solar 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 control constraints, etc.

[0101] Among them, the wind and solar output constraint condition is: 0≤P t W ≤P t W,max , 0≤P t S ≤P t S,max , where P W t is the actual output power of the fan in period t, P S t is the actual output power of the photovoltaic cell during period t.

[0102] The water balance constraints are: Where V H t , is the water storage capacity of the reservoir at time t, Q H t and q Ht is the inflow and outflow of the reservoir at time t.

[0103] The outbound flow constraints are: Where q Hmin is the minimum outflow of the reservoir, is the maximum outflow of the reservoir.

[0104] The power generation flow constraints are: Where QF Hmin is the minimum power generation flow of the reservoir, is the maximum power generation flow of the reservoir.

[0105] The water level constraints are: Where Z Hmin is the minimum operating water level of the reservoir, is the maximum operating water level of the reservoir.

[0106] The output constraints of the hydropower station are: In the formula, is the maximum output power of hydropower;

[0107] The power balance constraints are:

[0108] In the formula are the power generation of water, wind, light and hydrogen storage fuel cell under hydrogen storage regulation in period t, is the input power of the electrolyzer during period t, P outt is the net output power of the system.

[0109] The power constraint of the electrolyzer is:

[0110] In the formula is the hydrogen input power in period t, λ H is the hydrogen production efficiency of the electrolyzer, They are the hydrogen storage power, hydrogen storage power generation power, and hydrogen compression power in period t. ELC,mint and P ELC,maxt are the minimum and maximum input power of the electrolyzer during period t, ΔP ELC,min and ΔP ELC,max They are respectively the lower and upper limits of the electrolyzer power ramp.

[0111] The compressor power constraint is: Where η ELC and η YS They are the energy consumption of hydrogen production by electrolyzer and the energy consumption of hydrogen storage by compressor respectively.

[0112] The fuel cell power constraint is:

[0113] In the formula is the fuel cell output power during period t, λ FC For the efficiency of fuel cell power generation, and are the minimum and maximum output power of the fuel cell during period t, ΔP ELC,min and ΔP ELC,max They are respectively the lower and upper limits of the fuel cell power generation power ramp.

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

[0115] In some optional implementations, the objective functions include but are not limited to the objective function of minimizing the volatility of the complementary output of hydropower, wind power and solar power, the objective function of minimizing the amount of power abandoned in the combined system, and the objective function of minimizing the difference between the power generation of the combined system and the capacity of the transmission channel.

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

[0117] Where N is the standard deviation of the output fluctuation of the hydro-wind-solar hybrid system within a unit period, P H iter,t , P W iter,t , P S iter,t are the water, wind and solar power outputs in the tth period of the iterth iteration respectively. av is the average output value of the system within a unit period;

[0118] The minimum objective function of the joint system power abandonment is:

[0119] Where M is the total amount of abandoned electricity from hydropower, wind and solar power in a unit period.

[0120] The minimum objective function of the combined system power generation-transmission channel capacity difference is:

[0121] Where Z is the absolute value of the difference between the comprehensive output of water, wind and solar power and the capacity of the transmission channel at the iter iteration in the kth population, P transport Transmission channel capacity for the power grid.

[0122] Step S2, based on the traditional particle swarm algorithm, establish a generation strategy based on the initial power of water, wind and solar power, an inertia weight, and a learning factor adaptive change strategy with the number of iterations. Combined with the NSGA-II algorithm, the frontier level and crowding distance are used as the fitness function in the particle swarm algorithm to improve the traditional 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 photovoltaic power as boundaries, and the water, wind and photovoltaic power generation power in each period as decision variables, introduce the first improved strategy to obtain the initial population.

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

[0126] Among them, the improvement strategy of the minimum water level in each period is:

[0127] Where H mink,t is the lowest water level of the reservoir in the kth population during the t period, qave is the historical average flow of the reservoir, is the lowest water level of the reservoir during the kth population t-1 period.

[0128] The improvement strategy for the maximum water level in each period is:

[0129] Where H maxk,t is the highest water level of the reservoir in the kth population during period t, is the highest water level of the reservoir during the kth population t-1 period.

[0130] The improved strategy for water level initialization in each period is:

[0131] Where H k,t is the initial water level of the reservoir in the kth population in period t, H k,t is the initial water level of the reservoir in the kth population in period t-1, rand is a random function, and population is the number of particle swarms.

[0132] The improved strategy for initializing water outflow flow in each period is:

[0133] Where q Hk,t is the initial outbound flow of the kth population in period t, and repmat is a repeated array function.

[0134] The improved strategy for initializing the hydropower flow in each period is:

[0135] Where QF H k,t is the initial power generation flow of the kth population in period t, b 1 is a constant.

[0136] The improved strategy for initializing the abandoned hydropower flow in each period is:

[0137] Where Nq Hk,t is the initial current abandonment amount of the kth population in time period t.

[0138] The improved strategy for wind power generation initialization in each period is:

[0139] Where P W k,t is the initial wind power generation power of the kth population in period t, b 2 is a constant, and .* is array multiplication.

[0140] The improved strategy for photovoltaic power generation initialization in each period is:

[0141] In the formula is the initial photovoltaic power generation power of the kth population in period t, b 3 is a constant, and .* is array multiplication.

[0142] S22, introduce the second improvement strategy from the two parameters of inertia weight and empirical coefficient to improve the particle swarm optimization algorithm.

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

[0144] Among them, the adaptive inertia weight improvement strategy is:

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

[0146] The adaptive learning factor improvement strategy is:

[0147] Where C 1 is the individual learning experience coefficient, C 1s and C 1e are the preset initial and stop values, 1.5 and 0.5 respectively, C 2 is the group learning experience coefficient, C 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 frontier level and crowding distance in the Pareto optimal solution set as the fitness update coefficient in the particle swarm optimization, and combines the capacity of the electric transport channel as the fitness function to update and iterate the individual optimal position, historical optimal position and Pareto solution set of the particle.

[0149] In some optional implementations, the fitness includes:

[0150] Where F k,iter is the fitness of the kth population at the iterth iteration, Rank k,iter is the Pareto frontier level at the iter iteration in the kth population, Crowd k,iter Dp is the crowding distance at the iter iteration in the kth population.k,iter,out is the absolute value of the difference between the comprehensive output of water, wind and solar power and the capacity of the transmission channel at the iter iteration in the kth population, P transport Transmission channel capacity for the power grid.

[0151] The specific process of NSPSO algorithm solution is as follows:

[0152] Assume that the population is in a D-dimensional space, where there are K particles, defined as S = {X 1 ,X 2 ,…X K}, where S is the space set, X i ={X i1 ,X i2 ,…X id}, i = 1, 2…K, the solution of the i-th particle in this space contains the relevant information of position and velocity;

[0153] The position formula is: Among them U 1 , L 1 Indicates the upper and lower limits of the particle's position change during motion;

[0154] The speed formula is: Among them U 2 , L 2 Indicates the upper and lower limits of the particle's speed change during 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 level formula: F = {F 1 ,F 2 ,…,F n} where F i ={X i1 ,X i2 ,…,X im}

[0160] Crowding distance formula:

[0161] Where: W is the inertia weight, which inherits the speed of the particle in the past and represents the inertia characteristics of the particle. Through adaptive inertia weight, in the early stage of iteration, a larger W makes the algorithm less likely to fall into the local minimum and facilitates global search. In the later stage of iteration, a smaller W is conducive to local search and the convergence of the algorithm; C 1 , C 2 is the learning factor, C 1 It represents the individual cognition in the particle swarm algorithm, which means that in the iteration process, the particle needs to compare with its previous value to achieve the position change of the particle; C 2 It reflects public cognition, which means that in the iteration process, each particle needs to be compared with the best position currently recognized and known; r 1 、r 2 It is a random number, which is assigned a random number between 0 and 1 as the initial value through a random function. 1 、r 2 The assignment of is completely random, independent of each other, and does not affect each other. Through the adaptive learning factor, in the early stage of iteration, the large C 1 and Little C 2 This makes the particles have better self-learning ability and poorer social learning ability, which is beneficial to global search. 1 and Big C 2 This makes the particles have stronger social learning ability and weaker self-learning ability, which is conducive to the convergence of the algorithm. i is the i-th frontier level, indicating that after each iteration, the frontier level is updated according to the dominance relationship of the solution. Each solution is assigned a frontier level to indicate its relative superiority. and Is the solution X i The objective values ​​of the previous and next solutions on the objective m, and is the maximum and minimum value of all solutions on the target. It means that for solutions at the same frontier level, the optimal solution will be further selected through the crowding distance. The solution with a larger crowding distance indicates that it has a higher diversity in the target space, which helps to avoid the solution set from being too concentrated.

[0162] Step S3, using the improved particle swarm algorithm to optimize and solve the upper layer objective function to obtain the Pareto optimal solution set.

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

[0164] S31, perform parameter setting.

[0165] It involves wind power, photovoltaic power, hydropower station power generation flow and abandoned water flow, 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 generating unit, and the number of iterations is 1500.

[0166] S32, initializing the particles according to the set parameters.

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

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

[0169] Determine the individual optimal value p for each particle best and the optimal value G of the entire group best。

[0170] S34, based on the initial inertia weight data and the initial learning factor data, using the speed and position formula of the particle group, calculate the updated speed, position and fitness of the particles in each iteration.

[0171] S35, taking out the frontier level and crowding distance of each particle, performing non-dominated sorting, and recording the position of the particle with a frontier level of 1.

[0172] S36, using the adaptive inertia weight and learning factor formula to adjust the inertia weight w and learning factor c 1 、c 2 Make updates and continue the iterative process.

[0173] S37, proceed to the next step according to whether the number of iterations meets the termination condition; if so, output the Pareto optimal solution set, otherwise continue to loop steps S33 to S36 until the requirement is met.

[0174] Step S4, taking the Pareto optimal solution set as the boundary condition of the output power, and using mixed linear integer programming to optimize and solve the lower layer objective function.

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

[0176] S41, introducing the hydrogen storage constraint conditions in step S1 to construct a water-wind-solar hydrogen storage system;

[0177] S42, selecting the position of a particle in the Pareto solution set obtained in step S3 as the output power of the water-wind-solar in the lower model;

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

[0179] This embodiment further studies the complementary strategy of water, wind and solar output by analyzing the output characteristics of water, wind and solar. Based on the traditional particle swarm algorithm, the invention improves the generation strategy of the initial population and proposes a strategy for adaptively changing the inertia weight and learning factor with the number of iterations. At the same time, the frontier level and congestion distance in the NSGA-II algorithm are combined with the capacity of the power transmission channel as the fitness, forming a multi-objective particle swarm algorithm NSPSO, and establishing a dual-objective model with the minimum standard deviation of the output volatility of the water, wind and solar system and the minimum amount of abandoned electricity. The water, wind and solar complementary system is optimized and solved to obtain the Pareto optimal solution set that satisfies the minimum total output volatility and the minimum total abandoned electricity. The hydrogen storage model is introduced to establish the NSPSO-MILP two-layer optimization model, and the Pareto optimal solution set is prepared to be used to optimize and solve the water, wind and solar hydrogen system.

[0180] This embodiment uses the combined power generation as the basic data, comprehensively considers various factors including output volatility and power abandonment, and establishes a mathematical model for the regulation of water-wind-solar complementarity and green power consumption and hydrogen production. In the water-wind-solar complementarity, the peak load problem needs to be paid special attention to during the process of water-wind-solar joint grid-connected transmission. In the energy combination of water-wind-solar complementarity based on green power consumption and hydrogen production, the rapid regulation capability of hydrogen storage is used to coordinate the joint operation of the water-wind-solar system.

[0181] The process of exerting the regulatory capacity of green electricity consumption and hydrogen production is often affected by the volatility when hydropower, wind and solar power are connected to the grid. Therefore, a comprehensive model can be constructed to effectively solve the grid connection and consumption problems when hydropower, wind and solar power are coordinated. Based on the above theoretical basis, the present invention selects the minimum standard deviation of the output volatility of the hydropower, wind and solar power system, the minimum amount of abandoned electricity, and the minimum difference in the power generation of the combined system and the transmission channel capacity as the objective function, and establishes the optimization regulation model accordingly. Taking the comprehensive output of the combined system as the data basis, the regulatory capacity of green electricity consumption and hydrogen production is fully utilized.

[0182] The implementation of this embodiment has the following beneficial effects: This embodiment explores an optimization control mode for the water-wind-solar complementary combined green electricity consumption and hydrogen production system. First, by establishing a mathematical model of wind turbines, 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 water-wind-solar complementary combined green electricity consumption and hydrogen production system. The NSPSO algorithm provided by the present invention takes the minimization of the standard deviation of volatility of the combined output of the water, wind and solar systems and the amount of abandoned electricity as dual objective functions, and considers the stability of its operation and power balance, combines the basic constraints of wind power, photovoltaics and hydropower, and the forecast of typical days in specific areas and the output constraints of each energy source, establishes a combined power generation system of water, wind and solar complementarity, and simulates and further analyzes the optimal scheduling; secondly, the mathematical model of the water, wind, solar and hydrogen storage system is further deepened, and a multi-objective two-layer optimization model with the minimum standard deviation of volatility of the combined output of the water, wind and solar systems, the amount of abandoned electricity and the difference between the combined output and the capacity of the transmission channel is established. The upper model is optimized to obtain a solution set with the minimum standard deviation of volatility and the amount of abandoned electricity. At the same time, the difference in the capacity of the transmission channels in the lower model is also minimized, so that the system operation mode can respond to the output more flexibly, effectively solving the multi-dimensional and constrained difficulties in solving the water, wind, solar and hydrogen.

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

[0184] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the 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 above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0185] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0186] Embodiment 2

[0187] Figure 2 This is a flow chart of an embodiment of a double-layer optimization control method using the calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system of the present invention. Figure 2 As shown, the upper layer: the flow chart with the minimum power generation volatility and the minimum power abandonment includes the following steps:

[0188] S111, input grid transmission channel capacity.

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

[0190] S113, input the hydropower remaining load task curve.

[0191] S114, generating an initial population according to the transmission channel capacity of the power grid, the maximum output curve of wind and solar power, and the residual load task curve of hydropower.

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

[0193] S116, update the speed 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, go to step S118, otherwise go to step S113.

[0195] S118, output the Pareto solution set of the short-term multi-objective regulation model of water, wind and solar power, enter the lower-level combined power generation-transmission channel capacity difference minimum process, and enter step S211.

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

[0197] S212, input the hydropower, wind and solar power curtailment curve.

[0198] S213, input hydrogen to adjust the remaining load task curve.

[0199] S214, based on the actual output curve of hydropower, wind power and solar power, the curtailment curve of hydropower, wind power and solar power and the task curve of hydrogen regulation remaining load, Matlab+Cplex solution is performed.

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

[0201] S216, calculate the optimal value.

[0202] S217, determine whether the constraint conditions are met, if yes, go to step S218, otherwise go to step S215.

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

[0204] In order to verify the applicability of the NSPSO-MI LP proposed in the present invention in the optimization and control operation of the wind-solar complementary combined green electricity consumption and hydrogen production system of the hydropower station, the following example is used for verification. The model of this embodiment selects a combined power generation system including a river basin hydropower station, a wind farm and a photovoltaic power station, and the scheduling cycle is a single-day scheduling with an interval of hours. The data used is the basic data of a typical day in the area. The optimization scheduling solution is performed by using the NSPSO-MI LP two-layer model based on the Matlab program. To verify the effectiveness and applicability of the improved optimization method.

[0205] Analysis of typical day results of water-wind-solar complementarity: Figure 3 for Figure 2 Pareto solution set diagram in middle and upper level optimization. Figure 3 The Pareto front between the amount of power abandoned and the volatility of the hydro-wind-solar hybrid system under different output conditions is shown. The front shows a uniform distribution, indicating that there is a significant trade-off between the amount of power abandoned and the volatility. As the volatility decreases, the amount of power abandoned by the system increases, which shows that when reducing the volatility to improve the system's smoothing ability, there will be a certain degree of power loss. Therefore, when optimizing the balancing strategy of the hydro-wind-solar hybrid system, it is necessary to comprehensively consider the mutual influence between volatility and power abandoned and seek the optimal compromise solution.

[0206] Figure 4 for Figure 2 The first operation status diagram of water and wind power in the Pareto frontier of middle and upper layer optimization. Figure 4 The hydropower, wind and solar power output states with the lowest volatility and the highest amount of abandoned power in the Pareto front are shown. It can be seen that the hydropower output remains large when the wind and solar power output is low, and maintains a small output when the wind and solar power output is high, thereby effectively improving the stability of the hydropower, wind and solar power complementary power generation system. This regulation strategy ensures the smooth operation of the system under different wind and solar power output conditions. However, since the regulation of hydropower output leads to an increase in abandoned water flow, the overall amount of abandoned power also increases, showing a certain contradictory relationship.

[0207] Figure 5 for Figure 2 The second operation status diagram of concentrated hydropower and wind power in the Pareto solution of the middle and upper level optimization. Figure 5 The output states of hydropower, wind power and solar power with the highest volatility and the lowest power curtailment in the Pareto front are shown. It can be seen that hydropower also maintains a higher output when the output of wind power is low, and maintains a lower output when the output of wind power is high, so as to effectively reduce the power curtailment. However, in order to minimize the power curtailment, the regulation of hydropower shows different degrees of increase and decrease when the peak and valley of wind and solar power generation changes. Although the power curtailment was successfully reduced, it also led to an increase in volatility, so the overall volatility was higher.

[0208] Analysis of the results of the regulation of water-wind-solar complementary combined with green electricity consumption and hydrogen production: Figure 6 for Figure 2 The operation results in the middle and lower layer optimization, Figure 6 The operation of the water-wind-solar hybrid combined with green power consumption and hydrogen production control system under different output conditions is demonstrated. Under the joint control of the green power consumption and hydrogen production system, the system can effectively utilize the abandoned electricity. Figure 4 The obtained hydropower, wind and solar power output results are compared with the MI LP algorithm. The results show that when the total output of hydropower, wind and solar power exceeds the capacity of the transmission channel, the hydrogen storage system converts redundant electrical energy into hydrogen storage; when the output of hydropower, wind 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 of hydropower, wind and solar power and ensuring smooth operation.

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

[0210] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of the present invention, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A calculation method for a water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system, characterized in that: The steps include: S1. Establish a power generation model that meets the operating constraints of water, wind, solar and hydrogen, and select the minimum standard deviation of the output fluctuation of the water, wind and solar system and the minimum power abandonment of the combined system as the upper objective function, and the minimum difference between the power generation of the combined system and the capacity of the transmission channel as the lower objective function, and establish a water, wind and solar complementary combined hydrogen regulation model; S2, based on the traditional particle swarm algorithm, establishes the generation strategy based on the initial power of water, wind and solar power, the inertia weight, and the adaptive change strategy of the learning factor with the number of iterations. Combined with the NSGA-II algorithm, the frontier level and crowding distance are used as the fitness function in the particle swarm algorithm to improve the traditional particle swarm optimization algorithm; S3, using the improved particle swarm algorithm to optimize and solve the upper objective function to obtain the Pareto optimal solution set; S4, taking the Pareto optimal solution set as the boundary condition of the output power, the lower-level objective function is optimized and solved using mixed linear integer programming.

2. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 1 is characterized in that: The power generation model includes: Wind power generation model: Where P W,maxt is the maximum output power of the fan in period t, P Wr is the rated output power of the fan, V in is the cut-in wind speed, V out is the cut-out wind speed, V r is the rated wind speed, V t is the actual wind speed during period t; Photovoltaic power generation model: Where P S,maxt is the maximum output power of the photovoltaic cell in period t, P S is the rated output power of photovoltaic power generation, F S is the derating factor, GTI is the solar radiation intensity at time t, α p is the temperature correction factor, T c,t is the temperature of the photovoltaic cell at time t, T c,STC is the surface temperature of the photovoltaic array under standard test conditions; Hydroelectric power generation model: Where η Hg is the efficiency of the turbine in converting water energy into electrical energy, ρ is the density of water, g is the acceleration due to gravity, h Ht 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 calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 1 is characterized in that: S2, based on the traditional particle swarm algorithm, establishes a generation strategy based on the initial power of water, wind and solar power, an inertia weight, and a learning factor adaptive change strategy with the number of iterations, combines the NSGA-II algorithm, and uses the frontier level and crowding distance as the fitness function in the particle swarm algorithm to improve the traditional particle swarm optimization algorithm. The specific steps include: S21, taking the upper and lower water levels of the reservoir as the boundary, the maximum output power of wind power and photovoltaic power as the boundary, and the wind, solar and water power generation power in each period as the decision variable, introduce the first improved strategy to obtain the initial population; S22, introduce the second improvement strategy from the two parameters of inertia weight and empirical coefficient to improve the particle swarm optimization algorithm; S23, based on the NSGA-II algorithm, uses the frontier level and crowding distance in the Pareto optimal solution set as the fitness update coefficient in the particle swarm optimization, and combines the capacity of the electric transport channel as the fitness function to update and iterate the individual optimal position, historical optimal position and Pareto solution set of the particle.

4. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 1 is characterized in that: The step S3, using the improved particle swarm algorithm to optimize and solve the upper layer objective function to obtain the Pareto optimal solution set specifically includes: S31, setting parameters; S32, initializing the particles according to the set parameters; S33, solve the fitness of all particles in each time period; S34, according to the initial inertia weight data, the initial learning factor data, and the speed and position formula of the particle group, calculate the updated speed, position and fitness of the particle in each iteration; S35, taking out the frontier level and crowding distance of each particle, performing non-dominated sorting, and recording the position of the particle with a frontier level of 1; S36, using the adaptive inertia weight and learning factor formula to update the inertia weight w and the learning factors c1 and c2, and continue the iterative process; S37, proceed to the next step according to whether the number of iterations meets the termination condition; if so, output the Pareto optimal solution set, otherwise continue to loop steps S33 to S36 until the requirement is met.

5. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 1 is characterized in that: The step S4, using the Pareto optimal solution set as the boundary condition of the output power and using mixed linear integer programming to optimize and solve the lower layer objective function specifically includes: S41, introducing the hydrogen storage constraint conditions in step S1 to construct a water-wind-solar hydrogen storage system; S42, selecting the position of a particle in the Pareto solution set obtained in step S3 as the output power of the water-wind-solar in the lower model; S43, taking the minimum difference between the power generation of the combined system and the capacity of the transmission channel in step S1 as the objective function of the lower model, solving it through the Cplex solver in Matlab, and obtaining the operating power of hydropower, wind power, photovoltaic power, electrolyzer, fuel cell, and compressor in each time period.

6. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 1 is characterized in that: The constraints include: Wind and solar power output constraint: 0≤P t W ≤P t W,max , 0≤P t S ≤P t S,max , where P Wt is the actual output power of the fan in period t, P St is the actual output power of the photovoltaic cell in period t; Water balance constraints: Where V Ht , is the water storage capacity of the reservoir at time t, Q Ht and q Ht is the inflow and outflow of the reservoir at time t; Outbound flow constraints: In the formula, q Hmin is the minimum outflow of the reservoir, is the maximum outflow of the reservoir; Power generation flow constraints: Where, QF Hmin is the minimum power generation flow of the reservoir, is the maximum power generation flow of the reservoir; Water level constraints: In the formula, Z Hmin is the minimum operating water level of the reservoir, is the maximum operating water level of the reservoir; Output constraints of hydropower station: In the formula, is the maximum output power of hydropower; Power balance constraints: In the formula, are the power generation of water, wind, light and hydrogen storage fuel cell under hydrogen storage regulation in period t, is the input power of the electrolyzer during period t, P outt is the net output power of the system; Electrolyzer power constraints: In the formula, is the hydrogen input power in period t, λ H is the hydrogen production efficiency of the electrolyzer, are the hydrogen storage power, hydrogen storage power generation power, and hydrogen compression power in period t respectively; P ELC,mint and P ELC,maxt are the minimum and maximum input power of the electrolyzer during period t, ΔP ELC,min and ΔP ELC,max They are the lower and upper limits of the electrolyzer power ramp, respectively; Compressor power constraints: Where η ELC and η YS They are the energy consumption of hydrogen production by electrolyzer and hydrogen storage by compressor; Fuel cell power constraints: In the formula, is the fuel cell output power during period t, λ FC For the efficiency of fuel cell power generation, and are the minimum and maximum output power of the fuel cell during period t, ΔP ELC,min and ΔP ELC,max They are the lower and upper limits of the fuel cell power generation ramp, respectively; Green electricity consumption and hydrogen production control constraints:

7. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 1 is characterized in that: The objective function includes: Minimum objective function for the output volatility of hydro-wind-solar hybrid system: Where N is the standard deviation of the output fluctuation of the hydro-wind-solar hybrid system within a unit period, P Hiter,t , P Witer,t , P Siter,t are the water, wind and solar power outputs in the tth period of the iterth iteration respectively; P av is the average output value of the system within a unit period; Minimum objective function of joint system power abandonment: Where M is the total amount of abandoned electricity from hydropower, wind power and solar power in a unit period; The minimum objective function of the combined system power generation-transmission channel capacity difference is: Where Z is the absolute value of the difference between the comprehensive output of water, wind and solar power and the capacity of the transmission channel at the iter iteration in the kth population, P transport Transmission channel capacity for the power grid.

8. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 3 is characterized in that: The first improvement strategy includes: Improvement strategy for minimum water level in each period: In the formula, H mink,t is the lowest water level of the reservoir in the kth population during the t period, qave is the historical average flow of the reservoir, is the lowest water level of the reservoir during the kth population t-1 period; Improvement strategy for maximum water level in each period: In the formula, H maxk,t is the highest water level of the reservoir in the kth population during period t, is the highest water level of the reservoir during the kth population t-1 period; Improved strategy for water level initialization in each period: In the formula, H k,t is the initial water level of the reservoir in the kth population in period t, H k,t is the initial water level of the reservoir in the kth population in the t-1 period, rand is a random function, and population is the number of particle swarms; Improved strategy for initializing water outflow flow in each period: In the formula, q Hk,t is the initial outbound flow of the kth population in period t, repmat is the repeated array function; Improved strategy for initialization of hydropower flow in each period: Where, QF Hk,t is the initial power generation flow of the kth population in period t, b1 is a constant; Improved strategy for initializing abandoned hydropower flow in each period: In the formula, Nq Hk,t is the initial current abandonment amount of the kth population in period t; Improved strategies for wind power generation initialization in different time periods: Where P Wk,t is the initial wind power generation of the kth population in period t, b2 is a constant, and .* is array multiplication; Improvement strategy for photovoltaic power generation initialization in different time periods: In the formula, is the initial photovoltaic power generation of the kth population in period t, b3 is a constant, and .* is array multiplication.

9. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 3 is characterized in that: The second improvement strategy includes: Adaptive inertia weight improvement strategy: Where W is the inertia weight, W min and W max are the preset minimum and maximum inertia weights, which are 0.4 and 0.9 respectively, iter is the current iteration number, iter max is the maximum number of iterations, which is 1500; Adaptive learning factor improvement strategy: In the formula, C1 is the individual learning experience coefficient, C 1s and C 1e are the preset initial value and stop value, which are 1.5 and 0.5 respectively, C2 is the group learning experience coefficient, C 2s and C 2e The preset initial and stop values ​​are 0.5 and 1.5 respectively.

10. The calculation method of the water-wind-solar complementary power generation combined with green electricity consumption and hydrogen production control system according to claim 3 is characterized in that: The fitness includes: In the formula, F k,iter is the fitness of the kth population at the iterth iteration, Rank k,iter is the Pareto frontier level at the iter iteration in the kth population, Crowd k,iter Dp is the crowding distance at the iter iteration in the kth population; k,iter,out is the absolute value of the difference between the comprehensive output of water, wind and solar power and the capacity of the transmission channel at the iter iteration in the kth population, P transport Transmission channel capacity for the power grid.

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