CN115759360A - Two-stage optimization planning method, system and medium for wind-solar-hydrogen storage coupling system - Google Patents
Two-stage optimization planning method, system and medium for wind-solar-hydrogen storage coupling system Download PDFInfo
- Publication number
- CN115759360A CN115759360A CN202211344965.3A CN202211344965A CN115759360A CN 115759360 A CN115759360 A CN 115759360A CN 202211344965 A CN202211344965 A CN 202211344965A CN 115759360 A CN115759360 A CN 115759360A
- Authority
- CN
- China
- Prior art keywords
- hydrogen production
- hydrogen
- production equipment
- electrolytic
- hydrogen storage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 348
- 239000001257 hydrogen Substances 0.000 title claims abstract description 348
- 238000003860 storage Methods 0.000 title claims abstract description 148
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000008878 coupling Effects 0.000 title claims abstract description 33
- 238000010168 coupling process Methods 0.000 title claims abstract description 33
- 238000005859 coupling reaction Methods 0.000 title claims abstract description 33
- 238000013439 planning Methods 0.000 title claims abstract description 31
- 238000005457 optimization Methods 0.000 title claims description 96
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims abstract description 308
- 238000004519 manufacturing process Methods 0.000 claims abstract description 162
- 238000004088 simulation Methods 0.000 claims description 66
- 230000014509 gene expression Effects 0.000 claims description 20
- 230000009471 action Effects 0.000 claims description 16
- 230000005611 electricity Effects 0.000 claims description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000005868 electrolysis reaction Methods 0.000 claims description 3
- 238000012384 transportation and delivery Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 description 11
- 238000010248 power generation Methods 0.000 description 8
- 230000008901 benefit Effects 0.000 description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
- 229910052799 carbon Inorganic materials 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Landscapes
- Electrolytic Production Of Non-Metals, Compounds, Apparatuses Therefor (AREA)
Abstract
The invention relates to an energy system optimal configuration technology, in particular to a two-stage optimal planning method, a two-stage optimal planning system and a two-stage optimal planning medium for a wind-solar-hydrogen storage coupling system. According to the method provided by the invention, the two stages of the number of the electrolytic hydrogen production equipment and the capacity of the hydrogen storage tank are optimized for the wind-light-hydrogen storage coupling system, and the electrolytic hydrogen production equipment in the wind-light-hydrogen storage coupling system is accurately modeled, so that the characteristics of regional wind power photovoltaic resources are fully considered, the calculation precision and speed of the model are effectively guaranteed, and the optimized result can provide the most intuitive data support for the judgment and analysis of planners.
Description
Technical Field
The invention relates to an energy system optimal configuration technology, in particular to a two-stage optimal planning method, a two-stage optimal planning system and a two-stage optimal planning medium for a wind, light and hydrogen storage coupling system.
Background
In recent years, the progress of global energy transformation has been accelerated. In the aim of realizing double carbon of 'carbon peak reaching' and 'carbon neutralization', the energy is a main battlefield, and the electric power is a main force army. Under such a background, it is very important to construct a new power system mainly using new energy, and at present, a renewable energy distributed power generation system represented by photovoltaic power generation and wind power generation has been applied in a large scale. However, wind power and photovoltaic power generation rely on wind power, illumination and other resources with strong volatility, and cannot meet the requirement on stability of an energy supply system.
The hydrogen energy has the advantages of zero carbon, high energy density and super-long-term energy storage, and becomes an important technical means for solving the problem of power supply stability of a power grid in a deviation region. The wind-solar-hydrogen storage coupling system is constructed, the fluctuation of wind and light power generation can be effectively stabilized by optimizing the capacities of the electrolytic hydrogen production equipment and the hydrogen storage equipment, so that the new energy consumption capability is improved by a relatively economic means, and the problem of power supply reliability of a micro-grid system is solved. Therefore, the wind, light and hydrogen storage coupling system integrating wind power generation, photovoltaic power generation, electrolytic hydrogen production and hydrogen storage is widely concerned by academia and industry, and is built by a series of demonstration application devices. Wherein, it should be noted that, the alkaline electrolytic cell (ALK) is the only current device for large-scale commercial hydrogen production, taking into account the multidimensional indexes such as technical cost, capacity limit, degree of commercialization, etc.
In the existing capacity matching model of the electro-hydrogen coupling system, a photovoltaic and alkaline electrolysis cell electro-hydrogen integrated system optimization matching model is constructed in the literature 'design and operation integration optimization of wind-solar-hydrogen integrated energy system' (Vol. 43 power and energy, page 117 in No. 2), however, a distributed power supply is considered to be single, the benefit of a hydrogen storage tank is not considered, and the optimization modeling of a wind-solar-hydrogen storage fusion system cannot be adapted. A design and operation integrated optimization model of a wind-solar-hydrogen comprehensive energy system is constructed in the literature, namely capacity optimization configuration and day-ahead optimization scheduling of a wind-solar-hydrogen coupled power generation system (China electric power Vol. 53, no. 10, page 80), a capacity configuration scheme and a typical operation strategy of each device of the system are optimized, however, the operation characteristics of electrolytic hydrogen production equipment are not completely considered in the model, and the optimization result and the actual situation may have deviation. The literature, "Optimal Day-ahead Dispatch of An Alkaline electrolyte System centralized Thermal-electrical Properties and State-Transitional Dynamics" (Applied Energy volume 37, page 1180) considers factors such as operating cost, efficiency and life cycle, constructs a new Energy and hydrogen Energy integrated System Energy management multi-objective optimization model, but only considers the operating and efficiency characteristics of a single electrolytic cell, and does not consider a scene related to multi-electrolytic cell combined operation.
Disclosure of Invention
In view of the above, the first objective of the present invention is to provide a two-stage optimization planning method for a wind, light and hydrogen storage coupling system, which optimizes the wind, light and hydrogen storage coupling system in two stages, namely, the number of hydrogen production devices for electrolysis and the capacity of a hydrogen storage tank, so as to realize accurate modeling of the wind, light and hydrogen storage coupling system, fully consider regional wind and electricity photovoltaic resource characteristics, effectively ensure the calculation accuracy and speed of a model, and provide the most intuitive data support for planners to judge and analyze the optimization result.
Based on the same inventive concept, the second purpose of the invention is to provide a two-stage optimization planning system for a wind, light and hydrogen storage coupling system.
Based on the same inventive concept, a third object of the present invention is to provide a storage medium.
The first purpose of the invention can be achieved by the following technical scheme:
a wind-solar-hydrogen storage coupling system two-stage optimization planning method comprises the following steps:
acquiring wind and light resource data;
modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on the time sequence simulation;
modeling the hydrogen storage tank, constructing a hydrogen storage tank optimization model based on time sequence simulation, and calculating the capacity of the optimized hydrogen storage tank by using the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on the time sequence simulation;
and outputting the optimal capacity ratio.
Furthermore, the method for modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on time sequence simulation comprises the following steps:
establishing a logic state model of the electrolytic hydrogen production equipment;
setting integral constraints of a wind-solar-hydrogen optimization model based on time sequence simulation, wherein the integral constraints comprise integral equipment constraints of the electrolytic hydrogen production equipment, new energy output constraints and electric power balance constraints, and the integral equipment constraints of the electrolytic hydrogen production equipment comprise upper and lower power limit constraints of the electrolytic hydrogen production equipment, hydrogen production output constraints of the electrolytic hydrogen production equipment and logic state constraints of the electrolytic hydrogen production equipment;
setting a first-stage objective function by taking the maximum overall profit of the wind, light, electricity and hydrogen coupled system as an objective;
and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by adopting an optimization algorithm.
Further, the establishment of the state model of the electrolytic hydrogen production equipment comprises the following steps:
establishing an operation logic state model of the electrolytic hydrogen production equipment, dividing the operation state of the electrolytic hydrogen production equipment into three states of working, standby and idle, respectively using binary variables L, S and I to represent the working, standby and idle states, and when the value of the binary variable is 1, representing that the electrolytic hydrogen production equipment is in the operation state represented by the binary variable; when the value of the binary variable is 0, the electrolytic hydrogen production equipment is not in the operation state represented by the binary variable;
establishing an action logic state model of the electrolytic hydrogen production equipment, respectively using binary variables Y and Z to represent start-up and shut-down actions of the electrolytic hydrogen production equipment, and when the value of the binary variable is 1, representing that the electrolytic hydrogen production equipment is in an action state represented by the binary variable; when the value of the binary variable is 0, the electrolytic hydrogen production equipment is not in the action state represented by the binary variable;
and (3) establishing an input-output relation model of the action of the electrolytic hydrogen production equipment, wherein F represents the hydrogen yield of the alkaline electrolytic cell, and U represents the power consumed by the alkaline electrolytic cell.
Further, the logic state of the electrolytic hydrogen production equipment is constrained, and the expression is as follows:
wherein, L, S and I are binary variables respectively representing the working, standby and idle states of the electrolytic hydrogen production equipment, Y and Z are binary variables representing the starting and stopping actions of the electrolytic hydrogen production equipment, t represents a time index, the superscript represents the time of the electrolytic hydrogen production equipment, and the subscript n is an index of a module of the electrolytic hydrogen production equipment.
Further, the expression of the first stage objective function is:
wherein T is the total length of the simulation time, T is the time index, N is the number of the electrolytic hydrogen production equipment, N represents the module index of the electrolytic hydrogen production equipment, P h In order to achieve the selling price of the hydrogen,
representing the hydrogen production at time t of the nth electrolytic hydrogen production plant,P hinv for the investment cost of a single electrolytic hydrogen production plant, LT h In order to complete the life cycle of the electrolytic hydrogen production equipment,
and
respectively the start-up and shut-down states at time t of the nth alkaline cell module, P s And P d Respectively the startup cost and the shutdown cost of the alkaline electrolytic cell,
is the selling price of the electricity at the moment t of the large power grid,
the power provided for the large power grid at the moment t for the integrated system; and delta t is the simulation time step.
Further, modeling is carried out on the hydrogen storage tank, a hydrogen storage tank optimization model based on time sequence simulation is constructed, and the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on time sequence simulation are utilized to calculate the capacity of the optimized hydrogen storage tank:
setting integral constraints of a hydrogen storage tank optimization model based on time sequence simulation, wherein the integral constraints comprise integral equipment constraints of the hydrogen storage tank, new energy output constraints and electric power balance constraints, and the integral equipment constraints of the hydrogen storage tank comprise maximum capacity constraints of the hydrogen storage tank and dynamic capacity constraints of the hydrogen storage tank;
setting a second stage objective function with the maximum overall profit of the wind, light, electricity and hydrogen coupled system as an objective;
and calculating the optimized hydrogen storage tank loading capacity by adopting an optimization algorithm.
Further, the maximum capacity of the hydrogen storage tank is constrained, and the expression is as follows:
0≤Q t ≤Q max
wherein Q is t The hydrogen storage amount of the hydrogen storage tank at the time t; q max The maximum hydrogen storage capacity of the hydrogen storage tank;
the dynamic capacity of the hydrogen storage tank is restricted, and the expression is as follows:
wherein,
for the external delivery of hydrogen storage tanks, ∑ F t And represents the total hydrogen production of all the electrolytic hydrogen production equipment at the moment t.
Further, the expression of the second stage objective function is:
wherein T is the total length of the simulation time, T is the time index, N is the installed capacity of the optimized electrolytic hydrogen production equipment, N represents the module index of the electrolytic hydrogen production equipment, P h In order to achieve the selling price of the hydrogen,
represents the hydrogen production at time t of the nth electrolytic hydrogen production plant, P hinv For the investment cost of a single electrolytic hydrogen production plant, LT h For the full life cycle of an electrolytic hydrogen plant, P sinv Investment cost of hydrogen storage tank per unit volume, C s The gauge number of the hydrogen storage tank is set; LT (LT) s In order to ensure the full life cycle of the hydrogen storage tank,
and
respectively the start-up and shut-down states of the nth alkaline cell module at time t, P s And P d Respectively the startup cost and the shutdown cost of the alkaline electrolytic cell,
is the selling price of the electricity at the moment t of the large power grid,
the wind, solar and hydrogen storage coupling system provides power for a large power grid at the moment t; and delta t is the simulation time step.
The second purpose of the invention can be achieved by the following technical scheme:
a wind, light and hydrogen storage coupling system two-stage optimization planning system comprises:
the data acquisition module is used for acquiring wind and light resource data;
the wind-solar-hydrogen optimization model module is used for modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on the time sequence simulation;
the hydrogen storage tank optimization model module is used for modeling the hydrogen storage tank, constructing a hydrogen storage tank optimization model based on time sequence simulation, and calculating the capacity of the optimized hydrogen storage tank by using the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on the time sequence simulation;
and the output module is used for outputting the optimal capacity ratio.
The third purpose of the invention can be achieved by the following technical scheme:
a storage medium stores a program, and when the program is executed by a processor, the two-stage optimization planning method for the wind-solar-hydrogen storage coupling system is realized.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the invention, the optimization modeling of two stages of the number of the electrolytic hydrogen production equipment and the capacity of the hydrogen storage tank is carried out on the wind-solar-hydrogen storage coupling system, and compared with the model in the prior art, the variable scale in a single optimization process is reduced, so that the calculation speed of the model is improved.
(2) The invention adopts the fine modeling for the operation state of the electrolytic hydrogen production device and adopts the time sequence simulation method to carry out optimization planning, thereby improving the simulation precision of the wind-solar-hydrogen optimization model based on the time sequence simulation and reducing the error caused by the electrolytic hydrogen production device in the optimization planning method.
(3) The time sequence simulation method designed by the invention can fully consider the characteristics of regional new energy resources, simulate the actual operation condition of the power system by time intervals, including the starting and stopping condition of the alkaline electrolytic cell, the operation output, the hydrogen production quantity, the hydrogen storage quantity, the power interaction power with the main network and the like of each time section, is more comprehensive when boundary conditions are considered, and the calculation result is closer to the actual operation condition.
Drawings
FIG. 1 is a flowchart of a method of example 1 of the present invention;
FIG. 2 is a normalized annual wind power hourly output sequence of embodiment 1 of the present invention;
FIG. 3 is a normalized annual photovoltaic hourly power output sequence for example 1 of the present invention;
FIG. 4 is an operational schematic diagram of an electrolytic hydrogen production apparatus according to example 1 of the present invention;
FIG. 5 is a bar graph of the number relationship between the system benefit and the number of alkaline cells for the first stage objective function of example 1 of the present invention;
fig. 6 is a line graph of the system benefit of the second stage objective function of example 1 of the present invention as a function of the quantity between the scales of the hydrogen storage tanks.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
Example 1:
as shown in fig. 1, the embodiment provides an optimization planning method for a wind, light and hydrogen storage coupling system, which includes the following steps:
s100, acquiring wind and light resource data;
in this embodiment, the wind and light resource data is wind and light resource data of a certain area, and includes installed capacity of a wind power plant and installed capacity of a photovoltaic power plant, an annual wind power hourly output sequence and an annual photovoltaic hourly output sequence, and the data is normalized to obtain the total installed capacity of a new energy plant of the area, which is 40MW. The installed capacity of the wind power plant is 23MW, the installed capacity of the photovoltaic power station is 17MW, the hourly output sequence of the normalized annual wind power is shown in a graph 2, and the hourly output sequence of the normalized annual photovoltaic is shown in a graph 3.
S200, as shown in FIG. 4, modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity (namely the number of modules) of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on the time sequence simulation, wherein the method comprises the following steps:
s210, establishing a state model of the electrolytic hydrogen production equipment, comprising the following steps:
s211, establishing an operation logic state model of the electrolytic hydrogen production equipment, dividing the operation state of the electrolytic hydrogen production equipment into three states of working, standby and idle, respectively representing the working, standby and idle states by using binary variables L, S and I, and when the value of the binary variable is 1, representing that the electrolytic hydrogen production equipment is in the operation state represented by the binary variable; when the value of the binary variable is 0, the electrolytic hydrogen production equipment is not in the operation state represented by the binary variable; for example, when the alkaline electrolyzer equipment is in operation, L =1; otherwise, L =0.
S212, establishing an action logic state model of the electrolytic hydrogen production equipment, respectively representing start-up and stop actions of the electrolytic hydrogen production equipment by using binary variables Y and Z, and representing that the electrolytic hydrogen production equipment is in an action state represented by the binary variables when the value of the binary variables is 1; when the value of the binary variable is 0, it indicates that the electrolytic hydrogen production apparatus is not in the operation state indicated by the binary variable. For example, when the alkaline electrolytic cell is shifted from the standby state to the operating state, Y =1,z =0.
S213, establishing an operation input and output relation model of the electrolytic hydrogen production equipment, wherein F represents the hydrogen yield of the alkaline electrolytic cell, and U represents the power consumed by the alkaline electrolytic cell.
S220, setting integral constraints of a wind-solar-hydrogen optimization model based on time sequence simulation, wherein the integral constraints comprise integral equipment constraints of the electrolytic hydrogen production equipment, new energy output constraints and electric power balance constraints, and the integral equipment constraints of the electrolytic hydrogen production equipment comprise upper and lower power limit constraints of the electrolytic hydrogen production equipment, hydrogen production output constraints of the electrolytic hydrogen production equipment and logic state constraints of the electrolytic hydrogen production equipment;
in this embodiment, the expression of the new energy output constraint is:
wherein,
the output at the moment t of the wind power plant,
theoretical output of the wind power plant at the time t;
the output at the moment t of the photovoltaic power station,
the theoretical output of the photovoltaic power station at the moment t is obtained.
In this embodiment, the expression of the electric power balance constraint is:
wherein,
represents the nth electricityThe operating power of the hydrogen production equipment at the moment t is decomposed,
and the power provided for the large power grid at the moment t for the integrated system.
In this embodiment, the expression of the restriction on the upper and lower power limits of the electrolytic hydrogen production equipment is:
wherein,
represents the operating power of the nth electrolytic hydrogen production device in a standby state,
represents the minimum operating power of the nth electrolytic hydrogen production device in the working state,
represents the maximum operating power of the nth electrolytic hydrogen production device in the working state,
represents the current power of the nth electrolytic hydrogen plant,
showing a standby state of the nth electrolytic hydrogen production equipment at the moment t;
showing the working state of the nth electrolytic hydrogen production equipment at the moment t.
In this embodiment, the hydrogen production output constraint expression of the electrolytic hydrogen production equipment is as follows:
wherein,
representing the hydrogen production at time t of the nth electrolytic hydrogen production plant, a and b being common parameters.
In this embodiment, the expression of the constraint on the logic state of the electrolytic hydrogen production equipment is as follows:
wherein L, S and I are binary variables respectively representing the working, standby and idle states of the electrolytic hydrogen production equipment, Y and Z are binary variables representing the starting and stopping actions of the electrolytic hydrogen production equipment, t represents a time index, the superscript represents the time of the electrolytic hydrogen production equipment, and the subscript n represents the index of a module of the electrolytic hydrogen production equipment.
S230, setting a first-stage objective function with the maximum overall profit of the wind, light, electricity and hydrogen coupling system as an objective;
in this embodiment, the expression of the first-stage objective function is:
wherein T is the total length of the simulation time, T is the time index, N is the number of electrolytic hydrogen production equipment, N represents the module index of the electrolytic hydrogen production equipment, and P h In order to achieve the selling price of the hydrogen,
represents the hydrogen production at time t of the nth electrolytic hydrogen production plant, P hinv For the investment cost of a single electrolytic hydrogen production plant, LT h In order to complete the life cycle of the electrolytic hydrogen production equipment,
and
respectively the start-up and shut-down states of the nth alkaline cell module at time t, P s And P d Respectively the start-up cost and the shutdown cost of the alkaline electrolytic cell,
is the selling price of the electricity at the moment t of the large power grid,
the power provided for the large power grid at the moment t for the integrated system; and delta t is the simulation time step.
And S240, calculating and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by adopting an optimization algorithm.
In this example, the hydrogen production equipment is an alkaline electrolyzer, and the module parameters are shown in table 1:
TABLE 1 alkaline electrolyser module parameters
The electricity prices in each time period of the region are shown in table 2:
TABLE 2 electricity prices at each time period
In this embodiment, the simulation time step is 1h, and the simulation result of step S200 is shown in fig. 5.
S300, modeling is carried out on the hydrogen storage tank, a hydrogen storage tank optimization model based on time sequence simulation is constructed, and the installed capacity of the optimized electrolytic hydrogen production equipment and the capacity of the optimized hydrogen storage tank are calculated by utilizing the hydrogen storage tank optimization model based on the time sequence simulation, and the method comprises the following steps:
s310, setting integral constraints of a hydrogen storage tank optimization model based on time sequence simulation, wherein the integral constraints comprise integral equipment constraints of the hydrogen storage tank, new energy output constraints and electric power balance constraints, and the integral equipment constraints of the hydrogen storage tank comprise maximum capacity constraints of the hydrogen storage tank and dynamic capacity constraints of the hydrogen storage tank;
in this embodiment, the maximum capacity of the hydrogen storage tank is constrained by the expression:
0≤Q t ≤Q max
wherein Q is t The hydrogen storage amount of the hydrogen storage tank at the time t; q max The maximum hydrogen storage capacity of the hydrogen storage tank;
in this embodiment, the dynamic capacity of the hydrogen storage tank is constrained according to the expression:
wherein,
for the external delivery of hydrogen storage tanks, ∑ F t And represents the total hydrogen production of all the electrolytic hydrogen production equipment at the moment t.
The new energy output constraint and the electric power balance constraint in step S310 of the present embodiment have the same expressions as the corresponding constraints in step S210, and are not described herein again.
S320, setting a second stage objective function with the maximum overall profit of the wind, light, electricity and hydrogen coupling system as an objective;
in this embodiment, the expression of the second stage objective function is:
wherein T is the total length of the simulation time, T is the time index, N is the installed capacity of the optimized electrolytic hydrogen production equipment, N represents the module index of the electrolytic hydrogen production equipment, P h In order to achieve the selling price of the hydrogen,
represents the hydrogen production at time t, P, of the nth electrolytic hydrogen production plant hinv For the investment cost of a single electrolytic hydrogen production plant, LT h For the full life cycle of an electrolytic hydrogen plant, P sinv Investment cost of hydrogen storage tank per unit volume, C s The gauge number of the hydrogen storage tank is set; LT (LT) s In order to ensure the full life cycle of the hydrogen storage tank,
and
respectively the start-up and shut-down states of the nth alkaline cell module at time t, P s And P d Respectively the start-up cost and the shutdown cost of the alkaline electrolytic cell,
is the selling price of the electricity at the moment t of the large power grid,
the wind, solar and hydrogen storage coupling system provides power for a large power grid at the moment t; and delta t is the simulation time step.
And S330, calculating and calculating the optimized hydrogen storage filling capacity by adopting an optimization algorithm.
In this embodiment, the simulation time step is 1h, and the simulation result of step S300 is shown in fig. 6.
And S400, outputting the optimal capacity ratio.
In summary, in the embodiment, the wind-solar-hydrogen storage coupling system is subjected to optimization modeling in two stages, namely the number of the hydrogen production devices and the capacity of the hydrogen storage tank, and compared with the model in the prior art, the variable scale in a single optimization process is reduced, so that the calculation speed of the model is increased; in the embodiment, the operation state of the electrolytic hydrogen production device is finely modeled, and the time sequence simulation method is adopted for optimization planning, so that the simulation precision of the wind-solar-hydrogen optimization model based on time sequence simulation is improved, and the error caused by the electrolytic hydrogen production device in the optimization planning method is reduced; the time sequence simulation method designed by the embodiment can fully consider the characteristics of regional new energy resources, simulate the actual operation condition of the power system by time intervals, including the starting and stopping condition of the alkaline electrolytic cell, the operation output, the hydrogen production amount, the hydrogen storage amount, the power interaction power with the main network and the like of each time section, is more comprehensive when boundary conditions are considered, and the calculation result is closer to the actual operation condition.
Example 2:
the embodiment provides a two-stage optimization planning system of a wind, light and hydrogen storage coupling system, which comprises:
the data acquisition module is used for acquiring wind and light resource data;
the wind-solar-hydrogen optimization model module is used for modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on the time sequence simulation;
the hydrogen storage tank optimization model module is used for modeling the hydrogen storage tank, constructing a hydrogen storage tank optimization model based on time sequence simulation, and calculating the capacity of the optimized hydrogen storage tank by using the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on the time sequence simulation;
and the output module is used for outputting the optimal capacity ratio.
That is to say, in the above modules of this embodiment, the data obtaining module is used to implement step S100 in embodiment 1 of the present invention; the wind-solar-hydrogen optimization model module is used for realizing the step S200 of the embodiment 1, and the hydrogen storage tank optimization model module is used for realizing the step S300 of the embodiment 1; the output module is used to implement step S400 of embodiment 1. Since steps S100 to S400 have been described in detail in embodiment 1, for the sake of brevity of description, the detailed implementation process of each module in this embodiment is referred to in embodiment 1, and is not described again.
Example 3:
the embodiment provides a storage medium, which stores a program, and when the program is executed by a processor, the method for implementing the two-stage optimization planning of the wind, photovoltaic and hydrogen storage coupling system in the embodiment 1 of the present invention includes the following steps:
a wind, light and hydrogen storage coupling system two-stage optimization planning method comprises the following steps:
acquiring wind and light resource data;
modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on the time sequence simulation;
modeling the hydrogen storage tank, constructing a hydrogen storage tank optimization model based on time sequence simulation, and calculating the capacity of the optimized hydrogen storage tank by using the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on the time sequence simulation;
and outputting the optimal capacity ratio.
It should be noted that the computer readable storage medium of the present embodiment may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this embodiment, however, a computer readable signal medium may include a propagated data signal with a computer readable program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable storage medium may be written with a computer program for implementing the present embodiment in one or more programming languages, including an object oriented programming language such as Java, python, C + +, and conventional procedural programming languages, such as C, or similar programming languages, or a combination thereof. The program may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be understood that the embodiments described above are only a few embodiments of the present invention, and the present invention is not limited to the details of the above embodiments, and that any suitable changes or modifications by one of ordinary skill in the art may be made without departing from the scope of the present invention.
Claims (10)
1. A wind, light and hydrogen storage coupling system two-stage optimization planning method is characterized by comprising the following steps:
acquiring wind and light resource data;
modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on the time sequence simulation;
modeling the hydrogen storage tank, constructing a hydrogen storage tank optimization model based on time sequence simulation, and calculating the capacity of the optimized hydrogen storage tank by using the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on the time sequence simulation;
and outputting the optimal capacity ratio.
2. The two-stage optimization planning method for the wind, light and hydrogen storage coupling system according to claim 1, wherein the method comprises the following steps of modeling the hydrogen production equipment, constructing a wind, light and hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized hydrogen production equipment by using the wind, light and hydrogen optimization model based on time sequence simulation:
establishing a state model of electrolytic hydrogen production equipment;
setting integral constraints of a wind-solar-hydrogen optimization model based on time sequence simulation, wherein the integral constraints comprise integral equipment constraints of the electrolytic hydrogen production equipment, new energy output constraints and electric power balance constraints, and the integral equipment constraints of the electrolytic hydrogen production equipment comprise upper and lower power limit constraints of the electrolytic hydrogen production equipment, hydrogen production output constraints of the electrolytic hydrogen production equipment and logic state constraints of the electrolytic hydrogen production equipment;
setting a first-stage objective function by taking the maximum overall profit of the wind, light, electricity and hydrogen coupled system as an objective;
and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by adopting an optimization algorithm.
3. The wind, light, hydrogen storage and coupling system two-stage optimization planning method of claim 2, wherein the establishment of the state model of the electrolytic hydrogen production equipment comprises the following steps:
establishing an operation logic state model of the electrolytic hydrogen production equipment, dividing the operation state of the electrolytic hydrogen production equipment into three states of working, standby and idle, respectively using binary variables L, S and I to represent the working, standby and idle states, and when the value of the binary variable is 1, representing that the electrolytic hydrogen production equipment is in the operation state represented by the binary variable; when the value of the binary variable is 0, the electrolytic hydrogen production equipment is not in the operation state represented by the binary variable;
establishing an action logic state model of the electrolytic hydrogen production equipment, respectively using binary variables Y and Z to represent start-up and stop actions of the electrolytic hydrogen production equipment, and when the value of the binary variable is 1, representing that the electrolytic hydrogen production equipment is in an action state represented by the binary variable; when the value of the binary variable is 0, the electrolytic hydrogen production equipment is not in the action state represented by the binary variable;
and (3) establishing an input-output relation model of the action of the electrolytic hydrogen production equipment, wherein F represents the hydrogen yield of the alkaline electrolytic cell, and U represents the power consumed by the alkaline electrolytic cell.
4. The wind, light, hydrogen storage and coupling system two-stage optimization planning method of claim 3, wherein the electrolysis hydrogen production equipment is constrained in logic state, and the expression is as follows:
wherein, L, S and I are binary variables respectively representing the working, standby and idle states of the electrolytic hydrogen production equipment, Y and Z are binary variables representing the starting and stopping actions of the electrolytic hydrogen production equipment, t represents a time index, the superscript represents the time of the electrolytic hydrogen production equipment, and the subscript n is an index of a module of the electrolytic hydrogen production equipment.
5. The wind, solar and hydrogen storage coupled system two-stage optimization planning method according to claim 4, wherein the expression of the first-stage objective function is as follows:
wherein T is the total length of the simulation time, T is the time index, N is the number of the electrolytic hydrogen production equipment, N represents the module index of the electrolytic hydrogen production equipment, P h In order to achieve the selling price of the hydrogen,
represents the hydrogen production at time t of the nth electrolytic hydrogen production plant, P hinv For the investment cost of a single electrolytic hydrogen production plant, LT h For the full life cycle of the electrolytic hydrogen production plant,
and
respectively the start-up and shut-down states at time t of the nth alkaline cell module, P s And P d Respectively the startup cost and the shutdown cost of the alkaline electrolytic cell,
is the selling price of the large power grid at the time t,
the power is provided for the integrated system to the large power grid at the moment t; and delta t is the simulation time step.
6. The wind, solar and hydrogen storage coupling system two-stage optimization planning method according to claim 1, wherein modeling is performed on the hydrogen storage tank, a hydrogen storage tank optimization model based on time sequence simulation is constructed, and the optimized hydrogen storage tank capacity is calculated by using the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on time sequence simulation:
setting integral constraints of a hydrogen storage tank optimization model based on time sequence simulation, wherein the integral constraints comprise integral equipment constraints of the hydrogen storage tank, new energy output constraints and electric power balance constraints, and the integral equipment constraints of the hydrogen storage tank comprise maximum capacity constraints of the hydrogen storage tank and dynamic capacity constraints of the hydrogen storage tank;
setting a second stage objective function with the maximum overall profit of the wind, light, electricity and hydrogen coupled system as an objective;
and calculating the optimized hydrogen storage tank loading capacity by adopting an optimization algorithm.
7. The wind, solar and hydrogen storage coupled system two-stage optimization planning method of claim 6, wherein the maximum capacity of the hydrogen storage tank is constrained by an expression:
0≤Q t ≤Q max
wherein Q t The hydrogen storage amount of the hydrogen storage tank at the time t; q max The maximum hydrogen storage capacity of the hydrogen storage tank;
the dynamic capacity of the hydrogen storage tank is restricted, and the expression is as follows:
wherein,
for the external delivery of hydrogen storage tanks, ∑ F t Represents the total hydrogen production of all electrolytic hydrogen production plants at time t.
8. The wind, solar and hydrogen storage coupled system two-stage optimization planning method according to claim 7, wherein the expression of the second-stage objective function is as follows:
wherein T is the total length of the simulation time, T is the time index, N is the installed capacity of the optimized electrolytic hydrogen production equipment, N represents the module index of the electrolytic hydrogen production equipment, P h In order to achieve the selling price of the hydrogen,
represents the hydrogen production at time t of the nth electrolytic hydrogen production plant, P hinv For the investment cost of a single electrolytic hydrogen production plant, LT h For the full life cycle of an electrolytic hydrogen plant, P sinv Investment cost of hydrogen storage tank per unit volume, C s The gauge number of the hydrogen storage tank is set; LT (LT) s The life cycle of the hydrogen storage tank is as follows,
and
respectively the start-up and shut-down states at time t of the nth alkaline cell module, P s And P d Respectively the start-up cost and the shutdown cost of the alkaline electrolytic cell,
is the selling price of the electricity at the moment t of the large power grid,
the wind, solar and hydrogen storage coupling system provides power for a large power grid at the moment t; and delta t is the simulation time step.
9. A wind, light and hydrogen storage coupling system two-stage optimization planning system is characterized by comprising:
the data acquisition module is used for acquiring wind and light resource data;
the wind-solar-hydrogen optimization model module is used for modeling the electrolytic hydrogen production equipment, constructing a wind-solar-hydrogen optimization model based on time sequence simulation, and calculating the installed capacity of the optimized electrolytic hydrogen production equipment by using the wind-solar-hydrogen optimization model based on the time sequence simulation;
the hydrogen storage tank optimization model module is used for modeling the hydrogen storage tank, constructing a hydrogen storage tank optimization model based on time sequence simulation, and calculating the capacity of the optimized hydrogen storage tank by using the installed capacity of the optimized electrolytic hydrogen production equipment and the hydrogen storage tank optimization model based on the time sequence simulation;
and the output module is used for outputting the optimal capacity ratio.
10. A storage medium storing a program, wherein the program, when executed by a processor, implements the two-stage optimization planning method for the wind, solar and hydrogen storage coupled system according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211344965.3A CN115759360A (en) | 2022-10-31 | 2022-10-31 | Two-stage optimization planning method, system and medium for wind-solar-hydrogen storage coupling system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211344965.3A CN115759360A (en) | 2022-10-31 | 2022-10-31 | Two-stage optimization planning method, system and medium for wind-solar-hydrogen storage coupling system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115759360A true CN115759360A (en) | 2023-03-07 |
Family
ID=85354441
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211344965.3A Pending CN115759360A (en) | 2022-10-31 | 2022-10-31 | Two-stage optimization planning method, system and medium for wind-solar-hydrogen storage coupling system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115759360A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117477615A (en) * | 2023-12-28 | 2024-01-30 | 国网浙江省电力有限公司电力科学研究院 | Optimal configuration method and equipment for electric-hydrogen composite energy storage system |
-
2022
- 2022-10-31 CN CN202211344965.3A patent/CN115759360A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117477615A (en) * | 2023-12-28 | 2024-01-30 | 国网浙江省电力有限公司电力科学研究院 | Optimal configuration method and equipment for electric-hydrogen composite energy storage system |
| CN117477615B (en) * | 2023-12-28 | 2024-03-26 | 国网浙江省电力有限公司电力科学研究院 | Optimal configuration method and equipment for electric-hydrogen composite energy storage system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ju et al. | A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator | |
| Zhang et al. | Bi-level optimization dispatch of integrated-energy systems with P2G and carbon capture | |
| Zhang et al. | Optimal energy and reserve scheduling in a renewable-dominant power system | |
| Qiu et al. | Extended load flexibility of utility-scale P2H plants: Optimal production scheduling considering dynamic thermal and HTO impurity effects | |
| CN114362218B (en) | Scheduling method and device for multi-type energy storage in micro-grid based on deep Q learning | |
| CN115759360A (en) | Two-stage optimization planning method, system and medium for wind-solar-hydrogen storage coupling system | |
| CN116540545A (en) | Photovoltaic power generation hydrogen production cluster random optimization scheduling method based on ember process | |
| Zhu et al. | Energy optimal dispatch of the data center microgrid based on stochastic model predictive control | |
| Zantye et al. | THESEUS: A techno-economic design, integration and downselection framework for energy storage | |
| Qiu et al. | Multistage mixed-integer robust optimization for power grid scheduling: An efficient reformulation algorithm | |
| CN117060491B (en) | Operation optimization method, system, medium and equipment of wind-solar hybrid energy storage system | |
| CN114465226A (en) | Method for establishing multi-level standby acquisition joint optimization model of power system | |
| CN116562432A (en) | Novel power system planning-oriented source network load storage collaborative planning method and system | |
| CN115776138A (en) | Micro-grid capacity planning method and device considering multi-dimensional uncertainty and energy management strategy | |
| CN115425650A (en) | Power supply station microgrid configuration method, device, equipment and medium | |
| Xu et al. | A two-layer multi-energy management system for microgrids with solar, wind, and geothermal renewable energy | |
| Si et al. | Modeling the Heat-Hydrogen Balance Characteristic of Hydrogen Energy Storage and Cooperative Dispatch of Wind-Hydrogen Hybrid System | |
| Saadaoui et al. | Hybridization and energy storage high efficiency and low cost | |
| Han et al. | Research on the Flexibility Margin of an Electric–Hydrogen Coupling Energy Block Based on Model Predictive Control | |
| Su et al. | A novel data-driven robust method applied to OPF with uncertain wind power | |
| Ye et al. | Multi-Time Scale Collaborative Optimization of Distribution Network Based on Photovoltaic Generation Clusters | |
| Zhang et al. | An optimal capacity allocation method for integrated energy systems considering uncertainty | |
| CN118572703B (en) | Step water storage wind-solar-fire planning operation method, system, equipment and medium | |
| US20240362547A1 (en) | Operation plan creation device, operation plan creation method, operation plan creation program, and method for producing hydrogen | |
| Ge et al. | Monthly unit commitment with the consideration of wind farms correlation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |