Economic low-carbon dispatching method and device for green factory containing CCUS

Abstract

The invention relates to the technical field of low carbon, in particular to an economic low-carbon dispatching method and device for a CCUS-containing green factory. Comprising the steps of determining a demand response of a green factory in a preset historical duration; acquiring carbon dioxide data generated by a CCUS system in the green factory within a preset historical duration; constructing an economic low-carbon dispatching model of the green factory, wherein the economic low-carbon dispatching model is constructed based on a distributed power generation system, green certificate transaction, a CCUS system and demand response of the green factory; the economic low-carbon dispatching model is solved based on the product parameters, the demand response and the carbon dioxide data to obtain the low-carbon economic dispatching strategy of the green factory in the preset future duration, the low-carbon economic dispatching strategy of the green factory is optimized, the total profit of the green factory can be improved by 26.7% under the low-carbon economic dispatching strategy, and the economic benefit of the green factory can be improved by 26.7%. The total carbon emission is reduced by 10.4%, the carbon emission is obviously reduced, and green low-carbon production is realized.

Description

Technical Field

[0001] The present application relates to the field of low-carbon technology, and specifically to a method, device and storage medium for economic low-carbon scheduling of a green factory containing CCUS. Background Art

[0002] Traditional industrial manufacturing, especially high-energy-consuming green factories, are facing tremendous pressure to reduce emissions. In this context, carbon capture, utilization and storage (CCUS) technology has gradually been applied to high-carbon emission industries such as thermal power generation.

[0003] The existing low-carbon economic dispatch strategy schemes for green factories usually focus on one aspect, such as production efficiency or energy utilization, but lack a decision-making scheme that comprehensively considers grid power and CCUS. Therefore, a low-carbon dispatch model that combines grid power and CCUS is urgently needed to rationalize the low-carbon economic dispatch strategy of green factories and reduce carbon emissions while improving the economic benefits of green factories. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device and storage medium for economic low-carbon scheduling of a green factory containing CCUS, so as to solve the problems of low economic benefits and high carbon emissions caused by unreasonable low-carbon economic scheduling strategies in the prior art.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for economic low-carbon scheduling of a green factory containing CCUS, the method comprising:

[0006] Determine the demand response of the green plant at a preset historical time;

[0007] Obtain the carbon dioxide data generated by the CCUS system in the green factory within a preset historical period; build an economic low-carbon scheduling model for the green factory, where the economic low-carbon scheduling model is built based on the green factory's distributed power generation system, green certificate trading, CCUS system and demand response;

[0008] An economic low-carbon scheduling model is solved based on the product parameters, demand response and carbon dioxide data of the products to be produced in the green factory to obtain a low-carbon economic scheduling strategy for the green factory within a preset future time period, wherein the low-carbon economic scheduling strategy includes the working time and shutdown time of each generator set and production equipment in the green factory within the preset future time period.

[0009] In an embodiment of the present application, the green factory also includes multiple product storage areas, and constructing an economic and low-carbon scheduling model for the green factory includes: generating a comprehensive benefit function of the CCUS system based on carbon dioxide data; generating a power purchase cost function of the green factory and a green certificate comprehensive benefit function of the green certificate transaction according to demand response; obtaining the output power of the generator set, and generating a power generation cost function of the distributed power generation system according to the output power of the generator set; generating an objective function according to product parameters, the comprehensive benefit function of the CCUS system, the power purchase cost function, the power generation cost function and the green certificate comprehensive benefit function; obtaining the storage capacity of each product storage area, and generating constraints based on the storage capacity of all product storage areas; and constructing an economic and low-carbon scheduling model based on the objective function and constraints.

[0010] In an embodiment of the present application, the carbon dioxide data includes the carbon dioxide capture operating cost of the CCUS system, the unit carbon dioxide storage cost, the unit carbon dioxide transportation cost, the unit carbon dioxide tax, the unit carbon dioxide price, the carbon dioxide emissions within a preset historical period, the carbon dioxide capture within a preset historical period, and the carbon dioxide storage within a preset historical period. Generating a comprehensive benefit function of the CCUS system based on the carbon dioxide data includes: determining the carbon capture operating cost function of the CCUS system based on the carbon dioxide capture operating cost and the output power; determining the carbon storage cost function of the CCUS system based on the unit carbon dioxide storage cost and the carbon dioxide capture within a preset historical period; determining the carbon transportation cost function of the CCUS system based on the unit carbon dioxide transportation cost and the carbon dioxide storage within a preset historical period; determining the carbon penalty function of the CCUS system based on the unit carbon dioxide tax, the carbon dioxide emissions within a preset historical period, and the carbon dioxide capture within a preset historical period; determining the carbon benefit function of the CCUS system based on the unit carbon dioxide price and the carbon dioxide storage within a preset historical period; generating a comprehensive benefit function of the CCUS system based on the carbon capture operating cost function, the carbon storage cost function, the carbon transportation cost function, the carbon penalty function, and the carbon benefit function.

[0011] In an embodiment of the present application, the demand response includes the electricity purchased by the green factory from the power grid and the electricity sold by the green factory to the power grid. Generating the green factory's electricity purchase cost function based on the demand response includes: determining the green factory's purchase cost based on the electricity purchased by the green factory from the power grid and the purchase price; determining the green factory's electricity sales revenue based on the electricity sold by the green factory to the power grid and the selling price; generating the green factory's electricity purchase cost function based on the green factory's purchase cost and electricity sales revenue.

[0012] In an embodiment of the present application, the demand response includes a daily quota of renewable energy and renewable energy power generation. The green certificate comprehensive benefit function for green certificate trading generated according to the demand response includes: when the daily quota of renewable energy is greater than the power generation of renewable energy, the green certificate comprehensive benefit function is generated according to the daily quota of renewable energy, the power generation of renewable energy, the price of green certificates purchased by green factories, and the penalty coefficient; when the daily quota of renewable energy is less than or equal to the power generation of renewable energy, the green certificate comprehensive benefit function is generated according to the daily quota of renewable energy, the power generation of renewable energy and the price of green certificates sold by green factories.

[0013] In the embodiment of the present application, the product parameters include the type of product, the raw materials required for each product, the cost of the raw materials, and the value created by each product. The objective function is shown in expression (1):

[0014]

[0015] Among them, ω is the maximum total economic profit of the green factory, v i is the value of the i-th product, g i is the i-th product, n is the total number of product types, c j is the cost of the jth raw material, p j is the jth raw material, m-1 is the total number of raw materials except electricity, C e is the electricity purchase cost function of the green factory, C f is the power generation cost function of the green plant, R ccus is the comprehensive benefit function of CCUS system, R gc It is the comprehensive benefit function of green certificate for green factory’s green certificate transaction.

[0016] In an embodiment of the present application, the method also includes: before generating constraint conditions based on the storage capacity of all product storage areas, obtaining the processing time of each production equipment; determining the working status of the production equipment adjacent to each product storage area based on the storage capacity of each product storage area; determining the decision variables of each production equipment based on the working status of each production equipment; determining the production efficiency of each production equipment based on the decision variables and processing time of each production equipment; and updating the storage capacity of each product storage area based on the production efficiency of each production equipment.

[0017] In the embodiment of the present application, the constraint condition is that the updated storage capacity of each product storage area is less than the preset maximum storage capacity corresponding to each product storage area.

[0018] The second aspect of the present application provides a green factory economic low-carbon scheduling device containing CCUS, comprising:

[0019] a memory configured to store instructions;

[0020] The processor is configured to call instructions from the memory and implement the above-mentioned green factory economic low-carbon scheduling method containing CCUS when executing the instructions.

[0021] A third aspect of the present application provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the above-mentioned method for economical low-carbon scheduling of green factories containing CCUS.

[0022] Through the above technical scheme, it is possible to determine the demand response of the green factory within a preset historical period; obtain the carbon dioxide data generated by the CCUS system in the green factory within the preset historical period; build an economic and low-carbon scheduling model for the green factory, which is based on the distributed power generation system, green certificate trading, CCUS system and demand response of the green factory; solve the economic and low-carbon scheduling model based on product parameters, demand response and carbon dioxide data to obtain the low-carbon economic scheduling strategy of the green factory within a preset future period, optimize the low-carbon economic scheduling strategy of the green factory, and under this low-carbon economic scheduling strategy, the total profit of the green factory can be increased by 26.7%, and the total carbon emissions can be reduced by 10.4%, which significantly reduces carbon emissions and realizes green and low-carbon production.

[0023] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0025] Figure 1 A schematic diagram of a process flow of a method for economic low-carbon scheduling of a green factory containing CCUS according to an embodiment of the present application is schematically shown;

[0026] Figure 2 A schematic diagram schematically shows a low-carbon economic dispatch strategy for a green factory according to an embodiment of the present application;

[0027] Figure 3 The internal structure diagram of a computer device according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0029] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0030] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0031] Figure 1 The following schematically shows a flow chart of a method for economic low-carbon dispatching of a green factory containing CCUS according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides an economical and low-carbon scheduling method for a green factory containing CCUS, which may include the following steps.

[0032] Step 101: Determine the demand response of the green plant over a preset historical period.

[0033] Step 102: Obtain carbon dioxide data generated by the CCUS system in the green plant within a preset historical period.

[0034] Step 103: Construct an economic low-carbon dispatch model for the green factory, wherein the economic low-carbon dispatch model is constructed based on the distributed power generation system, green certificate trading, CCUS system and demand response of the green factory.

[0035] A green factory can be a green factory that follows the concept of green manufacturing and achieves green, low-carbon and circular production throughout the entire process by adopting a series of advanced technologies and management methods. The processor can also determine the demand response of the green factory within a preset historical period. The processor can obtain the carbon dioxide data generated by the CCUS (Carbon Capture, Utilization and Storage) system in the green factory within a preset historical period. The preset historical period can be determined by the user based on demand. The processor can build an economic low-carbon scheduling model for the green factory, wherein the economic low-carbon scheduling model is built based on the distributed power generation system, green certificate trading, CCUS system and demand response of the green factory.

[0036] In an embodiment of the present application, the green factory also includes multiple product storage areas, and constructing an economic and low-carbon scheduling model for the green factory includes: generating a comprehensive benefit function of the CCUS system based on carbon dioxide data; generating a power purchase cost function of the green factory and a green certificate comprehensive benefit function of the green certificate transaction according to demand response; obtaining the output power of the generator set, and generating a power generation cost function of the distributed power generation system according to the output power of the generator set; generating an objective function according to product parameters, the comprehensive benefit function of the CCUS system, the power purchase cost function, the power generation cost function and the green certificate comprehensive benefit function; obtaining the storage capacity of each product storage area, and generating constraints based on the storage capacity of all product storage areas; and constructing an economic and low-carbon scheduling model based on the objective function and constraints.

[0037] The green factory may also include multiple product storage areas. The processor may construct an economic low-carbon scheduling model for the green factory. Specifically, the processor may generate a comprehensive benefit function of the CCUS system based on carbon dioxide data. And generate a power purchase cost function for the green factory and a comprehensive benefit function of the green certificate transaction based on demand response. The processor may also obtain the output power of the generator set, and generate a power generation cost function of the distributed power generation system based on the output power of the generator set. In a specific embodiment, the power generation cost function of the distributed power generation system is C f =a e (P f (t)) 2 +b e P f (t)+c e , (t=1,…,T), where t represents the tth time gap within the preset historical duration, T represents the Tth time gap within the preset historical duration, P f (t) is the output power of the generator set in the tth time interval, a e 、b e 、c eis the cost coefficient of the generator set, which is a constant. The generator set can be an internal combustion generator.

[0038] After obtaining the comprehensive benefit function, electricity purchase cost function, power generation cost function and green certificate comprehensive benefit function of the CCUS system, the processor can generate an objective function based on the product parameters, the comprehensive benefit function, electricity purchase cost function, power generation cost function and green certificate comprehensive benefit function of the CCUS system. In an embodiment of the present application, the processor can also obtain the product parameters of the products to be produced by the green factory, wherein the product parameters may include the type of product, the raw materials required for each product, the cost of the raw materials and the value created by each product. In a specific embodiment, the products to be produced may be lithium batteries, carbon-zinc batteries, etc., and the raw materials may include battery cells, compressed foam, cooling plates, etc. The objective function is shown in expression (1):

[0039]

[0040] Among them, ω is the maximum total economic profit of the green factory, v i is the value of the i-th product, g i is the i-th product, n is the total number of product types, c j is the cost of the jth raw material, p j is the jth raw material, m-1 is the total number of raw materials except electricity, C e is the electricity purchase cost function of the green factory, C f is the power generation cost function of the green plant, R ccus is the comprehensive benefit function of CCUS system, R gc It is the comprehensive benefit function of green certificate for green factory’s green certificate transaction.

[0041] The processor can also obtain the storage capacity of each product storage area and generate constraints based on the storage capacity of all product storage areas. After obtaining the objective function and the constraints, the processor can build an economic low-carbon scheduling model based on the objective function and the constraints.

[0042] In an embodiment of the present application, the carbon dioxide data includes the carbon dioxide capture operating cost of the CCUS system, the unit carbon dioxide storage cost, the unit carbon dioxide transportation cost, the unit carbon dioxide tax, the unit carbon dioxide price, the carbon dioxide emissions within a preset historical period, the carbon dioxide capture within a preset historical period, and the carbon dioxide storage within a preset historical period. Generating a comprehensive benefit function of the CCUS system based on the carbon dioxide data includes: determining the carbon capture operating cost function of the CCUS system based on the carbon dioxide capture operating cost and the output power; determining the carbon storage cost function of the CCUS system based on the unit carbon dioxide storage cost and the carbon dioxide capture within a preset historical period; determining the carbon transportation cost function of the CCUS system based on the unit carbon dioxide transportation cost and the carbon dioxide storage within a preset historical period; determining the carbon penalty function of the CCUS system based on the unit carbon dioxide tax, the carbon dioxide emissions within a preset historical period, and the carbon dioxide capture within a preset historical period; determining the carbon benefit function of the CCUS system based on the unit carbon dioxide price and the carbon dioxide storage within a preset historical period; generating a comprehensive benefit function of the CCUS system based on the carbon capture operating cost function, the carbon storage cost function, the carbon transportation cost function, the carbon penalty function, and the carbon benefit function.

[0043] CO2 data include the operating costs of CO2 capture in CCUS systems ope , unit carbon dioxide storage cost r cc , unit carbon dioxide transportation cost r cs , unit carbon dioxide tax tax , unit carbon dioxide price r pri , CO2 emissions within a preset historical period∑ t Q emi (t), CO2 capture within the preset historical period∑ t Q cc (t) and the amount of carbon dioxide stored in the preset historical period ∑ t Q cs (t), (t=1, ..., T), t represents the tth time gap in the preset historical duration, and T represents the Tth time gap in the preset historical duration. For example, the preset historical duration may be 1 day, and the time gap may be set to 10 minutes, then T may be the 144th time gap.

[0044] The processor can generate a comprehensive benefit function of the CCUS system based on the CO2 data. Specifically, the processor can generate a comprehensive benefit function of the CCUS system based on the CO2 capture operating cost r opp And the output power P of the generator set f (t) Determine the carbon capture operating cost function of the CCUS system, that is, the carbon capture operating cost function of the CCUS system is C ope =∑rope ∑ t P f (t). The processor can store CO2 at a cost of r per unit. cc and the amount of CO2 captured within a preset historical period∑ t Q cc (t) Determine the carbon storage cost function of the CCUS system, that is, the carbon storage cost function of the CCUS system is C cc =∑r cc ∑ t Q cc (t). The processor can transport the unit CO2 at a cost of r cs and the amount of carbon dioxide stored within a preset historical period∑ t Q cs (t) Determine the carbon transportation cost function of the CCUS system, that is, the carbon transportation cost function of the CCUS system is C cs =∑r cs ∑ t Q cs (t). Processors can be taxed according to the unit CO2 tax r tax , CO2 emissions within a preset historical period∑ t Q emi (t) and the amount of carbon dioxide captured within a preset historical period ∑ t Q cc (t) Determine the carbon penalty function of the CCUS system, that is, the carbon penalty function of the CCUS system is The processor can calculate the unit CO2 price r pri and the amount of carbon dioxide stored within a preset historical period∑ t Q cs (t) Determine the carbon benefit function of the CCUS system, that is, the carbon benefit function of the CCUS system is After obtaining the carbon capture operating cost function, carbon storage cost function, carbon transportation cost function, carbon penalty function and carbon benefit function of the CCUS system, the processor can generate a comprehensive benefit function of the CCUS system according to the carbon capture operating cost function, carbon storage cost function, carbon transportation cost function, carbon penalty function and carbon benefit function of the CCUS system. Specifically, the comprehensive benefit function of the CCUS system can be

[0045] In an optional embodiment, the carbon dioxide emissions of the CCUS system at each time interval can be determined based on the ratio of carbon dioxide emissions to the output power of the generator set and the output power of the generator set. The carbon dioxide capture of the CCUS system at each time interval can be determined based on the carbon dioxide emissions at each time interval and the carbon capture absorption efficiency. The carbon dioxide storage of the CCUS system at each time interval can be determined based on the carbon dioxide capture at each time interval and the transportation loss efficiency.

[0046] In an embodiment of the present application, the demand response includes the electricity purchased by the green factory from the power grid and the electricity sold by the green factory to the power grid. Generating the green factory's electricity purchase cost function based on the demand response includes: determining the green factory's purchase cost based on the electricity purchased by the green factory from the power grid and the purchase price; determining the green factory's electricity sales revenue based on the electricity sold by the green factory to the power grid and the selling price; generating the green factory's electricity purchase cost function based on the green factory's purchase cost and electricity sales revenue.

[0047] The demand response may include the electricity purchased by the green plant from the grid and the electricity sold by the green plant to the grid. The processor may generate a cost function for purchasing electricity for the green plant based on the demand response. Specifically, the processor may calculate the cost function for purchasing electricity based on the electricity E purchased by the green plant from the grid. B (t) and the purchase price P buy Determine the purchase cost of the green factory E B (t)·P buy The processor can generate electricity based on the amount of electricity sold by the green plant to the grid. S (t) and selling price P Selling Determine the electricity sales revenue of the green factory. After obtaining the purchase cost and electricity sales revenue of the green factory, the processor can generate the electricity purchase cost function of the green factory according to the purchase cost and electricity sales revenue of the green factory, that is, the electricity purchase cost function C e =∑ t∈T (E B (t)·P buy -E s (t)·P Selling ), t represents the tth time gap within the preset historical time length, and T represents the Tth time gap within the preset historical time length.

[0048] In an embodiment of the present application, the demand response includes a daily quota of renewable energy and renewable energy power generation. The green certificate comprehensive benefit function for green certificate trading generated according to the demand response includes: when the daily quota of renewable energy is greater than the power generation of renewable energy, the green certificate comprehensive benefit function is generated according to the daily quota of renewable energy, the power generation of renewable energy, the price of green certificates purchased by green factories, and the penalty coefficient; when the daily quota of renewable energy is less than or equal to the power generation of renewable energy, the green certificate comprehensive benefit function is generated according to the daily quota of renewable energy, the power generation of renewable energy and the price of green certificates sold by green factories.

[0049] Demand response can include a daily renewable energy quota E res and renewable energy generation E N . The daily renewable energy quota refers to the minimum renewable energy proportion indicator for electricity consumption in each provincial administrative region stipulated by the state based on the national medium- and long-term total renewable energy utilization goals and energy development strategies and plans. The processor can generate a comprehensive green certificate benefit function based on demand response. Specifically, the processor can determine whether the daily renewable energy quota is greater than the renewable energy power generation. In the case that the daily renewable energy quota is greater than the renewable energy power generation, the processor can generate a comprehensive green certificate benefit function based on the daily renewable energy quota, renewable energy power generation, the price of green certificates purchased by green factories, and the penalty coefficient. That is, in E res >E N In the case of Among them, c gc,b The price of buying green certificates for green factories, β pen is the penalty coefficient. When the daily renewable energy quota is less than or equal to the renewable energy power generation, the processor can generate a comprehensive green certificate revenue function based on the daily renewable energy quota, renewable energy power generation, and the price of green certificates sold by green factories. res ≤E N In the case of Among them, c gc,s The price of selling green certificates for green factories.

[0050] In an embodiment of the present application, the method also includes: before generating constraint conditions based on the storage capacity of all product storage areas, obtaining the processing time of each production equipment; determining the working status of the production equipment adjacent to each product storage area based on the storage capacity of each product storage area; determining the decision variables of each production equipment based on the working status of each production equipment; determining the production efficiency of each production equipment based on the decision variables and processing time of each production equipment; and updating the storage capacity of each product storage area based on the production efficiency of each production equipment.

[0051] Before generating constraint conditions according to the storage capacity of all product storage areas, the processor may obtain the processing time of each production equipment. The processor may determine the working state of the production equipment adjacent to each product storage area according to the storage capacity of each product storage area. For example, a green factory may include multiple production lines, and the production equipment and product storage areas of each production line are connected in sequence. For any product storage area, when the storage capacity of the product storage area is zero, the processor may determine that a production equipment behind the product storage area is in a hungry state. Alternatively, when the storage capacity of the product storage area is the maximum capacity, the processor may determine that the previous production equipment of the product storage area is in a hungry state. When the capacity of the product storage area is between zero and the maximum capacity, the processor may determine that the production equipment before and after the product storage area are in a normal working state. When determining the working state of each production equipment, the processor may determine the decision variable of each production equipment according to the working state of each production equipment. For example, for any production equipment, when the working state of the production equipment is a hungry state, the processor may determine that the decision variable of the production equipment takes a value of 0. When the production equipment is in a normal working state, the processor may determine that the decision variable of the production equipment takes a value of 1. When the decision variables of each production device are determined, the processor can determine the production efficiency of each production device according to the decision variables and processing time of each production device. After determining the production efficiency of each production device, the processor can update the storage capacity of each product storage area according to the production efficiency of each production device.

[0052] For example, the processor can calculate the time interval T according to the preset history time. s The storage capacity of each product storage area in each time interval determines the working state of the production equipment adjacent to each product storage area, and then determines the decision variables of each production equipment. The decision variables can be expressed in the form of a function as Z ab (t), specifically, when the working state of the b-th production equipment on the a-th production line is a normal working state such as a processing state, a production state, etc., Z ab (t) takes the value of 1. When the working state of the b-th production equipment on the a-th production line is the starvation state, Z ab (t) takes the value of 0. The processor can calculate the processing time T of each production equipment according to the processing time T p and Z ab (t) Determine the production efficiency of each production equipment. The production efficiency can be expressed in functional form as The processor may determine that the updated storage capacity of each product storage area is determined based on the storage capacity of each product storage area before the update and the production efficiency of the corresponding production equipment. In an optional embodiment, the updated storage capacity of each product storage area may be equal to the sum of the storage capacity of each product storage area before the update and the production efficiency of the corresponding production equipment. In an embodiment of the present application, the constraint condition may be that the updated storage capacity of each product storage area is less than the preset maximum storage capacity corresponding to each product storage area. The preset maximum storage capacity may be determined by the user according to demand to ensure that the green factory can operate stably and efficiently and improve the production efficiency of the green factory.

[0053] Step 104: Solve the economic low-carbon scheduling model based on the product parameters, demand response and carbon dioxide data of the products to be produced by the green factory to obtain the low-carbon economic scheduling strategy of the green factory within a preset future time, wherein the low-carbon economic scheduling strategy includes the working time and shutdown time of each generator set and production equipment in the green factory within the preset future time.

[0054] After obtaining the product parameters, demand response and carbon dioxide data of the green factory, the processor can solve the economic low-carbon scheduling model based on the product parameters, demand response and carbon dioxide data to obtain the low-carbon economic scheduling strategy of the green factory within the preset future duration, wherein the low-carbon economic scheduling strategy may include the working time and shutdown time of each generator set and production equipment in the green factory within the preset future duration. In an optional embodiment, the preset future duration can be determined by the user based on the efficiency of the green factory in producing products.

[0055] For example, Figure 2 As shown, the green factory may include a machine (corresponding to the production equipment above) M 11 、M 12 、M 21 、M 22 、M 23 、M 31 、M 01 、M 02 、M 03 、M 04 The preset future duration can be set to 1 day, and the time interval can be set to 10 minutes, that is, each hour includes 6 time intervals, and a total of 144 time intervals in a day. The processor can solve the economic low-carbon scheduling model to obtain the machine M 11 、M 12 、M 21 、M 22 、M 23 、M 31 、M 01 、M 02 、M 03 、M04 Low-carbon economic dispatch strategy in each time slot.

[0056] Specifically, the machine tool M 11 It needs to be opened in the 1st time slot at 1 o'clock, the 1st and 5th time slots at 2 o'clock and 3 o'clock, and the 3rd and 6th time slots at 4 o'clock and 5 o'clock, and closed at other times.

[0057] Machine Tool 12 It needs to be opened at the second time slot at 1 o'clock, the first, second, third and fourth time slots at 2 o'clock, 3 o'clock, 4 o'clock, 5 o'clock, 6 o'clock and 7 o'clock, and closed at other times.

[0058] Machine Tool 21 It must be opened from 1:00 to 11:00, in the second time slot at 12:00, the first, second and sixth time slots at 19:00, the first, second, third and fifth time slots at 20:00, the first, second, third and fifth time slots at 21:00, the first, third, fourth and sixth time slots at 22:00, and the first, second and third time slots at 23:00. It must be closed at other times.

[0059] Machine Tool 22 It must be opened in the 1st to 6th time slots from 1:00, 2:00 to 13:00, the 1st, 2nd, 3rd and 6th time slots from 18:00, the 1st, 2nd, 3rd, 4th and 6th time slots from 19:00, the 1st, 2nd, 4th, 5th and 6th time slots from 20:00, the 1st, 2nd, 3rd, 4th and 6th time slots from 21:00, the 1st time slot at 22:00, the 1st, 2nd, 3rd and 5th time slots from 23:00, and the 1st to 3rd time slots from 24:00, and it must be closed at other times.

[0060] Machine Tool 23In the 3rd to 6th time slots at 1 o'clock, the 1st, 2nd, 3rd, 5th and 6th time slots at 2 o'clock and 3 o'clock, the 2nd to 6th time slots at 4 o'clock, 5 o'clock and 6 o'clock, the 1st, 2nd, 4th, 5th and 6th time slots at 7 o'clock and 8 o'clock, the 1st, 3rd, 4th, 5th and 6th time slots at 9 o'clock and 10 o'clock, the 1st, 3rd, 4th, 5th and 6th time slots at 11 o'clock, the 1st, 3rd, 4th, 5th and 6th time slots at 12 o'clock, 13 o'clock and 14 o'clock. The 2nd and 3rd time slots, the 1st, 2nd, 4th and 6th time slots at 18:00, the 1st, 4th, 5th and 6th time slots at 19:00, the 1st, 2nd, 4th, 5th and 6th time slots at 20:00, the 1st, 2nd and 5th time slots at 21:00, the 1st time slot at 22:00, the 2nd, 3rd and 5th time slots at 23:00, and the 1st to 3rd time slots at 24:00 need to be opened, and they are closed at other times.

[0061] Machine Tool 31 It needs to be opened in the second time slot at 1 o'clock, the first, fourth and sixth time slots at 2 o'clock, the first, second and third time slots at 3 o'clock, the first and second time slots at 4 o'clock, 5 o'clock and 6 o'clock, and closed at other times.

[0062] Machine Tool 01 The 4th and 6th time slots at 1 o'clock, the 6th time slot from 2 to 11 o'clock, 12 o'clock, the 2nd to 6th time slots at 13 o'clock, 22 o'clock and 23 o'clock, and the 1st to 3rd time slots at 24 o'clock need to be opened, and they should be closed at other times.

[0063] Machine Tool 02 It needs to be opened in the 5th and 6th time slots at 1 o'clock, the 1st to 4th time slots from 2 o'clock to 23 o'clock, and 24 o'clock, and it should be closed at other times.

[0064] Machine Tool 03 It needs to be opened in the 6th time slot at 1 o'clock, the 1st to 5th time slots from 2 o'clock to 23 o'clock, and 24 o'clock, and it should be closed at other times.

[0065] Machine Tool 04 It must be opened from 2:00 to 24:00 and closed at other times.

[0066] After getting the machine M 11 、M 12 、M 21 、M 22 、M 23 、M 31 、M 01 、M 02 、M 03、M 04 After presetting the low-carbon economic dispatch strategy in the future, the processor can control the machine M 11 、M 12 、M 21 、M 22 、M 23 、M 31 、M 01 、M 02 、M 03 、M 04 Work in accordance with the low-carbon economic dispatch strategy to complete the production of products. In the process of each generator set and production equipment in the green factory working in accordance with the low-carbon economic dispatch strategy, the processor can obtain the carbon emissions generated by the generator set of the green factory and the carbon emissions generated by the power purchase from the power grid, and determine the actual carbon emissions of the green factory based on the carbon emissions generated by the generator set and the carbon emissions generated by the power purchase from the power grid.

[0067] Through the above technical solution, the low-carbon economic scheduling strategy of the green factory is optimized, and under this low-carbon economic scheduling strategy, the total profit of the green factory can be increased by 26.7%, and the total carbon emissions can be reduced by 10.4%, which significantly reduces carbon emissions and realizes green and low-carbon production.

[0068] Figure 1 FIG. 1 is a flow chart of a method for economic low-carbon dispatch of a green plant containing CCUS in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0069] The embodiment of the present application also provides a green factory economic low-carbon scheduling device containing CCUS, including:

[0070] a memory configured to store instructions;

[0071] The processor is configured to call instructions from the memory and implement the above-mentioned green factory economic low-carbon scheduling method containing CCUS when executing the instructions.

[0072] An embodiment of the present application also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned green factory economic low-carbon scheduling method containing CCUS.

[0073] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure) and a database (not shown in the figure) connected via a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02 and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store product parameters, demand response, carbon dioxide data and other data. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, a green factory economic low-carbon scheduling method containing CCUS is implemented.

[0074] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0075] An embodiment of the present application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented: determining the demand response of a green factory within a preset historical period; obtaining the carbon dioxide data generated by a CCUS system in the green factory within a preset historical period; constructing an economic and low-carbon scheduling model for the green factory, wherein the economic and low-carbon scheduling model is constructed based on the distributed power generation system, green certificate trading, CCUS system, and demand response of the green factory; solving the economic and low-carbon scheduling model based on product parameters, demand response, and carbon dioxide data of products to be produced by the green factory to obtain a low-carbon economic scheduling strategy for the green factory within a preset future period, wherein the low-carbon economic scheduling strategy includes the working time and shutdown time of each power generation unit and production equipment in the green factory within the preset future period.

[0076] In one embodiment, a green factory includes multiple product storage areas, and constructing an economic and low-carbon scheduling model for the green factory includes: generating a comprehensive benefit function of the CCUS system based on carbon dioxide data; generating a power purchase cost function of the green factory and a green certificate comprehensive benefit function of green certificate trading according to demand response; obtaining the output power of the generator set, and generating a power generation cost function of the distributed power generation system according to the output power of the generator set; generating an objective function according to product parameters, the comprehensive benefit function of the CCUS system, the power purchase cost function, the power generation cost function and the green certificate comprehensive benefit function; obtaining the storage capacity of each product storage area, and generating constraints according to the storage capacity of all product storage areas; and constructing an economic and low-carbon scheduling model according to the objective function and the constraints.

[0077] In one embodiment, the carbon dioxide data includes the carbon dioxide capture operating cost, unit carbon dioxide storage cost, unit carbon dioxide transportation cost, unit carbon dioxide tax, unit carbon dioxide price, carbon dioxide emissions within a preset historical period, carbon dioxide capture within a preset historical period, and carbon dioxide storage within a preset historical period of the CCUS system. Generating a comprehensive benefit function of the CCUS system based on the carbon dioxide data includes: determining the carbon capture operating cost function of the CCUS system based on the carbon dioxide capture operating cost and the output power; determining the carbon storage cost function of the CCUS system based on the unit carbon dioxide storage cost and the carbon dioxide capture within a preset historical period; determining the carbon transportation cost function of the CCUS system based on the unit carbon dioxide transportation cost and the carbon dioxide storage within a preset historical period; determining the carbon penalty function of the CCUS system based on the unit carbon dioxide tax, carbon dioxide emissions within a preset historical period, and carbon dioxide capture within a preset historical period; determining the carbon benefit function of the CCUS system based on the unit carbon dioxide price and the carbon dioxide storage within a preset historical period; generating a comprehensive benefit function of the CCUS system based on the carbon capture operating cost function, the carbon storage cost function, the carbon transportation cost function, the carbon penalty function, and the carbon benefit function.

[0078] In one embodiment, the demand response includes the electricity purchased by the green factory from the power grid and the electricity sold by the green factory to the power grid. Generating the green factory's electricity purchase cost function based on the demand response includes: determining the green factory's purchase cost based on the electricity purchased by the green factory from the power grid and the purchase price; determining the green factory's electricity sales revenue based on the electricity sold by the green factory to the power grid and the sales price; generating the green factory's electricity purchase cost function based on the green factory's purchase cost and electricity sales revenue.

[0079] In one embodiment, the demand response includes the daily quota of renewable energy and the power generation of renewable energy. The green certificate comprehensive benefit function for green certificate trading generated according to the demand response includes: when the daily quota of renewable energy is greater than the power generation of renewable energy, the green certificate comprehensive benefit function is generated according to the daily quota of renewable energy, the power generation of renewable energy, the price of green certificates purchased by green factories and the penalty coefficient; when the daily quota of renewable energy is less than or equal to the power generation of renewable energy, the green certificate comprehensive benefit function is generated according to the daily quota of renewable energy, the power generation of renewable energy and the price of green certificates sold by green factories.

[0080] In one embodiment, the product parameters include the type of product, the raw materials required for each product, the cost of the raw materials, and the value created by each product. The objective function is shown in expression (1):

[0081]

[0082] Among them, ω is the maximum total economic profit of the green factory, v i is the value of the i-th product, g i is the i-th product, n is the total number of product types, c j is the cost of the jth raw material, p j is the jth raw material, m-1 is the total number of raw materials except electricity, C e is the electricity purchase cost function of the green factory, C f is the power generation cost function of the green plant, R ccus is the comprehensive benefit function of CCUS system, R gc It is the comprehensive benefit function of green certificate for green factory’s green certificate transaction.

[0083] In one embodiment, the method also includes: obtaining the processing time of each production equipment before generating constraints based on the storage capacity of all product storage areas; determining the working status of the production equipment adjacent to each product storage area based on the storage capacity of each product storage area; determining the decision variables of each production equipment based on the working status of each production equipment; determining the production efficiency of each production equipment based on the decision variables and processing time of each production equipment; and updating the storage capacity of each product storage area based on the production efficiency of each production equipment.

[0084] In one embodiment, the constraint condition is that the updated storage capacity of each product storage area is less than the preset maximum storage capacity corresponding to each product storage area.

[0085] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the program steps of the method for initializing economic low-carbon scheduling of a green plant containing CCUS.

[0086] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0087] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0091] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0092] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0093] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0094] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for economic low-carbon dispatching of green factories containing CCUS, characterized in that: The method comprises: Determining a demand response of the green plant over a preset historical period; Obtaining carbon dioxide data generated by the CCUS system in the green plant within the preset historical period; Constructing an economic and low-carbon dispatch model of the green factory, wherein the economic and low-carbon dispatch model is constructed based on the distributed power generation system, green certificate trading, CCUS system and demand response of the green factory; The economic low-carbon scheduling model is solved based on the product parameters of the product to be produced by the green factory, the demand response and the carbon dioxide data to obtain the low-carbon economic scheduling strategy of the green factory within a preset future time period, wherein the low-carbon economic scheduling strategy includes the working time and shutdown time of each generator set and production equipment in the green factory within the preset future time period.

2. The method for economic low-carbon dispatching of green factories containing CCUS according to claim 1, characterized in that: The green factory also includes a plurality of product storage areas, and the economic low-carbon scheduling model for building the green factory includes: generating a comprehensive benefit function of the CCUS system according to the carbon dioxide data; Generate the electricity purchase cost function of the green factory and the green certificate comprehensive benefit function of the green certificate transaction according to the demand response; Acquire the output power of the generator set, and generate a power generation cost function of the distributed power generation system according to the output power of the generator set; Generate an objective function according to the product parameters, the comprehensive benefit function of the CCUS system, the power purchase cost function, the power generation cost function and the green certificate comprehensive benefit function; Obtain the storage capacity of each product storage area, and generate constraint conditions based on the storage capacity of all product storage areas; The economic low-carbon scheduling model is constructed according to the objective function and the constraint conditions.

3. The method for economic low-carbon dispatching of green factories containing CCUS according to claim 2, characterized in that: The carbon dioxide data includes the carbon dioxide capture and operation cost of the CCUS system, the unit carbon dioxide storage cost, the unit carbon dioxide transportation cost, the unit carbon dioxide tax, the unit carbon dioxide price, the carbon dioxide emissions within the preset historical period, the carbon dioxide capture within the preset historical period, and the carbon dioxide storage within the preset historical period. Generating the comprehensive benefit function of the CCUS system based on the carbon dioxide data includes: Determining a carbon capture operating cost function of the CCUS system according to the carbon dioxide capture operating cost and the output power; Determining a carbon storage cost function of the CCUS system according to the unit carbon dioxide storage cost and the carbon dioxide capture amount within the preset historical period; Determining a carbon transportation cost function of the CCUS system according to the unit carbon dioxide transportation cost and the carbon dioxide storage amount within the preset historical time period; Determining a carbon penalty function of the CCUS system according to the unit carbon dioxide tax, the carbon dioxide emissions within the preset historical period, and the carbon dioxide capture within the preset historical period; Determining a carbon revenue function of the CCUS system according to the unit carbon dioxide price and the carbon dioxide storage amount within the preset historical period; A comprehensive benefit function of the CCUS system is generated according to the carbon capture operation cost function, the carbon storage cost function, the carbon transportation cost function, the carbon penalty function and the carbon benefit function.

4. The method for economic low-carbon dispatching of green factories containing CCUS according to claim 2, characterized in that: The demand response includes the electricity purchased by the green factory from the power grid and the electricity sold by the green factory to the power grid. The power purchase cost function of the green factory generated according to the demand response includes: Determining the purchase cost of the green plant according to the electricity purchased by the green plant from the power grid and the purchase price; Determine the electricity sales revenue of the green factory according to the electricity sold by the green factory to the power grid and the selling price; The electricity purchase cost function of the green factory is generated according to the purchase cost and electricity sales income of the green factory.

5. The method for economic low-carbon dispatching of green factories containing CCUS according to claim 2, characterized in that: The demand response includes the renewable energy daily quota and renewable energy power generation. The green certificate comprehensive income function for green certificate trading generated according to the demand response includes: In the case where the renewable energy daily quota is greater than the renewable energy power generation, the green certificate comprehensive benefit function is generated according to the renewable energy daily quota, the renewable energy power generation, the price of the green certificate purchased by the green factory and the penalty coefficient; When the renewable energy daily quota is less than or equal to the renewable energy power generation, the green certificate comprehensive benefit function is generated according to the renewable energy daily quota, the renewable energy power generation and the price of the green certificates sold by the green factory.

6. The method for economic low-carbon dispatching of green factories containing CCUS according to claim 2, characterized in that: The product parameters include the type of product, the raw materials required for each product, the cost of the raw materials, and the value created by each product. The objective function is shown in expression (1): Where ω is the maximum total economic profit of the green factory, v i is the value of the i-th product, g i is the i-th product, n is the total number of product types, c j is the cost of the jth raw material, p j is the jth raw material, m-1 is the total number of raw materials except electricity, C e is the electricity purchase cost function of the green factory, C f is the power generation cost function of the green plant, R ccus is the comprehensive benefit function of the CCUS system, R gc It is the comprehensive income function of green certificates for the green factory’s green certificate transactions.

7. The method for economic low-carbon dispatching of green factories containing CCUS according to claim 2, characterized in that: The method further comprises: Before generating constraints based on the storage capacity of all product storage areas, obtain the processing time of each production equipment; Determine the working status of production equipment adjacent to each product storage area based on the storage capacity of each product storage area; Determine the decision variables of each production equipment according to the working status of each production equipment; Determine the production efficiency of each production equipment based on the decision variables and processing time of each production equipment; Update the storage capacity of each product storage area according to the production efficiency of each production equipment.

8. The method for economic low-carbon dispatching of green factories containing CCUS according to claim 7, characterized in that: The constraint condition is that the updated storage capacity of each product storage area is less than the preset maximum storage capacity corresponding to each product storage area.

9. A green factory economic low-carbon dispatching device containing CCUS, characterized in that: include: a memory configured to store instructions; A processor is configured to call the instructions from the memory and implement the economic low-carbon dispatch method of a green factory containing CCUS according to any one of claims 1 to 8 when executing the instructions.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for causing a machine to execute the economic low-carbon dispatching method for a green factory containing CCUS according to any one of claims 1 to 8.

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