A two-stage optimization operation method for low-carbon demand response of molten salt manufacturing process considering grid dynamic carbon potential

Abstract

The present application relates to a kind of low carbon demand response two-stage optimization operation method of molten salt manufacturing process considering power grid dynamic carbon potential, comprising: constructing molten salt manufacturing process dynamic carbon energy flow model;In optimization first stage, with carbon emission minimum, production capacity highest, electric energy consumption and steam consumption minimum as optimization goal, system optimal production rhythm is obtained by using NSGA-III algorithm solution;Considering that molten salt manufacturing enterprise accesses distribution network specific node, based on carbon emission flow theory, the dynamic carbon potential of the node that enterprise accesses power grid is calculated;In optimization second stage, with electricity cost minimum and electricity carbon emission minimum as dual optimization goal, according to the price of peak, flat, valley period and the dynamic carbon potential change of grid-connected point, production plan is optimized, and the optimal production time considering economy and environmental protection is obtained.Compared with prior art, the present application can effectively enhance the accommodation capacity of regional power grid to new energy, improve the energy-saving and carbon-reducing efficiency of molten salt manufacturing.

Description

Technical Field

[0001] The present invention relates to the technical field of regional integrated energy optimization and scheduling, and in particular to a two-stage optimization operation method for low-carbon demand response in a molten salt manufacturing process taking into account the dynamic carbon potential of a power grid. Background Art

[0002] As the upstream raw material for molten salt energy storage, the molten salt manufacturing process has high energy consumption and large carbon emissions. Molten salt enterprises are typical high-energy-consuming enterprises, and there is an urgent need to study appropriate carbon energy flow modeling and energy-saving and carbon-reduction operation optimization methods to improve their ability to participate in the interactive interaction between power grid supply and demand.

[0003] Existing research reveals that the entire production process of molten salt enterprises involves numerous steps, with complex carbon and energy flow relationships, forming a network structure. Insufficient buffer utilization during the production process and complex equipment energy consumption characteristics make carbon emissions calculation difficult and energy efficiency low. Existing research on energy conservation and carbon reduction in product manufacturing processes primarily applies to discrete manufacturing processes. Overall, there is a lack of solutions for dynamic carbon energy flow modeling and energy conservation and carbon reduction optimization scheduling in continuous manufacturing processes for high-energy-consuming enterprises, such as molten salt manufacturing. For example, Chinese patent CN116401810A discloses a Petri net-based carbon energy flow modeling and energy conservation and carbon reduction optimization method for molten salt manufacturing processes. This solution uses Petri nets to model the carbon energy flow of the molten salt manufacturing process and define a P / T system. The model is optimized by constructing multiple objective functions and constraints. Finally, the model is solved using the NSGA-II (fast non-dominated sorting genetic algorithm) with an elitist strategy to determine the optimal production rate of the equipment under different macrocycles. This approach only reduces excessive carbon dioxide emissions caused by some equipment in the molten salt production process and fails to effectively improve energy conservation and carbon reduction efficiency.

[0004] Furthermore, from the perspective of the current new power system, the massive integration of renewable energy into the grid will lead to significant volatility and intermittency, placing the onus on users to absorb and absorb these new energy sources. Traditional constant carbon emission factors fail to reflect the differences in carbon emissions resulting from users' electricity consumption over time, leaving users with little incentive to adjust their electricity usage. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned existing technologies and to provide a two-stage optimization operation method for low-carbon demand response of the molten salt manufacturing process taking into account the dynamic carbon potential of the power grid. It can enhance the regional power grid's ability to absorb new energy, while improving the energy-saving and carbon reduction efficiency of molten salt manufacturing enterprises, and effectively realize the coordinated interaction between the power grid and power users.

[0006] The object of the present invention can be achieved by the following technical solution: a two-stage optimization operation method for low-carbon demand response of a molten salt manufacturing process considering the dynamic carbon potential of the power grid, comprising the following steps:

[0007] S1. Based on the STN (State Task Network) modeling rules, a dynamic carbon energy flow model for the molten salt manufacturing process is constructed.

[0008] S2. In the first optimization phase, the NSGA-III algorithm is used to solve the optimal production rhythm for the internal production process of the molten salt manufacturing enterprise, with the lowest carbon emissions, highest production capacity, and lowest electricity and steam consumption as the optimization objectives. The optimization is based on the equipment production capacity constraints, buffer capacity constraints, collaborative cascade equipment production capacity constraints, production conditions and chemical reaction equilibrium constraints.

[0009] S3. Considering the molten salt manufacturing enterprise connected to a specific node of the distribution network, the dynamic carbon potential of the node connected to the grid is calculated based on the carbon emission flow theory.

[0010] In the second stage of optimization, with the dual optimization goals of minimizing electricity costs and minimizing carbon emissions, the production plan is optimized based on electricity prices during peak, flat, and off-peak periods, as well as the dynamic carbon potential of the grid connection point, to obtain the optimal production time that takes into account both economic and environmental factors.

[0011] S5. Control the working state of the molten salt manufacturing system accordingly according to the optimal production rhythm and optimal production time.

[0012] Furthermore, step S1 specifically combines the state task network with the power of the production task equipment according to the STN modeling rules to establish a power demand model of the production equipment, so as to decouple the molten salt manufacturing process into independent state nodes and task nodes, wherein the state nodes include raw materials, intermediate materials, final products, electricity consumption, steam consumption and carbon emissions;

[0013] The task nodes include solution pump, neutralization reactor, evaporation crystallizer, thickener, centrifuge and dryer.

[0014] Furthermore, the dynamic carbon energy flow model of the molten salt manufacturing process in step S1 includes the STN network material flow matrix and the carbon energy flow matrix :

[0015] ,

[0016] ,

[0017] ,

[0018] ,

[0019] Among them, among them, The first raw material is 25% sodium carbonate. The second raw material is 25% dilute nitric acid. is the first intermediate material, i.e., a mixed solution, is the second intermediate material, i.e., the mixed solution, The third intermediate material is a neutralization solution with a set concentration. The fourth intermediate material is molten salt crystals. The fifth intermediate material is the molten salt crystal. The sixth intermediate material is the molten salt crystal. The final product is 99.7% sodium nitrate salt. is electrical energy, i.e., electricity consumption in the production process, is steam, i.e. steam consumption in the production process, Carbon emissions, that is, carbon emissions during the production process;

[0020] ~ They correspond to the first solution pump, the second solution pump, the neutralization reactor, the third solution pump, the evaporation crystallizer, the thickener, the centrifuge and the dryer respectively. is the power of the first solution pump, is the power of the second solution pump, is the power of the neutralization reactor, is the power of the third solution pump, is the power of the evaporation crystallizer, is the power of the thickener, is the power of the centrifuge, is the power of the dryer, is the steam consumption of the neutralization reactor, is the steam consumption of the evaporation crystallizer, is the steam consumption of the dryer, is the carbon emission of the first solution pump, is the carbon emission of the second solution pump, To neutralize the carbon emissions of the reactor, is the carbon emission of the third solution pump, is the carbon emission of the evaporation crystallizer, is the carbon emission of the thickener, is the carbon emission of the centrifuge, Carbon emissions from the dryer.

[0021] Furthermore, the optimization goal in step S2 is specifically:

[0022] ,

[0023] ,

[0024] ,

[0025] ,

[0026] ,

[0027] ,

[0028] ,

[0029] ,

[0030] ,

[0031] ,

[0032] ,

[0033] ,

[0034] Among them, the total carbon emissions Equal to direct carbon emissions from chemical reactions Indirect carbon emissions corresponding to equipment energy consumption , Represents the types of intermediate and final products in the molten salt manufacturing process, Represents the operating rate of producing the intermediate product or final product, Represents the power of the device that consumes electrical energy, Represents the power of the equipment consuming steam; represents direct carbon emissions from chemical reactions, Represents the indirect carbon emissions corresponding to the energy consumption of the equipment; Represents the relative molecular mass of the acidic raw material, Represents the relative molecular mass of the basic prototype, represents the total amount of reactants, 、 、 、 、 、 They represent the carbon emissions of the solution pump, neutralization reactor, evaporation crystallizer, thickener, centrifuge, and dryer respectively. Representative equipment i The duration of a macro cycle, Representative equipment i The power of a macrocycle, Representative equipment iThe amount of steam consumed per hour in a macro cycle, represents the carbon emission factor of electricity, represents the steam carbon emission factor.

[0035] The equipment production capacity constraint in step S2 is specifically subject to the actual operating rate of the equipment, and is used to constrain each equipment from exceeding the maximum production rate:

[0036] ,

[0037] in, 、 Representing the i The minimum and maximum operating speed of each device, Representative i The operating speed of each device;

[0038] The buffer capacity constraint is specifically:

[0039] ,

[0040] in, 、 Representing the i The minimum and maximum storage capacity of each cache device, Representative i The current storage capacity of each device;

[0041] The production capacity constraint of the collaborative cascade equipment specifically considers that when there is no buffer between the equipment, the upper and lower equipment are connected by pipes or conveyor belts, and materials are continuously transferred. Since pipe blockage or conveyor belt pause cannot occur, storage between the equipment is impossible:

[0042] ,

[0043] in, 、 Represent the production rates of upstream and downstream equipment respectively, Represents the upstream material conversion rate;

[0044] The production conditions constrain equipment production capacity in the following ways: when the intermediate storage area is idle, meaning its buffer capacity is less than 30%, this means the production capacity of the upstream equipment must be higher than that of the downstream equipment to ensure the efficient filling of the intermediate storage area. Conversely, when the intermediate storage area is full, meaning its buffer capacity exceeds 70%, the production capacity of the downstream equipment must exceed that of the upstream equipment to ensure timely consumption of products in the storage area and avoid backlogs. When the intermediate storage area is in operation, meaning its buffer capacity is between 30% and 70%, the production equipment should operate at the lowest energy consumption ratio.

[0045] The chemical reaction equilibrium constraint is used to constrain the ratio of acidic and alkaline raw materials entering the neutralization reactor to be consistent:

[0046] ,

[0047] in, and represents the proportionality coefficient, Represents the delivery rate of acidic raw materials, Represents the delivery rate of alkaline raw materials.

[0048] Furthermore, the step S3 specifically obtains the system's power flow distribution through power flow calculation, and then calculates the carbon potential vectors of all nodes in combination with the carbon emission intensity of different generator sets.

[0049] Furthermore, the power flow distribution of the system includes:

[0050] 1) Branch flow distribution matrix

[0051] The branch power flow distribution matrix is: N Order square matrix, use It is used to describe the active power flow distribution of the power system:

[0052] ,

[0053] ,

[0054] ,

[0055] 2) Unit injection distribution matrix

[0056] The unit injection distribution matrix is ​​a K × N The square matrix of order can be expressed as , which is used to describe the connection between all generator sets and the power system, and reflects the specific situation of the active power injected into the system by the generator sets:

[0057] ,

[0058] 3) Load distribution matrix

[0059] The load distribution matrix is ​​a M × N The square matrix of order is marked as , which is used to describe the connection between all electrical loads and the power system and accurately quantify the active load:

[0060] ,

[0061] 4) Node active flux matrix

[0062] The node active flux matrix is ​​an N-order square matrix, marked as , used to describe the specific contribution of the generator set to the node and the carbon potential between nodes in the system, i ,make represents the set of branches where the flow flows into node 𝑖, For branch s The active power is:

[0063] ,

[0064] According to the definitions of the above three matrices, The matrix i The row diagonal elements are equal to Matrix and Matrix i The sum of the column elements, let , then:

[0065] ,

[0066] in, for N + K A row vector, where all elements are 1. pass and The matrix is ​​generated directly.

[0067] Furthermore, the node carbon potential vector is specifically:

[0068] ,

[0069] ,

[0070] ,

[0071] in, is the nodal carbon potential vector, For the i The carbon potential of each node, is the contribution of power generation equipment to the node, is the carbon emission intensity vector of the generator set, For the k The carbon emission intensity of each power generating unit.

[0072] Furthermore, the lowest electricity cost in step S4 is specifically:

[0073] ,

[0074] ,

[0075] in, Represents the first j The electricity cost of each machine, Representative j The power consumption of each machine, represents the price of electricity at time t, represents the weighted electricity price within a day, Represents the total electricity cost after production transfer, represents the initial production time, Represents the time of transfer.

[0076] Furthermore, the minimum carbon emission of electricity consumption in step S4 is specifically:

[0077] ,

[0078] ,

[0079] in, Represents the first j The carbon emissions of each machine, Representative Moment t The dynamic carbon potential, represents the weighted dynamic carbon potential within a day, Represents the total carbon emissions after transfer production.

[0080] Compared with the prior art, the present invention has the following advantages:

[0081] The present invention first constructs a dynamic carbon energy flow model of the molten salt manufacturing process according to the modeling rules of the STN state task network, and then designs and optimizes the first stage and the second stage respectively. Among them, the first stage is for the internal production process of the molten salt manufacturing enterprise, considering multiple optimization targets such as production capacity and carbon emissions, by making full use of the material storage capacity of the buffer area, increasing the operation flexibility of the frequency conversion equipment before and after the storage area, breaking the production continuity for the operation rate matching restriction of the frequency conversion equipment before and after, so that each device can work at the optimal energy consumption operation rate as much as possible, and can obtain the optimal production rhythm; the second stage considers the interactive energy-saving and carbon reduction optimization of the molten salt manufacturing process under the dynamic carbon potential of the power grid and the time-of-use electricity price, and the power demand can be transferred from peak and high carbon to non-peak and non-high carbon time periods through demand response, so as to obtain the optimal production time that takes into account the economic and environmental protection of the enterprise. The present invention can not only effectively enhance the regional power grid's ability to absorb new energy, but also significantly improve the energy-saving and carbon reduction efficiency of molten salt manufacturing enterprises, thereby realizing the collaborative interaction between the power grid and power users.

[0082] This paper proposes a carbon energy flow modeling approach based on the STN network. This approach decouples the production process into independent state nodes and task nodes, abstracts the operation and scheduling of the entire molten salt production process into nodes in a topological network, and integrates the state-task network with the power, materials, and carbon emissions of production equipment. Compared with traditional modeling methods, this approach better reflects the energy flow characteristics of each device in the system at each moment, providing a clearer description of the equipment's carbon emissions.

[0083] In the first optimization stage, the present invention targets the internal production process of the molten salt manufacturing enterprise with the lowest carbon emissions, the highest production capacity, and the lowest electricity consumption and steam consumption as the optimization goals. In combination with the set equipment production capacity constraints, buffer capacity constraints, collaborative cascade equipment production capacity constraints, production conditions constraints on equipment production capacity, and chemical reaction equilibrium constraints, the NSGA-III algorithm is adopted to solve the optimal production rhythm of the system, which can effectively improve the efficiency of energy saving and carbon reduction.

[0084] The present invention considers the connection of a molten salt manufacturing enterprise to a specific node of the distribution network, and based on the carbon emission flow theory, calculates the dynamic carbon potential of the node where the enterprise is connected to the grid. Then, in the second stage of optimization, the dual optimization goals of minimizing electricity cost and minimizing carbon emissions from electricity are taken. According to the electricity prices during peak, flat and valley periods and the dynamic carbon potential changes of the grid connection point, the production plan is optimized, which can obtain the optimal production time that takes into account both economy and environmental protection, and effectively optimize the operation of the entire molten salt manufacturing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 Schematic diagram of the method flow of the present invention;

[0086] Figure 2 The STN network model of the molten salt manufacturing process in Example 2;

[0087] Figure 3 This is a flow chart for solving the optimal production rhythm optimization model within the molten salt manufacturing enterprise in Example 2;

[0088] Figure 4 This is a power consumption curve of the molten salt manufacturing system under short-term orders in Example 2;

[0089] Figure 5 This is a graph showing the electricity cost of the molten salt manufacturing system under short-term orders in Example 2;

[0090] Figure 6 This is a graph showing carbon emissions from electricity generated by the molten salt manufacturing system under short-term orders in Example 2;

[0091] Figure 7 This is a power consumption curve of the molten salt manufacturing system under daily production in Example 2;

[0092] Figure 8This is a graph showing the electricity cost of the molten salt manufacturing system under daily production in Example 2;

[0093] Figure 9 This is a graph showing the carbon emissions from electricity generated by the molten salt manufacturing system under daily production in Example 2;

[0094] Figure 10 is the carbon potential of the node where the molten salt enterprise is located in Example 2;

[0095] Figure 11 This is a low-carbon demand response flow chart considering the dynamic carbon potential of node electricity and electricity price signals in Example 2;

[0096] Figure 12 This is a power consumption curve diagram before and after optimization of the molten salt manufacturing system under short-term orders in Example 2;

[0097] Figure 13 This is the electricity cost curve before and after optimization of the molten salt manufacturing system under short-term orders in Example 2;

[0098] Figure 14 This is a graph showing the carbon emissions before and after optimization of the molten salt manufacturing system under short-term orders in Example 2;

[0099] Figure 15 This is a power consumption curve diagram of the molten salt manufacturing system before and after optimization under daily production in Example 2;

[0100] Figure 16 This is a graph showing the electricity cost before and after optimization of the molten salt manufacturing system under daily production in Example 2;

[0101] Figure 17 This is a graph showing the carbon emissions before and after optimization of the molten salt manufacturing system under daily production in Example 2. DETAILED DESCRIPTION

[0102] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0103] Example 1

[0104] like Figure 1 As shown, a two-stage optimization operation method for low-carbon demand response of a molten salt manufacturing process considering the dynamic carbon potential of the power grid includes the following steps:

[0105] S1. Based on the modeling rules of the STN state task network, a dynamic carbon energy flow model of the molten salt manufacturing process is constructed;

[0106] S2. In the first optimization phase, the NSGA-III algorithm is used to solve the optimal production rhythm for the internal production process of the molten salt manufacturing enterprise, with the lowest carbon emissions, highest production capacity, and lowest electricity and steam consumption as the optimization objectives. The optimization is based on the equipment production capacity constraints, buffer capacity constraints, collaborative cascade equipment production capacity constraints, production conditions and chemical reaction equilibrium constraints.

[0107] S3. Considering the molten salt manufacturing enterprise connected to a specific node of the distribution network, the dynamic carbon potential of the node connected to the grid is calculated based on the carbon emission flow theory.

[0108] In the second stage of optimization, with the dual optimization goals of minimizing electricity costs and minimizing carbon emissions, the production plan is optimized based on electricity prices during peak, flat, and off-peak periods, as well as the dynamic carbon potential of the grid connection point, to obtain the optimal production time that takes into account both economic and environmental factors.

[0109] S5. Control the working state of the molten salt manufacturing system accordingly according to the optimal production rhythm and optimal production time.

[0110] Example 2

[0111] This embodiment applies the method proposed in the first embodiment to optimize the operation scheduling of the molten salt manufacturing process. The main contents include:

[0112] Step 1: Based on the modeling rules of the STN network, a dynamic carbon energy flow model of the molten salt manufacturing process is constructed;

[0113] Step 2: In the first phase, multiple optimization objectives, such as production capacity and carbon emissions, are considered for the internal production process of the molten salt manufacturing enterprise. By fully utilizing the material storage and discharge capacity of the buffer area, the operating flexibility of the frequency conversion equipment before and after the storage area is increased. This overcomes the operating rate matching restrictions imposed by production continuity on the frequency conversion equipment before and after, allowing each device to operate at the optimal energy consumption operating rate as much as possible, thereby achieving energy conservation and carbon reduction. The NSGA-III algorithm is used to obtain the optimal production rhythm of the system.

[0114] Step 3: Comprehensively consider the specific nodes of the molten salt manufacturing enterprise connected to the distribution network and calculate the dynamic carbon potential of the node connected to the grid based on the carbon emission flow theory;

[0115] Step 4: The second phase proposes energy-saving and carbon-reduction optimization of the molten salt manufacturing process through supply-demand interaction, taking into account the dynamic carbon potential of the grid's electricity and time-of-use electricity prices. Demand response can shift electricity demand from peak, high-carbon periods to off-peak, low-carbon periods, achieving the optimal production time that balances economic and environmental performance.

[0116] Wherein, step 1 specifically includes:

[0117] The STN (state task network) combines the state task network with the power of production task equipment to establish a power demand model for production equipment. Based on the STN network method, the production process is decoupled into independent state nodes and task nodes.

[0118] In this embodiment, the molten salt manufacturing enterprise uses 25% sodium carbonate and 25% dilute nitric acid as raw materials, and generates sodium nitrate, carbon dioxide and water through a neutralization reaction. In order to obtain high-purity sodium nitrate salt, the reaction solution is first subjected to evaporation and crystallization to a concentration of about 80%, and then treated with a thickener to reduce the water content to about 11.11%. The water content is then reduced to 5.2% through centrifugation technology. Finally, a drying process is used to produce sodium nitrate salt with a purity of up to 99.7%. This production process mainly emits water and carbon dioxide, and the company's electricity and steam mainly rely on external purchases. In this way, an STN network model of the molten salt manufacturing process is constructed, and the operation and scheduling of the entire molten salt production process are abstracted as nodes in the topological network for operation, combining the state task network with the power, materials, and carbon emissions of the production equipment.

[0119] The corresponding STN network material flow matrix and the carbon energy flow matrix as follows:

[0120] ,

[0121] , (1)

[0122] ,

[0123] , (2)

[0124] Represents the transformation and loss of materials after they pass through the equipment. It represents the electricity and steam demand of each device and the carbon emissions of the device under different macro cycles.

[0125] The definitions of the symbols in the correlation matrix are shown in Tables 1 and 2.

[0126] Table 1 Status nodes and their meanings in the STN network model

[0127]

[0128] Table 2 Task nodes and their meanings in the STN network model

[0129]

[0130] The STN network model of the molten salt manufacturing process in this embodiment is as follows Figure 2 shown.

[0131] Step 2 specifically includes:

[0132] 1) Objective function

[0133] The operating rates of related equipment in the molten salt manufacturing process are used as adjustable optimization variables. Focusing on the molten salt production process, the objective function is set to minimize carbon emissions, maximize production capacity, and minimize electricity and steam consumption for optimization:

[0134] , (3)

[0135] , (4)

[0136] , (5)

[0137] , (6)

[0138] Where: Total carbon emissions Equal to direct carbon emissions from chemical reactions Indirect carbon emissions corresponding to equipment energy consumption , Represents the types of intermediate and final products in the molten salt manufacturing process, Represents the operating rate of producing the intermediate product or final product, Represents the power of the device that consumes electrical energy, Represents the power of the equipment consuming steam;

[0139] in, represents direct carbon emissions from chemical reactions, The indirect carbon emissions corresponding to the energy consumption of the representative equipment are calculated using the following formula:

[0140] , (7)

[0141] , (8)

[0142] , (9)

[0143] , (10)

[0144] , (11)

[0145] , (12)

[0146] , (13)

[0147] , (14)

[0148] Where: Represents the relative molecular mass of the acidic raw material, Represents the relative molecular mass of the basic prototype, represents the total amount of reactants, 、 、 、 、 、 They represent the carbon emissions of the solution pump, neutralization reactor, evaporation crystallizer, thickener, centrifuge, and dryer respectively. Representative equipment i The duration of a macro cycle, Representative equipment i The power of a macrocycle, Representative equipment i The amount of steam consumed per hour in a macro cycle, represents the carbon emission factor of electricity, represents the steam carbon emission factor.

[0149] 2) Constraints

[0150] The parameter values ​​are usually subject to actual conditions such as the maximum production rate of the relevant equipment and the buffer capacity. There are five main constraints: equipment production capacity constraints, buffer capacity constraints, collaborative cascade equipment production capacity constraints, production conditions constraints on equipment production capacity, and chemical reaction equilibrium constraints.

[0151] Subject to the actual operating speed of the equipment, each device cannot exceed the maximum production rate:

[0152] , (15)

[0153] in, 、 Representing the i The minimum and maximum operating speed of each device, Representative i The operating speed of the device.

[0154] Buffer capacity constraints:

[0155] , (16)

[0156] in, 、 Representing the i The minimum and maximum storage capacity of each cache device, Representative iThe current storage capacity of the device.

[0157] Production capacity constraints of collaborative cascade equipment: When there is no buffer between devices, the upper and lower devices are connected by pipes or conveyor belts, and materials are continuously converted. Since pipe blockage or conveyor belt pause cannot occur, storage cannot be performed between devices.

[0158] , (17)

[0159] in, 、 Represent the production rates of upstream and downstream equipment respectively, Represents the upstream material conversion rate.

[0160] Production conditions constrain equipment production capacity: Specifically, when the intermediate storage area is idle (i.e., its buffer capacity is below 30%), the production capacity of the upstream equipment must be higher than that of the downstream equipment to ensure the efficient filling of the intermediate storage area. Conversely, when the intermediate storage area is full (i.e., its buffer capacity exceeds 70%), the production capacity of the downstream equipment must exceed that of the upstream equipment to ensure timely consumption of product in the storage area and avoid backlogs. When the intermediate storage area is operating (i.e., its buffer capacity is between 30% and 70%), production equipment should operate at the lowest energy consumption ratio.

[0161] Chemical reaction equilibrium constraint: Chemical reaction will occur during the molten salt manufacturing process. In order to ensure sufficient chemical reaction, the ratio of acid and alkaline raw materials entering the neutralization reactor must be consistent. Therefore

[0162] , (18)

[0163] in, and represents the proportionality coefficient, Represents the delivery rate of acidic raw materials, Represents the delivery rate of alkaline raw materials.

[0164] In the first stage, the optimal production rhythm optimization model solution process of the molten salt manufacturing enterprise is considered. Figure 3 As shown, according to the above process, the short-term molten salt production optimization process event table is shown in Table 3; the daily molten salt production optimization process event table is shown in Table 4.

[0165] Table 3 Short-term molten salt production optimization process event table

[0166]

[0167] Table 4 Molten salt production optimization process event table

[0168]

[0169] The relevant curve of power production of molten salt manufacturing system under short-term orders is as follows Figures 4 to 6 As shown; the relevant curve of power production of molten salt manufacturing system under daily production is as follows Figures 7 to 9 shown.

[0170] Step 3 specifically includes:

[0171] In this embodiment, the regional network where the molten salt manufacturing enterprise is located has N nodes, the system also includes K The system power flow distribution is obtained through power flow calculation.

[0172] 1) Branch flow distribution matrix

[0173] The branch power flow distribution matrix is: N Order square matrix, use It is used to describe the active power flow distribution of the power system. As shown in the following formula:

[0174] , , , (19)

[0175] 2) Unit injection distribution matrix

[0176] The unit injection distribution matrix is ​​a K × N The square matrix of order can be expressed as , which is used to describe in detail the connection relationship between all generator sets and the power system, and further reflect the specific situation of the active power injected into the system by the generator set. As shown in the following formula:

[0177] , (20)

[0178] 3) Load distribution matrix

[0179] The load distribution matrix is ​​a M × N The square matrix of order is marked as , which is used to elaborate the connection between all electrical loads and the power system and accurately quantify the active load. As shown in the following formula:

[0180] , (twenty one)

[0181] 4) Node active flux matrix

[0182] The node active flux matrix is ​​an N-order square matrix, marked as , which is used to elaborate on the specific contribution of the generator sets in the system to the nodes and between the nodes to the carbon potential.

[0183] For Node i ,make represents the set of branches where the flow flows into node 𝑖, For branch s The active power is:

[0184] , (twenty two)

[0185] According to the definitions of the above three matrices, The matrix i The row diagonal elements are equal to Matrix and Matrix i The sum of the elements in the column.

[0186] If the order , then:

[0187] , (twenty three)

[0188] Where: for N + K A row vector of order , where all elements are 1. Therefore Available through and The matrix is ​​generated directly.

[0189] 5) Carbon emission intensity vector of power generation units

[0190] Different generator sets have different carbon emission characteristics. k ( k =1,2,…, K The carbon emission intensity of each generator set is , then the carbon emission intensity vector of the generator set can be expressed as:

[0191] , (twenty four)

[0192] 6) Node carbon potential vector

[0193] The primary calculation target of the carbon emission flow of the power system is the carbon potential of all nodes. i ( i =1,2,…, N ) nodes is , then the node carbon potential vector can be expressed as:

[0194] , (25)

[0195] At the same time, the node carbon potential can be obtained by 、 、 The matrix is ​​directly generated, and the calculation formula of the node carbon potential is shown in Equations (26) and (27):

[0196] , (26)

[0197] , (27)

[0198] In the formula It will directly reflect the contribution of different power generation equipment to the node.

[0199] In this embodiment, the carbon potential of the node where the molten salt enterprise is located is as follows: Figure 10 shown.

[0200] Step 4 specifically includes:

[0201] The second stage considers the low-carbon demand response of the dynamic carbon potential of node electricity and electricity price signals. By optimizing the allocation of power resources, enterprises will not only optimize production plans according to electricity prices during peak, flat and valley periods, but also optimize production plans according to the dynamic carbon potential changes of grid connection points, and find the balance point between the economy and environmental protection of the production process.

[0202] Focusing on the economy and environmental protection of the molten salt production process, we set up dual optimization goals, namely, the lowest electricity cost and the lowest carbon emissions from electricity use.

[0203] Lowest electricity cost:

[0204] , (28)

[0205] , (29)

[0206] in, represents the electricity cost of the jth machine after production transfer, represents the power consumption of the jth machine, represents the price of electricity at time t, represents the weighted electricity price within a day, Represents the total electricity cost after production transfer, represents the initial production time, Represents the time of transfer.

[0207] Lowest carbon emissions from electricity consumption:

[0208] , (30)

[0209] , (31)

[0210] in, Represents the first j The carbon emissions of each machine, Representative Moment t The dynamic carbon potential, represents the weighted dynamic carbon potential within a day, Represents the total carbon emissions after transfer production.

[0211] In this embodiment, the low-carbon demand response process in the second stage considering the dynamic carbon potential of node electricity and electricity price signals is as follows: Figure 11 According to the above process, the power production related curves before and after the optimization of the short-term molten salt manufacturing system are obtained as follows: Figures 12 to 14 As shown; the power production related curves before and after the optimization of the daily molten salt manufacturing system are as follows Figures 15 to 17 As shown in the figure, the comparison shows that after optimization using this solution, the power consumption and carbon emissions of the molten salt manufacturing process can be significantly reduced, achieving the goal of efficient energy conservation and carbon reduction.

[0212] Compared with traditional modeling methods, the carbon energy flow modeling based on the STN network proposed in this solution can better reflect the energy flow characteristics of each device in the system at each time and provide a clearer description of the carbon emissions of the equipment;

[0213] This solution takes into account multiple optimization objectives such as production capacity and carbon emissions for the internal production process of the molten salt manufacturing enterprise in the first phase. By fully utilizing the material storage and discharge capacity of the buffer area, the operating flexibility of the frequency conversion equipment before and after the storage area is increased, and the operating rate matching restrictions of the frequency conversion equipment before and after the production continuity are broken, so that each device can operate at the optimal energy consumption operating rate as much as possible, thereby achieving the purpose of energy saving and carbon reduction. The NSGA-III algorithm is used to obtain the optimal production rhythm of the system.

[0214] This solution also incorporates a second-stage production optimization based on the production rhythms derived from the first stage. Taking into account the specific nodes where molten salt manufacturers connect to the distribution network, and based on carbon emission flow theory, the dynamic carbon potential of these nodes is calculated. This approach proposes an energy-saving and carbon-reduction optimization strategy based on the supply-demand interaction of the molten salt manufacturing process, taking into account the dynamic carbon potential of the grid's electricity consumption and time-of-use electricity prices. Demand response can shift electricity demand from peak, high-carbon periods to off-peak, low-carbon periods, achieving the optimal production time that balances economic and environmental performance.

Claims

1. A two-stage optimization operation method for low-carbon demand response in a molten salt manufacturing process considering the dynamic carbon potential of the power grid, characterized in that: The following steps are involved: S1. Based on the modeling rules of the STN state task network, a dynamic carbon energy flow model of the molten salt manufacturing process is constructed; S2. In the first stage of optimization, the NSGA-III algorithm is used to solve the optimal production rhythm of the system, targeting the internal production process of the molten salt manufacturing enterprise with the lowest carbon emissions, highest production capacity, and lowest electricity and steam consumption as the optimization objectives, combined with the set constraints; S3. Considering the molten salt manufacturing enterprise connected to a specific node of the distribution network, the dynamic carbon potential of the node connected to the grid is calculated based on the carbon emission flow theory. In the second stage of optimization, with the dual optimization goals of minimizing electricity costs and minimizing carbon emissions, the production plan is optimized based on electricity prices during peak, flat, and off-peak periods, as well as the dynamic carbon potential of the grid connection point, to obtain the optimal production time that takes into account both economic and environmental factors. S5. Control the working state of the molten salt manufacturing system accordingly according to the optimal production rhythm and optimal production time; Step S1 specifically combines the state task network with the power of the production task equipment according to the STN modeling rules to establish a power demand model for the production equipment, so as to decouple the molten salt manufacturing process into independent state nodes and task nodes, where the state nodes include raw materials, intermediate materials, final products, electricity consumption, steam consumption and carbon emissions; Task nodes include solution pump, neutralization reactor, evaporation crystallizer, thickener, centrifuge and dryer; The dynamic carbon energy flow model of the molten salt manufacturing process in step S1 includes the STN network material flow matrix and the carbon energy flow matrix : ， ， ， ， in, The first raw material is 25% sodium carbonate. The second raw material is 25% dilute nitric acid. is the first intermediate material, i.e., a mixed solution, is the second intermediate material, i.e., the mixed solution, The third intermediate material is a neutralization solution with a set concentration. The fourth intermediate material is molten salt crystals. The fifth intermediate material is the molten salt crystal. The sixth intermediate material is the molten salt crystal. The final product is 99.7% sodium nitrate salt. is electrical energy, i.e., electricity consumption in the production process, is steam, i.e. steam consumption in the production process, Carbon emissions, that is, carbon emissions during the production process; ~ They correspond to the first solution pump, the second solution pump, the neutralization reactor, the third solution pump, the evaporation crystallizer, the thickener, the centrifuge and the dryer respectively. is the power of the first solution pump, is the power of the second solution pump, is the power of the neutralization reactor, is the power of the third solution pump, is the power of the evaporation crystallizer, is the power of the thickener, is the power of the centrifuge, is the power of the dryer, is the steam consumption of the neutralization reactor, is the steam consumption of the evaporation crystallizer, is the steam consumption of the dryer, is the carbon emission of the first solution pump, is the carbon emission of the second solution pump, To neutralize the carbon emissions of the reactor, is the carbon emission of the third solution pump, is the carbon emission of the evaporation crystallizer, is the carbon emission of the thickener, is the carbon emission of the centrifuge, Carbon emissions from dryers; The optimization objectives in step S2 are specifically: ， ， ， ， ， ， ， ， ， ， ， ， Among them, the total carbon emissions Equal to direct carbon emissions from chemical reactions Indirect carbon emissions corresponding to equipment energy consumption , Represents the types of intermediate and final products in the molten salt manufacturing process, Represents the operating rate of producing the intermediate product or final product, Represents the power of the equipment that consumes electrical energy, Represents the power of the equipment consuming steam; represents direct carbon emissions from chemical reactions, Represents the indirect carbon emissions corresponding to the energy consumption of the equipment; Represents the relative molecular mass of the acidic raw material, Represents the relative molecular mass of the basic prototype, represents the total amount of reactants, 、 、 、 、 、 They represent the carbon emissions of the solution pump, neutralization reactor, evaporation crystallizer, thickener, centrifuge, and dryer respectively. Representative equipment i The duration of a macro cycle, Representative equipment i The power of a macrocycle, Representative equipment i The amount of steam consumed per hour in a macro cycle, represents the carbon emission factor of electricity, represents the steam carbon emission factor; The constraints in step S2 include equipment production capacity constraints, buffer capacity constraints, collaborative cascade equipment production capacity constraints, production condition constraints on equipment production capacity, and chemical reaction equilibrium constraints. The equipment production capacity constraints are specifically subject to the actual operating rate of the equipment and are used to constrain each equipment to not exceed the maximum production rate: ， in, 、 Representing the i The minimum and maximum operating speed of each device, Representative i The operating speed of each device; The buffer capacity constraint is specifically: ， in, 、 Representing the i The minimum and maximum storage capacity of each cache device, Representative i The current storage capacity of each device; The production capacity constraint of the collaborative cascade equipment specifically considers that when there is no buffer between the equipment, the upper and lower equipment are connected by pipes or conveyor belts, and materials are continuously transferred. Since pipe blockage or conveyor belt pause cannot occur, storage between the equipment is impossible: ， in, 、 Represent the production rates of upstream and downstream equipment respectively, Represents the upstream material conversion rate; The production conditions constrain equipment production capacity in the following ways: when the intermediate storage area is idle, meaning its buffer capacity is less than 30%, the production capacity of the upstream equipment must be higher than that of the downstream equipment. Conversely, when the intermediate storage area is full, meaning its buffer capacity exceeds 70%, the production capacity of the downstream equipment must exceed that of the upstream equipment. Furthermore, when the intermediate storage area is in operation, meaning its buffer capacity is between 30% and 70%, the production equipment must operate at the lowest energy consumption ratio. The chemical reaction equilibrium constraint is used to constrain the ratio of acidic and alkaline raw materials entering the neutralization reactor to be consistent: ， in, and represents the proportionality coefficient, Represents the delivery rate of acidic raw materials, Represents the delivery rate of alkaline raw materials; Step S3 specifically obtains the system's power flow distribution through power flow calculation, and then calculates the carbon potential vectors of all nodes based on the carbon emission intensity of different generator sets. The system's power flow distribution includes: 1) Branch flow distribution matrix The branch power flow distribution matrix is: N Order square matrix, use It is used to describe the active power flow distribution of the power system: ， ， ， 2) Unit injection distribution matrix The unit injection distribution matrix is ​​a K × N The square matrix of order can be expressed as , which is used to describe the connection between all generator sets and the power system, and reflects the specific situation of the active power injected into the system by the generator sets: ， 3) Load distribution matrix The load distribution matrix is ​​a M × N The square matrix of order is marked as , which is used to describe the connection between all electrical loads and the power system and accurately quantify the active load: ， 4) Node active flux matrix The node active flux matrix is ​​an N-order square matrix, marked as , used to describe the specific contribution of the generator set to the node and the carbon potential between nodes in the system, i ,make represents the set of branches where the flow flows into node 𝑖, For branch s The active power is: ， The matrix i The row diagonal elements are equal to Matrix and Matrix i The sum of the column elements, let , then: ， in, for N + K A row vector, where all elements are 1. pass and Matrix direct generation; The node carbon potential vector is specifically: ， ， ， in, is the nodal carbon potential vector, For the i The carbon potential of each node, is the contribution of power generation equipment to the node, is the carbon emission intensity vector of the generator set, For the k The carbon emission intensity of each power generating unit.

2. The two-stage optimization operation method for low-carbon demand response of the molten salt manufacturing process considering the dynamic carbon potential of the power grid according to claim 1 is characterized in that: The lowest electricity cost in step S4 is specifically: ， ， in, Represents the first j The electricity cost of each machine, Representative j The power consumption of each machine, represents the price of electricity at time t, represents the weighted electricity price within a day, Represents the total electricity cost after production transfer, represents the initial production time, Represents the time of transfer.

3. The two-stage optimization operation method for low-carbon demand response of the molten salt manufacturing process considering the dynamic carbon potential of the power grid according to claim 2 is characterized in that: The minimum carbon emission of electricity consumption in step S4 is specifically: ， ， in, Represents the first j The carbon emissions of each machine, Representative Moment t The dynamic carbon potential, represents the weighted dynamic carbon potential within a day, Represents the total carbon emissions after transfer production.

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