CN115600858A - Wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment - Google Patents
Wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment Download PDFInfo
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Abstract
The invention provides an economical optimization scheduling method of a wind-solar energy storage hydrogen production system considering wind abandoning and light abandoning punishment, which comprises the following steps: s1, establishing mathematical models of all parts of a wind-solar hydrogen production system; s2, considering the wind and light abandoning punishment cost, and establishing a day-ahead optimal scheduling model of the wind and light storage hydrogen production system; s3, carrying out optimization solution on the optimized scheduling model by adopting a self-adaptive simulated annealing particle swarm algorithm; and S4, setting the wind-solar hydrogen production system according to the power grid electricity purchasing power, the storage battery running power, the wind power generation power, the photovoltaic power generation power and the electrolytic cell running power obtained by optimization solution in each period, and realizing the economic optimization scheduling of the wind-solar hydrogen production system. According to the method, the wind and light abandoning punishment cost is considered, the constraint conditions are adjusted, and the influence of time-of-use electricity price and demand side hydrogen response on a day-ahead scheduling model of the wind and light storage hydrogen production system is considered, so that the scheduling method is more in line with actual production, and the overall economy of the system can be improved.
Description
Technical Field
The invention belongs to the technical field of hydrogen production and storage optimization scheduling of new energy, and particularly relates to an economical optimization scheduling method of a wind-solar energy storage hydrogen production system considering wind abandoning and light abandoning punishment.
Background
In recent years, in the face of the gradual depletion of fossil energy and the environmental problems such as air pollution, climate change and the like caused by the depletion, the steady promotion of energy structure optimization and the realization of the orderly replacement of fossil energy by renewable energy are common consensus in energy policies of countries in the world. Renewable energy sources, however, are still deficient in maintaining the stability of the power supply due to the limitations of the prior art capabilities and the power infrastructure. Renewable energy power generation is greatly influenced by natural conditions, and has the characteristic of intermittent power generation, which can have adverse effects on the stability and safety of a power grid system. In an electric power system, an energy storage technology is an effective technology for solving the problems caused by large-scale renewable energy power generation grid connection. The hydrogen energy storage can electrolyze water from enough and surplus renewable energy power to prepare hydrogen and store the hydrogen, so that large-scale energy storage in renewable energy transformation is realized. The water electrolysis hydrogen production technology is a core technology in large-scale new energy hydrogen production.
Scholars at home and abroad make a great deal of research on the efficiency improvement of water electrolysis equipment and the optimization of the scheduling method of the wind-light hydrogen storage and production system, the research on the optimization scheduling mainly comprises two aspects of the optimization of the structure of the wind-light hydrogen storage and production system and the improvement of an optimization algorithm, and the final purpose of the optimization is to improve the economy of the system. The improvement of the optimization algorithm is mostly the optimization of convergence speed and accuracy.
The particle swarm optimization algorithm (PSO) has good effect on the optimal scheduling of the wind-solar hydrogen production system. The particle swarm optimization algorithm is a random search algorithm based on a population, and inspiration of the random search algorithm comes from natural behaviors of bird communities in food search. Similar to other search algorithms, PSO starts with a randomly generated population of possible solutions, with particles deciding on the next step through their own experience and best experience of the same age, by iteratively calculating the globally optimal solution that converges on the problem step by step. Each particle in the cluster is a potential solution to the problem and corresponds to a fitness value determined by its position. The speed of the particles determines the moving direction and distance of the particles, and the speed is dynamically adjusted along with the movement of the particles and other particles, so that the optimization of an individual in a feasible solution space is realized.
The PSO algorithm has good effect when being applied to the optimal scheduling of the wind-solar hydrogen production system, but when the traditional particle swarm algorithm is used for solving the model, a local optimal solution can be involved in the iterative computation process, and a global optimal solution cannot be obtained. Moreover, the prior art determination of objective functions and constraint conditions is not compatible with actual production, resulting in a less economical system.
Disclosure of Invention
The invention aims to provide an economic optimization scheduling method of a wind-solar energy storage hydrogen production system considering wind abandonment and light abandonment punishment, which establishes a hydrogen production system day-ahead processing model with the lowest daily operation cost of the system, improves the traditional PSO algorithm by utilizing the characteristics of a simulated annealing algorithm, and solves by utilizing an improved self-adaptive simulated annealing particle swarm algorithm, thereby obtaining a hydrogen production system day-ahead scheduling scheme with the lowest daily operation cost and overcoming the technical problems in the prior art.
Therefore, the technical scheme provided by the invention is as follows:
a wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment comprises the following steps:
s1: establishing mathematical models of all parts of the wind-solar energy storage hydrogen production system, including a photovoltaic power generation model, a wind power generation model, a storage battery energy storage equipment model and an alkaline electrolytic cell model;
s2: considering wind and light abandoning punishment cost, obtaining daily operation cost of each part according to each part mathematical model of the wind-light storage hydrogen production system in the S1, taking the sum of the daily operation cost of the system as a target function, establishing an optimized scheduling model, and determining each constraint of the system during normal operation, wherein the constraint comprises a system power balance constraint condition, a storage battery energy storage system constraint, a power grid interactive power constraint and a hydrogen production constraint;
s3: performing optimization solution on the optimization scheduling model by adopting a self-adaptive simulated annealing particle swarm algorithm to obtain the power grid electricity purchasing power, the storage battery operation power, the wind power generation power, the photovoltaic power generation power and the electrolytic cell operation power of each time period corresponding to 24-hour hourly lowest daily operation cost in a typical day of the wind-solar hydrogen storage system;
s4: and setting the wind-solar hydrogen production system according to the power grid electricity purchasing power, the storage battery running power, the wind power generation power, the photovoltaic power generation power and the electrolytic bath running power obtained in the step S3, and realizing the economic optimization scheduling of the wind-solar hydrogen production system.
In S1, a photovoltaic power generation model and a wind power generation model are respectively as follows:
s11: photovoltaic power generation model:
in the formula (I), the compound is shown in the specification,
the output power of the photovoltaic array at the illumination intensity G (t); g STC 、T STC 、P STC Respectively representing the illumination intensity, the photovoltaic array temperature and the maximum output power in a standard test environment; k is a temperature coefficient, and generally k = -0.45; t (T) is the surface temperature of the photovoltaic array at time T;
s12: the wind power generation model comprises:
in the wind turbine generator modelInput wind speed v and predicted output power P of single wind turbine t The relationship of (c) is:
in the formula: v is the actual wind speed at the height of the hub of the wind turbine generator in m/s; v. of i To cut into the wind speed; v. of o Cutting out the wind speed; v. of n Rated wind speed; p n Is rated power of the wind turbine generator, kW; rho is air density, kg/m 3 (ii) a R is the length of the blade of the wind turbine generator, m;
the total schedulable wind power predicted by the wind power plant in the t period is represented as follows:
in the formula: m is the total number of the wind turbine generators;
the predicted output power of the jth wind turbine generator set at the moment t is obtained; Δ t is the adjacent time period.
In the S1, the storage battery energy storage equipment model and the alkaline electrolytic cell model are respectively as follows:
s13: storage battery energy storage device model:
the accumulator device plays a role of peak clipping and valley filling, and the mathematical model of the accumulator device can be expressed as follows:
in the formula: e (t) is the total energy of the storage battery in the period t, kW.h; σ is the self-discharge efficiency of the battery;
and
are respectively storage batteriesThe device charges and discharges power, kW, in a period t; eta ch And η dis Charging and discharging efficiencies for the battery device, respectively;
s14: alkaline cell model:
Q el ＝P el ·Δt·ρ·η el
in the formula: q el For hydrogen production, m 3 ；P el Inputting power, kW, for the alkaline electrolytic cell; delta t is the running time of the electrolytic cell, h; rho is the electrolytic cell electricity-hydrogen conversion parameter, m 3 /kW·h；η el For the working efficiency of the electrolytic cell,%.
The optimized scheduling model established in S2 is as follows:
C day ＝C new +C pen +C ele +C sto +C grid +C com
in the formula, C day 、C new 、C pen 、C ele 、C sto 、C grid 、C com Scheduling the daily operation cost, the wind power and photovoltaic power station operation cost, the wind and light abandoning punishment cost, the alkaline electrolytic cell hydrogen production cost, the energy storage device maintenance cost, the power purchasing cost to the power grid and the hydrogen storage cost of a hydrogen storage tank for the system respectively;
wherein, the running cost C of the wind power and photovoltaic power station new ＝K w P w (t)+K pv P pv (t), wherein: k w 、K pv The running cost of the wind power station and the photovoltaic power station is unit/kW; p w (t)、P pv (t) scheduling power, kW, of wind power and photovoltaic in a time period t respectively;
wind and light abandoning punishment cost
In the formula: k is a radical of formula w 、k pv Punishing cost coefficients of wind abandoning and light abandoning respectively;
hydrogen production cost of alkaline electrolytic cell C ele (t)＝P el (t)×C per_ele +C water In the formula: p el (t) the input power of the alkaline electrolytic cell is kW in the period of t; c per_ele The operating cost of the alkaline electrolytic cell is yuan/kilowatt hour; c water For water consumption cost, yuan/Nm 3 ；
Maintenance cost C of storage battery energy storage device sto (t)＝P bat (t)×C per_bat In the formula, P bat (t) is the charging/discharging power of the accumulator, P bat (t)＝E(t)/t；C per_bat Maintenance cost per unit electric quantity of the storage battery device, yuan/kilowatt-hour;
hydrogen storage cost of hydrogen storage tank C com (t)＝Q el ×C per_com In the formula: q el For hydrogen production, m 3 ；C per_com Is the unit hydrogen storage cost per Nm of the hydrogen storage tank 3 ；
Cost C of power purchase of power grid grid (t)＝P grid (t)×C per_grid In the formula: p grid (t) purchasing power, kW, from the power grid by the system in a period t; c per_grid The power supply is the time-sharing electricity price of power grid industrial power selling, yuan/kilowatt hour.
In the S2, the system power balance constraint condition, the storage battery energy storage system constraint, the power grid interaction power constraint and the hydrogen production constraint are respectively as follows:
s221: system power balance constraint:
in the formula: p is renew (t) renewable energy output, namely wind-solar hybrid power generation in t periodMaximum power output, kW; p is bat (t) the charging/discharging power of the storage battery in a period of t, wherein the discharging power of the storage battery is a positive value, and the charging power is a negative value, kW; p grid (t) purchasing power, kW, from the power grid by the system in a period t; p el (t) the input power of the alkaline electrolytic cell in the period of t is kW;
s222: and (3) constraint of a storage battery energy storage system:
the capacity of the storage battery is related to the capacity of the previous period and the charge-discharge power and the self-discharge amount in the previous period, and the state of charge of the storage battery in the t period is as follows:
in the formula:
the state of charge of the storage battery at t time interval; sigma is the self-discharge rate of the storage battery; eta c Charge/discharge efficiency for the battery; e bat The total capacity of the storage battery is kW.h;
is the same as P bat (t)；
S223: and power grid interaction power constraint:
in the formula:
and
respectively purchasing minimum power and maximum power, kW, from a power grid for the system;
s224: and (3) restricting the operation power of the electrolytic cell:
in the formula:
and
respectively the minimum running power and the maximum running power in the production state of the alkaline electrolytic cell, kW;
s225: hydrogen production constraints
In the formula:
is hydrogen storage margin, nm, of the system for the period t 3 ；
Hydrogen production rate, nm, of alkaline electrolyzer for t time period 3 /h；
For the demand side hydrogen load in the period t, nm 3 ；
Hydrogen gas balance, nm, of the system for the planned end period of the day-ahead operation 3 。
S222, the constraint of the storage battery energy storage system further comprises the constraint of upper and lower limits of charge and discharge power and the constraint of upper and lower limits of the charge state of the storage battery, and the constraint of the upper and lower limits of the charge state of the storage battery is as follows:
the upper and lower limits of the charging and discharging power of the storage battery are constrained as follows:
in the formula:
and
respectively setting a charging power limit value and a discharging power limit value, kW, of the storage battery;
the upper and lower limits of the charge state of the storage battery are constrained as follows:
in the formula:
and
respectively, a minimum state of charge value and a maximum state of charge value of the storage battery.
The optimization solving process of the optimized scheduling model by adopting the self-adaptive simulated annealing particle swarm optimization is as follows:
step 1: setting initial parameters including a population size N, a maximum iteration number M, a variable dimension D, an initial temperature and a temperature attenuation coefficient;
and 2, step: inputting parameters of wind power, photovoltaic, energy storage battery and electrolytic cell equipment to restrict and generate initial particles, including particle positions and speeds;
and 3, step 3: calculating the fitness value of each particle in the initial population, finding the optimal value of the objective function, and recording the individual optimal position and the global optimal position;
and 4, step 4: setting the initial temperature of simulated annealing, and adaptively changing omega and c 1 、c 2 (ii) a Omega is an inertia weight coefficient; c. C 1 Represents a self-recognition factor; c. C 2 Represents a social cognitive factor;
and 5: performing simulated annealing algorithm neighborhood search on the particles according to Metropolis criterion;
step 6: updating the optimal positions of all particles and the global optimal position of the population by adopting a speed updating formula;
and 7: judging whether the maximum iteration times is reached, if not, returning to the step S34; otherwise, stopping iteration;
and step 8: and outputting the current optimal particles, namely the optimizing result, and terminating the algorithm.
The speed updating formula in the step 6 is as follows:
in the formula (I), the compound is shown in the specification,
represents the velocity of the ith particle at the k +1 th iteration;
and
respectively representing the speed and the position of the ith particle at the kth iteration; omega is an inertia weight coefficient; c. C 1 Represents a self-recognition factor; c. C 2 Represents a social cognitive factor; r is 1 And r 2 Is a random number; p is a radical of ibest The individual optimal position of the ith particle.
The beneficial effects of the invention are:
the wind-solar energy storage hydrogen production system economical optimization scheduling method considering the wind abandoning and light abandoning punishment provided by the invention takes economy as a priority target, considers the wind abandoning and light abandoning punishment cost in an objective function, establishes a hydrogen production system day-ahead scheduling model with the lowest system day operation cost, adjusts constraint conditions on the basis, considers the influence of time-of-use electricity price and demand side hydrogen response on the wind-solar energy storage hydrogen production system day-ahead scheduling model, not only enables the scheduling method to be more in line with actual production, but also can improve the overall economy of the system.
The invention adopts a parameter self-adaptive algorithm and utilizes an improved self-adaptive simulated annealing particle swarm algorithm to solve, thereby obtaining the day-ahead scheduling scheme of the hydrogen production system with the lowest daily operation cost. The optimal scheduling method considers the intermittency of new energy power generation in real power production, can reflect the production process of the wind-solar hydrogen production more truly, and is beneficial to the realization of economic scheduling.
As will be described in further detail below.
Drawings
FIG. 1 is a flow chart of the adaptive simulated annealing particle swarm algorithm of the present invention;
FIG. 2 is a structural diagram of the components of a wind-solar hydrogen production system;
FIG. 3 is a graph of a photovoltaic power generation model and a wind power generation model;
FIG. 4 is a hydrogen load demand curve;
FIG. 5 is a day-ahead scheduling plan of a wind-solar hydrogen production system;
FIG. 6 is a schematic diagram of the hydrogen production rate of the alkaline electrolytic cell and the hydrogen balance of the hydrogen storage tank;
fig. 7 is a schematic diagram of battery energy storage device state of charge.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the invention to those skilled in the art. The terminology used in the description of the exemplary embodiments is not intended to be limiting of the invention.
Unless otherwise defined, terms (including 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 belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Example 1:
the embodiment provides an economical optimization scheduling method of a wind-solar energy storage hydrogen production system considering wind abandonment and light abandonment punishment, which comprises the following steps:
s1: establishing mathematical models of all parts of the wind-solar energy storage hydrogen production system, wherein the mathematical models comprise a photovoltaic power generation model, a wind power generation model, a storage battery energy storage equipment model and an alkaline electrolytic cell model;
s2: considering the wind and light abandoning punishment cost, obtaining the daily operation cost of each part according to each part mathematical model of the wind and light storage hydrogen production system in S1, taking the sum of the daily operation cost of the system as a target function, establishing an optimized scheduling model, and determining each constraint of the system during normal operation, wherein the constraint comprises a system power balance constraint condition, a storage battery energy storage system constraint, a power grid interaction power constraint and a hydrogen production constraint;
s3: performing optimization solution on the optimization scheduling model by adopting a self-adaptive simulated annealing particle swarm algorithm to obtain the power grid electricity purchasing power, the storage battery operation power, the wind power generation power, the photovoltaic power generation power and the electrolytic cell operation power of each time period corresponding to 24-hour hourly lowest daily operation cost in a typical day of the wind-solar hydrogen storage system;
s4: and setting the wind-solar hydrogen production system according to the power grid electricity purchasing power, the storage battery running power, the wind power generation power, the photovoltaic power generation power and the electrolytic bath running power obtained in the step S3, and realizing the economic optimization scheduling of the wind-solar hydrogen production system.
The wind-solar energy storage hydrogen production system economic optimization scheduling method considering the wind abandoning and light abandoning punishment provided by the invention takes economy as a priority target, builds a hydrogen production system day-ahead scheduling model with the lowest system day operation cost according to the wind abandoning and light abandoning punishment cost in a target function, and adjusts constraint conditions on the basis, so that the scheduling method is more in line with actual production, and the overall economy of the system can be improved.
Example 2:
on the basis of embodiment 1, the present embodiment provides an economic optimization scheduling method for a wind-solar energy storage hydrogen production system considering wind abandoning and light abandoning punishments, and in S1, a photovoltaic power generation model and a wind power generation model are respectively:
s11: photovoltaic power generation model:
the output power of the photovoltaic battery pack changes along with the changes of uncertain factors such as sunlight irradiation intensity, temperature and the like, and the output power of the photovoltaic array is as follows:
in the formula (I), the compound is shown in the specification,
the output power of the photovoltaic array at the illumination intensity G (t); g STC 、T STC 、P STC Respectively representing the illumination intensity, the photovoltaic array temperature and the maximum output power in a standard test environment; k is the temperature coefficient, generally k = -0.45; t (T) is the surface temperature of the photovoltaic array at time T;
s12: the wind power generation model comprises:
single wind turbine generator input wind speed v and predicted output power P in wind turbine generator model t The relationship of (1) is:
in the formula: v is the actual wind speed at the hub height of the wind turbine generator, m/s; v. of i To cut into the wind speed; v. of o Cutting out the wind speed; v. of n Rated wind speed; p is n The rated power of the wind turbine is kW; rho is air density, kg/m 3 (ii) a R is the length of the blade of the wind turbine generator, m;
the schedulable wind power total amount predicted by the wind power plant in the period t is represented as:
in the formula: m is the total number of the wind turbine generators;
the predicted output power of the jth wind turbine generator set at the time t is obtained; Δ t is the adjacent time;
s13: storage battery energy storage equipment model:
the storage battery device plays a role in peak clipping and valley filling, and the mathematical model of the storage battery device can be expressed as follows:
in the formula: e (t) is the total energy of the storage battery in the period t, kW.h; sigma is the self-discharge efficiency of the storage battery;
and
charging and discharging power, kW, of the storage battery device in a time period t respectively; eta ch And η dis Charging and discharging efficiencies for the battery device, respectively;
s14: alkaline cell model:
the expression of the electrolytic cell electricity-gas conversion relation and the generated hydrogen gas volume number is as follows:
Q el ＝P el ·Δt·ρ·η el
in the formula: q el For hydrogen production, m 3 ；P el Inputting power, kW, for the alkaline electrolytic cell; delta t is the running time of the electrolytic cell, h; rho is the electrolytic cell electricity-hydrogen conversion parameter, m 3 /kW·h；η el For the working efficiency of the electrolytic cell,%.
In the embodiment, mathematical models of all parts of the wind-solar hydrogen production system are established, and output characteristic curves of wind power and photovoltaic power are obtained in a mathematical modeling mode.
Example 3:
on the basis of the embodiment 1, the embodiment provides an economic optimization scheduling method for a wind-solar energy storage hydrogen production system considering wind abandoning and light abandoning punishments, and the establishment of an optimization scheduling model in S2 is as follows:
C day ＝C new +C pen +C ele +C sto +C grid +C com
in the formula, C day 、C new 、C pen 、C ele 、C sto 、C grid 、C com Scheduling the daily operation cost, the wind power and photovoltaic power station operation cost, the wind and light abandoning punishment cost, the alkaline electrolytic cell hydrogen production cost, the energy storage device maintenance cost, the power purchasing cost to the power grid and the hydrogen storage cost of a hydrogen storage tank for the system respectively;
wherein, the running cost C of the wind power and photovoltaic power station new ＝K w P w (t)+K pv P pv (t), wherein: k w 、K pv The running cost of the wind power station and the photovoltaic power station is unit/kW; p w (t)、P pv (t) scheduling power, kW, of wind power and photovoltaic in a time period t respectively;
wind and light abandoning punishment cost
In the formula: k is a radical of w 、k pv Punishing cost coefficients of wind abandoning and light abandoning respectively;
hydrogen production cost of alkaline electrolytic cell C ele (t)＝P el (t)×C per_ele +C water In the formula: p el (t) the input power of the alkaline electrolytic cell in the period of t is kW; c per_ele The operating cost of the alkaline electrolytic cell is yuan/kilowatt hour; c water For water consumption cost, yuan/Nm 3 ；Nm 3 Representing standard cubic meters.
Assuming that the electrolytic cell produces 1/Nm of water per production 3 Hydrogen gas, water consumption 0.89kg, electrolysis water cost 10 yuan/t, production 1/Nm 3 The water consumption cost of hydrogen is 0.0089 yuan.
Maintenance cost C of storage battery energy storage device sto (t)＝P bat (t)×C per_bat In the formula, P bat (t) is the charging/discharging power of the accumulator, P bat (t)＝E(t)/t；C per_bat Maintenance cost per unit electric quantity of the storage battery device, yuan/kilowatt hour;
hydrogen storage cost of hydrogen storage tank C com (t)＝Q el ×C per_com In the formula: q el For hydrogen production, m 3 ；C per_com For the unit hydrogen storage cost of the hydrogen storage tank, yuan/Nm 3 ；
Cost C of power purchase of power grid grid (t)＝P grid (t)×C per_grid In the formula: p grid (t) purchasing power, kW, from the power grid by the system in a period t; c per_grid The power supply is the time-sharing electricity price of power grid industrial power selling, yuan/kilowatt hour.
In the embodiment, wind and light abandoning penalty cost is considered, the sum of daily operating cost of the system is taken as a target function, time-of-use electricity price is considered, and a day-ahead optimization scheduling model of the wind-light hydrogen storage system based on the daily operating cost of the system is established, so that the scheduling method is more in line with actual production.
Example 4:
on the basis of the embodiment 1, the embodiment provides an economic optimization scheduling method for a wind-solar energy storage and hydrogen production system considering wind abandoning and light abandoning punishments, and the system power balance constraint condition, the storage battery energy storage system constraint, the power grid interaction power constraint and the hydrogen production constraint in the step S2 are respectively as follows:
s221: system power balance constraint:
in the formula: p renew (t) is renewable energy output, namely wind-solar hybrid power generation at tMaximum output power in a time period, kW; p bat (t) the charging/discharging power of the storage battery in a period of t, wherein the discharging power of the storage battery is a positive value, and the charging power is a negative value, kW; p grid (t) purchasing power, kW, from the power grid by the system in a period t; p is el (t) the input power of the alkaline electrolytic cell in the period of t is kW;
s222: and (3) constraint of a storage battery energy storage system:
the capacity of the storage battery is related to the capacity of the previous period and the charge-discharge power and the self-discharge amount in the previous period, and the state of charge of the storage battery in the t period is as follows:
in the formula:
the state of charge of the storage battery at t time interval; sigma is the self-discharge rate of the storage battery; eta c Charge/discharge efficiency for the battery; e bat The total capacity of the storage battery is kW.h;
is the same as P bat (t)；
S223: and power grid interaction power constraint:
in the formula:
and
respectively purchasing minimum power and maximum power, kW, from a power grid for the system;
s224: and (3) restricting the operation power of the electrolytic cell:
in the formula:
and
respectively the minimum running power and the maximum running power in the production state of the alkaline electrolytic cell, kW;
s225: hydrogen production constraints
In the formula:
is hydrogen storage margin, nm, of the system for the period t 3 ；
Hydrogen production rate, nm, of the alkaline cell for a period of time t 3 /h；
For the demand side hydrogen load in the period t, nm 3 ；
Hydrogen gas balance, nm, of the system for the planned end period of the day-ahead operation 3 。
The method determines the state constraint and the power constraint of the system during normal operation, adds a load end hydrogen demand response behavior to the wind-solar hydrogen storage system model considering power interaction, and considers the influence of the time-of-use electricity price and demand side hydrogen response on the day-ahead scheduling model of the wind-solar hydrogen storage system.
Example 5:
on the basis of embodiment 4, this embodiment provides an economic optimization scheduling method for a wind-solar energy storage and hydrogen production system considering wind abandoning and light abandoning punishment, and S222 constraints of a storage battery energy storage system further include upper and lower limits of charge and discharge power constraints and upper and lower limits of a storage battery state of charge constraints, which are specifically as follows:
the upper and lower limits of the charge and discharge power of the storage battery are constrained as follows:
in the formula:
and
respectively a charging power limit value and a discharging power limit value, kW, of the storage battery;
the upper and lower limits of the charge state of the storage battery are constrained as follows:
in the formula:
and
respectively, a minimum state of charge value and a maximum state of charge value of the storage battery.
Besides the capacity change constraint, the storage battery also provides charging and discharging power upper and lower limit constraints, capacity upper and lower limit constraints and storage battery capacity equal beginning and end in days, so as to meet the storage battery safety constraint.
Example 6:
on the basis of embodiment 4, this embodiment provides an economic optimization scheduling method for a wind-solar energy storage hydrogen production system considering wind abandoning and light abandoning punishments, and as shown in fig. 1, an optimization solution process of an optimization scheduling model by using a self-adaptive simulated annealing particle swarm algorithm is as follows:
step 1: setting initial parameters including a population size N, a maximum iteration number M, a variable dimension D, an initial temperature and a temperature attenuation coefficient;
and 2, step: inputting wind power, photovoltaic, energy storage batteries and electrolytic cell equipment parameters to restrict and generate initial particles, including particle positions and speeds;
and step 3: calculating the fitness value of each particle in the initial population, finding the optimal value of the objective function, and recording the individual optimal position and the global optimal position;
and 4, step 4: setting the initial temperature of simulated annealing, and adaptively changing omega and c 1 、c 2 (ii) a Omega is an inertia weight coefficient; c. C 1 Represents a self-cognition factor; c. C 2 Represents a social cognition factor;
and 5: performing simulated annealing algorithm neighborhood search on the particles according to Metropolis criterion;
step 6: updating the optimal positions of all particles and the global optimal position of the population by adopting a speed updating formula;
and 7: judging whether the maximum iteration times is reached, if not, returning to the step S34; otherwise, stopping iteration;
and step 8: and outputting the current optimal particles, namely the optimizing result, and terminating the algorithm.
The invention utilizes the self-adaptive simulated annealing particle swarm algorithm to solve, thereby obtaining the day-ahead scheduling scheme of the hydrogen production system with the lowest daily operation cost. The optimal scheduling method considers the intermittency of new energy power generation in real power production, can reflect the production process of the wind-solar hydrogen production more truly, and is beneficial to the realization of economic scheduling.
Example 7:
on the basis of embodiment 6, the present embodiment provides an economic optimization scheduling method for a wind-solar energy storage hydrogen production system considering wind abandoning and light abandoning punishments, and the speed updating formula in step 6 is as follows:
in the formula (I), the compound is shown in the specification,
represents the velocity of the ith particle at the k +1 th iteration;
and
respectively representing the speed and the position of the ith particle at the kth iteration; omega is an inertia weight coefficient; c. C 1 Represents a self-recognition factor; c. C 2 Represents a social cognitive factor; r is 1 And r 2 Is a random number; p is a radical of ibest The individual optimal position of the ith particle.
The speed updating formula of the existing standard particle swarm algorithm is as follows:
in the formula:
and
respectively representing the speed and the position of the ith particle at the kth iteration; omega is an inertia weight coefficient, and the development and exploration capacity of an algorithm can be controlled; c. C 1 、c 2 Respectively representing self-cognition factors and social cognition factors; r is 1 And r 2 Is a random number; p is a radical of ibest An individual optimal position for the ith particle; g best Is the best position of the population.
In order to improve the optimization speed and accuracy of the particle swarm optimization and avoid falling into a local optimal solution, the following hyperbolic tangent function is adopted to control the inertia weight coefficient to perform nonlinear adaptive change in the embodiment:
ω＝(ω max +ω min )/2+tanh(-4+8×(k max -k)/k max )(ω max -ω min )/2
in the formula: omega max And omega min The maximum value and the minimum value of the inertia weight coefficient are respectively and frequently taken as 0.95 and 0.4; k is the current number of iterations, k max Is the maximum number of iterations.
Learning factor c 1 、c 2 The expression is as follows:
in the formula: c. C 1 (k)、c 2 (k) Are respectively a learning factor c 1 、c 2 The value at the kth iteration; c. C 1max 、c 1min 、c 2max 、c 2min Are respectively a learning factor c 1 、c 2 Maximum and minimum values of.
Introducing simulated annealing operation, guiding population to accept differential solution with certain probability according to Metropolis criterion and temperature, adopting roulette strategy to obtain p i Determining global optimal candidate value p' g Rewrite speed update formula:
example 8:
in order to further explain the specific implementation process of the method of the present invention, the present embodiment takes a certain wind and light hydrogen storage system as an example to perform specific example analysis.
1. Example parameters
The structure of the wind-solar energy and light energy storage hydrogen production system is shown in figure 2, and the capacity of a wind turbine generator set is 700kW, and the capacity of a photovoltaic power station is 900kW. The upper limit of the power purchasing power of the system to a superior power grid is 400kW, the power grid sells power by adopting a peak-valley time-of-use power price mechanism, and specific data are shown in Table 1.
TABLE 1 time-of-use electricity price data of electric network
And the optimization model determines a daily scheduling plan by taking 1h as a time interval, a wind-solar combined power generation system model is established in MATLAB/Simulink according to the mathematical model given in S1, and the output power of the wind generating set and the photovoltaic system is calculated in the Simulink model by utilizing the wind speed, the temperature and the solar irradiation intensity data. The wind power and photovoltaic predicted output curves in the system are shown in fig. 3.
The operating parameters of each energy storage device in the system are shown in table 2.
TABLE 2 energy storage device operating parameters
The hydrogen load demand curve is shown in fig. 4, and the values of other parameters are shown in table 3.
TABLE 3 evaluation of other parameters
In order to evaluate the performance of the economic operation optimization model of the wind-solar hydrogen storage system and improve the effectiveness of an optimization algorithm, the algorithm in the invention adopts a self-adaptive simulated annealing particle swarm algorithm to solve in MATLAB software.
2. Analysis of example results
(1) Day-ahead operation plan of system and state of energy storage equipment
FIG. 5 is a schematic diagram of a day-ahead scheduling plan of a wind-solar energy storage hydrogen production system, wherein the daily operation cost of the system under the day-ahead operation plan is 9.61 multiplied by 10 3 . As shown in fig. 5, the electricity purchasing period of the power grid is 04:00-07: period 00, 04: time 00Segment power grid electricity purchasing 398kW,05: power purchase 400kW of a power grid in a period of 00 hours, 06: power purchase of a power grid in a period of 00 hours of 398kW,07: 398kW is carried out in the power purchasing period of 00 power grids; battery operating power 01:00-24: the 00 period is-35.2 kW, 217kW, 10kW, -472kW, 295kW, -141kW, -323kW, 0kW, 369kW, -178kW, 186kW, -140kW, -63.1kW, 180kW, 89.1kW, -473kW, -104kW, 343kW, 72.5kW, -78.6kW, 20.7kW, -23kW, -167kW, 59.3kW respectively; positive values indicate discharge, negative values indicate charging wind power 01:00-24: the 00 time period is 428kW, 417kW, 340kW, 308kW, 280kW, 315kW, 343kW, 298kW, 276kW, 292kW, 288kW, 298kW, 320kW, 124kW, 293kW, 258kW, 255kW, 263kW, 278kW, 235kW, 400kW, 432kW, 399kW, 235kW respectively. The photovoltaic power generation time period is 07:00-19:00, the photovoltaic power generation power is respectively 70kW, 206kW, 326kW, 446kW, 521kW, 596kW, 654kW, 667kW, 614kW, 517kW, 637kW, 196kW and 49.9kW.
FIG. 6 is a schematic diagram of hydrogen production rate of the electrolytic cell and hydrogen remaining amount of the hydrogen storage tank in the system. As can be seen from fig. 5 and 6, the electrolysis cell increases the electrolysis power in the time period when the wind and light output is large, the electricity price of the power grid is at the valley or the hydrogen load is low, and the hydrogen is replenished to the hydrogen storage tank after the hydrogen demand in the time period is met. For example 04:00-07: and in the 00 time period, when the electricity price of the power grid is in a valley state and the photovoltaic output is very low, the system buys a large amount of hydrogen produced by low-price electrically-driven electrolytic tank equipment from the power grid, and stores hydrogen energy for the subsequent increased hydrogen load demand. When the wind and light output is small, the electricity price of the power grid is high or the hydrogen demand of the load end is higher than the hydrogen production rate corresponding to the wind and light output, the hydrogen demand can be met by the residual hydrogen of the hydrogen storage tank and the electrolytic hydrogen production of the electrolytic cell. At 14:00-15: and 00, the phenomenon of wind abandonment exists in the time period, because the hydrogen demand of the load end is lower, the hydrogen allowance of the hydrogen storage tank is sufficient, the system does not need to drive an electrolytic cell to produce a large amount of hydrogen, and the electric energy is excessive and is consumed everywhere.
Fig. 7 is a schematic state of charge diagram of the energy storage device of the storage battery, and it can be seen from fig. 5 and 6 that the storage battery is charged to store energy when the hydrogen demand is low, the wind-solar output is too large, or the electricity price of the power grid is low; when the hydrogen demand is high, the wind and light output is insufficient or the electricity price of the power grid is peak, the electrolytic cell is maintained to work through the discharge of the storage battery. The storage battery plays a role in peak clipping and valley filling, and the energy storage equipment increases the stability and reliability of system operation.
(2) System operating cost analysis
The section calculates the operation cost of each part in the wind-solar hydrogen production system, adopts a self-adaptive simulated annealing particle swarm optimization (BSAPSO) to solve for 10 times, and then takes an average value as a final calculation result, and the result is shown in Table 4.
TABLE 4 running cost of each part of the system
As can be seen from table 1, under the energy storage parameters of the example, the system still has a certain wind and light abandoning phenomenon, which is mainly because the model cannot realize complete consumption of wind and light energy by selling electricity to the power grid, the system can only purchase electricity from the superior power grid but cannot sell redundant electric energy to the superior power grid, and the system does not have a function of bidirectional power exchange with the power grid. In addition, the limited capacity of the energy storage battery is also one of the reasons, and the capacity of the energy storage battery is 800kWh in the present example.
(3) Algorithmic comparison
Table 5 shows that the adaptive simulated annealing particle swarm optimization (BSAPSO) has a better effect when the objective function value of the standard PSO and the BSAPSO are compared when the model optimization result is solved.
TABLE 5 comparison of objective function values for two algorithms
The daily operation cost of the wind-solar hydrogen storage system calculated by the standard particle swarm algorithm is usually 1.41 multiplied by 10 4 The daily operation cost of the system calculated by the self-adaptive simulated annealing particle swarm algorithm is only about 1.02 multiplied by 10 4 The cost is saved by about 28 percent. This is because the former is trapped in the local optimal solution in the iterative computation process, and cannot jump out the local optimal solution to search the global optimal solution. A simulated annealing operator is added in the BSAPSO algorithm, the method has the characteristic of probability jump and can help jump out a local optimal solution in the search process. In addition, the global search capability of the algorithm is enhanced by the self-adaptive change of key parameters such as the inertia weight coefficient omega and the learning factor.
The above examples are merely illustrative of the present invention and should not be construed as limiting the scope of the invention, which is intended to be covered by the claims and any design similar or equivalent to the scope of the invention.
Claims (8)
1. A wind-solar energy storage hydrogen production system economical optimization scheduling method considering wind abandoning and light abandoning punishment is characterized by comprising the following steps:
s1: establishing mathematical models of all parts of the wind-solar energy storage hydrogen production system, wherein the mathematical models comprise a photovoltaic power generation model, a wind power generation model, a storage battery energy storage equipment model and an alkaline electrolytic cell model;
s2: considering wind and light abandoning punishment cost, obtaining daily operation cost of each part according to each part mathematical model of the wind-light storage hydrogen production system in the S1, taking the sum of the daily operation cost of the system as a target function, establishing an optimized scheduling model, and determining each constraint of the system during normal operation, wherein the constraint comprises a system power balance constraint condition, a storage battery energy storage system constraint, a power grid interactive power constraint and a hydrogen production constraint;
s3: performing optimization solution on the optimization scheduling model by adopting a self-adaptive simulated annealing particle swarm algorithm to obtain the power grid electricity purchasing power, the storage battery operation power, the wind power generation power, the photovoltaic power generation power and the electrolytic cell operation power of each time period corresponding to 24-hour hourly lowest daily operation cost in a typical day of the wind-solar hydrogen storage system;
s4: and setting the wind and light hydrogen production system according to the power grid electricity purchasing power, the storage battery running power, the wind power generation power, the photovoltaic power generation power and the electrolytic bath running power obtained in the step S3, and realizing the economic optimization scheduling of the wind and light hydrogen production system.
2. The economic optimization scheduling method of the wind-solar energy storage hydrogen production system considering the wind abandoning and light abandoning punishment of claim 1, wherein in S1, the photovoltaic power generation model and the wind power generation model are respectively as follows:
s11: photovoltaic power generation model:
in the formula (I), the compound is shown in the specification,
the output power of the photovoltaic array at the illumination intensity G (t); g STC 、T STC 、P STC Respectively representing the illumination intensity, the photovoltaic array temperature and the maximum output power in a standard test environment; k is temperature coefficient, k = -0.45; t (T) is the surface temperature of the photovoltaic array at time T;
s12: the wind power generation model comprises:
single wind turbine generator input wind speed v and predicted output power P in wind turbine generator model t The relationship of (c) is:
in the formula: v is the actual wind speed at the hub height of the wind turbine generator, m/s; v. of i The wind speed is cut in; v. of o Cutting out the wind speed; v. of n Rated wind speed; p n The rated power of the wind turbine is kW; rho is air density, kg/m 3 (ii) a R is the length of the blade of the wind turbine generator, m;
the total schedulable wind power predicted by the wind power plant in the t period is represented as follows:
in the formula: m is the total number of the wind turbine generators; p t j The predicted output power of the jth wind turbine generator set at the moment t is obtained; Δ t is the adjacent time period.
3. The economic optimization scheduling method of the wind-solar energy storage hydrogen production system considering the wind abandoning and light abandoning punishment of claim 1, wherein the storage battery energy storage device model and the alkaline electrolytic cell model in the S1 are respectively as follows:
s13: storage battery energy storage equipment model:
the storage battery device plays a role in peak clipping and valley filling, and the mathematical model of the storage battery device can be expressed as follows:
in the formula: e (t) is the total energy of the storage battery in the period t, kW.h; sigma is the self-discharge efficiency of the storage battery;
and
charging and discharging power, kW, of the storage battery device in a time period t respectively; eta ch And η dis Charging and discharging efficiencies for the battery device, respectively;
s14: alkaline cell model:
Q el ＝P el ·Δt·ρ·η el
in the formula: q el For hydrogen production, m 3 ；P el Inputting power, kW, for the alkaline electrolytic cell; delta t is the running time of the electrolytic cell, h; rho is the electrolytic cell electricity-hydrogen conversion parameter, m 3 /kW·h；η el The working efficiency of the electrolytic cell is high.
4. The wind-solar energy storage hydrogen production system economic optimization scheduling method considering wind abandoning and light abandoning punishment according to claim 1, wherein the establishment of the optimization scheduling model in S2 is as follows:
C day ＝C new +C pen +C ele +C sto +C grid +C com
in the formula, C day 、C new 、C pen 、C ele 、C sto 、C grid 、C com Scheduling the daily operation cost, the wind power and photovoltaic power station operation cost, the wind and light abandoning punishment cost, the alkaline electrolytic cell hydrogen production cost, the energy storage device maintenance cost, the power purchasing cost to the power grid and the hydrogen storage cost of a hydrogen storage tank for the system respectively;
wherein, the running cost C of the wind power and photovoltaic power station new ＝K w P w (t)+K pv P pv (t), wherein: k w 、K pv The running cost of the wind power station and the photovoltaic power station is unit/kW; p is w (t)、P pv (t) scheduling power, kW, of wind power and photovoltaic in a time period t respectively;
wind and light abandoning punishment cost
In the formula: k is a radical of w 、k pv Punishing cost coefficients of wind abandoning and light abandoning respectively;
hydrogen production cost of alkaline electrolytic cell C ele (t)＝P el (t)×C per_ele +C wate In the formula: p el (t) the input power of the alkaline electrolytic cell in the period of t is kW; c per_ele The operating cost of the alkaline electrolytic cell is yuan/kilowatt hour; c water For water consumption cost, yuan/Nm 3 ；
Maintenance cost C of storage battery energy storage device sto (t)＝P bat (t)×C per_bat In the formula, P bat (t) the charge/discharge power of the battery, P bat (t)＝E(t)/t；C per_bat Maintenance cost per unit electric quantity of the storage battery device, yuan/kilowatt hour;
hydrogen storage cost of hydrogen storage tank C com (t)＝Q el ×C per_com In the formula: q el For hydrogen production, m 3 ；C per_com Is the unit hydrogen storage cost per Nm of the hydrogen storage tank 3 ；
Cost C of power purchase of power grid grid (t)＝P grid (t)×C per_grid In the formula: p is grid (t) purchasing electric power, kW, from the power grid by the system at the time period t; c per_grid The method is time-sharing electricity price, yuan/kilowatt hour, of the power grid industrial electricity selling.
5. The wind-solar energy storage and hydrogen production system economic optimization scheduling method considering wind abandoning and light abandoning punishment according to claim 1, wherein the system power balance constraint condition, the storage battery energy storage system constraint, the power grid interaction power constraint and the hydrogen production constraint in the S2 are respectively as follows:
s221: system power balance constraint:
in the formula, P renew (t) the output of renewable energy, namely the maximum output power of the wind-solar hybrid power generation in a period of t, kW; p is bat (t) the charging/discharging power of the storage battery in the period of t, wherein the discharging power of the storage battery is a positive value, and the charging power is a negative value, kW; p grid (t) purchasing power, kW, from the power grid by the system in a period t; p is el (t) the input power of the alkaline electrolytic cell is kW in the period of t;
s222: and (3) constraint of a storage battery energy storage system:
the capacity of the storage battery is related to the capacity of the previous period and the charge-discharge power and the self-discharge amount in the previous period, and the state of charge of the storage battery in t period is as follows:
in the formula:
the state of charge of the storage battery at t time interval; sigma is the self-discharge rate of the storage battery; eta c Charge/discharge efficiency for the battery; e bat kW.h is the total capacity of the storage battery;
is the same as P bat (t)；
S223: power grid interaction power constraint:
in the formula:
and
purchasing minimum power and maximum power, kW, from a power grid respectively for a system;
s224: and (3) restricting the operation power of the electrolytic cell:
in the formula:
and
respectively in the production state of the alkaline electrolytic cellSmall and maximum operating power, kW;
s225: hydrogen production constraints
In the formula:
is hydrogen storage margin, nm, of the system during the period t 3 ；
Hydrogen production rate, nm, of alkaline electrolyzer for t time period 3 /h；
Hydrogen load on demand side for t period, nm 3 ；
Hydrogen gas balance, nm, of the system for the planned end period of the day-ahead operation 3 。
6. The method for optimizing and scheduling the economy of the wind-solar energy storage hydrogen production system considering the wind abandoning and light abandoning punishment of claim 5, wherein the method comprises the following steps: s222, the constraint of the storage battery energy storage system further comprises the constraint of upper and lower limits of charge and discharge power and the constraint of upper and lower limits of the charge state of the storage battery, and the constraint is as follows:
the upper and lower limits of the charge and discharge power of the storage battery are constrained as follows:
in the formula:
and
respectively setting a charging power limit value and a discharging power limit value, kW, of the storage battery;
the upper and lower limits of the charge state of the storage battery are constrained as follows:
in the formula:
and
respectively, a minimum state of charge value and a maximum state of charge value of the storage battery.
7. The wind-solar energy storage and hydrogen production system economic optimization scheduling method considering wind abandoning and light abandoning punishment according to claim 1, wherein the method comprises the following steps: the optimization solving process of the optimized scheduling model by adopting the self-adaptive simulated annealing particle swarm optimization is as follows:
step 1: setting initial parameters including a population size N, a maximum iteration number M, a variable dimension D, an initial temperature and a temperature attenuation coefficient;
step 2: inputting wind power, photovoltaic, energy storage batteries and electrolytic cell equipment parameters to restrict and generate initial particles, including particle positions and speeds;
and step 3: calculating the fitness value of each particle in the initial population, finding the optimal value of the objective function, and recording the individual optimal position and the global optimal position;
and 4, step 4: setting the initial temperature of simulated annealing, and adaptively changing omega and c 1 、c 2 (ii) a Omega is an inertia weight coefficient; c. C 1 Represents a self-cognition factor; c. C 2 Represents a social cognitive factor;
and 5: performing simulated annealing algorithm neighborhood search on the particles according to Metropolis criterion;
step 6: updating the optimal positions of all particles and the global optimal position of the population by adopting a speed updating formula;
and 7: judging whether the maximum iteration times is reached, if not, returning to the step S34; otherwise, stopping iteration;
and 8: and outputting the current optimal particles, namely the optimizing result, and terminating the algorithm.
8. The wind-solar energy storage and hydrogen production system economic optimization scheduling method considering wind abandoning and light abandoning punishment of claim 7, wherein: the speed updating formula in the step 6 is as follows:
in the formula (I), the compound is shown in the specification,
represents the velocity of the ith particle at the k +1 th iteration;
and
respectively representing the speed and the position of the ith particle at the kth iteration; omega is an inertia weight coefficient; c. C 1 Represents a self-cognition factor; c. C 2 Represents a social cognitive factor; r is 1 And r 2 Is a random number; p is a radical of ibest The individual optimal position of the ith particle.
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