A power plant planning method and system for marine ranching

Abstract

The present invention discloses a power plant planning method, system, electronic device and computer-readable storage medium for ocean ranches, and belongs to the technical field of power plant planning. The present invention comprehensively considers five factors, namely, economy, environment, employment, social impact and energy security, in the power plant planning optimization method for ocean ranches. The relative importance of each factor is determined by specifying the priority (i.e., order) for the lexicographic multi-objective function; or the quantitative priority of multiple objective functions is achieved by assigning weights to each factor of the weighted sum multi-objective function. It can improve the accuracy and robustness of ocean ranch power plant planning, and solves the problem in the prior art that "a single objective function may not fully meet the multi-dimensional requirements of power plant planning."

Description

Technical Field

[0001] The present application relates to the technical field of power plant planning, and in particular to a power plant planning method and system for marine ranches. Background Art

[0002] The statements in this section merely mention background technology related to the present application and do not necessarily constitute prior art.

[0003] "Marine ranching" refers to the use of large-scale fishery facilities and systematic management systems in a certain sea area, using the natural marine ecological environment to gather artificially released economic marine organisms, just like grazing cattle and sheep on land, and carrying out planned and purposeful marine stocking of fish, shrimp, shellfish, algae and other marine resources at sea. my country is a major maritime country, and marine ranching, as an important part of the marine economy, has developed rapidly in recent years.

[0004] The geographical location of the ocean ranch makes its energy supply scarce, so it is particularly necessary to plan power plants for the ocean ranch. However, in the existing technology, when planning power plants for ocean ranches, the focus is on factors such as energy security, energy trading fairness, and sustainability, and uncertain factors such as demand forecasting, renewable energy technology iteration, and reliable capacity of power plants are not considered, which affects the accurate planning of ocean ranch power plants, affects the reliable operation of ocean ranch power plants, and further affects the setting and realization of energy development goals. Summary of the invention

[0005] In order to address the deficiencies of the prior art, the present application provides a method, system, electronic device and computer-readable storage medium for planning a power plant for an ocean ranch, which takes into account many uncertain factors and conducts multi-dimensional power plant planning for an ocean ranch.

[0006] In the first aspect, the present application provides a power plant planning method for marine ranching;

[0007] A power plant planning method for marine ranching, including:

[0008] Obtain the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants in the ocean ranch, and establish a multi-objective planning model based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants with the goal of minimizing economic costs, carbon emissions and social costs;

[0009] The existing actual power demand is obtained, and an uncertain demand scenario is generated according to the existing power demand. Based on the uncertain demand scenario, the multi-objective planning model is solved under the set constraints to obtain the types of new power plants in the ocean ranch and the number of new power plants of different types.

[0010] Furthermore, it also includes:

[0011] According to different types of new power plants and the number of new power plants of different types, the corresponding total cost, total carbon dioxide emissions, total social cost, power plant type dependency score, total power generation and employment coefficient are calculated;

[0012] The optimal type of new power plant and the number of new power plants of different types are selected based on total cost, total carbon dioxide emissions, total social cost, power plant type dependence score, total power generation and employment coefficient.

[0013] Furthermore, the multi-objective planning model is established based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants, with the goal of minimizing economic costs, carbon emissions and social costs, including:

[0014] According to the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of existing power plants, with the goal of minimizing economic costs, carbon emissions and social costs, establish economic cost function, carbon emission function and social cost function;

[0015] The economic cost function, carbon emission function and social cost function are randomly sorted by the lexicographic order method to determine the different solving orders of the economic cost function, carbon emission function and social cost function;

[0016] Through the weighted sum method, quantitative decisions are made on the weights of the economic cost function, carbon emission function and social cost function to generate different multi-objective functions;

[0017] A multi-objective planning model is established based on the economic cost function, carbon emission function, social cost function and different multi-objective functions.

[0018] Furthermore, the economic cost function is expressed as

[0019]

[0020] Among them, GE i represents the economic cost coefficient of existing power plant type i, GN i represents the economic cost coefficient of a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, Nis for the electricity generation of new power plants;

[0021] The carbon emission function is expressed as

[0022]

[0023] Among them, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, EE i is the emission factor for the existing type i power plant, NE i is the emission factor for a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants;

[0024] The social cost function is expressed as

[0025]

[0026] Among them, SC i is the social compensation rate of type i power plant, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, E is is the power generation of the existing power plant, p s To predict power demand, N is The power generation capacity of the new power plant.

[0027] Furthermore, the obtaining of the existing actual power demand and generating an uncertain demand scenario according to the actual power demand includes:

[0028] Obtain the existing actual power demand, and randomly generate demand forecast scenarios based on the actual power demand and the predicted power demand;

[0029] According to the uncertainty of demand development, the probability of occurrence of demand forecast scenarios is assumed, and different demand forecast scenarios are cross-multiplied to obtain uncertain demand scenarios.

[0030] Further, the demand forecasting scenarios include the actual power demand being 10%-20% lower than the forecasted power demand, the actual power demand being equal to the forecasted power demand, and the actual power demand being 10%-20% higher than the forecasted power demand;

[0031] Assume that the probability of actual power demand being 10%-20% lower than predicted power demand is 0.2-0.4, the probability of actual power demand being equal to predicted power demand is 0.3-0.5, and the probability of actual power demand being 10%-20% higher than predicted power demand is 0.2-0.4.

[0032] Furthermore, the set constraints include predicted power demand constraints, carbon emission constraints, economic cost constraints, social compensation rate constraints, power plant technical specification constraints and robustness constraints.

[0033] In the second aspect, the present application provides a power plant planning system for marine ranching;

[0034] A power plant planning system for marine ranching, comprising:

[0035] The multi-objective planning model building module is configured to: obtain the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants of the ocean ranch, and establish a multi-objective planning model based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants with the goal of minimizing economic costs, carbon emissions and social costs;

[0036] The power plant planning acquisition module is configured to: obtain the existing actual power demand, and generate an uncertain demand scenario based on the existing power demand; based on the uncertain demand scenario, under the set constraints, solve the multi-objective planning model to obtain the type of new power plant for the ocean ranch and the number of new power plants of different types.

[0037] In a third aspect, the present application provides an electronic device;

[0038] An electronic device includes a memory and a processor, and computer instructions stored in the memory and running on the processor. When the computer instructions are run by the processor, the steps of the power plant planning method for ocean ranches are completed.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium;

[0040] A computer-readable storage medium is used to store computer instructions, which, when executed by a processor, complete the steps of the above-mentioned power plant planning method for ocean ranches.

[0041] Compared with the prior art, the beneficial effects of this application are:

[0042] 1. The technical solution provided by this application comprehensively considers the five factors of economy, environment, employment, social impact and energy security in the optimization of power plant planning in marine ranches. By specifying the priority (i.e., order) of the lexicographic multi-objective function to determine the relative importance of each factor; or by assigning weights to each factor of the weighted sum multi-objective function to achieve the quantitative priority of multiple objective functions, the problem that a single objective function may not fully meet the multi-dimensional requirements of power plant planning is solved.

[0043] 2. The technical solution provided by the present application uses an efficiency measurement method based on multiple aspects of power plant planning to determine a decision-making tool for power plant planning. A lexicographic multi-objective function is used to specify a variety of priorities (i.e., order), and a weighted sum multi-objective function is used to convert each factor into a cost according to its weight. These methods are compared through data envelopment analysis to determine a decision-making tool for power plant planning.

[0044] 3. The technical solution provided by this application, when comparing the priorities of randomly generated objective functions, in the process of converting various factors into costs according to weights through a weighted sum multi-objective function, the ratio of the three functions, economic cost function, carbon emission function and social cost function, is 1: carbon tax: 1; thereby, this application can be compatible with different carbon tax scenarios when applied to marine ranches in different regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings in the specification, which constitute a part of the present application, are used to provide further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute improper limitations on the present application.

[0046] Figure 1 A schematic diagram of a process flow provided for an embodiment of the present application. DETAILED DESCRIPTION

[0047] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present application have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0049] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0050] Embodiment 1

[0051] In the prior art, energy research focuses on energy security, energy trading fairness, and sustainability. However, when planning a power plant for marine ranches, many uncertainties, employment, and social damage should be considered. Therefore, this application provides a power plant planning method for marine ranches.

[0052] Next, combine Figure 1 A power plant planning method for ocean ranches disclosed in this embodiment is described in detail.

[0053] This embodiment provides a power plant planning method for ocean ranches.

[0054] A power plant planning method for ocean ranching includes the following steps:

[0055] S1. Obtain the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants in the ocean ranch. Based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants, establish a multi-objective planning model with the goal of minimizing economic costs, carbon emissions and social costs; among which, the power plant equipment information includes the power plant equipment investment cost, the number of major equipment, the energy consumption (carbon emission) of major equipment, the power plant equipment investment cost / equipment replacement years, equipment maintenance cost, equipment operation cost, energy consumption cost, major equipment such as gas turbines, coal-fired generators, etc.; social compensation information includes land cost, sea pollution cost, fishery loss cost and noise cost. It includes the following steps:

[0056] S101. Obtain the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants in the ocean ranch, and determine the economic cost function, carbon emission function and social cost function with the goal of minimizing economic cost, carbon emission and social cost.

[0057] Specifically, according to the planned size of new power plants, and according to the equipment investment cost, equipment replacement life, equipment maintenance cost, equipment operation cost and energy consumption cost of existing power plants, the economic cost coefficient is fitted to determine the economic cost function; according to the carbon emissions generated by the energy consumption of existing power plants when generating electricity (carbon emissions of major equipment and the number of major equipment), the carbon emission factor is fitted to determine the carbon emission function, and carbon emissions are mainly caused by the energy consumption of non-renewable energy (gas turbines, coal-fired generators, etc.); based on historical power plant data, the size of new power plants is predicted, and according to the land cost, sea pollution cost, fishery loss cost and noise cost, the social compensation amount is fitted to determine the social cost function.

[0058] The economic cost function is established by stochastic optimization and robust optimization models, which include the annual capital expenditure and operating expenditure of each power plant of a given type in the planning of sea ranch power plants.

[0059] The economic cost function of each new power plant is expressed as

[0060]

[0061] Among them, GE i represents the economic cost coefficient of existing power plant type i, GN i represents the economic cost coefficient of a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants;

[0062] The carbon emission function is expressed as

[0063]

[0064] Among them, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, EE i is the emission factor for the existing type i power plant, NE i is the emission factor for a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants;

[0065] The social cost function is expressed as

[0066]

[0067] Among them, SC i is the social compensation rate of type i power plant, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, E is is the power generation of the existing power plant, p s To predict power demand, N is The power generation capacity of the new power plant.

[0068] S102. Based on the lexicographically ordered multi-objective function, each objective function is randomly sorted by enumeration to determine different solution orders for the economic cost function, the carbon emission function, and the social cost function.

[0069] S103. When formulating the multi-objective function of the marine ranch based on the weighted sum multi-objective function, all objective functions are converted into costs by making quantitative decisions on each weight; according to the costs, weights of the economic cost function, carbon emission function and social cost function are allocated to generate multi-objective functions with different weights; for the carbon emission function, the carbon emission function is converted into costs by multiplying the carbon emissions by the carbon tax per unit amount.

[0070] S104. Establish a multi-objective planning model based on the economic cost function, carbon emission function, social cost function and different multi-objective functions.

[0071] S2. Obtain the current actual power demand and generate an uncertain demand scenario based on the current power demand. The specific steps include:

[0072] S201. Obtain the existing actual power demand, and randomly generate demand forecast scenarios based on the actual power demand and the predicted power demand; wherein the demand forecast scenarios include the actual power demand being 15% lower than the predicted power demand, the actual power demand being equal to the predicted power demand, and the actual power demand being 15% higher than the predicted power demand.

[0073] S202. According to the uncertainty of demand development and based on the uncertainty related to future population growth and economic growth, it is assumed that the probability of occurrence of demand forecast scenarios is 0.3, 0.4, and 0.3 respectively, and the different demand forecast scenarios are cross-multiplied to obtain uncertain demand scenarios.

[0074] S3. Based on the uncertain demand scenario and under the set constraints, the multi-objective planning model is solved to obtain the power generation of the new power plant, the capacity of the new power plant and the power generation of the existing power plant in the ocean ranch; among which, the set constraints include supply security, environmental protection, economic competitiveness, power plant technical specifications and robustness constraints.

[0075] Specifically, the supply security constraint means that the power generation of new power plants + the power generation of existing power plants >= the predicted power demand, which is expressed as

[0076]

[0077] Environmental protection constraints refer to the carbon emissions of new power plants + carbon emissions of existing power plants <= carbon emission cap, expressed as

[0078]

[0079] The economic competitiveness constraint refers to the economic cost coefficient of the new power plant <= the economic cost coefficient of the old power plant * 1.1, which is expressed as

[0080] GN i ≤GE i×1.1;

[0081] The technical specification of a power plant refers to the social compensation rate of a newly built power plant not exceeding the rated social compensation rate, expressed as

[0082] SC i ≤SC e (i=1,2,3,…,T+T 0 );

[0083] The robustness constraint means that the type dependency score of the new power plant does not exceed the rated power plant type dependency score, which is expressed as

[0084] H s ≤H se .

[0085] Under different uncertain demand scenarios, input the type of new power plant i, the number of new power plants n and the power generation P of the power plant. Through the above-mentioned multi-objective function, under the constraints, obtain the type of new power plant and the number of new power plants of different types with the lowest economic cost (or the lowest carbon emission cost or the lowest social cost or the lowest weighted sum cost).

[0086] S4. Calculate the corresponding total power generation and employment coefficient according to the types of new power plants under different situations and the number of new power plants of different types; select the optimal types of new power plants and the number of new power plants of different types according to the total power generation and employment coefficient. The specific steps include:

[0087] The efficiency score of each power plant planning combination is calculated using the power plant planning combination generated by the multi-objective programming model. The decision-making unit in this method is generated by the dot product of different power plant planning combinations and uncertain demand scenarios. In order to expand the criteria beyond the three aspects of optimization mentioned above, three additional considerations (power plant type dependency score, total power generation and employment) are added to the efficiency measurement stage after optimization. Therefore, the efficiency described in the present invention is defined according to the degree to which the power plant planning combination treats the six aspects of ocean ranch power plant planning (total cost, total carbon dioxide emissions, total social cost, power plant type dependency score, total power generation and employment).

[0088] In order to effectively meet the above multiple efficiency assessment requirements, each decision unit defines four inputs and two outputs. Both the inputs and outputs come from the optimization decision variables of the power plant planning model. The inputs include the total cost, total carbon dioxide emissions, total social cost and power plant type dependency score (derived from a comprehensive assessment of the power plant's own costs, environment, social damage and safety) corresponding to the types and numbers of new power plants of different types under different situations. The outputs are the total power generation and employment related to energy and social benefits, respectively. The total power generation is obtained by the power plant type i, number n and power generation P; employment is obtained by the power plant type i, number n and power generation P through the attached table.

[0089] Among them, the total cost of each decision-making unit is calculated by the economic cost function, the total carbon dioxide emissions of each decision-making unit are calculated by the carbon emission function, and the total social cost of each decision-making unit is calculated by the social cost function; in order to ensure a safe energy supply, the diversification of power generation methods is crucial, and the diversification of power generation methods effectively buffers the impact of the energy system from the power supply side. In this embodiment, the basis for the power plant type dependency score of the marine ranch planning is the Hirschman-Herfindahl Index (HHI).

[0090] The HHI formula for scenario s is:

[0091]

[0092]

[0093] Among them, s is is the proportional share of power plant type i in scenario s, with HHI values ​​in the range [1 / N,1]. A higher HHI value means that the energy system is more dependent on a single main energy supply source.

[0094] The total power generation is derived from the decision variables in the power plant planning model and is measured in gigawatt hours [GWh]. The total power generation demand is the most important constraint, and if each decision unit shares the same amount of forecast power, the total power generation cost will be equal.

[0095] The commissioning, production and equipment upgrade of new power plants can provide more employment opportunities, so employment is abstracted as the product of the power plant employment coefficient (in job-years / megawatt [job-year / MW]) and the new power plant capacity (in megawatt [MW]). The employment factors of various power plants are listed in Appendix 1.

[0096] Table 1

[0097]

[0098] Specifically, data envelopment analysis is used to comprehensively score the advantages and disadvantages of the six aspects. Let n be the number of decision-making units, and each decision-making unit has 4 inputs and 2 outputs. ij Denote the input i of decision unit j, let y rj Represents the output r of decision unit j.

[0099] The envelope model formula is as follows:

[0100] θ * =Minθ

[0101]

[0102]

[0103] λ≥0:j=1,2,…,n.

[0104] Among them, λ j represents the linear coefficient of decision unit j, and θ is the relative efficiency score of decision unit o. An efficiency score of 1 indicates that the decision unit is effective.

[0105] The types of new power plants corresponding to the six aspects with the highest efficiency scores and the number of new power plants of different types are taken as the optimal ones, and the power plant planning of the ocean ranch is carried out.

[0106] Embodiment 2

[0107] This embodiment discloses a power plant planning system for ocean ranches, including:

[0108] The multi-objective planning model building module is configured to: obtain the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants of the ocean ranch, and establish a multi-objective planning model based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants with the goal of minimizing economic costs, carbon emissions and social costs;

[0109] The power plant planning acquisition module is configured to: obtain the existing actual power demand, and generate an uncertain demand scenario based on the existing power demand; based on the uncertain demand scenario, under the set constraints, solve the multi-objective planning model to obtain the power generation of the new power plant of the ocean ranch, the capacity of the new power plant and the power generation of the existing power plant.

[0110] It should be noted that the multi-objective planning model building module and the power plant planning acquisition module correspond to the steps in Embodiment 1, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in Embodiment 1. It should be noted that the modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0111] Embodiment 3

[0112] Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned power plant planning method for ocean ranches are completed.

[0113] Embodiment 4

[0114] Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned power plant planning method for ocean ranches are completed.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0118] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0119] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A power plant planning method for marine ranching, characterized in that: include: Obtain the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants in the ocean ranch, and establish a multi-objective planning model based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants with the goal of minimizing economic costs, carbon emissions and social costs; Obtain the existing actual power demand and generate an uncertain demand scenario based on the existing power demand; based on the uncertain demand scenario, solve the multi-objective planning model under the set constraints to obtain the types of new power plants in the ocean ranch and the number of new power plants of different types; The social compensation information includes land cost, sea pollution cost, fishery loss cost and noise cost; The multi-objective planning model is established based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants, with the goal of minimizing economic costs, carbon emissions and social costs, including the following steps: S101, obtaining the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants of the ocean ranch, with the goal of minimizing economic cost, carbon emission and social cost, and determining the economic cost function, carbon emission function and social cost function; Specifically, according to the planned new power plant volume, the existing power plant equipment investment cost, equipment replacement life, equipment maintenance cost, equipment operation cost and energy consumption cost, the economic cost coefficient is fitted to determine the economic cost function; According to the carbon emissions generated by the energy consumed by existing power plants during power generation, the carbon emission factor is fitted to determine the carbon emission function; based on historical power plant data, the size of new power plants is predicted, and according to land costs, sea pollution costs, fishery loss costs and noise costs, the social compensation amount is fitted to determine the social cost function; The economic cost function is established by stochastic optimization and robust optimization models, which include annual capital expenditures and operating expenditures for each power plant of a given type when planning the sea ranch power plant; The economic cost function is expressed as Among them, GE i represents the economic cost coefficient of existing power plant type i, GN i represents the economic cost coefficient of a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants; The carbon emission function is expressed as Among them, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, EE i is the emission factor for the existing type i power plant, NE i is the emission factor for a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants; The social cost function is expressed as Among them, SC i is the social compensation rate of type i power plant, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants; S102, randomly sorting the economic cost function, the carbon emission function and the social cost function by lexicographic order method, and determining different solving orders for the economic cost function, the carbon emission function and the social cost function; specifically: based on the lexicographic order multi-objective function, randomly sorting each objective function by enumeration, and determining different solving orders for the economic cost function, the carbon emission function and the social cost function; S103. Quantitatively determine the weights of the economic cost function, carbon emission function and social cost function through the weighted sum method to generate different multi-objective functions; Specifically, when formulating the multi-objective function of the marine ranch based on the weighted sum multi-objective function, all objective functions are converted into costs by making quantitative decisions on each weight; according to the cost, the weights of the economic cost function, carbon emission function and social cost function are allocated to generate multi-objective functions with different weights; for the carbon emission function, the carbon emission function is converted into cost by multiplying the carbon emission amount by the carbon tax per unit amount; S104. Establish a multi-objective planning model based on the economic cost function, the carbon emission function, the social cost function and different multi-objective functions; The obtaining of the existing actual power demand and generating an uncertain demand scenario according to the actual power demand includes: Obtain the existing actual power demand, and randomly generate demand forecast scenarios based on the actual power demand and the predicted power demand; According to the uncertainty of demand development, the probability of occurrence of demand forecast scenarios is assumed, and different demand forecast scenarios are cross-multiplied to obtain uncertain demand scenarios.

2. The method for planning a power plant for ocean ranching as claimed in claim 1, characterized in that: include: According to different types of new power plants and the number of new power plants of different types, the corresponding total cost, total carbon dioxide emissions, total social cost, power plant type dependency score, total power generation and employment coefficient are calculated; The optimal type of new power plant and the number of new power plants of different types are selected based on total cost, total carbon dioxide emissions, total social cost, power plant type dependence score, total power generation and employment coefficient.

3. The method for planning a power plant for ocean ranching as claimed in claim 1, characterized in that: The demand forecast scenarios include the actual power demand being 10%-20% lower than the forecast power demand, the actual power demand being equal to the forecast power demand, and the actual power demand being 10%-20% higher than the forecast power demand; Assume that the probability of actual power demand being 10%-20% lower than predicted power demand is 0.2-0.4, the probability of actual power demand being equal to predicted power demand is 0.3-0.5, and the probability of actual power demand being 10%-20% higher than predicted power demand is 0.2-0.

4.

4. The method for planning a power plant for ocean ranching as claimed in claim 1, characterized in that: The set constraints include predicted power demand constraints, carbon emission constraints, economic cost constraints, social compensation rate constraints, power plant technical specification constraints and robustness constraints.

5. A power plant planning system for marine ranching, characterized by: include: The multi-objective planning model building module is configured to: obtain the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants of the ocean ranch, and establish a multi-objective planning model based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants with the goal of minimizing economic costs, carbon emissions and social costs; The power plant planning acquisition module is configured to: obtain the existing actual power demand, generate an uncertain demand scenario based on the existing power demand; based on the uncertain demand scenario, solve the multi-objective planning model under set constraints to obtain the types of new power plants in the ocean ranch and the number of new power plants of different types; The social compensation information includes land cost, sea pollution cost, fishery loss cost and noise cost; The multi-objective planning model is established based on the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants, with the goal of minimizing economic costs, carbon emissions and social costs, including the following steps: S101, obtaining the power generation information, type information, carbon dioxide emission information, power plant equipment information and social compensation information of the existing power plants of the ocean ranch, with the goal of minimizing economic cost, carbon emission and social cost, and determining the economic cost function, carbon emission function and social cost function; Specifically, according to the planned new power plant volume, the existing power plant equipment investment cost, equipment replacement life, equipment maintenance cost, equipment operation cost and energy consumption cost, the economic cost coefficient is fitted to determine the economic cost function; According to the carbon emissions generated by the energy consumed by existing power plants during power generation, the carbon emission factor is fitted to determine the carbon emission function; based on historical power plant data, the size of new power plants is predicted, and according to land costs, sea pollution costs, fishery loss costs and noise costs, the social compensation amount is fitted to determine the social cost function; The economic cost function is established by stochastic optimization and robust optimization models, which include annual capital expenditures and operating expenditures for each power plant of a given type when planning the sea ranch power plant; The economic cost function is expressed as Among them, GE i represents the economic cost coefficient of existing power plant type i, GN i represents the economic cost coefficient of a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants; The carbon emission function is expressed as Among them, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, EE i is the emission factor for the existing type i power plant, NE i is the emission factor for a new power plant of type i, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants; The social cost function is expressed as Among them, SC i is the social compensation rate of type i power plant, T and T 0 denotes the set of type i active power plants and type i obsolete power plants, E is is the power generation of the existing power plant, p s To predict power demand, N is for the electricity generation of new power plants; S102, based on the lexicographically ordered multi-objective function, randomly sorting each objective function by enumeration, and determining different solving orders for the economic cost function, the carbon emission function, and the social cost function; S103. Quantitatively determine the weights of the economic cost function, carbon emission function and social cost function through the weighted sum method to generate different multi-objective functions; Specifically, when formulating the multi-objective function of the marine ranch based on the weighted sum multi-objective function, all objective functions are converted into costs by making quantitative decisions on each weight; according to the cost, the weights of the economic cost function, carbon emission function and social cost function are allocated to generate multi-objective functions with different weights; for the carbon emission function, the carbon emission function is converted into cost by multiplying the carbon emission amount by the carbon tax per unit amount; S104. Establish a multi-objective planning model based on the economic cost function, the carbon emission function, the social cost function and different multi-objective functions; The obtaining of the existing actual power demand and generating an uncertain demand scenario according to the actual power demand includes: Obtain the existing actual power demand, and randomly generate demand forecast scenarios based on the actual power demand and the predicted power demand; According to the uncertainty of demand development, the probability of occurrence of demand forecast scenarios is assumed, and different demand forecast scenarios are cross-multiplied to obtain uncertain demand scenarios.

6. The power plant planning system for ocean ranching as claimed in claim 5 is characterized in that: include: According to different types of new power plants and the number of new power plants of different types, the corresponding total cost, total carbon dioxide emissions, total social cost, power plant type dependency score, total power generation and employment coefficient are calculated; The optimal type of new power plant and the number of new power plants of different types are selected based on total cost, total carbon dioxide emissions, total social cost, power plant type dependence score, total power generation and employment coefficient.

7. The power plant planning system for ocean ranching as claimed in claim 5 is characterized in that: The demand forecast scenarios include the actual power demand being 10%-20% lower than the forecast power demand, the actual power demand being equal to the forecast power demand, and the actual power demand being 10%-20% higher than the forecast power demand; Assume that the probability of actual power demand being 10%-20% lower than predicted power demand is 0.2-0.4, the probability of actual power demand being equal to predicted power demand is 0.3-0.5, and the probability of actual power demand being 10%-20% higher than predicted power demand is 0.2-0.

4.

8. The power plant planning system for ocean ranching as claimed in claim 5 is characterized in that: The set constraints include predicted power demand constraints, carbon emission constraints, economic cost constraints, social compensation rate constraints, power plant technical specification constraints and robustness constraints.

9. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps described in any one of claims 1 to 4 are completed.

10. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the steps described in any one of claims 1 to 4.

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