Method and device for creating collaborative optimization model of low-carbon hydrogen production system

Abstract

The present invention belongs to the technical field of power systems, and discloses a method and device for creating a collaborative optimization model of a low-carbon hydrogen production system. The method includes: dividing various types of generator sets and hydrogen production devices in the target low-carbon hydrogen production system into block groups to obtain a plurality of block groups; constructing continuous variables representing the aggregated behavior of the generator sets in the block groups, and establishing a generator set model based on the continuous variables; wherein the continuous variables represent the online capacity and start-stop capacity of the block groups; obtaining a piecewise linear function by performing piecewise linearization processing on the electrolytic hydrogen production efficiency function, and establishing an electrolytic hydrogen production device model based on the piecewise linear function; establishing a collaborative optimization model of the low-carbon hydrogen production system based on the generator set model, the electrolytic hydrogen production device model, cost constraints and additional constraints. It solves the problems of difficult creation of collaborative optimization models and poor model accuracy in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly relates to a method and device for creating a collaborative optimization model of a low-carbon hydrogen production system. Background Art

[0002] With the development of renewable energy, the global installed capacity of wind power and photovoltaic has grown rapidly. The spatio-temporal distribution differences between the output of renewable energy and the load demand have led to the problems of wind curtailment and light curtailment. As a clean secondary energy source, hydrogen energy has the advantages of being clean, efficient, convenient for transportation and storage, etc., and can achieve long-term energy storage; electrolytic hydrogen production is a commonly used and relatively convenient hydrogen production method, which can achieve efficient energy conversion and does not produce secondary pollution. In a new power system containing a large amount of renewable energy, making full use of curtailed wind and light for electrolytic hydrogen production can not only maximize the consumption of renewable energy and improve the phenomena of wind curtailment and light curtailment, but also the produced hydrogen can be used for hydrogen fuel cell power generation to play a role in peak shaving and valley filling.

[0003] In the power system optimization model, due to the need to consider the start-stop problems of individual units, its collaborative optimization problem is usually a difficult-to-handle mixed-integer programming problem (MILP). Mixed-integer programming is an NP-hard problem. As the scale of the mathematical model expands, its calculation time and solution time will increase exponentially with the increase of discrete variables. When performing long-term scale optimization of a large-scale power system, there is often a problem of too long calculation time. Currently, most of the research on the problem of too long MILP solution time focuses on how to improve existing algorithms and simplify scenarios to reduce the number of discrete variables, etc. Although these studies can reduce the calculation time to a certain extent, on the one hand, the mixed-integer nature of the model has not changed, and the optimization problem is still an NP-hard problem. On the other hand, simplifying scenarios often affects the accuracy and usability of the optimization results. In addition, in a low-carbon hydrogen production system, the electro-hydrogen efficiency function of the electrolytic hydrogen production device is also non-linear. Since it is difficult to describe the mapping relationship of non-linear functions in the optimization model, this causes certain difficulties for the establishment of a collaborative optimization model.

[0004] Therefore, providing a method and device for creating a collaborative optimization model of a low-carbon hydrogen production system to solve the technical problems of difficult creation of the collaborative optimization model and poor model accuracy has become an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] The embodiments of the present invention provide a method and device for creating a collaborative optimization model of a low-carbon hydrogen production system to solve the problems of difficult creation of collaborative optimization models and poor model accuracy in the prior art. To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a preamble to the subsequent detailed description.

[0006] According to the first aspect of the embodiments of the present invention, a method for creating a collaborative optimization model of a low-carbon hydrogen production system is provided.

[0007] In some embodiments, the method includes:

[0008] Dividing various types of generator sets and hydrogen production devices in the target low-carbon hydrogen production system into block groups to obtain a plurality of block groups;

[0009] Constructing continuous variables representing the aggregated behavior of the generator sets in the block group and establishing a generator set model based on the continuous variables; wherein, the continuous variables represent the online capacity and start-stop ability of the block group;

[0010] Obtaining a piecewise linear function by performing piecewise linearization on the electrolytic hydrogen production efficiency function, and establishing an electrolytic hydrogen production device model based on the piecewise linear function;

[0011] Based on the generator set model, the electrolytic hydrogen production device model, cost constraints, and additional constraints, establishing a collaborative optimization model of the low-carbon hydrogen production system.

[0012] Optionally, the constructing continuous variables representing the aggregated behavior of the generator sets in the block group and establishing a generator set model based on the continuous variables specifically includes:

[0013] Introducing discrete variables representing the aggregated behavior of the generator sets in the block group;

[0014] Performing continuous processing on the discrete variables to obtain continuous variables, representing the overall operating state of the generator sets in the block group;

[0015] Establishing a generator set model based on the continuous variables.

[0016] Optionally, the step of introducing discrete variables representing the aggregated behavior of the generator sets in the block group includes: the expression of the discrete variables is:

[0017]

[0018]

[0019]

[0020] Among them, and respectively represent the online capacity, start-up capacity, and shutdown capacity of block group k at time t. J represents the total number of generator sets in a certain block group. X t represents the operating state of the unit, and S t represents the start-up behavior of the unit, and U t represents the shutdown behavior of the unit. represents the nameplate capacity of the unit.

[0021] Optionally, the step of obtaining continuous variables by continuousizing the discrete variables and representing the overall operating state of the generator sets in the block group includes:

[0022] When using three continuous variables to approximately represent the discrete variables, the continuous variables need to satisfy the following constraint conditions:

[0023]

[0024] Among them, represents the total capacity of block group k, respectively represent the online capacity, start-up capacity, and shutdown capacity of block group k at time t.

[0025] Optionally, the step of obtaining a piecewise linear function by piecewise linearizing the electrolytic hydrogen production efficiency function includes: when multiple electrolytic hydrogen production devices in the block group are in a synchronous operation mode, the expression of the piecewise linear function is:

[0026] x k,t,1 = x k,xk,2 =... = x k,t,J

[0027] Among them, x k,t,n is the continuous variable of block group k at time t, and n is 1, 2..., J.

[0028] Optionally, the step of obtaining a piecewise linear function by piecewise linearizing the electrolytic hydrogen production efficiency function further includes that when multiple electrolytic hydrogen production devices in the block group are in an asynchronous operation mode, the expression of the piecewise linear function is:

[0029]

[0030] Among them, x H,t,j and x h,t,j+1 are special ordered set variables used as linearization weighting factors. h represents the hth ordered set variable, H represents the total number of ordered set variables, and bin t,j+1Indicates the switching state of the (j + 1)-th electrolytic hydrogen production device in the block group at time t.

[0031] Optionally, when multiple electrolytic hydrogen production devices in the block group are in an asynchronous operation mode, in the expression of the piecewise linear function, x h,t,j The constraint conditions to be satisfied include:

[0032]

[0033]

[0034] x h,t,j ≥ 0

[0035]

[0036] x h,t,j + x h-1,t,j ≥ 0 cannot hold continuously

[0037] Wherein, and respectively represent the efficiency and load of the h-th point, P w,t,j and P s,t,j respectively represent the wind curtailment amount and light curtailment amount input to the j-th electrolyzer at time t in the block group, η sys,t,j represents the hydrogen production efficiency of the j-th electrolytic hydrogen production device in the block group at time t, EC cap represents the capacity of the electrolyzer, x h,t,j is a special ordered set variable as a linearization weighting factor, bin t,j represents the switching state of the j-th electrolytic hydrogen production device in the block group at time t, and J represents the total number of electrolyzers.

[0038] Optionally, the objective function of the collaborative optimization model is:

[0039] min C f + C v + C h

[0040] Wherein, C f represents the daily operation and maintenance cost of the units in block group k; C v represents the fuel cost and start-stop cost of the units, C h represents the hydrogen production cost.

[0041] Optionally, the expression of the daily operation and maintenance cost of the units in block group k is:

[0042]

[0043] In the formula, and respectively represent the operation and maintenance costs of various thermal power units, wind turbines, and photovoltaic units within block group k; represents the total installed capacity of the i-th type of thermal power unit; respectively represent the total installed capacities of wind turbines and photovoltaic units within block group k.

[0044] Optionally, the expressions for the fuel cost and start-stop cost of the unit are:

[0045]

[0046] In the formula, represents the fuel cost of thermal power units within block group k; represents the start-up cost of thermal power units within the k-th block group, represents the output power of the i-th type of thermal power unit within the k-th block group at time t, represents the start-up capacity of the i-th type of thermal power unit within the k-th block group at time t, N represents the total number of types of thermal power units, and T represents the overall operation duration.

[0047] Optionally, the expression for the hydrogen production cost of the unit is:

[0048]

[0049] In the formula, C capital,k represents the investment cost of the electrolytic hydrogen production device within block group k, C wat,k represents the cost of purchasing water for all electrolytic devices within block group k, C 0&M represents the operation and maintenance cost of the electrolytic hydrogen production device, R o,k and R heat,k represent the income obtained by selling the by-products (oxygen and heat) of the electrolytic hydrogen production process within block group k, EC cap,k represents the total capacity of the electrolytic devices within block group k, A hyd,k represents the total hydrogen production within block group k.

[0050] Optionally, the additional constraints include at least one of the following:

[0051] Power generation power and installed capacity constraints, flexibility constraints, system power balance constraints, and hydrogen production system constraints.

[0052] Optionally, the expression for the power generation power and installed capacity constraints is:

[0053]

[0054]

[0055]

[0056]

[0057] In the formula, represents the output power of the i-th type of thermal unit in block group k at time t, respectively represent the output powers of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, and α t,k and β t,k represent the hourly capacity factors of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, and represent the capacity credits of wind energy and solar energy in block group k, and D max,k represents the maximum load of block group k, and N represents the total number of types of thermal units.

[0058] Optionally, the expression of the flexibility constraint is:

[0059]

[0060]

[0061] In the formula, and respectively represent the increase / decrease ramp ratios of the i-th type of unit in block group k, and respectively represent the start / stop ramp constraints of the i-th type of unit in block group k, represents the output power of the i-th type of thermal unit in block group k at time t, respectively represent the start-up capacity and shutdown capacity of block group k at time t, and μ i are respectively the minimum and maximum output ratios of the i-th type of thermal unit in area k at t hours.

[0062] Optionally, the expression of the system power balance constraint is:

[0063]

[0064]

[0065] In the formula, D t,k represents the total load of block group k at time t, represents the output power of the i-th type of thermal unit in block group k at time t, respectively represent the output powers of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, represents the power transmitted from block group j to block group k at time t, represents the fixed power output from region k to the external power grid of the research region at the t-th hour, and L j,k represents the maximum and minimum transmission capacities from block group j to block group k.

[0066] According to the second aspect of the embodiments of the present invention, a device for creating a collaborative optimization model based on a low-carbon hydrogen production system is provided.

[0067] In some embodiments, the device includes:

[0068] A block division unit, configured to divide various types of generator sets and hydrogen production devices in the target low-carbon hydrogen production system into block groups, obtaining a plurality of block groups;

[0069] A variable conversion unit, configured to construct continuous variables representing the aggregated behavior of the generator sets in the block group, and establish a generator set model based on the continuous variables; wherein, the continuous variables represent the online capacity and start-stop ability of the block group;

[0070] A function processing unit, configured to obtain a piecewise linear function by performing piecewise linearization processing on the electrolytic hydrogen production efficiency function, and establish an electrolytic hydrogen production device model based on the piecewise linear function;

[0071] A result output unit, configured to establish a collaborative optimization model of the low-carbon hydrogen production system based on the generator set model, the electrolytic hydrogen production device model, cost constraints, and additional constraints.

[0072] According to the third aspect of the embodiments of the present invention, a computer device is provided.

[0073] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0074] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0075] The method and device for creating a collaborative optimization model of a low-carbon hydrogen production system provided by the present invention divide various types of generator sets and hydrogen production devices in the system into blocks, transform the optimization object of the optimization problem from a single unit to a block group, and use discrete variables representing the overall behavior of the block group to replace the discrete variables representing the behavior of a single unit in the group. At the block group level, the mixed integer programming problem is continuousized, and continuous variables are used to replace discrete variables; the nonlinear function is linearized, and a piecewise linear function is used to replace the nonlinear function. And comprehensively considering the operating cost of the unit and the hydrogen production cost, combined with other constraint conditions of the power system and the hydrogen production device, a collaborative optimization model of the low-carbon hydrogen production system based on block learning is established. By dividing the block group, the discrete variables are simplified, the number of discrete variables is reduced, and the system calculation complexity is reduced; in addition, by continuousizing the discrete variables and linearizing the nonlinear function, the collaborative optimization model of the low-carbon hydrogen production system is transformed into a linear programming problem, which greatly reduces the solution time of the optimization model and is of great significance for the long-term scale optimization of large-scale power systems, simplifies the optimization model of the low-carbon hydrogen production system, and reduces the solution time of the optimization model. It solves the technical problems of difficult creation of the collaborative optimization model and poor model accuracy.

[0076] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0078] Figure 1 is one of the flowcharts of a method for creating a collaborative optimization model of a low-carbon hydrogen production system shown according to an exemplary embodiment;

[0079] Figure 2 is the second of the flowcharts of a method for creating a collaborative optimization model of a low-carbon hydrogen production system shown according to an exemplary embodiment;

[0080] Figure 3 is a schematic diagram of the error when there are three units in the block group in a usage scenario;

[0081] Figure 4 is a schematic diagram of the error when there are five units in the block group in a usage scenario;

[0082] Figure 5 is a schematic diagram of the electricity-hydrogen efficiency function;

[0083] Figure 6It is a schematic structural diagram of a device for creating a collaborative optimization model based on a low-carbon hydrogen production system shown according to an exemplary embodiment;

[0084] Figure 7 It is a schematic structural diagram of a computer device shown according to an exemplary embodiment. Detailed implementation manners

[0085] The following description and drawings fully illustrate the specific embodiments herein, enabling those skilled in the art to practice them. Parts and features of some embodiments may be included in or substituted for parts and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents of the claims. In this document, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a structure, device or equipment including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such structure, device or equipment. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the structure, device or equipment including the said element. The embodiments herein are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0086] The terms "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. in this document indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing this document and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", "coupled" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal connection of two elements. It can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0087] In this document, unless otherwise stated, the term "plurality" means two or more.

[0088] In this text, the character " / " indicates an "or" relationship between the preceding and following objects. For example, A / B means: A or B.

[0089] In this text, the term "and / or" is a description of the associative relationship of an object, indicating that three relationships can exist. For example, A and / or B means: A or B, or, the three relationships of A and B.

[0090] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0091] Please refer to Figure 1 , Figure 1 is a flowchart of a method for creating a collaborative optimization model of a low-carbon hydrogen production system shown according to an exemplary embodiment.

[0092] In a specific implementation manner, the method for creating a collaborative optimization model of the low-carbon hydrogen production system provided by the present invention includes the following steps:

[0093] Step S101: Divide various types of generator sets and hydrogen production devices in the target low-carbon hydrogen production system into block groups; that is to say, divide various types of generator sets and hydrogen production devices in the system into block groups, and transform the optimization object of the optimization problem from a single unit to a block group. In this way, the discrete variables are simplified through block group division, the number of discrete variables is reduced, and the system calculation complexity is reduced.

[0094] Specifically, the units in the low-carbon hydrogen production system include thermal power generator sets, wind power generator sets, solar power generator sets, electrolytic hydrogen production devices, and hydrogen fuel cell units. In order to improve the speed of collaborative optimization of the low-carbon hydrogen production system planning operation, reduce the calculation complexity, and reduce the solution time of the optimization model, when constructing the collaborative optimization model of the low-carbon hydrogen production system, first, various types of generator sets and electrolytic hydrogen production devices with similar operating characteristics in adjacent geographical regions are respectively grouped into blocks to obtain multiple block groups, thereby transforming the optimization problem from optimizing the behavior of a single unit to optimizing the behavior of a block group.

[0095] Step S102: Construct continuous variables representing the aggregated behavior of the block group, and establish a generator set model based on the continuous variables; wherein, the continuous variables represent the online capacity and start-stop ability of the block group; that is to say, when establishing the generator set model, use discrete variables representing the overall behavior of the block group to replace the discrete variables representing the behavior of individual units in the group, and then continuously process the mixed-integer programming problem at the block group level, and use continuous variables to replace discrete variables to approximately represent the online capacity and start-up and shutdown capabilities of the block group.

[0096] Step S103: Obtain a piecewise linear function by performing piecewise linearization on the electrolytic hydrogen production efficiency function, and establish an electrolytic hydrogen production device model based on the piecewise linear function; when establishing the electrolytic hydrogen production device model, establish a piecewise linear function through a set of continuous variables to approximately represent the non-linear electricity-to-hydrogen efficiency function of all electrolytic devices in the block, linearize the efficiency function of the electrolytic hydrogen production device, and use the piecewise linear function to replace the non-linear function. In this way, by continuousizing the discrete variables and linearizing the non-linear function, the collaborative optimization model of the low-carbon hydrogen production system is transformed into a linear programming problem, significantly reducing the solution time of the optimization model.

[0097] Step S104: Based on the generator set model, the electrolytic hydrogen production device model, cost constraints, and additional constraints, establish a collaborative optimization model. Finally, comprehensively considering the operating cost of the unit and the hydrogen production cost, combined with the constraint conditions of the power system and the hydrogen production device, establish a collaborative optimization model of the low-carbon hydrogen production system based on block learning, transforming the original mixed-integer programming problem into a linear programming problem.

[0098] In step S102, as Figure 2 shown, construct continuous variables representing the aggregated behavior of the generator sets in the block group, and establish a generator set model based on the continuous variables, specifically including the following steps:

[0099] Step S201: Introduce discrete variables representing the aggregated behavior of the generator sets in the block group.

[0100] For a single generator set, three binary variables are usually used to describe the operating state of the unit at each time point: X t represents the operating state of the unit, X t =1 indicates that the unit is operating at time t, S t represents the start-up behavior of the unit, S t =1 indicates that the unit starts up at time t, U t represents the shutdown behavior of the unit U t =1 indicates that the unit shuts down at time t. The three binary variables satisfy the following constraints:

[0101] X t =X t-1 +S t -U t Equation (1)

[0102] The output power of a single unit at time t satisfies the following constraints:

[0103]

[0104] Among them, p i (t) represents the output power of a single unit at time t, Indicates the nameplate capacity of the unit, α i (t) and respectively represent the ratios of the minimum power output and the maximum power output to the nameplate capacity of the unit.

[0105] Step S202: Continuously process the discrete variables to obtain continuous variables, representing the overall operating state of the generator sets in the block group.

[0106] Specifically, after grouping various generator sets into blocks, three discrete variables are introduced to represent the aggregation behavior of all generator sets within the block group, which respectively represent the online capacity and the starting and stopping capabilities of block group k at time t. The definition formulas of each discrete variable are as follows:

[0107]

[0108]

[0109]

[0110] Among them, J represents the total number of generator sets within a certain block group, indicating the nameplate capacity of the unit.

[0111] In the present invention, three continuous variables are used to approximately represent the above three discrete variables. These three continuous variables need to satisfy the following constraint conditions:

[0112]

[0113] Among them, represents the total capacity of block group k, which is defined as the sum of the nameplate capacities of all units within the block group. Its definition formula is as follows:

[0114]

[0115] The total output power of the i-th type of unit in block group k satisfies:

[0116]

[0117] Among them, and respectively represent the minimum and maximum output ratios of the i-th type of unit in block group k at time t.

[0118] Similar to formula (1), for the three continuous variables representing the overall operating state of the block group, their relationship can be expressed as follows:

[0119]

[0120] It can be seen that the change in the online capacity from time (t-1) to time t depends on the total capacity of the shutdown units and the total capacity of the startup units at time t. In Equation (9), since the shutdown capacity and the startup capacity can cancel each other out, the continuous method proposed by the present invention not only transforms the MILP problem into a linear programming problem with high computational efficiency, but also significantly reduces the number of decision variables.

[0121] Step S203: Establish a generator set model based on the continuous variables.

[0122] As Figure 3 and Figure 4 shown, for a block group with J units, its actual total online capacity of the block group should be certain discrete values among them. After the present invention performs continuous processing on it, it can take any value between them, which will cause a certain error in the calculation. This error will decrease as the number of units in the block group increases, because after the number of units increases, the number of possible discrete values increases, and the discrete value density increases within a certain continuous interval, resulting in the discrete values being closer to the continuous values, so the error decreases.

[0123] Figure 3 and Figure 4 respectively show the errors when there are three units (150GW, 250GW, 350GW) in the block group and when there are five units (100GW, 150GW, 200GW, 250GW, 300GW) in the block group. It can be seen that when the number of units in the block group increases from three to five, the error after continuous processing is significantly reduced.

[0124] In some embodiments, the electrolytic hydrogen production efficiency function in the above step S103 is specifically linearized in segments by adopting the following strategy.

[0125] The electrolytic hydrogen production devices are also divided into block groups. When multiple electrolytic hydrogen production devices are operating simultaneously in a block, it is necessary to specify whether their operating mode is synchronous or asynchronous. When they operate synchronously, multiple electrolytic hydrogen production devices have the same output at time t; when they operate asynchronously, multiple electrolytic hydrogen production devices can only be started one by one.

[0126] For the electrolytic hydrogen production device, its electricity-to-hydrogen efficiency function is generally a non-linear function, as Figure 5 shown.

[0127] Specifically, a set of continuous variables x k,t,nA piecewise linear function is established to approximately represent this non - linear function:

[0128] When the block group is in the synchronous operation mode:

[0129] x h,t,1 =x h,t,2 =…=x h,t,J Formula (10) When the block group is in the asynchronous operation mode:

[0130]

[0131] Wherein, x h,t,j satisfies the following constraints:

[0132]

[0133]

[0134] x h,t,j ≥0 Formula (14)

[0135]

[0136] Wherein, and respectively represent the efficiency and load of the h - th point, P w,t,j and P s,t,j respectively represent the wind rejection amount and light rejection amount input to the j - th electrolyzer at time t in the block group, η sys,t,j represents the hydrogen production efficiency of the j - th electrolytic hydrogen production device in the block group at time t, EC cap represents the capacity of the electrolyzer, x h,t,j is a special ordered set variable as a linearization weighting factor, bin t,j represents the switching state of the j - th electrolytic hydrogen production device in the block group at time t, J represents the total number of electrolyzers. In addition, there are not allowed to be more than two consecutive x k,t,j non - zero, that is, x h,t,j +x h-1,t,j ≥0 cannot hold continuously.

[0137] In the above step S104, considering the unit operation cost and hydrogen production cost comprehensively, using the above results of variable continuousization and function linearization, combined with other constraint conditions of the power system and hydrogen production devices, a collaborative optimization model of a low - carbon hydrogen production system based on block learning is established.

[0138] Among them, the objective function of the collaborative optimization model is:

[0139] min C f +C v +C h Formula (16)

[0140] Among them, C f represents the daily operation and maintenance cost of the units within the block group k; C v represents the fuel cost and start-stop cost of the units, and C h represents the hydrogen production cost.

[0141]

[0142]

[0143]

[0144] and respectively represent the operation and maintenance costs of various thermal power units, wind turbines, and photovoltaic units within the block group k; represents the total installed capacity of the i-th type of thermal power unit; respectively represent the total installed capacities of wind turbines and photovoltaic units within the block group k; represents the fuel cost of the thermal power units within the block group k; represents the start-up cost of the thermal power units within the block group k, represents the output power of the i-th type of thermal power unit within the k-th block group at time t, represents the start-up capacity of the i-th type of thermal power unit within the k-th block group at time t, N represents the total number of types of thermal power units, and T represents the overall operation duration.

[0145] C capital,k represents the investment cost of the electrolytic hydrogen production device within the block group k, and C wat,k represents the cost of purchasing water for all electrolytic devices within the block group k, and C O&M represents the operation and maintenance cost of the electrolytic hydrogen production device, and R o,k and R heat,k represent the income obtained by selling the by-products (oxygen and heat) of the electrolytic hydrogen production process within the block group k, and EC cap,k represents the total capacity of the electrolytic devices within the block group k, and A hyd,k represents the total hydrogen production within the block group k, which is represented by the following formula:

[0146]

[0147] Assuming that the water consumption, oxygen output, and heat output during the electrolysis process are functions of the hydrogen production, it can be defined as:

[0148] C wat,k =α·A hyd,k ·p wat Formula (21)

[0149] R o,k = β·A hyd,k ·p o Equation (22)

[0150] R heat,k = γ·A hyd,k ·p heat Equation (23)

[0151] Wherein, p wat 、p o 、p heat respectively represent the unit prices of water, oxygen, and heat, and α, β, and γ represent the water consumption, oxygen production, and heat production for producing a unit of hydrogen.

[0152] Furthermore, the collaborative optimization model of the low-carbon hydrogen production system should also include the following constraint conditions:

[0153] (1) Power generation power and installed capacity constraints:

[0154]

[0155]

[0156]

[0157]

[0158] Wherein, represents the output power of the i-th type of thermal power unit in block group k at time t, respectively represent the output powers of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, α t,k and β t,k represent the hourly capacity factors of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, and represent the capacity credits of wind energy and solar energy in block group k, D max,k represents the maximum load of block group k, and N represents the total number of types of thermal power units.

[0159] (2) Flexibility constraints. The ramp constraints of the units in the block group are as follows:

[0160]

[0161]

[0162] Wherein, and respectively represent the increase / decrease ramp ratios of the i-th type of unit in block group k, and respectively represent the start-up / shutdown ramp constraint of the i-th type of unit in block group k represents the output power of the i-th type of thermal unit in block group k at time t respectively represent the start-up capacity and shutdown capacity of block group k at time t and μ i are respectively the minimum and maximum output ratios of the i-th type of thermal unit in area k at t hours

[0163] Using the above continuous variables, the minimum start-stop time constraint formula is as follows:

[0164]

[0165]

[0166] where and respectively represent the minimum start-up / shutdown time of the i-th type of unit in block group k

[0167] (3) System power balance constraint:

[0168]

[0169]

[0170] where D t,k represents the total load of block group k at time t represents the output power of the i-th type of thermal unit in block group k at time t respectively represent the output powers of the wind power generation unit and the photovoltaic power generation unit in block group k at time t represents the power transmitted from block group j to block group k at time t represents the fixed power output from area k to the external power grid of the research area at the t-th hour and L j,k represent the maximum and minimum transmission capacities from block group j to block group k

[0171] (4) Hydrogen production system constraint, the input power constraint of the electrolysis equipment is as follows:

[0172] P w,t,k ≥0 Equation (34)

[0173] P s,t,k ≥0 Equation (35)

[0174] bin t,k ·EC cap,k / N·U1 ≤ Pw,t,k +P s,t,k ≤bin t,k ·EC cap,k / N·U K Formula (36)

[0175]

[0176]

[0177] As shown in Formulas (34)-(38), the input power of the electrolysis equipment in block group k should satisfy the power constraint of the equipment and the constraints of curtailed wind and curtailed light power. Among them, P cw,t,k and P cs,t,k respectively represent the curtailed wind and curtailed light power in block group k at time t.

[0178] The constraints on the utilization rate of curtailed wind and curtailed light are as follows:

[0179]

[0180]

[0181] Among them, respectively represent the expected utilization rates of curtailed wind and curtailed light in block group k.

[0182] In the above specific implementation manner, the method for creating a collaborative optimization model of the low-carbon hydrogen production system provided by the present invention divides various types of generator sets and hydrogen production devices in the system into blocks, transforms the optimization object of the optimization problem from a single unit to a block group, and uses discrete variables representing the overall behavior of the block group to replace the discrete variables representing the behavior of a single unit in the group. At the block group level, the mixed-integer programming problem is continuously processed, and continuous variables are used to replace discrete variables; the nonlinear function is linearized, and a piecewise linear function is used to replace the nonlinear function. And comprehensively considering the operating cost of the unit and the hydrogen production cost, combined with other constraint conditions of the power system and the hydrogen production device, a collaborative optimization model of the low-carbon hydrogen production system based on block learning is established.

[0183] By dividing the block group, the discrete variables are simplified, the number of discrete variables is reduced, and the system calculation complexity is reduced; in addition, by continuously processing the discrete variables and linearizing the nonlinear function, the collaborative optimization model of the low-carbon hydrogen production system is transformed into a linear programming problem, which greatly reduces the solution time of the optimization model, has important significance for the long-time scale optimization of large-scale power systems, simplifies the optimization model of the low-carbon hydrogen production system, and reduces the solution time of the optimization model. It solves the technical problems of difficult creation of the collaborative optimization model and poor model accuracy.

[0184] Furthermore, the problems of long solution time and NP-hardness in solving mixed-integer programming are solved. Moreover, the error of the discrete variable continuousization method proposed by the present invention will decrease as the number of units in the block group increases, which can ensure the calculation accuracy while simplifying the calculation.

[0185] In one embodiment, the present invention further provides a device for creating a collaborative optimization model based on a low-carbon hydrogen production system. As Figure 6 shown, the device includes:

[0186] A block division unit 601, configured to divide various types of generator sets and hydrogen production devices in the target low-carbon hydrogen production system into block groups, obtaining a plurality of block groups;

[0187] A variable conversion unit 602, configured to construct continuous variables representing the aggregated behavior of the generator sets in the block group, and establish a generator set model based on the continuous variables; wherein, the continuous variables represent the online capacity and start-stop capacity of the block group;

[0188] A function processing unit 603, configured to obtain a piecewise linear function by performing piecewise linearization processing on the electrolytic hydrogen production efficiency function, and establish an electrolytic hydrogen production device model based on the piecewise linear function;

[0189] A result output unit 604, configured to establish a collaborative optimization model of the low-carbon hydrogen production system based on the generator set model, the electrolytic hydrogen production device model, cost constraints, and additional constraints.

[0190] In the above specific embodiment, the device for creating a collaborative optimization model based on a low-carbon hydrogen production system provided by the present invention divides various types of generator sets and hydrogen production devices in the system into block groups, converts the optimization object of the optimization problem from a single unit to a block group, and uses discrete variables representing the overall behavior of the block group to replace the discrete variables representing the behavior of individual units in the group. At the block group level, the mixed-integer programming problem is continuously processed, and continuous variables are used to replace discrete variables; the nonlinear function is linearized, and a piecewise linear function is used to replace the nonlinear function. And comprehensively considering the operating cost of the unit and the hydrogen production cost, combined with other constraint conditions of the power system and the hydrogen production device, a collaborative optimization model of the low-carbon hydrogen production system based on block learning is established. By dividing the block group, the discrete variables are simplified, the number of discrete variables is reduced, and the system calculation complexity is reduced; in addition, by continuously processing the discrete variables and linearizing the nonlinear function, the collaborative optimization model of the low-carbon hydrogen production system is transformed into a linear programming problem, greatly reducing the solution time of the optimization model, which is of great significance for the long-term scale optimization of large-scale power systems, simplifies the optimization model of the low-carbon hydrogen production system, and reduces the solution time of the optimization model. The technical problems of difficult creation of the collaborative optimization model and poor model accuracy are solved.

[0191] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structural diagram may be as Figure 7 shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0192] Those skilled in the art can understand that Figure 7 the structure shown in

[0193] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0194] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0195] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0196] The present invention is not limited to the structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for creating a collaborative optimization model of a low-carbon hydrogen production system, characterized in that The method includes: Dividing various generator sets and hydrogen production devices in the target low-carbon hydrogen production system into blocks to obtain multiple block groups; Constructing continuous variables characterizing the aggregation behavior of generator sets in the block group, and establishing a generator set model based on the continuous variables; wherein, the continuous variables represent the online capacity and start-stop ability of the block group; Obtaining a piecewise linear function by performing piecewise linearization on the electrolytic hydrogen production efficiency function, and establishing an electrolytic hydrogen production device model based on the piecewise linear function; Establishing a collaborative optimization model for the low-carbon hydrogen production system based on the generator set model, the electrolytic hydrogen production device model, cost constraints, and additional constraints; The step of constructing continuous variables characterizing the aggregation behavior of generator sets in the block group and establishing a generator set model based on the continuous variables specifically includes: Introducing discrete variables characterizing the aggregation behavior of generator sets in the block group; Performing continuous processing on the discrete variables to obtain continuous variables, representing the overall operating state of the generator sets in the block group; Establishing a generator set model based on the continuous variables; The step of obtaining a piecewise linear function by performing piecewise linearization on the electrolytic hydrogen production efficiency function includes: when multiple electrolytic hydrogen production devices in the block group are in a synchronous operation mode, the expression of the piecewise linear function is: x h,t,1 = x h,t,2 = … = x h,t,J where x h,t,n is a weight variable as a linearization weight factor, and n is 1, 2, …, J; When multiple electrolytic hydrogen production devices in the block group are in an asynchronous operation mode, the expression of the piecewise linear function is: where x H,t,j and x h,t,j+1 are special ordered set variables as linearization weighting factors, h represents the h-th successor variable, H represents the total number of successor variables, and bin t,j+1 represents the switching state of the (j + 1)-th electrolytic hydrogen production device in the block group at time t; When multiple electrolytic hydrogen production devices in the block group are in an asynchronous operation mode, in the expression of the piecewise linear function, x h,t,j The constraint conditions to be satisfied include: x h,t,j ≥0 x h,t,j +x h-1,t,j ≥0 cannot hold continuously Among them, and represent the efficiency and load of the h-th point respectively, P w,t,j and P s,t,j represent the wind rejection volume and light rejection volume input to the j-th electrolyzer at time t in the block group respectively, η sys,t,j represents the hydrogen production efficiency of the j-th electrolytic hydrogen production device in the block group at time t, EC cap represents the capacity of the electrolyzer, x h,t,j is a special ordered set variable as a linearization weighting factor, bin t,j represents the switching state of the j-th electrolytic hydrogen production device in the block group at time t, and J represents the total number of electrolyzers.

2. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 1, wherein The step of introducing discrete variables characterizing the aggregation behavior of generator sets in the block group includes: the expression of the discrete variables is: Among them, and respectively represent the online capacity, start-up ability, and shutdown ability of block group k at time t. J represents the total number of generator sets in a certain block group, and X t represents the operating state of the unit, and S t represents the start-up behavior of the unit, and U t represents the shutdown behavior of the unit. represents the nameplate capacity of the unit.

3. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 2, characterized in that, The step of performing continuous processing on the discrete variables to obtain continuous variables, representing the overall operating state of the generator sets in the block group, includes: Usage When three continuous variables are used to approximately represent the discrete variable, the following constraint conditions shall be satisfied by the continuous variables: Among them, represents the total capacity of block group k, respectively represent the online capacity, start-up ability and shutdown ability of block group k at time t.

4. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to any one of claims 1-3, characterized in that The objective function of the collaborative optimization model is: min C f +C v +C h Among them, C f represents the daily operation and maintenance cost of the units within the block group k; C v represents the fuel cost and start-stop cost of the units, and C h represents the hydrogen production cost.

5. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 4, characterized in that The expression of the daily operation and maintenance cost of the units in block group k is: In the formula, and respectively represent the operation and maintenance costs of various thermal power units, wind turbines, and photovoltaic units within block group k; represents the total installed capacity of the i-th type of thermal power unit; respectively represent the total installed capacities of wind turbines and photovoltaic units within block group k.

6. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 5, wherein The expressions of the fuel cost and start-stop cost of the units are: In the formula, represents the fuel cost of the thermal power units in block group k; represents the start-up cost of the thermal power units in the k-th block group, represents the output power of the i-th type of thermal power unit in the k-th block group at time t, represents the start-up capacity of the i-th type of thermal power unit in the k-th block group at time t, N represents the total number of types of thermal power units, and T represents the total operating duration.

7. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 5, wherein The expression of the hydrogen production cost of the units is: Where C capital,k represents the investment cost of the electrolytic hydrogen production device within block group k, C wat,k represents the cost of purchasing water for all electrolysis devices within block group k, C O&M represents the operation and maintenance cost of the electrolytic hydrogen production device, R o,k and R heat,k represent the income obtained from selling the by-products (oxygen and heat) of the electrolytic hydrogen production process within block group k, EC cap,k represents the total capacity of the electrolysis devices within block group k, A hyd,k represents the total hydrogen production within block group k.

8. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to any one of claims 1 to 3, characterized in that The additional constraints include at least one of the following: Power generation power and installed capacity constraints, flexibility constraints, system power balance constraints, and hydrogen production system constraints.

9. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 8, wherein The expression of the power generation power and installed capacity constraints is: In the formula, represents the output power of the i-th type of thermal power unit in block group k at time t, respectively represent the output powers of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, α t,k and β t,k represent the hourly capacity factors of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, and represent the capacity credits of wind energy and solar energy in block group k, D max,k represents the maximum load of block group k, and N represents the total number of types of thermal power units.

10. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 8, wherein The expression of the flexibility constraint is: In the formula, and respectively represent the increase / decrease ramp rate of the i-th type of unit in block group k, and respectively represent the start-up / shut-down ramp constraints of the i-th type of unit in block group k, represents the output power of the i-th type of thermal unit in block group k at time t, respectively represent the start-up capacity and shut-down capacity of block group k at time t, and μ i are respectively the minimum and maximum output ratios of the i-th type of thermal unit in area k at t hours.

11. The method for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 8, wherein The expression of the system power balance constraint is: Where D t,k represents the total load of block group k at time t, represents the output power of the i-th type of thermal unit in block group k at time t, respectively represent the output powers of the wind power generation unit and the photovoltaic power generation unit in block group k at time t, represents the power transmitted from block group j to block group k at time t, represents the fixed power output from area k to the external grid of the research area in the t-th hour, and L j,k represent the maximum and minimum transmission capacities from block group j to block group k.

12. An apparatus for creating a collaborative optimization model of a low-carbon hydrogen production system, characterized in that, The device includes: A block division unit for dividing various generator sets and hydrogen production devices in the target low-carbon hydrogen production system into blocks to obtain multiple block groups; A variable conversion unit for constructing continuous variables characterizing the aggregation behavior of generator sets in the block group and establishing a generator set model based on the continuous variables; wherein, the continuous variables represent the online capacity and start-stop ability of the block group; A function processing unit for obtaining a piecewise linear function by performing piecewise linearization on the electrolytic hydrogen production efficiency function and establishing an electrolytic hydrogen production device model based on the piecewise linear function; A result output unit, configured to establish a collaborative optimization model of a low-carbon hydrogen production system based on the generator set model, the electrolytic hydrogen production device model, cost constraints, and additional constraints; In the function processing unit, the steps of obtaining a piecewise linear function by performing piecewise linearization on the electrolytic hydrogen production efficiency function include: when multiple electrolytic hydrogen production devices in a block group are in a synchronous operation mode, the expression of the piecewise linear function is: x h,t,1 = x h,t,2 = … = x h,t,J where x h,t,n is a weight variable as a linearization weight factor, and n is 1, 2,..., J; When multiple electrolytic hydrogen production devices in a block group are in an asynchronous operation mode, the expression of the piecewise linear function is: where x H,t,j and x h,t,j+1 are special ordered set variables as linearization weighting factors, h represents the h-th successor variable, H represents the total number of successor variables, and bin t,j+1 represents the switching state of the (j + 1)-th electrolytic hydrogen production device in the block group at time t; When multiple electrolytic hydrogen production devices in the block group are in an asynchronous operation mode, in the expression of the piecewise linear function, x h,t,j The constraint conditions to be satisfied include: x h,t,j ≥0 x h,t,j +x h-1,t,j ≥0 cannot hold continuously Among them, and represent the efficiency and load of the h-th point respectively, P w,t,j and P s,t,j represent the wind curtailment volume and photovoltaic curtailment volume input to the j-th electrolyzer at time t in the block group respectively, η sys,t,j represents the hydrogen production efficiency of the j-th electrolytic hydrogen production device in the block group at time t, EC cap represents the capacity of the electrolyzer, x h,t,j is a special ordered set variable as a linearized weighting factor, bin t,j represents the switching state of the j-th electrolytic hydrogen production device in the block group at time t, and J represents the total number of electrolyzers.

13. The device for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 12, wherein The steps of introducing discrete variables characterizing the aggregated behavior of generator sets in a block group include: the expression of the discrete variables is: Among them, and respectively represent the online capacity, start-up ability, and shutdown ability of block group k at time t. J represents the total number of generating units in a certain block group, and X t represents the operating state of the unit, and S t represents the start-up behavior of the unit, and U t represents the shutdown behavior of the unit, represents the nameplate capacity of the unit.

14. The device for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 13, wherein The steps of obtaining a continuous variable by performing continuous processing on the discrete variable to represent the overall operating state of the generator sets in the block group include: Use When three continuous variables are used to approximately represent the discrete variable, the continuous variables need to satisfy the following constraint conditions: Among them, represents the total capacity of block group k, respectively represent the online capacity, start-up ability, and shutdown ability of block group k at time t.

15. The device for creating a collaborative optimization model of a low-carbon hydrogen production system according to any one of claims 12-14, wherein The objective function of the collaborative optimization model is: minC f +C v +C h Among them, C f represents the daily operation and maintenance cost of the units within the kth block group; C v represents the fuel cost and start-stop cost of the units, and C h represents the hydrogen production cost.

16. The device for creating a collaborative optimization model of a low-carbon hydrogen production system according to claim 15, wherein The expression of the daily operation and maintenance cost of the units in block group k is: In the formula, and respectively represent the operation and maintenance costs of various thermal power units, wind turbines, and photovoltaic units within the block group k; represents the total installed capacity of the i-th type of thermal power unit; respectively represent the total installed capacities of wind turbines and photovoltaic units within the block group k.

17. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 11.

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