A micro energy network planning optimization method based on economic and environmental comprehensive evaluation

Abstract

The present application relates to a kind of micro energy network planning optimization method based on economic-environmental comprehensive evaluation, comprising the following steps: establishing micro energy network system optimization configuration model, the objective function of micro energy network system optimization configuration model is comprehensive evaluation index objective function, wherein, the comprehensive evaluation index objective function is obtained by the weighted processing of economic cost objective function and environmental protection cost objective function based on random weighting method, and economic cost objective function is determined by annual average fixed cost, annual operating cost, annual maintenance cost and annual average carbon trading income;Establish optimization scheduling constraint condition;Micro energy network system optimization configuration model is solved based on linear / nonlinear-branch and bound algorithm optimization, and the optimal configuration scheme is obtained.Compared with prior art, the present application introduces carbon trading income index, has the advantages that the conflict of economic cost and environmental cost is considered, the planning is more reasonable, etc.

Description

Technical Field

[0001] This invention relates to the field of microgrid planning and optimization, and in particular to a method for microgrid planning and optimization based on a comprehensive economic and environmental evaluation. Background Technology

[0002] With social development and continuous population growth, human demand for energy has increased dramatically. Fossil fuels are gradually being depleted, and environmental pollution is becoming increasingly serious. The concept of microgrids has opened up a new avenue for solving energy problems. Microgrids consist of numerous devices, and how to rationally allocate these devices to meet different load demands and ensure the entire system operates at its optimal state is a problem that researchers have been exploring.

[0003] CN 111507529 A discloses a microgrid capacity optimization planning method based on a two-layer game theory approach involving economic, environmental, and dynamic energy pricing. This method includes steps such as designing a framework for the two-layer game theory approach, establishing an upper-level environmental-economic evolutionary game planning model, establishing a lower-level energy dynamic pricing strategy master-slave game model, and establishing a multi-energy conversion coupling hub model. It can balance the conflicting economic and environmental benefits in the planning problem, while also considering the dynamic characteristics of energy prices, balancing the conflicting interests between microgrid operators and users, and improving planning efficiency and economy. However, its shortcoming lies in that environmental benefits only consider pollution control costs, neglecting the economic benefits of energy conservation and emission reduction from using new energy sources such as wind and solar power, resulting in a relative disconnect between economic and environmental indicators. Summary of the Invention

[0004] The purpose of this invention is to provide a micro-energy grid planning optimization method based on a comprehensive economic and environmental evaluation. It introduces carbon trading revenue indicators and organically combines economic and environmental indicators to measure the benefits of energy conservation and emission reduction brought about by using wind and solar power generation, thereby reducing the cost of building renewable energy power generation systems and shortening the construction cycle.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A microgrid planning and optimization method based on comprehensive economic and environmental evaluation includes the following steps:

[0007] An optimal configuration model for a microgrid system is established. The objective function of the optimal configuration model is a comprehensive evaluation index objective function. The comprehensive evaluation index objective function is obtained by weighting the economic cost objective function and the environmental cost objective function using a random weighting method. The economic cost objective function is determined by the annual average fixed cost, annual operating cost, annual maintenance cost, and annual average carbon trading revenue.

[0008] Establish optimal scheduling constraints;

[0009] The optimal configuration scheme of the microgrid system is obtained by optimizing the solution of the linear / nonlinear branch and bound algorithm.

[0010] The economic cost objective function C yx for:

[0011] C yx =f ins +f run +f main -f ct

[0012] In the formula: f ins Indicates the average annual fixed cost; f run Indicates annual operating cost; f main Indicates annual maintenance cost; f ct This represents the average annual carbon trading revenue.

[0013] The average annual fixed cost is:

[0014]

[0015] In the formula: r represents the discount rate, in %; n represents the service life of the equipment; C GT For gas turbine engine cost; C BA It is the cost of energy storage batteries; C PV For photovoltaic costs; C WT For the cost of the wind turbine; C HSE Cost of thermal and cold storage units; C RB Cost of waste heat boiler; C AbC Cost of lithium bromide refrigerator; C AC Cost of electric refrigeration unit.

[0016] The annual operating cost is:

[0017]

[0018] In the formula: f gas For natural gas costs; f grid Cost of purchasing electricity from the grid; p gas For natural gas prices; P GT(t) represents the output power of the gas turbine; η e For gas turbine power generation efficiency; HV gas p represents the calorific value of natural gas. grid (t) represents the time-of-use electricity price of the power grid; P grid (t) represents the amount of electricity purchased from the grid; T represents the number of hours in a year.

[0019] The annual maintenance cost is:

[0020]

[0021] In the formula: C GT_m For gas turbine maintenance costs, C BA_m It is the maintenance cost of energy storage batteries, C WT_m For wind turbine maintenance costs, C PV_m For photovoltaic maintenance costs, To reduce the maintenance costs of thermal and cold storage units, For the maintenance costs of waste heat boilers, For the maintenance cost of lithium bromide refrigerators, C AC_m This represents the maintenance cost of the electric chiller; n indicates the number of units installed for each component, and its subscript indicates the corresponding component.

[0022] The average annual carbon trading revenue is:

[0023]

[0024] In the formula: P PV and P WP Represents the annual power generation of photovoltaic and wind power, respectively; M is the carbon dioxide emission factor; p ct This indicates the price of carbon trading.

[0025] The objective function for environmental protection costs is:

[0026]

[0027] In the formula: C wr The objective function is the environmental cost; F i P represents the pollutant emissions per unit power of microgrid equipment. gi It provides power to microgrid devices.

[0028] The comprehensive evaluation index objective function is obtained by weighting the economic cost objective function and the environmental protection cost objective function using the random weighting method, as follows:

[0029]

[0030] In the formula: η1 and η2 are both random numbers, C sum For the comprehensive evaluation index objective function, C yxLet C be the objective function of economic cost. wr The objective function is the environmental cost.

[0031] The optimization scheduling constraints include: linear inequality constraints, linear equality constraints, decision variable boundary constraints, nonlinear inequality constraints, nonlinear equality constraints, integer constraints, and binary constraints.

[0032] The solution process of the linear / nonlinear branch and bound algorithm is as follows:

[0033] Step 3-1) Initialize the MILP subproblem, solve the MINLP relaxation problem at the root node to obtain its solution set, and initialize the linearized point set. At the nodes, MILP subproblems are constructed using the idea of ​​external approximation, where,

[0034] The MINLP relaxation problem is as follows:

[0035] Z NLPR(lI,uI) =min f(x,y)

[0036] stg(x,y)≤0,

[0037] x i ∈X,l I ≤y≤u I .

[0038] Step 3-2) Solve the MILP subproblem based on the branch and bound algorithm. Select a node in the branch and bound tree and solve the LP subproblem. If the LP subproblem is infeasible, delete the node and search the branch and bound tree again; otherwise, assume... For the solution to the LP subproblem, if y is not an integer, then branch processing is performed on the current node; if y is an integer, then the first NLP subproblem is solved; if the first NLP subproblem is feasible, let... The optimal solution to the first NLP subproblem is defined; if the function value of the optimal solution to the first NLP subproblem is less than the current upper bound, the upper bound is updated; if the first NLP subproblem is infeasible, then let... The optimal solution to the second NLP subproblem; utilizing the solution provided by the first or second NLP subproblem. Update the linearized point set And update the MILP subproblem;

[0039] The first NLP subproblem is:

[0040]

[0041]

[0042] x∈X.

[0043] The second NLP subproblem is:

[0044]

[0045]

[0046] μ≥0,

[0047] x∈X,μ∈R m .

[0048] Step 3-3) Check if the branch and bound tree is empty. If it is empty, the algorithm terminates; otherwise, repeat step 3-2).

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] (1) Based on traditional economic and environmental indicators, this invention introduces carbon trading revenue indicators, organically combining economic and environmental indicators to establish a comprehensive evaluation index that can measure the benefits of energy conservation and emission reduction brought about by using wind and solar power generation, thereby reducing the cost of building renewable energy power generation systems and shortening the construction cycle.

[0051] (2) This invention fully considers the operating characteristics of wind and solar power generation, hydrogen production, energy storage, gas turbine generator sets, etc., and establishes a micro energy network objective function model. The model takes into account the characteristics of each device, the simulation is more accurate, and the optimized operation results obtained by solving are more in line with reality.

[0052] (3) The present invention adopts a random weighting method to transform a multi-objective problem into a single-objective problem, which can find the optimal solution of the objective function from different search directions and the search results are better.

[0053] (4) Based on the linear / nonlinear branch and bound algorithm, this invention optimizes the solution of the mixed integer nonlinear programming problem MINLP and obtains the optimal solution of the comprehensive evaluation index. It has good convergence, can find the global optimal solution, and can converge quickly. The algorithm has good stability. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention;

[0055] Figure 2 This is a schematic diagram of a regional microgrid system structure.

[0056] Figure 3 A diagram showing the transaction prices and volumes of carbon exchanges in various regions;

[0057] Figure 4 The flowchart shows the linear / nonlinear branch and bound algorithm. Detailed Implementation

[0058] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0059] A microgrid is a smart energy network that integrates distributed energy production, transmission, conversion, storage, and consumption. Based on a smart grid and widely combined with internet information technology, it enables localized consumption of distributed energy for end-user energy supply. Microgrids not only have a high proportion of renewable energy integration but also achieve a basic balance between local energy production and consumption load through energy storage and optimized allocation. They enable multi-energy complementarity among various distributed energy sources such as wind, solar, natural gas, and biomass, achieving efficient integration of electricity, heat, gas, and cooling within the region. Furthermore, they can flexibly interact with the public power grid as needed. The system structure of a microgrid is as follows: Figure 2 As shown.

[0060] In the construction of a microgrid system, the output characteristics and cost of each component need to be considered to select the optimal capacity, thereby achieving the desired result. In building the microgrid optimization configuration model, this invention uses economy and environmental friendliness as optimization objectives; therefore, the objective function includes both minimizing economic cost and minimizing environmental cost. Since the system comprises multiple components, each component's characteristics are subject to its own constraints. Therefore, the constraints of the overall model include constraints on the output characteristics of each component, as well as overall system constraints, such as power balance constraints. Through the above objective function and constraints, an optimization model can be established to analyze the configuration strategy of the microgrid system.

[0061] Specifically, this invention provides a microgrid planning optimization method based on a comprehensive economic and environmental evaluation, such as... Figure 1 As shown, it includes the following steps:

[0062] Step 1) Establish a microgrid system optimization configuration model, wherein the objective function of the microgrid system optimization configuration model is a comprehensive evaluation index objective function;

[0063] The objective function of the microgrid system configuration optimization model is a comprehensive evaluation index objective function that simultaneously considers both the economic cost and environmental cost of system configuration.

[0064] A) Economic cost objective function

[0065] The economic cost objective function C yx for:

[0066] C yx =f ins +f run +f main-f ct

[0067] In the formula: f ins Indicates the average annual fixed cost; f run Indicates annual operating cost; f main Indicates annual maintenance cost; f ct This represents the average annual carbon trading revenue; all units are in yuan.

[0068] A1) Average Annual Fixed Costs

[0069] This embodiment assumes a planning period of 15 years, and averages the fixed costs of the microgrid over an annual period to obtain the average annual fixed cost:

[0070]

[0071] In the formula: r represents the discount rate, in %; n represents the service life of the equipment; C GT For the cost of the gas turbine; C BA It is the cost of energy storage batteries; C PV For photovoltaic costs; C WT For the cost of the wind turbine; C HSE Cost of thermal and cold storage units; C RB Cost of waste heat boiler; C AbC Cost of lithium bromide refrigerator; C AC Cost of electric refrigeration unit.

[0072] In this embodiment, the discount rate is set to 8%.

[0073] A2) Annual operating costs

[0074] The annual operating cost of a microgrid system includes two parts: the cost of natural gas consumed by the generator sets and the cost of purchasing electricity from the grid to meet the electricity load demand. Generally, based on typical daily or annual load curves, the real-time power of the microgrid generator sets and the electricity purchased from the grid are obtained through operational simulation. Then, the power generation and the amount of electricity purchased from the grid are calculated to obtain the annual cost of natural gas consumed by the generator sets and the annual cost of electricity purchased from the grid.

[0075] The annual operating cost is:

[0076]

[0077] In the formula: f gas Natural gas cost, in yuan; f grid The cost of purchasing electricity from the grid is expressed in yuan; p gas This refers to the price of natural gas, expressed in yuan / Nm³. 3 ;P GT (t) represents the output power of the gas turbine, in kW; η eFor gas turbine power generation efficiency; HV gas This refers to the calorific value of natural gas, expressed in MJ / Nm³. 3 ;p grid (t) represents the time-of-use electricity price of the power grid, in yuan / kWh; P grid (t) represents the electricity purchased from the grid, in kWh; T represents the number of hours in a year.

[0078] A3) Annual maintenance cost

[0079] The annual maintenance cost is:

[0080]

[0081] In the formula: C GT_m For gas turbine maintenance costs, C BA_m It is the maintenance cost of energy storage batteries, C WT_m For wind turbine maintenance costs, C PV_m For photovoltaic maintenance costs, To reduce the maintenance costs of thermal and cold storage units, For the maintenance costs of waste heat boilers, For the maintenance cost of lithium bromide refrigerators, C AC_m This represents the maintenance cost of the electric chiller; n indicates the number of units installed for each component, and its subscript indicates the corresponding component.

[0082] A4) Average Annual Carbon Trading Revenue

[0083] With the official launch of the national carbon emissions trading system, calculations based on existing policies and data indicate that wind and solar power projects can generate an additional revenue of 1.3 to 7.4 fen per kilowatt-hour. The emission factors of different regional power grids range from 0.6894 to 0.9139 t CO2 / MWh, with a national average of 0.7819 t CO2 / MWh. This means that 1 MWh of renewable energy can earn approximately 0.78 tons of CCERs (1 unit of CCER can offset 1 ton of carbon dioxide equivalent emissions). A diagram illustrating the transaction prices and volumes of carbon exchanges across various regions is shown below. Figure 3 As shown.

[0084] The average annual carbon trading revenue is:

[0085]

[0086] In the formula: P PV and P WP represents the annual power generation of photovoltaic and wind power, respectively, in kWh; M is the carbon dioxide emission factor, which is taken as 0.7819tCO2 / MWh in this embodiment; p ct This indicates the carbon trading price, expressed in yuan / t CO2.

[0087] B) Environmental cost objective function

[0088] To meet the requirements of sustainable development, microgrid systems should consider both economic efficiency and environmental friendliness. The main pollutants emitted by microgrid systems during operation are air pollutants such as CO2, SO2, and NOx.

[0089] The objective function for environmental protection costs is:

[0090]

[0091] In the formula: C wr The objective function is the environmental cost; F i The pollutant emissions per unit power of microgrid equipment, expressed in kg / kWh; P gi Power output by microgrid devices, measured in kW.

[0092] C) Optimization function of comprehensive evaluation index

[0093] When considering the multi-objective optimization problem of microgrid system operation, which comprehensively considers both economic efficiency and environmental friendliness, the system energy optimization problem will be classified as a multi-objective optimization problem since there is often no positive correlation between economic efficiency and environmental friendliness. For a multi-objective problem, its Pareto optimal solution will be a solution set, not just a unique solution. This invention will use a random weighting method to transform the multi-objective problem into a single-objective problem. Compared with the fixed weighting method, the random weighting method will randomly assign weights to a certain objective in each run, while ensuring that the total weight of all objectives is 1, thereby enabling the search for the optimal solution of the objective function from different search directions. The final optimization result will be the minimum value after multiple runs.

[0094] The comprehensive evaluation index objective function is obtained by weighting the economic cost objective function and the environmental protection cost objective function using the random weighting method, as follows:

[0095]

[0096] In the formula: η1 and η2 are both random numbers, C sum For the comprehensive evaluation index objective function, C yx Let C be the objective function of economic cost. wr The objective function is the environmental cost.

[0097] Step 2) Establish optimal scheduling constraints;

[0098] The optimization scheduling constraints include: linear inequality constraints, linear equality constraints, decision variable boundary constraints, nonlinear inequality constraints, nonlinear equality constraints, integer constraints, and binary constraints.

[0099] Step 3) Solve the microgrid system optimization configuration model based on the linear / nonlinear branch and bound algorithm to obtain the optimal configuration scheme.

[0100] In optimizing resource allocation, the variables involved include both continuous and discrete variables. The discrete variable is the number of units in each allocation, while the continuous variable is the actual load percentage for each allocation. Therefore, the problem studied is a mixed-integer nonlinear programming problem (MINLP). MINLP problems are a class of nonlinear programming problems involving both continuous and discrete variables. In general, the MINLP model can be expressed in the following form:

[0101] min x f(x)

[0102] stAx≤b

[0103] A eq x = b eq

[0104] l b ≤x≤u b

[0105] c(x)≤d

[0106] c eq (x)=d eq

[0107] x i ∈Z,x j ∈{0,1}

[0108] Where f is a scalar function containing a nonlinear objective function, which is subject to the following constraints:

[0109] 1. Linear inequality constraint: A is an m×n sparse matrix, and b is an m×1 vector;

[0110] 2. Linear equality constraint: A eq It is a k×n sparse matrix, b eq It is a k×1 vector;

[0111] 3. Boundary constraints for decision variables: b and u b There are n×1 vectors, where -inf or inf represents the lower or upper bound of the unbounded vector, respectively;

[0112] 4. Nonlinear inequality constraints: c is a u×1 vector of a function containing nonlinear inequality constraints, and d is a u×1 vector;

[0113] 5. Nonlinear equality constraints: c eq It is a v×1 vector of functions containing nonlinear equality constraints, d eq It is a v×1 vector;

[0114] 6. Integer constraint: x i These are decision variables and must be integers (...-2, -1, 0, 1, 2...).

[0115] 7. Binary constraint: x j It is a decision variable and must be a binary number (0,1), where i≠j. The choice of binary constraint depends on the specific problem.

[0116] By using a linear / nonlinear branch-and-bound algorithm (LP / NLP-BB), the x-values ​​that satisfy all constraints can be selected to minimize the objective function. Compared to genetic algorithms, the LP / NLP-BB algorithm exhibits better convergence in solving MINLP problems, finding the global optimum and having a shorter solution time. In contrast, genetic algorithms, when faced with multivariate MINLP problems, have longer solution times and often only find local optima.

[0117] The solution process of the linear / nonlinear branch and bound algorithm is as follows:

[0118] Step 3-1) Initialize the MILP subproblem, solve the MINLP relaxation problem at the root node to obtain its solution set, and initialize the linearized point set. At the nodes, MILP subproblems are constructed using the idea of ​​external approximation, where,

[0119] The MINLP relaxation problem is as follows:

[0120] Z NLPR(lI,uI) =min f(x,y)

[0121] stg(x,y)≤0,

[0122] x i ∈X,l I ≤y≤u I .

[0123] Step 3-2) Solve the MILP subproblem based on the branch and bound algorithm. Select a node in the branch and bound tree and solve the LP subproblem. If the LP subproblem is infeasible, delete the node and search the branch and bound tree again; otherwise, assume... For the solution to the LP subproblem, if If the result is not an integer, then branch processing is performed on the current node. If the integer is true, then solve the first NLP subproblem; if the first NLP subproblem is feasible, then assume... The optimal solution to the first NLP subproblem is defined; if the function value of the optimal solution to the first NLP subproblem is less than the current upper bound, the upper bound is updated; if the first NLP subproblem is infeasible, then let... The optimal solution to the second NLP subproblem; utilizing the solution provided by the first or second NLP subproblem. Update the linearized point set And update the MILP subproblem;

[0124] The first NLP subproblem is:

[0125]

[0126]

[0127] x∈X.

[0128] The second NLP subproblem is:

[0129]

[0130]

[0131] μ≥0,

[0132] x∈X,μ∈R m .

[0133] Step 3-3) Check if the branch and bound tree is empty. If it is empty, the algorithm terminates; otherwise, repeat step 3-2).

[0134] The feasibility of the microgrid planning and optimization method described in this invention is illustrated below through a specific implementation result.

[0135] The planning of microgrid configuration depends on the characteristics of the region's electricity, cooling, and heating loads. Table 1 shows the maximum values ​​of electricity, cooling, and heating loads for a typical hotel building during summer, spring / autumn transition season, and winter on a typical day.

[0136] Table 1

[0137] Typical Seasonal Days Electrical load / kW Cooling load / kW Heat load / kW summer 5512 4256 263 Transitional season 5637 2592 2254 winter 5458 518 2754

[0138] The microgrid configuration involved in this embodiment mainly includes: a gas turbine, a waste heat boiler, a lithium bromide chiller, an electric chiller, a wind turbine, a photovoltaic power generation unit, a thermal storage unit, and a cold storage unit. The specific capacity of each configuration is shown in Table 2.

[0139] Table 2

[0140]

[0141] Based on the load demand and microgrid parameters in Tables 1 and 2, the planning optimization solution is obtained using the method described in this invention, and the calculation results are as follows:

[0142] a. Typical summer daily requirements: 1 x 200kW gas turbine, 5 x 1000kW gas turbines, 1 x 5000kW waste heat boiler, 1 x 4000kW lithium bromide chiller, 1 x 1000kW electric chiller, 388 x 320W photovoltaic panels, 1 x 200kW wind turbine, and 1 x 500kW cold storage unit. Under this configuration, the total fixed cost is 36.24 million yuan, the average annual fixed cost is 4.23 million yuan, the average annual operation and maintenance cost is 5.29 million yuan, the average annual carbon trading revenue is 89,000 yuan, and the average annual fuel cost is 36.02 million yuan. Annual CO2 emissions are 14,200 tons, annual CO emissions are 22.7 tons, annual SO2 emissions are 5.89 tons, and annual NOx emissions are 10.87 tons.

[0143] b. Typical winter day requirements: 2 x 200kW gas turbines, 5 x 1000kW gas turbines, 1 x 5000kW waste heat boiler, 1 x 1000kW lithium bromide chiller, 1 x 500kW electric chiller, 541 x 320W photovoltaic panels, 1 x 200kW wind turbine, 1 x 500kW electric energy storage unit, and 1 x 500kW cold energy storage unit. Under this configuration, the total fixed cost is 35.92 million yuan, the average annual fixed cost is 4.2 million yuan, the average annual operation and maintenance cost is 4.32 million yuan, the average annual carbon trading revenue is 94,000 yuan, and the average annual fuel cost is 37.09 million yuan. Annual CO2 emissions are 14,800 tons, annual CO emissions are 23.6 tons, annual SO2 emissions are 6.14 tons, and annual NOx emissions are 11.34 tons.

[0144] c. Typical daily requirements during the spring and autumn transition season: 5 x 1000kW gas turbines, 1 x 5000kW waste heat boiler, 1 x 4000kW lithium bromide chiller, 1 x 500kW electric chiller, 463 x 320W photovoltaic panels, 1 x 200kW wind turbine, 3 x 500kW wind turbines, 1 x 1000kW electric energy storage unit, and 1 x 500kW cold energy storage unit. Under this configuration, the total fixed cost is 40.34 million yuan, the average annual fixed cost is 4.71 million yuan, the average annual operation and maintenance cost is 4.3 million yuan, the average annual carbon trading revenue is 469,900 yuan, and the average annual fuel cost is 34.95 million yuan. Annual CO2 emissions are 13,700 tons, annual CO emissions are 21.81 tons, annual SO2 emissions are 5.67 tons, and annual NOx emissions are 10.47 tons.

[0145] Table 3 shows the number of components calculated for three typical days.

[0146] Table 3

[0147]

[0148] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A microgrid planning and optimization method based on comprehensive economic and environmental evaluation, characterized in that, Includes the following steps: An optimal configuration model for a microgrid system is established. The objective function of the optimal configuration model is a comprehensive evaluation index objective function. The comprehensive evaluation index objective function is obtained by weighting the economic cost objective function and the environmental cost objective function using a random weighting method. The economic cost objective function is determined by the annual average fixed cost, annual operating cost, annual maintenance cost, and annual average carbon trading revenue. Establish optimal scheduling constraints; The optimal configuration scheme of the microgrid system is obtained by optimizing the solution of the linear / nonlinear branch and bound algorithm. The economic cost objective function for: In the formula: This represents the average annual fixed cost; Indicates annual operating costs; Indicates annual maintenance cost; This represents the average annual carbon trading revenue; The average annual carbon trading revenue is: In the formula: and These represent the annual power generation from photovoltaic and wind power, respectively. M It is a carbon dioxide emission factor; Indicates the carbon trading price; The solution process of the linear / nonlinear branch and bound algorithm is as follows: Step 3-1) Initialize the MILP subproblem, solve the MINLP relaxation problem at the root node to obtain its solution set, and initialize the linearized point set. At the nodes, MILP subproblems are constructed using the idea of ​​external approximation, where, The MINLP relaxation problem is as follows: Step 3-2) Solve the MILP subproblem based on the branch and bound algorithm. Select a node in the branch and bound tree and solve the LP subproblem. If the LP subproblem is infeasible, delete the node and search the branch and bound tree again; otherwise, assume... For the solution to the LP subproblem, if If the result is not an integer, then branch processing is performed on the current node. If the integer is true, then solve the first NLP subproblem; if the first NLP subproblem is feasible, then assume... The optimal solution to the first NLP subproblem is defined; if the function value of the optimal solution to the first NLP subproblem is less than the current upper bound, the upper bound is updated; if the first NLP subproblem is infeasible, then let... The optimal solution to the second NLP subproblem; utilizing the solution provided by the first or second NLP subproblem. Update the linearized point set And update the MILP subproblem; The first NLP subproblem is: The second NLP subproblem is: Step 3-3) Check if the branch and bound tree is empty. If it is empty, the algorithm terminates; otherwise, repeat step 3-2.

2. The microgrid planning and optimization method based on economic and environmental comprehensive evaluation according to claim 1, characterized in that, The average annual fixed cost is: In the formula: r This represents the discount rate, expressed in % (%). n Indicates the number of years the equipment has been in use; Cost of gas turbine; It's the cost of energy storage batteries; For photovoltaic costs; For the cost of the wind turbine; Cost of thermal and cold storage units; Cost of waste heat boiler; Cost of lithium bromide refrigerator; Cost of electric refrigeration unit.

3. The microgrid planning and optimization method based on economic and environmental comprehensive evaluation according to claim 1, characterized in that, The annual operating cost is: In the formula: For natural gas costs; Cost of purchasing electricity from the grid; For natural gas prices; This refers to the output power of the gas turbine. For gas turbine power generation efficiency; The calorific value of natural gas; Time-of-use pricing for the power grid; To purchase electricity from the power grid; T It represents the number of hours in a year.

4. The microgrid planning and optimization method based on economic and environmental comprehensive evaluation according to claim 1, characterized in that, The annual maintenance cost is: In the formula: For gas turbine maintenance costs, It is the maintenance cost of energy storage batteries. For wind turbine maintenance costs, For photovoltaic maintenance costs, To reduce the maintenance costs of thermal and cold storage units, For the maintenance costs of waste heat boilers, For the maintenance cost of lithium bromide refrigerators, Maintenance costs for electric chillers; n This indicates the number of units installed for each component, with the subscript indicating the corresponding component.

5. The microgrid planning and optimization method based on economic and environmental comprehensive evaluation according to claim 1, characterized in that, The objective function for environmental protection costs is: In the formula: The objective function is the environmental cost. This refers to the pollutant emissions per unit power of microgrid equipment. It provides power to microgrid devices.

6. The microgrid planning and optimization method based on economic and environmental comprehensive evaluation according to claim 1, characterized in that, The comprehensive evaluation index objective function is obtained by weighting the economic cost objective function and the environmental protection cost objective function using the random weighting method, as follows: In the formula: , All are random numbers. The objective function for the comprehensive evaluation index is as follows: Let the objective function be the economic cost. The objective function is the environmental cost.

7. The microgrid planning and optimization method based on economic and environmental comprehensive evaluation according to claim 1, characterized in that, The optimization scheduling constraints include: linear inequality constraints, linear equality constraints, decision variable boundary constraints, nonlinear inequality constraints, nonlinear equality constraints, integer constraints, and binary constraints.

Images