Green electricity consumption distribution method, system and equipment based on industrial chain and medium

Abstract

The invention is suitable for the technical field of green electricity distribution, and provides a green electricity consumption distribution method, system and device based on an industrial chain and a medium. The target variables comprise direct green electricity consumption of a product, upstream direct raw material green electricity consumption in an industrial chain and upstream indirect product green electricity consumption in the industrial chain; determining constraint conditions according to the target function, wherein the constraint conditions include that the green electricity quantity of the product does not exceed the total amount of electric energy consumed for producing the product, the green electricity quantity of the product does not exceed the green electricity quantity owned by an enterprise, and the green electricity transaction time of the product needs to be matched with the production cycle; constructing an initial distribution model of the green electricity consumption of the product according to the target function and the constraint condition; optimizing the initial distribution model by using a particle swarm algorithm; and inputting the obtained variable parameters of the target product into the optimized model to generate an optimal green power distribution scheme of the target product, thereby effectively reducing the waste of green power resources of the product in an industrial chain.

Description

Technical Field

[0001] The present invention relates to the technical field of green electricity distribution, and in particular to a method, system, device and medium for distributing green electricity consumption based on an industrial chain. Background Art

[0002] Green electricity is electric energy converted from renewable energy. There is no carbon emission in the production process, so it has green attributes and environmental value. Broadly speaking, green electricity consumption includes two categories: green electricity and green certificates. Green electricity refers to the amount of electricity generated by renewable energy, which is a physical consumption; green certificates refer to electronic certificates issued for the amount of electricity generated by renewable energy, which is an environmental attribute subscription under the separation of certificates and electricity. The circulation of green certificates can give full play to the relative positive externality advantages of renewable energy.

[0003] In the existing technology, under the requirements of the quota system, commodity manufacturers purchase green electricity contracts through trading centers to complete direct green electricity consumption. This part of green electricity consumption will be passed downstream along the supply chain with commodities, and downstream enterprises will complete indirect green electricity consumption by purchasing upstream components. In the process of allocating green electricity, enterprises consider aspects such as ensuring corporate electricity consumption, controlling the proportion of retained electricity, participating in green electricity transactions, and implementing retained electricity allocation. When allocating to a certain product, the impact of the green electricity flow of the entire industrial chain on the green electricity consumption of the product is not considered, making the green electricity consumption allocation accuracy inaccurate.

[0004] In view of this, a method, system, device and medium for allocating green electricity consumption based on the industrial chain are needed. Summary of the invention

[0005] The embodiments of the present application provide a method, system, device and medium for allocating green electricity consumption based on an industrial chain, which are used to solve the problem of insufficient accuracy in allocating green electricity consumption based on an industrial chain.

[0006] A first aspect of the embodiment of the present application provides a method for allocating green electricity consumption based on an industrial chain, including:

[0007] Constructing an objective function based on target variables, wherein the target variables include direct green electricity consumption of products, direct green electricity consumption of raw materials in the middle and upper reaches of the industrial chain, and indirect green electricity consumption of products in the middle and upper reaches of the industrial chain;

[0008] Determine constraints based on the objective function, where the constraints include that the green electricity of the product must not exceed the total amount of electricity consumed in producing the product, the green electricity of the product must not exceed the green electricity owned by the enterprise, and the green electricity trading time of the product must match the production cycle;

[0009] Constructing an initial allocation model for product green electricity consumption according to the objective function and the constraint conditions;

[0010] Optimizing the initial allocation model using a particle swarm algorithm;

[0011] The obtained variable parameters of the target product are input into the optimized model to generate the optimal green electricity allocation plan for the target product.

[0012] Furthermore, the expression for constructing the objective function based on the target variable is as follows:

[0013]

[0014] Where: Q n,t Allocate green electricity consumption for the industrial chain of product n in time t.

[0015] Furthermore, the direct green electricity consumption of the product Q dir.re The expression used as the target variable is as follows:

[0016] Q dir.re =kq1(t)+kq2(t);

[0017] Where: q1(t) is the direct energy consumption of the product, q2(t) is the energy loss of the product, and k is the total energy consumption coefficient;

[0018] The direct raw material green electricity consumption in the upstream and middle reaches of the industrial chain Q indir.re The expression used as the target variable is as follows:

[0019]

[0020] Where: q i (t) is the green electricity consumption of the i-th direct raw material in the upstream of the product;

[0021] The green electricity consumption of indirect products in the upstream and midstream of the industrial chain is Q ondir.re The expression used as the target variable is as follows:

[0022]

[0023] Where: q j (t) is the green electricity consumption of the jth indirect product in the upstream of the product, and λ is the consumption weight of the upstream indirect product in the industrial chain.

[0024] Furthermore, the expressions of the constraints are as follows:

[0025] Q dir.re ≤Q cons,m ;

[0026] Q dir.re ≤Q dis,m ;

[0027] T∈Tpc ;

[0028] Where: Q cons,m is the total amount of electricity consumed in production, Q dis,m is the green electricity owned by the enterprise, T is the green electricity trading time of the product, T pc For the production cycle.

[0029] Furthermore, the optimizing the initial allocation model by using a particle swarm algorithm includes:

[0030] Initializing parameters, including the number of particle swarms, the number of iterations, the position and speed of the particle swarms;

[0031] Determine the fitness function;

[0032] The position and speed of the particle group in each iteration are updated according to the fitness function to determine the optimal particle position and speed, thereby determining the optimization parameters corresponding to the initial allocation model.

[0033] Furthermore, the fitness function f(y) is expressed as follows:

[0034]

[0035] In the formula: y is the actual result, y ′ For the prediction results.

[0036] Furthermore, the expressions of the position and velocity of the particle group are as follows:

[0037]

[0038] Where: s0 is the position of the particle group, v0 is the velocity of the particle group.

[0039] A second aspect of the embodiment of the present application provides a green electricity consumption allocation system based on an industrial chain, including:

[0040] An objective function construction unit, used to construct an objective function based on the objective variables, wherein the objective variables include the direct green electricity consumption of the product, the direct green electricity consumption of raw materials in the middle and upper reaches of the industrial chain, and the indirect green electricity consumption of products in the middle and upper reaches of the industrial chain;

[0041] A constraint determination unit, used to determine the constraint conditions according to the objective function, wherein the constraint conditions include that the green electricity amount of the product shall not exceed the total amount of electric energy consumed in producing the product, the green electricity amount of the product shall not exceed the green electricity amount owned by the enterprise, and the green electricity trading time of the product shall match the production cycle;

[0042] An initial allocation model construction unit, used to construct an initial allocation model for product green electricity consumption according to the objective function and the constraint conditions;

[0043] An initial allocation model optimization unit, used for optimizing the initial allocation model using a particle swarm algorithm;

[0044] The optimal green electricity allocation plan generating unit is used to input the acquired variable parameters of the target product into the optimized model to generate the optimal green electricity allocation plan of the target product.

[0045] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein when the processor executes the computer-readable instructions, the steps of the green electricity consumption allocation method based on the industrial chain as described in any one of the above are implemented.

[0046] A fourth aspect of an embodiment of the present application provides a readable storage medium, which stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the steps of the green electricity consumption allocation method based on the industrial chain as described in any one of the above are implemented.

[0047] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0048] In this application, the objective function is first constructed based on the green electricity consumption of a certain product in the industrial chain, including direct green electricity consumption, the green electricity consumption of the upstream direct raw materials of the product, and the green electricity consumption of the upstream indirect products of the product, and then the constraints are determined, where the constraints include that the green electricity consumption of the product must not exceed the total amount of electricity consumed in the production of the product, the green electricity consumption of the product must not exceed the green electricity owned by the enterprise, and the green electricity trading time of the product must match the production cycle; then, the initial allocation model is constructed based on the objective function and the constraints, and the initial model is optimized using the particle swarm algorithm. Finally, the optimal green electricity allocation plan for a certain target product is generated based on the optimized allocation model. This application determines the overall optimal allocation of a certain product in the industrial chain by setting the variables and constraints of the objective function, so that when the entire industrial chain is understood, the green electricity allocation is avoided from being inaccurate, effectively reducing the waste of green electricity resources of the product in the industrial chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of an embodiment of a method for allocating green electricity consumption based on an industrial chain in the present invention;

[0050] Figure 2 This is a schematic diagram of the green current transfer structure of the industrial chain in the present invention;

[0051] Figure 3 This is a schematic diagram of the power battery industry chain in the present invention;

[0052] Figure 4 It is a structural block diagram of an embodiment of a green electricity consumption allocation system based on an industrial chain in the present invention;

[0053] Figure 5 The figure is a structural block diagram of a computer device in the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In this embodiment, the green electricity consumption allocation method based on the industrial chain is used to improve the accuracy of green electricity allocation of products in the industrial chain, thereby reducing the waste of green electricity resources. The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, without specific limitation.

[0056] Embodiment 1

[0057] See also Figure 1 , an embodiment of a method for allocating green electricity consumption based on an industrial chain in the present invention comprises the following steps:

[0058] S11. Construct an objective function based on the target variables, where the target variables include the direct green electricity consumption of the product, the direct green electricity consumption of raw materials in the middle and upper reaches of the industrial chain, and the indirect green electricity consumption of products in the middle and upper reaches of the industrial chain;

[0059] In actual applications, the green electricity consumption of an enterprise is evenly distributed across all product lines. When calculating the green electricity consumption of a single product, it is not comprehensive to use only the direct green electricity consumption of the enterprise as the green electricity consumption allocated to the product. Therefore, the present invention considers the entire industrial chain to calculate the green electricity consumption of a certain product, and obtains the optimal total green electricity consumption that should be allocated to the product based on all the green electricity consumption generated by the entire industrial chain.

[0060] like Figure 2 Take the product flow process of the industrial chain shown in the figure as an example. This process includes the product name, product model, production batch, node link relationship and product flow direction of all nodes in the upstream and downstream of the industrial chain. As shown in the figure, the terminal product of the industrial chain is P0. There are four companies A, B, C and D in the industrial chain. In order to accurately know the energy consumption and green electricity consumption level of P0 in the whole production cycle, it is necessary to clarify the corresponding relationship and product flow information between P0 and all its upstream raw materials P1-P5. The traceability directions are A->B->D and A->B->C respectively. It should be noted that the industrial chain structure is usually very complex. Figure 3Taking the power battery as an example, if we trace back to its terminal products (vehicles and charging piles), there are hundreds of companies involved, including raw material companies that are directly in contact, that is, batteries, motors or electronic controls, and indirect product companies, that is, battery raw materials, as well as upstream companies of battery raw materials, such as minerals, and the same intermediate product may be provided by different companies. For the convenience of calculation, the direct green electricity consumption of the product (the direct green electricity consumption of the terminal product P0), the direct green electricity consumption of raw materials in the middle and upper reaches of the industrial chain (the green electricity consumption of direct raw materials P1), and the green electricity consumption of indirect products in the middle and upper reaches of the industrial chain (the green electricity consumption of P2, P3, and P4) are used as target variables to determine the objective function.

[0061] Specifically, the direct green electricity consumption of the product is Q dir.re The expression used as the target variable is as follows:

[0062] Q dir.re =kq1(t)+kq2(t);

[0063] Where: q1(t) is the direct energy consumption of the product, q2(t) is the energy loss of the product, and k is the total energy consumption coefficient.

[0064] Green electricity consumption of direct raw materials in the middle and upper reaches of the industrial chain Q ondir.re The expression used as the target variable is as follows:

[0065]

[0066] Where: q i (t) is the green electricity consumption of the i-th direct raw material in the upstream of the product.

[0067] Green electricity consumption of indirect products in the middle and upper reaches of the industrial chain Q ondir.re The expression used as the target variable is as follows:

[0068]

[0069] Where: q j (t) is the green electricity consumption of the jth indirect product in the upstream of the product, and λ is the consumption weight of the upstream indirect product in the industrial chain.

[0070] The expression of the objective function is as follows:

[0071]

[0072] Where: Q n,t Allocate green electricity consumption for the industrial chain of product n in time t.

[0073] From the above expression, we can see that we need to solve the minimum allocated green electricity amount of the product.

[0074] S12. Determine the constraints based on the objective function. The constraints include that the green electricity of the product must not exceed the total amount of electricity consumed in producing the product, the green electricity of the product must not exceed the green electricity owned by the enterprise, and the green electricity trading time of the product must match the production cycle;

[0075] In this embodiment, the green electricity quantity shall not exceed the total amount of electric energy consumed in producing the product, the green electricity quantity of the product shall not exceed the green electricity quantity owned by the enterprise, and the green electricity trading time of the product shall match the production cycle. The expressions used as the constraints are as follows:

[0076] Q dir.re ≤Q cons,m ;

[0077] Q dir.re ≤Q dis,m ;

[0078] T∈T pc ;

[0079] Where: Q cons,m is the total amount of electricity consumed in production, Q dis,m is the green electricity owned by the enterprise, T is the green electricity trading time of the product, T pc For the production cycle.

[0080] S13. Construct an initial allocation model for product green electricity consumption based on the objective function and constraints;

[0081] In this embodiment, the initial allocation model is an optimal green electricity allocation plan based on the product disaster and the industrial chain, which is obtained based on the above objective function and constraint conditions.

[0082] S14. Optimizing the initial allocation model using a particle swarm algorithm; comprising the following steps:

[0083] 1. Initialize parameters, including the number of particle swarms, number of iterations, position and speed of the particle swarm;

[0084] The model is initialized, the number of particle swarms is set to 120, the number of iterations is set to 60, the search dimension is set to d=6, and the optimal solution is found through iterative updates.

[0085] Initialize the position and speed of the particle swarm and randomly generate the position of the i-th particle as X i =(l,m), l is the initial learning rate, the interval is [0.001,0.1], m is the number of iterations of the initial allocation model, the interval is [10,200].

[0086] 2. Determine the fitness function;

[0087] The expression of the fitness function f(y) is as follows:

[0088]

[0089] In the formula: y is the actual result, y ′ For the prediction results.

[0090] 3. Update the position and speed of the particle swarm in each iteration according to the fitness function to determine the optimal particle position and speed, thereby determining the optimization parameters corresponding to the initial allocation model.

[0091] The position and velocity of the particle swarm are updated in each iteration. The formulas for the particle position s0 and velocity v0 are as follows:

[0092]

[0093] The particle velocity update formula is as follows:

[0094]

[0095] The position update formula is as follows:

[0096]

[0097] Where: o = 1, 2, ..., O, m is the number of iterations of the initial allocation model, ω is the inertia factor, is the velocity of particle o in the d-dimensional space in the m-th iteration, c1 and c2 are learning factors, and λ is a random number between [0,1]. is the position of o in the d dimension in the mth iteration, The current position of o in dimension d during the m iterations, is the position of the global extreme value of o in d dimensions in m iterations.

[0098] S15. Input the obtained variable parameters of the target product into the optimized model to generate the optimal green electricity allocation plan for the target product.

[0099] In this embodiment, after optimizing the initial allocation model using the particle swarm algorithm, the variable parameters of a target product are randomly obtained, that is, the data corresponding to the target parameters mentioned above, and these data are used as model input parameters and input into the optimized model to generate the optimal green electricity allocation plan for the target product in the entire industrial chain. The allocation plan here refers to the overall minimum allocation amount of a single product in the industrial chain, and the specific allocation amount in the node also needs to calculate the proportion of green electricity consumption of the node in the industrial chain. The specific calculation process of the proportion is not repeated here.

[0100] The above embodiment calculates the green electricity allocation based on the products in the industrial chain by considering the flow of products in the industrial chain, so that the green electricity allocation is more accurate and the waste of green electricity resources is greatly reduced.

[0101] Embodiment 2

[0102] See also Figure 4 An embodiment of a green electricity consumption allocation system based on an industrial chain in the present invention includes the following steps:

[0103] An objective function construction unit 101 is used to construct an objective function based on the objective variables, where the objective variables include the direct green electricity consumption of the product, the direct green electricity consumption of raw materials in the middle and upper reaches of the industrial chain, and the indirect green electricity consumption of products in the middle and upper reaches of the industrial chain;

[0104] The constraint determination unit 102 is used to determine the constraint according to the objective function, wherein the constraint includes that the green electricity of the product shall not exceed the total amount of electric energy consumed in producing the product, the green electricity of the product shall not exceed the green electricity owned by the enterprise, and the green electricity trading time of the product shall match the production cycle;

[0105] An initial allocation model building unit 103 is used to build an initial allocation model for product green electricity consumption according to the objective function and constraint conditions;

[0106] The initial allocation model optimization unit 104 is used to optimize the initial allocation model using a particle swarm algorithm;

[0107] The optimal green electricity allocation plan generating unit 105 is used to input the acquired variable parameters of the target product into the optimized model to generate the optimal green electricity allocation plan for the target product.

[0108] For the specific definition of the distribution system, please refer to the definition of the distribution method above, which will not be repeated here. Each module in the above distribution system can be implemented in whole or in part by software, hardware and their combination. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0109] Embodiment 3

[0110] See also Figure 5 The present invention provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0111] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0112] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0113] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0114] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for allocating green electricity consumption based on the industrial chain, characterized in that: include: Constructing an objective function based on target variables, wherein the target variables include direct green electricity consumption of products, direct green electricity consumption of raw materials in the middle and upper reaches of the industrial chain, and indirect green electricity consumption of products in the middle and upper reaches of the industrial chain; Determine constraints based on the objective function, where the constraints include that the green electricity of the product must not exceed the total amount of electricity consumed in producing the product, the green electricity of the product must not exceed the green electricity owned by the enterprise, and the green electricity trading time of the product must match the production cycle; Constructing an initial allocation model for product green electricity consumption according to the objective function and the constraint conditions; Optimizing the initial allocation model using a particle swarm algorithm; The obtained variable parameters of the target product are input into the optimized model to generate the optimal green electricity allocation plan for the target product.

2. The green electricity consumption allocation method based on the industrial chain according to claim 1 is characterized in that: The expression for constructing the objective function based on the target variable is as follows: Where: Q n,t Allocate green electricity consumption for the industrial chain of product n in time t.

3. The green electricity consumption allocation method based on the industrial chain according to claim 2 is characterized in that: The direct green electricity consumption of the product is Q dir.re The expression used as the target variable is as follows: Q dir.re =kq1(t)+kq2(t); Where: q1(t) is the direct energy consumption of the product, q2(t) is the energy loss of the product, and k is the total energy consumption coefficient; The direct raw material green electricity consumption in the upstream and middle reaches of the industrial chain Q indir.re The expression used as the target variable is as follows: Where: q i (t) is the green electricity consumption of the i-th direct raw material in the upstream of the product; The green electricity consumption of indirect products in the upstream and midstream of the industrial chain is Q ondir.re The expression used as the target variable is as follows: Where: q j (t) is the green electricity consumption of the jth indirect product in the upstream of the product, and λ is the consumption weight of the upstream indirect product in the industrial chain.

4. The green electricity consumption allocation method based on the industrial chain according to claim 1 is characterized in that: The expressions of the constraints are as follows: Q dir.re ≤Q cons,m ; Q dir.re ≤Q dis,m ; T∈T pc ; Where: Q cons,m is the total amount of electricity consumed in production, Q dis,m is the green electricity owned by the enterprise, T is the green electricity trading time of the product, T pc For the production cycle.

5. The green electricity consumption allocation method based on the industrial chain according to claim 1 is characterized in that: The method of optimizing the initial allocation model by using a particle swarm algorithm comprises: Initializing parameters, including the number of particle swarms, the number of iterations, the position and speed of the particle swarms; Determine the fitness function; The position and speed of the particle group in each iteration are updated according to the fitness function to determine the optimal particle position and speed, thereby determining the optimization parameters corresponding to the initial allocation model.

6. The green electricity consumption allocation method based on the industrial chain according to claim 5 is characterized in that: The expression of the fitness function f(y) is as follows: In the formula: y is the actual result, y ′ For the prediction results.

7. The green electricity consumption allocation method based on the industrial chain according to claim 5 is characterized in that: The expressions for the position and velocity of the particle group are as follows: Where: s0 is the position of the particle group, v0 is the velocity of the particle group.

8. A green electricity consumption allocation system based on the industrial chain, characterized in that: include: An objective function construction unit, used to construct an objective function based on the objective variables, wherein the objective variables include the direct green electricity consumption of the product, the direct green electricity consumption of raw materials in the middle and upper reaches of the industrial chain, and the indirect green electricity consumption of products in the middle and upper reaches of the industrial chain; A constraint determination unit, used to determine the constraint according to the objective function, wherein the constraint includes that the green electricity of the product shall not exceed the total amount of electric energy consumed in producing the product, the green electricity of the product shall not exceed the green electricity owned by the enterprise, and the green electricity trading time of the product shall match the production cycle; An initial allocation model construction unit, used to construct an initial allocation model for product green electricity consumption according to the objective function and the constraint conditions; An initial allocation model optimization unit, used for optimizing the initial allocation model using a particle swarm algorithm; The optimal green electricity allocation plan generating unit is used to input the acquired variable parameters of the target product into the optimized model to generate the optimal green electricity allocation plan of the target product.

9. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the steps of the green electricity consumption allocation method based on the industrial chain are implemented as described in any one of claims 1 to 7.

10. A readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the steps of the green electricity consumption allocation method based on the industrial chain are implemented.

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