Farming and pastoral zero-carbon park power utilization scheduling method considering electricity-carbon coupling market linkage

Abstract

The invention discloses an agricultural and pastoral zero-carbon park power utilization scheduling method considering electricity-carbon coupling market linkage. The method comprises the steps that S1, a carbon emission calculation model is constructed based on biological derived chain carbon flow characteristics of an agricultural and pastoral zero-carbon park; s2, constructing a power generation merchant income calculation model based on the electricity-carbon coupling market based on carbon emission in the power generation process of the power generation merchant in combination with an electricity market transaction mechanism; s3, constructing a total electricity utilization cost calculation model based on the characteristics of electricity purchase and power generation of the agricultural and pastoral zero-carbon park in combination with the carbon emission calculation model; s4, constructing a power utilization multi-objective optimization problem which aims at maximizing the total power generation income of the power producer and minimizing the total cost of power purchase and power generation of the agricultural and pastoral zero-carbon park; s5, solving a power utilization multi-objective optimization problem through a multi-objective particle swarm algorithm to obtain an optimal power utilization strategy; and guiding the electricity purchasing and generating work of the agricultural and pastoral zero-carbon park based on the optimal electricity utilization strategy. The energy utilization efficiency of the agricultural and pastoral zero-carbon park can be improved.

Description

Technical Field

[0001] The present invention relates to the field of electricity consumption planning for zero-carbon agricultural and animal husbandry parks, and in particular to an electricity consumption scheduling method for zero-carbon agricultural and animal husbandry parks that takes into account the linkage of an electricity-carbon coupling market. Background Art

[0002] With the proposal of the "dual carbon" goal, all walks of life are actively exploring new paths for energy conservation and emission reduction. As one of the important sources of carbon emissions, the transformation of agriculture and animal husbandry to low carbon and zero carbon is particularly urgent. Among them, the agricultural and animal husbandry zero-carbon park, as a new model for the green development of agriculture and animal husbandry, aims to achieve self-sufficiency in energy and zero growth in carbon emissions within the park by integrating advanced technologies such as renewable energy, smart microgrids, and carbon sink management. Such parks usually combine local natural resources and agricultural and animal husbandry production characteristics to build a bio-derivative chain, realize the resource utilization of waste and multi-level conversion of energy, and form a closed-loop ecosystem. However, the energy management and power dispatching of agricultural and animal husbandry zero-carbon parks still face many challenges.

[0003] The energy structure of the agricultural and animal husbandry zero-carbon park is complex and diverse, including solar energy, wind energy, biomass energy and other renewable energy sources. The supply of these energy sources is intermittent and uncertain, which brings great challenges to the power balance and stable operation of the park. In order to achieve efficient use of energy and stable operation of the park, a scientific power dispatching mechanism must be established to ensure timely purchase of electricity when energy supply is insufficient and effective power generation and storage when energy is in excess.

[0004] With the gradual establishment and improvement of the carbon trading market, the electricity consumption behavior of the agricultural and animal husbandry zero-carbon park, as an important participant in carbon emissions, not only affects the economic benefits of the park, but also directly affects the carbon footprint and carbon credit of the park. Therefore, how to minimize carbon emissions and electricity purchase costs while ensuring the normal electricity demand of the park has become an important consideration for the electricity dispatch of the agricultural and animal husbandry zero-carbon park. The electricity dispatch of the agricultural and animal husbandry zero-carbon park also needs to consider the interaction with the electricity market. With the gradual opening of the electricity market and the introduction of the competition mechanism, the demand response capabilities of power generators and power users have been significantly enhanced. As an important participant in the electricity market, the electricity dispatch strategy of the agricultural and animal husbandry zero-carbon park should fully consider factors such as market price fluctuations and electricity supply and demand conditions to maximize economic benefits. The electricity dispatch of the agricultural and animal husbandry zero-carbon park also needs to take into account the sustainable development goals of the park. While ensuring the normal operation of the park, it should reduce dependence on external energy as much as possible, improve the utilization rate of renewable energy, and promote the park to develop in a more green, low-carbon and sustainable direction.

[0005] In summary, the electricity dispatch of zero-carbon agricultural and animal husbandry parks is a multi-dimensional issue involving energy management, carbon emission control, electricity market transactions and sustainable development goals. Therefore, how to design an electricity dispatch method for zero-carbon agricultural and animal husbandry parks that comprehensively considers energy structure, carbon emissions, electricity market transactions and sustainable development goals is a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of the deficiencies of the above-mentioned prior art, the technical problem to be solved by the present invention is: how to provide an electricity scheduling method for a zero-carbon agricultural and animal husbandry park that takes into account the linkage of the electricity-carbon coupling market, and realize the comprehensive optimization of the park's energy management by comprehensively considering multiple factors such as power generation, electricity purchase, and carbon emissions, thereby improving the energy utilization efficiency of the zero-carbon agricultural and animal husbandry park, reducing energy waste and carbon emissions, and promoting the park to develop in a more green, low-carbon, and sustainable direction.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] A method for dispatching electricity consumption in an agricultural and animal husbandry zero-carbon park considering the linkage between electricity and carbon market, comprising:

[0009] S1: Construct a carbon emission calculation model based on the carbon flow characteristics of the bio-derived chain in the agricultural and animal husbandry zero-carbon park;

[0010] S2: Based on the carbon emissions during the power generation process of power generators and the power market trading mechanism, a power generator revenue calculation model based on the electricity-carbon coupling market is constructed; the total power generation revenue of the power generator is calculated through the power generator revenue calculation model;

[0011] S3: Based on the characteristics of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park and the carbon emission calculation model, a total electricity cost calculation model is constructed; the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park is calculated through the total electricity cost calculation model;

[0012] S4: Based on the power generator profit calculation model and the total electricity cost calculation model, a multi-objective optimization problem of electricity consumption is constructed with the goal of maximizing the total power generation revenue of the power generator and minimizing the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park;

[0013] S5: Solve the multi-objective optimization problem of electricity consumption through the multi-objective particle swarm algorithm to obtain the optimal electricity consumption strategy; guide the electricity purchase and power generation work of the agricultural and animal husbandry zero-carbon park based on the optimal electricity consumption strategy.

[0014] Preferably, in step S1, the calculation formula of the carbon emission calculation model is:

[0015] E=∑(E j ×ω j );

[0016] E j =∑(Tjn ×δ jn );

[0017] Where: E represents the biomass carbon emissions of the agricultural and animal husbandry zero-carbon park; E j represents the emission of the jth greenhouse gas; T jn represents the amount of the nth emission source of the jth greenhouse gas; δ jn Represents the emission coefficient, i.e., the greenhouse gas emissions caused by the activity data of a unit emission source; ω j represents the global warming potential value of the jth greenhouse gas.

[0018] Preferably, in step S2, the calculation formula of the power generator profit calculation model is:

[0019]

[0020] Where: U i G It represents the total revenue of power generation of generator i in the target period T; represents the electricity selling price of generator i in period t (t∈T), that is, the electricity purchasing price of the agricultural and animal husbandry zero-carbon park in period t; represents the amount of electricity sold by generator i in period t, that is, the amount of electricity purchased by the agricultural and animal husbandry zero-carbon park in period t; and They represent the power generation cost and carbon trading cost of generator i in period t respectively.

[0021] Preferably, in step S2, the power generation cost is calculated by the following formula:

[0022]

[0023] Where: represents the power generation cost of power generator i; a i represents the quadratic term coefficient of generator i; b i represents the linear coefficient of power generator i; c i is the no-load cost; It indicates that the electricity sold by power generator i is the electricity purchased by the agricultural and animal husbandry zero-carbon park.

[0024] Preferably, in step S2, the carbon trading cost is calculated by the following formula:

[0025]

[0026] Where: represents the carbon trading cost of power generator i; τ represents the carbon trading price; θ represents the carbon trading penalty coefficient; λ represents the carbon trading reward coefficient; v represents the length of the positive interval of carbon trading; l represents the length of the negative interval of carbon trading; k represents the number of transactions; Represents the carbon trading volume of power generator i, where the carbon trading volume is associated with the electricity purchase amount of the agricultural and animal husbandry zero-carbon park.

[0027] Preferably, in step S3, the calculation formula of the total electricity cost calculation model is:

[0028]

[0029] Where: C represents the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park during the target period T; represents the electricity purchase price of the agricultural and animal husbandry zero-carbon park in period t, which is equivalent to represents the amount of electricity purchased by the agricultural and animal husbandry zero-carbon park in period t, which is equivalent to represents the power generation cost of the agricultural and animal husbandry zero-carbon park in period t; represents the power generation of the zero-carbon agriculture and animal husbandry park in period t; represents the price of carbon emission rights; It represents the carbon emission coefficient of the agricultural and animal husbandry zero-carbon park when purchasing electricity; E represents the biocarbon emissions of the agricultural and animal husbandry zero-carbon park; K represents the free carbon quota allocated to the agricultural and animal husbandry zero-carbon park during period t.

[0030] Preferably, in step S4, the objective function of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park is expressed as:

[0031]

[0032] Preferably, in step S4, the constraints of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park include:

[0033] P i Gmin ≤P i G ≤P i Gmax ;

[0034] Where: P t GU represents the electricity selling price of generator i; P t Gmin and P t Gmax They represent the upper and lower limits of power generation pricing set by generator i respectively.

[0035] Preferably, in step S4, the constraints of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park include:

[0036]

[0037] Where: E sMax Indicates the upper limit of carbon quota sales; E bMax Indicates the lower limit for the sale of carbon quotas.

[0038] Preferably, in step S5, the processing steps of the multi-objective particle swarm algorithm include:

[0039] S501: randomly generate a group of particles, each particle represents a power usage strategy; assign a velocity vector to each particle for moving in the search space;

[0040] S502: Calculate the objective function value of each particle;

[0041] S503: performing non-dominated sorting on the particles according to the objective function value, and calculating the crowding distance of each particle;

[0042] S504: Update the speed and position of the particle according to the historical optimal position and the global optimal position of the particle; introduce an external archive to store the currently found Pareto optimal solution set;

[0043] S505: Select a group of particles from the current particle swarm and the external archive as candidates for the next generation particle swarm;

[0044] S506: using tournament selection, roulette wheel selection, or other selection strategies to determine which particles will be retained to the next generation;

[0045] S507: Repeat steps S502 to S506 until a predetermined number of iterations is reached or other stop conditions are met;

[0046] S508: Extract the Pareto optimal solution set from the external archive as the optimal electricity utilization strategy.

[0047] Compared with the prior art, the electricity dispatching method for zero-carbon agricultural and animal husbandry parks considering the linkage of electricity-carbon coupling market in the present invention has the following beneficial effects:

[0048] First, by deeply analyzing the carbon flow characteristics of the bio-derived chain of the agricultural and animal husbandry zero-carbon park, the present invention can accurately quantify the carbon emissions of the park, provide basic data support for realizing the linkage of the electricity-carbon coupling market, and help the park better understand and respond to the impact of the carbon market. Then, the present invention constructs a power generator revenue calculation model based on the electricity-carbon coupling market based on the carbon emissions of the power generator in the power generation process combined with the electricity market trading mechanism, and constructs a total electricity cost calculation model based on the characteristics of the power purchase and power generation of the agricultural and animal husbandry zero-carbon park combined with the carbon emission calculation model, and calculates the total power generation revenue of the power generator and the total cost of power purchase and power generation in the agricultural and animal husbandry zero-carbon park through the power generator revenue calculation model and the total electricity cost calculation model, respectively. The two models take into account the carbon emissions of the power generator in the power generation process and the electricity market trading mechanism as well as the characteristics of the power purchase and power generation of the agricultural and animal husbandry zero-carbon park, and can accurately calculate the total power generation revenue and the total electricity cost, which provides clear optimization goals and constraints for the subsequent multi-objective optimization problem of electricity consumption. Secondly, based on the power generation company revenue calculation model and the total electricity cost calculation model, the present invention constructs a multi-objective optimization problem of electricity consumption with the goal of maximizing the total power generation revenue of the power generation company and minimizing the total cost of purchasing and generating electricity in the agricultural and animal husbandry zero-carbon park. This multi-objective optimization problem helps to reduce the electricity cost of the park while ensuring the revenue of the power generation company and achieve a win-win situation of economic and environmental benefits. Finally, the present invention solves the multi-objective optimization problem of electricity consumption through a multi-objective particle swarm algorithm to obtain the optimal electricity consumption strategy. This strategy fully considers the linkage effect of the electricity-carbon coupling market, and can minimize carbon emissions and electricity costs while ensuring the power supply of the park. The efficiency and robustness of the multi-objective particle swarm algorithm also ensure the real-time and reliability of the scheduling strategy.

[0049] In summary, the present invention achieves comprehensive optimization of park energy management by comprehensively considering multiple factors such as power generation, power purchase, and carbon emissions, which helps to improve the energy utilization efficiency of agricultural and animal husbandry zero-carbon parks, reduce energy waste and carbon emissions, and promote the park to develop in a more green, low-carbon, and sustainable direction. At the same time, the present invention promotes the coordinated development of the electricity market and the carbon market through the linkage of the electricity-carbon coupling market, helps to promote the optimization and transformation of the energy structure, promotes the utilization and development of clean energy, and provides strong support for achieving the "dual carbon" goal. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0051] Figure 1 This is a logical block diagram of the electricity dispatching method for zero-carbon agricultural and animal husbandry parks considering the linkage between electricity and carbon markets. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but only represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0053] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invention product is usually placed when used, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In addition, the terms "horizontal", "vertical", etc. do not mean that the components are required to be absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] The following is a further detailed description through specific implementation methods:

[0055] Example:

[0056] This embodiment discloses a method for dispatching electricity consumption in an agricultural and animal husbandry zero-carbon park taking into account the linkage of the electricity-carbon coupling market.

[0057] like Figure 1 As shown in the figure, the electricity dispatching method for zero-carbon agricultural and animal husbandry parks considering the linkage between electricity and carbon market includes:

[0058] S1: Construct a carbon emission calculation model based on the carbon flow characteristics of the bio-derived chain in the agricultural and animal husbandry zero-carbon park;

[0059] S2: Based on the carbon emissions during the power generation process of power generators and the power market trading mechanism, a power generator revenue calculation model based on the electricity-carbon coupling market is constructed; the total power generation revenue of the power generator is calculated through the power generator revenue calculation model;

[0060] S3: Based on the characteristics of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park and the carbon emission calculation model, a total electricity cost calculation model is constructed; the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park is calculated through the total electricity cost calculation model;

[0061] S4: Based on the power generator profit calculation model and the total electricity cost calculation model, a multi-objective optimization problem of electricity consumption is constructed with the goal of maximizing the total power generation revenue of the power generator and minimizing the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park;

[0062] S5: Solve the multi-objective optimization problem of electricity consumption through the multi-objective particle swarm algorithm (MOPSO) to obtain the optimal electricity consumption strategy; guide the electricity purchase and power generation work of the agricultural and animal husbandry zero-carbon park based on the optimal electricity consumption strategy.

[0063] First, by deeply analyzing the carbon flow characteristics of the bio-derived chain of the agricultural and animal husbandry zero-carbon park, the present invention can accurately quantify the carbon emissions of the park, provide basic data support for realizing the linkage of the electricity-carbon coupling market, and help the park better understand and respond to the impact of the carbon market. Then, the present invention constructs a power generator revenue calculation model based on the electricity-carbon coupling market based on the carbon emissions of the power generator in the power generation process combined with the electricity market trading mechanism, and constructs a total electricity cost calculation model based on the characteristics of the power purchase and power generation of the agricultural and animal husbandry zero-carbon park combined with the carbon emission calculation model, and calculates the total power generation revenue of the power generator and the total cost of power purchase and power generation in the agricultural and animal husbandry zero-carbon park through the power generator revenue calculation model and the total electricity cost calculation model, respectively. The two models take into account the carbon emissions of the power generator in the power generation process and the electricity market trading mechanism as well as the characteristics of the power purchase and power generation of the agricultural and animal husbandry zero-carbon park, and can accurately calculate the total power generation revenue and the total electricity cost, which provides clear optimization goals and constraints for the subsequent multi-objective optimization problem of electricity consumption. Secondly, based on the power generation company revenue calculation model and the total electricity cost calculation model, the present invention constructs a multi-objective optimization problem of electricity consumption with the goal of maximizing the total power generation revenue of the power generation company and minimizing the total cost of purchasing and generating electricity in the agricultural and animal husbandry zero-carbon park. This multi-objective optimization problem helps to reduce the electricity cost of the park while ensuring the revenue of the power generation company and achieve a win-win situation of economic and environmental benefits. Finally, the present invention solves the multi-objective optimization problem of electricity consumption through a multi-objective particle swarm algorithm to obtain the optimal electricity consumption strategy. This strategy fully considers the linkage effect of the electricity-carbon coupling market, and can minimize carbon emissions and electricity costs while ensuring the power supply of the park. The efficiency and robustness of the multi-objective particle swarm algorithm also ensure the real-time and reliability of the scheduling strategy.

[0064] In summary, the present invention achieves comprehensive optimization of park energy management by comprehensively considering multiple factors such as power generation, power purchase, and carbon emissions, which helps to improve the energy utilization efficiency of agricultural and animal husbandry zero-carbon parks, reduce energy waste and carbon emissions, and promote the park to develop in a more green, low-carbon, and sustainable direction. At the same time, the present invention promotes the coordinated development of the electricity market and the carbon market through the linkage of the electricity-carbon coupling market, helps to promote the optimization and transformation of the energy structure, promotes the utilization and development of clean energy, and provides strong support for achieving the "dual carbon" goal.

[0065] In the specific implementation process, the calculation formula of the carbon emission calculation model is:

[0066] E=∑(E j ×ω j );

[0067] E j =∑(T jn ×δ jn );

[0068] Where: E represents the biomass carbon emissions of the agricultural and animal husbandry zero-carbon park; E j represents the emission of the jth greenhouse gas; T jn represents the amount of the nth emission source of the jth greenhouse gas; δ jn Represents the emission coefficient, i.e., the greenhouse gas emissions caused by the activity data of a unit emission source; ω j represents the global warming potential value of the jth greenhouse gas.

[0069] In the specific implementation process, the calculation formula of the power generator profit calculation model is:

[0070]

[0071] Where: It represents the total revenue of power generation of generator i in the target period T; represents the electricity selling price of generator i in period t (t∈T), that is, the electricity purchasing price of the agricultural and animal husbandry zero-carbon park in period t; represents the amount of electricity sold by generator i in period t, that is, the amount of electricity purchased by the agricultural and animal husbandry zero-carbon park in period t; and They represent the power generation cost and carbon trading cost of generator i in period t respectively.

[0072] In the specific implementation process, the power generation cost is calculated by the following formula:

[0073]

[0074] Where: represents the power generation cost of power generator i; a i represents the quadratic term coefficient of generator i; b i represents the linear coefficient of power generator i; c i is the no-load cost; It indicates that the electricity sold by power generator i is the electricity purchased by the agricultural and animal husbandry zero-carbon park.

[0075] In the specific implementation process, the carbon trading cost is calculated by the following formula:

[0076]

[0077] Where: represents the carbon trading cost of power generator i; τ represents the carbon trading price; θ represents the carbon trading penalty coefficient; λ represents the carbon trading reward coefficient; v represents the length of the positive interval of carbon trading; l represents the length of the negative interval of carbon trading; k represents the number of transactions; It represents the carbon trading volume of power generator i, where the carbon trading volume is associated with the electricity purchase amount of the agricultural and animal husbandry zero-carbon park. For example, a mapping relationship can be set up so that the corresponding carbon trading volume is generated for each amount of electricity used.

[0078] In the specific implementation process, the calculation formula of the total electricity cost calculation model is:

[0079]

[0080] Where: C represents the total cost of purchasing and generating electricity in the zero-carbon agriculture and animal husbandry park within the target period T (which can be 24 hours, i.e. one day); P t GU represents the electricity purchase price of the agricultural and animal husbandry zero-carbon park in period t, which is equivalent to Q t GU represents the amount of electricity purchased by the agricultural and animal husbandry zero-carbon park in period t, which is equivalent to P t RU represents the power generation cost of the agricultural and animal husbandry zero-carbon park in period t; Q t RU represents the power generation of the zero-carbon agriculture and animal husbandry park in period t; represents the price of carbon emission rights; It represents the carbon emission coefficient of the agricultural and animal husbandry zero-carbon park when purchasing electricity; E represents the biocarbon emissions of the agricultural and animal husbandry zero-carbon park; K represents the free carbon quota allocated to the agricultural and animal husbandry zero-carbon park during period t.

[0081] In the specific implementation process, the objective function of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park is expressed as:

[0082]

[0083] In the specific implementation process, the constraints of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park include:

[0084] P i Gmin ≤P i G ≤P i Gmax ;

[0085] Where: P t GU represents the electricity selling price of generator i; P t Gmin and P t Gmax They represent the upper and lower limits of power generation pricing set by generator i respectively.

[0086] In the specific implementation process, the constraints of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park include:

[0087]

[0088] Where: E sMax Indicates the upper limit of carbon quota sales; EbMax Indicates the lower limit for the sale of carbon quotas.

[0089] In the specific implementation process, the processing steps of the multi-objective particle swarm algorithm include:

[0090] S501: randomly generate a group of particles, each particle represents a power usage strategy; assign a velocity vector to each particle for moving in the search space;

[0091] S502: Calculate the objective function value of each particle (i.e., the total revenue of power generation and the total cost of power purchase and power generation);

[0092] S503: performing non-dominated sorting on the particles according to the objective function value, and calculating the crowding distance of each particle;

[0093] S504: Update the speed and position of the particle according to the historical optimal position of the particle and the global optimal position (i.e., the optimal solution in the Pareto frontier); introduce an external repository to store the currently found Pareto optimal solution set;

[0094] S505: Select a group of particles from the current particle swarm and the external archive as candidates for the next generation particle swarm;

[0095] S506: using tournament selection, roulette wheel selection, or other selection strategies to determine which particles will be retained to the next generation;

[0096] S507: Repeat steps S502 to S506 until a predetermined number of iterations is reached or other stop conditions are met;

[0097] S508: Extract the Pareto optimal solution set from the external archive as the optimal electricity utilization strategy.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for dispatching electricity consumption in agricultural and animal husbandry zero-carbon parks considering the linkage of electricity-carbon coupling market, characterized in that: include: S1: Construct a carbon emission calculation model based on the carbon flow characteristics of the bio-derived chain in the agricultural and animal husbandry zero-carbon park; S2: Based on the carbon emissions during the power generation process of power generators and the power market trading mechanism, a power generator revenue calculation model based on the electricity-carbon coupling market is constructed; the total power generation revenue of the power generator is calculated through the power generator revenue calculation model; S3: Based on the characteristics of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park and the carbon emission calculation model, a total electricity cost calculation model is constructed; the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park is calculated through the total electricity cost calculation model; S4: Based on the power generator profit calculation model and the total electricity cost calculation model, a multi-objective optimization problem of electricity consumption is constructed with the goal of maximizing the total power generation revenue of the power generator and minimizing the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park; S5: Solve the multi-objective optimization problem of electricity consumption through the multi-objective particle swarm algorithm to obtain the optimal electricity consumption strategy; guide the electricity purchase and power generation work of the agricultural and animal husbandry zero-carbon park based on the optimal electricity consumption strategy.

2. The method for dispatching electricity consumption in an agricultural and animal husbandry zero-carbon park considering the linkage between electricity and carbon coupling market as claimed in claim 1 is characterized by: In step S1, the calculation formula of the carbon emission calculation model is: And=∑(And j ×ω j ); E j =∑(T jn ×δ jn ); Where: E represents the biomass carbon emissions of the agricultural and animal husbandry zero-carbon park; E j represents the emission of the jth greenhouse gas; T jn represents the amount of the nth emission source of the jth greenhouse gas; δ jn Represents the emission coefficient, i.e., the greenhouse gas emissions caused by the activity data of a unit emission source; ω j represents the global warming potential value of the jth greenhouse gas.

3. According to the method for electricity dispatching in an agricultural and animal husbandry zero-carbon park taking into account the linkage between electricity and carbon coupling market as claimed in claim 1, in step S2, the calculation formula of the power generator profit calculation model is: Where: It represents the total revenue of power generation of generator i in the target period T; represents the electricity selling price of power generator i in period t, that is, the electricity purchasing price of the agricultural and animal husbandry zero-carbon park in period t; It represents the amount of electricity sold by power generator i in period t, that is, the amount of electricity purchased by the agricultural and animal husbandry zero-carbon park in period t; and They represent the power generation cost and carbon trading cost of generator i in period t respectively.

4. The electricity dispatching method for zero-carbon agricultural and animal husbandry parks considering the linkage between electricity and carbon coupling markets as claimed in claim 3, in step S2, the power generation cost is calculated by the following formula: Where: represents the power generation cost of power generator i; a i represents the quadratic term coefficient of generator i; b i represents the linear coefficient of power generator i; c i is the no-load cost; It indicates that the electricity sold by power generator i is the electricity purchased by the agricultural and animal husbandry zero-carbon park.

5. The method for dispatching electricity consumption in an agricultural and animal husbandry zero-carbon park considering the linkage between electricity and carbon coupling market as claimed in claim 3, in step S2, the carbon trading cost is calculated by the following formula: Where: represents the carbon trading cost of power generator i; τ represents the carbon trading price; θ represents the carbon trading penalty coefficient; λ represents the carbon trading reward coefficient; v represents the length of the positive interval of carbon trading; l represents the length of the negative interval of carbon trading; k represents the number of transactions; Represents the carbon trading volume of power generator i, where the carbon trading volume is associated with the electricity purchase amount of the agricultural and animal husbandry zero-carbon park.

6. The electricity dispatching method for zero-carbon agricultural and animal husbandry parks considering the linkage of electricity-carbon coupling market as claimed in claim 3, in step S3, the calculation formula of the total electricity cost calculation model is: Where: C represents the total cost of electricity purchase and power generation in the agricultural and animal husbandry zero-carbon park within the target period T; P t GU represents the electricity purchase price of the agricultural and animal husbandry zero-carbon park in period t, which is equivalent to represents the amount of electricity purchased by the agricultural and animal husbandry zero-carbon park in period t, which is equivalent to P t RU represents the power generation cost of the agricultural and animal husbandry zero-carbon park in period t; represents the power generation of the zero-carbon agriculture and animal husbandry park in period t; represents the price of carbon emission rights; It represents the carbon emission coefficient of the agricultural and animal husbandry zero-carbon park when purchasing electricity; E represents the biocarbon emissions of the agricultural and animal husbandry zero-carbon park; K represents the free carbon quota allocated to the agricultural and animal husbandry zero-carbon park during period t.

7. The electricity dispatching method for zero-carbon agricultural and animal husbandry parks considering the linkage of electricity-carbon coupling market as claimed in claim 6, in step S4, the objective function of the multi-objective optimization problem of electricity purchase in zero-carbon agricultural and animal husbandry parks is expressed as:

8. The method for dispatching electricity consumption in an agricultural and animal husbandry zero-carbon park considering the linkage between electricity and carbon coupling market as claimed in claim 7, in step S4, the constraints of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park include: P i Gmin ≤P i G ≤P i Gmax ; Where: P t GU represents the electricity selling price of generator i; P t Gmin and P t Gmax They represent the upper and lower limits of power generation pricing set by generator i respectively.

9. The method for dispatching electricity consumption in an agricultural and animal husbandry zero-carbon park considering the linkage between electricity and carbon coupling market as claimed in claim 7, in step S4, the constraints of the multi-objective optimization problem of electricity purchase in the agricultural and animal husbandry zero-carbon park include: Where: E sMax Indicates the upper limit of carbon quota sales; E bMax Indicates the lower limit for the sale of carbon quotas.

10. The method for dispatching electricity consumption in an agricultural and animal husbandry zero-carbon park considering the linkage between electricity and carbon coupling market as claimed in claim 9, wherein in step S5, the processing steps of the multi-objective particle swarm algorithm include: S501: randomly generate a group of particles, each particle represents a power usage strategy; Assign a velocity vector to each particle for moving in the search space; S502: Calculate the objective function value of each particle; S503: performing non-dominated sorting on the particles according to the objective function value, and calculating the crowding distance of each particle; S504: Update the speed and position of the particle according to the historical optimal position and the global optimal position of the particle; introduce an external archive to store the currently found Pareto optimal solution set; S505: Select a group of particles from the current particle swarm and the external archive as candidates for the next generation particle swarm; S506: using tournament selection, roulette wheel selection, or other selection strategies to determine which particles will be retained to the next generation; S507: Repeat steps S502 to S506 until a predetermined number of iterations is reached or other stop conditions are met; S508: Extract the Pareto optimal solution set from the external archive as the optimal electricity utilization strategy.

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