Microgrid planning method and system based on improved normal boundary crossing method

Abstract

The invention discloses a micro-grid planning method and system based on an improved normal boundary crossing method. The method comprises the following steps: constructing an objective function of a micro-grid planning model considering ecological, climate and human settlement influences; constructing constraint conditions of the micro-grid planning model considering environmental benefit constraints; constructing a compact form of a micro-grid planning model; solving to obtain a Pareto leading edge based on an improved normal boundary crossing method of Euclidean distance correction for a compact micro-grid planning model; and selecting an optimal compromise solution from the Pareto leading edge to obtain a micro-grid expansion planning scheme. According to the method, ecological, climate and human settlement influences are integrated, the modeling environmental benefits are quantified, and the environmental adaptability of micro-grid planning is improved; according to model nonlinearity caused by environmental benefit modeling, an improved normal boundary crossing method based on Euclidean distance correction is designed for solving, the solution set uniformity of the Pareto front is improved, aggregation or vacant areas of the solution set are avoided, and a candidate scheme set with higher coverage is provided for decision makers.

Description

Technical Field

[0001] The present invention belongs to the field of power systems and relates to microgrid planning technology, and in particular to a microgrid planning method and system based on an improved normal boundary intersection method. Background Art

[0002] Microgrids, as a key enabler for achieving a green and low-carbon energy transition, are gaining widespread adoption and application. Microgrids integrate multiple power sources, load types, and energy storage devices, playing a vital role in enhancing power supply reliability and promoting the integration of renewable energy. Therefore, developing optimal planning models for nano- and microgrid clusters has become a key research area.

[0003] In planning models, common planning objectives include improving the economic benefits, environmental benefits, and reliability of the power system. Based on historical information on wind speed, sunlight, and load, Dufo et al. established a two-objective optimization problem that considers the cost and emission minimization of independent wind, solar, diesel, and energy storage systems. Diab et al. established an optimal configuration model for independent wind, solar, diesel, and energy storage microgrids based on different control strategies. Khalilpour et al. studied the multi-period planning problem of photovoltaic and energy storage for grid-connected photovoltaic and energy storage microgrids based on the photovoltaic and load curves throughout the year. Hoppmann et al. used a full-year operation simulation for photovoltaic and energy storage grid-connected microgrids to conduct a technical and economic analysis of photovoltaic and energy storage investment issues.

[0004] Furthermore, the normal boundary intersection method is often used to solve multi-objective programming models, as the standard multi-objective particle swarm optimization algorithm and multi-objective genetic algorithm have randomness and are prone to falling into local optimality. Duan Ziyue et al. used the normal boundary intersection method and fuzzy membership function theory to address the unstable operating conditions that may occur in flexible low-frequency offshore wind power systems, solving the problem of optimizing the stable operating mode of a multi-objective system that takes into account operating efficiency and stability margin. Tan Zhukui et al. used an improved generalized normal boundary intersection method to solve the Pareto frontier of a multi-objective optimization scheduling model for an electricity-gas interconnected system, providing dispatchers with a variety of decision-making solutions.

[0005] However, the above studies still have the following deficiencies: (1) At the modeling level, the modeling of environmental benefits simply focuses on indicators such as carbon emissions, and does not fully quantify the comprehensive impact of ecological, climate, and human settlement factors on microgrid planning. (2) At the solution level, the existing normal boundary intersection method has the problem of uneven solution distribution, making it difficult to obtain a uniform Pareto frontier solution set. Summary of the Invention

[0006] Purpose of the invention: In order to overcome the deficiencies in the prior art, a microgrid planning method and system based on an improved normal boundary intersection method is provided.

[0007] Technical solution: To achieve the above objectives, the present invention provides a microgrid planning method based on an improved normal boundary intersection method, comprising the following steps:

[0008] S1: Construct the objective function of the microgrid planning model taking into account the impact of ecology, climate, and human settlement;

[0009] S2: Constraints for constructing a microgrid planning model that takes environmental benefit constraints into account;

[0010] S3: constructing a compact form of the microgrid planning model based on the objective function constructed in step S1 and the constraints constructed in step S2;

[0011] S4: For the compact microgrid planning model, the Pareto frontier is obtained by using the improved normal boundary intersection method based on Euclidean distance correction;

[0012] S5: Select the optimal compromise solution from the Pareto frontier to obtain the microgrid expansion planning scheme.

[0013] Furthermore, the objective function of the microgrid planning model taking into account the impact of ecology, climate, and human settlement in step S1 includes an economic cost objective function, an environmental benefit objective function, and a reliability objective function.

[0014] Furthermore, the establishment of the economic cost objective function in step S1 includes:

[0015] Microgrid planning aims to achieve the minimum total economic cost, that is, the minimum sum of planning and operating costs:

[0016]

[0017] Where: is the economic cost objective function; To plan costs; is the total scene set; is the total time period set; variable subscript Indicates the Scenario; variable subscript Indicates time period ; The cost of purchasing electricity for the microgrid; is the gas turbine fuel cost; is the incentive cost for adjustable load; The cost of charging and discharging energy storage.

[0018] For planning costs :

[0019]

[0020] Where: is the cost of expanding the unit energy storage capacity; building capacity for energy storage; The cost of expanding the capacity of new energy units per unit; Expand capacity for new energy units; is a candidate energy storage set; A collection of candidate new energy units;

[0021] Regarding running costs:

[0022]

[0023]

[0024]

[0025]

[0026] Where: The electricity purchase price; The power of purchased electricity; is the gas turbine fuel cost coefficient; Output power for the gas turbine; is the load incentive cost coefficient; is the flexible load power; is the load demand value under no-load excitation; 、 are the energy storage charging and discharging cost coefficients respectively; 、 are the energy storage charging efficiency and energy storage discharging efficiency respectively; and are the energy storage charging power and energy storage discharging power respectively;

[0027] Among them, the adjustable load incentive cost For nonlinear terms containing absolute values, the large M method is used for linearization:

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] in, is the introduced continuous auxiliary variable; is the introduced 0-1 auxiliary variable; is a large positive constant.

[0034] Furthermore, the establishment of the environmental benefit objective function in step S1 includes:

[0035] On the basis of carbon emissions, we further introduce three dimensions: ecological impact, climate impact, and human settlement impact to construct an environmental benefit objective function:

[0036]

[0037] in, is the environmental benefit objective function; The environmental benefit objective function in the existing microgrid planning; 、 、 are the weight factors for ecological impact, climate impact, and human settlement impact, respectively; is the ecological impact factor; is the climate influencing factor; The impact factor of human settlement.

[0038] Furthermore, the establishment of the reliability objective function in step S1 includes:

[0039] In microgrid planning, reliability aims to minimize wind and solar power curtailment and load shedding, as expressed as follows:

[0040]

[0041] in, is the reliability objective function; Indicates the amount of wind and solar power curtailment; Indicates load shedding amount; Penalty costs for units that curtail wind and solar power; is the unit load shedding penalty cost; For the new energy unit collection; For load collection.

[0042] Furthermore, the constraints in step S2 include external power purchase constraints, gas turbine constraints, flexible load constraints, energy storage constraints, planning constraints, and environmental benefit constraints, specifically:

[0043] External power purchase constraints: For external power purchases, including upper and lower limits:

[0044]

[0045] in, is the maximum power purchased by the microgrid;

[0046] Gas turbine constraints: For gas turbines, including upper and lower output constraints and ramp constraints:

[0047]

[0048]

[0049]

[0050] in, 、 are the upper and lower limits of the gas turbine active power respectively; is the maximum upward climbing power of the gas turbine; is the maximum downward climbing power of the gas turbine;

[0051] Adjustable load constraints: For adjustable loads, there are constraints on the total load amount and the load translation amount:

[0052]

[0053]

[0054] in, 、 They are the maximum and minimum load power demands respectively;

[0055] Energy storage constraints: The regulation capability of energy storage is modeled as follows:

[0056]

[0057]

[0058] in, and They are Moment and The remaining energy stored at the end of the moment; 、 are the energy storage charging and discharging efficiency respectively; 、 are the energy storage charging and discharging power respectively; is the net charging power of energy storage;

[0059] Energy storage regulation constraints include upper and lower limits on charge and discharge power, and upper and lower limits on storage capacity:

[0060]

[0061]

[0062]

[0063]

[0064] in, 、 are the upper and lower limits of energy storage capacity of the energy storage battery respectively; 、 They are the upper limits of charging and discharging power of the energy storage battery respectively; 、 are the charging and discharging status 0 / 1 variables of the energy storage battery respectively;

[0065] Planning constraints: For planning energy storage:

[0066]

[0067]

[0068]

[0069] in, A 0-1 auxiliary variable for planning energy storage, where 0 means no expansion and 1 means expansion; To plan the maximum capacity of energy storage;

[0070] For planning new energy:

[0071]

[0072] in, It is a 0-1 auxiliary variable for planning new energy, where 0 means no expansion and 1 means expansion; To plan the maximum capacity of new energy;

[0073] Environmental benefit constraints include ecological red line constraints, ecological buffer zone distance constraints, and human settlement environment suitability constraints. Specifically:

[0074] Ecological red line constraints:

[0075]

[0076] in, Indicates the 0 / 1 variable indicating whether the region will build new energy units. Represents the set of ecological red line coverage areas;

[0077] Ecological buffer zone distance constraints:

[0078]

[0079] in, Indicates construction area Ecologically sensitive areas the distance between them; It is a collection of areas that can be planned for new energy; It is a collection of ecologically sensitive areas; represents the distance of ecological buffer zone;

[0080] Constraints on the suitability of the human settlement environment:

[0081]

[0082] in, Building a region for new energy and settlements the distance between them; Gathering for areas where residents gather; It represents the suitability distance of human settlement environment.

[0083] Furthermore, the compact form of the microgrid planning model in step S3 is:

[0084]

[0085] in, is the decision variable; represents transpose; 、 are the first coefficient matrix and the second coefficient matrix of the economic cost objective function respectively; is the nonlinear form of the environmental benefit objective function; 、 are the first coefficient matrix and the second coefficient matrix of the reliability objective function respectively; 、 are the coefficient matrix and constant term of the inequality constraints respectively; 、 are the coefficient matrix and constant term of the inequality constraints respectively; 、 The decision variables The lower and upper bound matrices of .

[0086] Furthermore, the step S4 includes:

[0087] A1: Determine the Pareto frontier boundary points and the Pareto frontier auxiliary surface;

[0088] For the three sets of objective functions in the multi-objective optimization problem, each objective function is optimized separately to obtain the corresponding optimal solution. The three sets of optimal solutions are recorded as Pareto frontier boundary points.

[0089] The optimal solution corresponding to the optimization of the economic cost objective function is , then the objective function value is 、 、 ; The optimal solution corresponding to the optimization of the environmental benefit objective function is , then the objective function value is 、 、 ; The optimal solution corresponding to the optimization of the reliability objective function is , then the objective function value is 、 、 ;

[0090] Note that the Pareto frontier point 、 、 The determined triangular plane is the auxiliary surface of the Pareto frontier;

[0091] A2: Normalize the three objective functions in the model so that their values ​​are all between [0, 1], eliminating the effects of dimension and magnitude on the uniformity of the solution:

[0092]

[0093]

[0094]

[0095] in, is the normalized economic cost objective function; 、 are the maximum and minimum values ​​of the economic cost objective function respectively; is the normalized environmental benefit objective function; 、 are the maximum and minimum values ​​of the environmental benefit objective function respectively; is the normalized reliability objective function; 、 are the maximum and minimum values ​​of the reliability objective function respectively;

[0096] After normalization, the Pareto frontier boundary points become normalized Pareto frontier boundary points, and the Pareto frontier auxiliary surface becomes normalized Pareto frontier auxiliary surface.

[0097] A3: Obtain the normalized Pareto frontier point set;

[0098] On the normalized Pareto front auxiliary surface, select a set of evenly distributed points , represented as the normalized Pareto frontier point 、 、 Three-point linear combination:

[0099]

[0100]

[0101] in, 、 、 They are the normalized Pareto front auxiliary surface and the normalized Pareto front boundary points. 、 、 The weight coefficient of

[0102] Pass every point , make a perpendicular line to the normalized Pareto front auxiliary surface, calculate the following optimization model, and get the point set projected to the normalized Pareto front surface :

[0103]

[0104] in, express and distance;

[0105] A4: Adaptive adjustment of point set uniformity based on Euclidean distance correction, including the following steps:

[0106] A4-1: Calculate the set of adjacent points on the normalized Pareto frontier 、 Euclidean distance between :

[0107]

[0108] in, express The values ​​of the axis coordinates; express The values ​​of the axis coordinates; express The values ​​of the axis coordinates;

[0109] A4-2: Calculate the average Euclidean distance between all adjacent point sets :

[0110]

[0111] in, represents the total number of point sets;

[0112] A4-3: Setting the adjustment threshold ;

[0113] like , on the normalized Pareto front auxiliary surface, point The three-dimensional coordinate position of is updated as follows:

[0114]

[0115] in, It is a parameter that can be set dynamically, and its value range is between [0,1];

[0116] For updated position , make a perpendicular line to the normalized Pareto front auxiliary surface, and get the point set projected onto the normalized Pareto front surface ; Recalculate at this time :like , then increase ;like , then reduce ;like , then fix the current Location;

[0117] like , put the point The three-dimensional coordinate position of is updated as follows:

[0118]

[0119] For updated position , make a perpendicular line to the normalized Pareto front auxiliary surface, and get the point set projected onto the normalized Pareto front surface ; Recalculate at this time :like , then reduce ;like , then increase ;like , then fix the current Location;

[0120] like , then it indicates that the point with dot If the spacing is within a reasonable range, no adjustment is made.

[0121] Furthermore, the specific method of selecting the optimal compromise solution from the Pareto front in step S5 includes:

[0122] For the normalized economic cost objective function , normalized environmental benefit objective function , normalized reliability objective function , and solve their satisfaction through the following formulas:

[0123]

[0124] in, Indicates the The satisfaction of the normalized objective function is between [0,1]; For the A normalized objective function, For the The maximum value of the normalized objective function, For the The minimum value of the normalized objective function;

[0125] After calculating the satisfaction of each normalized objective function, the comprehensive satisfaction of the solution is determined by the following formula:

[0126]

[0127] in, is the number of normalized objective functions, is the comprehensive satisfaction value;

[0128] Among all the solutions on the Pareto frontier, the solution with the highest overall satisfaction is the optimal compromise solution.

[0129] The present invention also provides a microgrid planning system based on an improved normal boundary intersection method, comprising:

[0130] The objective function construction module is used to construct the objective function of the microgrid planning model taking into account the impact of ecology, climate, and human settlement;

[0131] A constraint condition construction module is used to construct the constraint conditions of the microgrid planning model taking into account environmental benefit constraints;

[0132] a model optimization module for building a compact form of the microgrid planning model;

[0133] The model solving module is used to solve the Pareto frontier based on the improved normal boundary intersection method corrected by Euclidean distance, select the optimal compromise solution from the Pareto frontier, and obtain the microgrid expansion planning scheme.

[0134] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0135] (1) The present invention integrates ecological, climate, and human settlement impacts, quantifies modeling environmental benefits, and embeds them into the objective function and constraints, thereby improving the environmental adaptability of microgrid planning;

[0136] (2) In order to solve the nonlinearity of the model caused by environmental benefit modeling, the present invention designs an improved normal boundary intersection method based on Euclidean distance correction to solve the Pareto frontier, which improves the uniformity of the solution set of the Pareto frontier and avoids the occurrence of clustered or vacant areas in the solution set. It provides decision makers with a more comprehensive set of candidate solutions, facilitating more reasonable decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] Figure 1 Schematic diagram of the process of the present invention;

[0138] Figure 2 Schematic diagram of the Pareto frontier boundary points and the Pareto frontier auxiliary surface;

[0139] Figure 3 Schematic diagram of the normalized Pareto frontier boundary points and the normalized Pareto frontier auxiliary surface;

[0140] Figure 4 Schematic diagram of the point set projected onto the Pareto frontier;

[0141] Figure 5 A diagram showing the Pareto frontier solution obtained by the method of the present invention;

[0142] Figure 6 The following is a graph showing the Pareto frontier solutions obtained by three different algorithms. DETAILED DESCRIPTION

[0143] The present invention is further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0144] Example 1:

[0145] like Figure 1 As shown, this embodiment provides a microgrid planning method based on an improved normal boundary intersection method, comprising the following steps:

[0146] S1: Construct the objective function of the microgrid planning model taking into account the impact of ecology, climate, and human settlement;

[0147] S2: Constraints for constructing a microgrid planning model that takes environmental benefit constraints into account;

[0148] S3: constructing a compact form of the microgrid planning model based on the objective function constructed in step S1 and the constraints constructed in step S2;

[0149] S4: For the compact microgrid planning model, the Pareto frontier is obtained by using the improved normal boundary intersection method based on Euclidean distance correction;

[0150] S5: Select the optimal compromise solution from the Pareto frontier to obtain the microgrid expansion planning scheme.

[0151] The objective functions of the microgrid planning model taking into account the impact of ecology, climate, and human settlement in step S1 include an economic cost objective function, an environmental benefit objective function, and a reliability objective function;

[0152] The establishment of the economic cost objective function includes:

[0153] Microgrid planning aims to achieve the minimum total economic cost, that is, the minimum sum of planning and operating costs:

[0154]

[0155] Where: is the economic cost objective function; To plan costs; is the total scene set; is the total time period set; variable subscript Indicates the Scenario; variable subscript Indicates time period ; The cost of purchasing electricity for the microgrid; is the gas turbine fuel cost; is the incentive cost for adjustable load; The cost of charging and discharging energy storage.

[0156] For planning costs :

[0157]

[0158] Where: is the cost of expanding the unit energy storage capacity; Expand capacity for energy storage; The cost of expanding the capacity of new energy units per unit; Expand capacity for new energy units; is a candidate energy storage set; A collection of candidate new energy units;

[0159] Regarding running costs:

[0160]

[0161]

[0162]

[0163]

[0164] Where: The electricity purchase price; The power of purchased electricity; is the gas turbine fuel cost coefficient; Output power for the gas turbine; is the load incentive cost coefficient; is the flexible load power; is the load demand value under no-load excitation; 、 are the energy storage charging and discharging cost coefficients respectively; 、 are the energy storage charging efficiency and energy storage discharging efficiency respectively; and are the energy storage charging power and energy storage discharging power respectively;

[0165] Among them, the adjustable load incentive cost For nonlinear terms containing absolute values, the large M method is used for linearization:

[0166]

[0167]

[0168]

[0169]

[0170]

[0171] in, is the introduced continuous auxiliary variable; is the introduced 0-1 auxiliary variable; is a large positive constant.

[0172] The establishment of the environmental benefit objective function includes:

[0173] In existing microgrid planning, environmental benefits are usually measured by carbon emissions, namely:

[0174]

[0175] in, The environmental benefit objective function in the existing microgrid planning; is the carbon emission cost of gas turbines; is the carbon price; Carbon emission factors for gas turbine power generation; The output power of the gas turbine.

[0176] The above modeling method fails to fully reflect the nonlinear interference of distributed energy construction on regional ecosystems. Therefore, this embodiment further introduces three dimensions, namely ecological impact, climate impact, and human settlement impact, based on carbon emissions to construct an environmental benefit objective function:

[0177]

[0178] in, is the environmental benefit objective function; The environmental benefit objective function in the existing microgrid planning; 、 、 are the weight factors for ecological impact, climate impact, and human settlement impact, respectively; is the ecological impact factor; is the climate influencing factor; The impact factor of human settlement.

[0179] The following describes the modeling methods for each factor:

[0180] First, ecological impact factors:

[0181] The ecological impact factor is used to characterize the degree of interference of new energy units on the ecosystem, mainly considering the following three aspects:

[0182] 1. Interference of wind turbines on birds ( ): The high-speed rotating blades of wind turbines can easily cause bird strikes and deaths, especially in migratory bird corridors, wetland protection areas, and other areas. The quantitative modeling is as follows:

[0183]

[0184] in, is the installed capacity of wind power, is the distance between the wind turbine and the bird habitat, 、 The formulas are The first coefficient and the second coefficient of .

[0185] The above function shows that the risk of bird collision damage from wind turbines varies with wind turbine installed capacity and distance. As the installed capacity increases, the risk of bird collision increases. However, considering that high-capacity wind turbines kill fewer birds per unit capacity, , to reflect the decreasing marginal risk of increasing capacity; , using exponential decay, indicating that the farther the wind turbine is from the bird habitat, the lower the collision risk. In this embodiment, the ecological impact factor item, Take 0.18, Take 0.20.

[0186] 2. Damage to surface vegetation and animal habitats caused by photovoltaic units ( ): Large areas of land are used for photovoltaic deployment, resulting in the fragmentation of plant communities and the restriction of animal activities. The quantitative modeling is as follows:

[0187]

[0188] in, is the photovoltaic installed capacity, is the distance between the photovoltaic unit and the ecologically sensitive area, 、 The formulas are The first coefficient and the second coefficient of .

[0189] The above function describes the impact of new energy unit planning on vegetation destruction and animal habitat loss. It is stated that as the installed capacity of new energy increases, the land area and construction interference required will increase, and further capacity increases will use more developed land, and the marginal loss will slow down. In addition, In other words, the farther away the unit is from the ecologically sensitive area, the smaller its ecological impact is. item, Take 0.09, Take 0.10.

[0190] 3. Disturbance of soil and water bodies by new energy units ( ): The planning and construction of new energy units will change the hydrological and geomorphological structure and increase the risk of soil erosion. The quantitative modeling is as follows:

[0191]

[0192] in, is the distance between the new energy unit and the water and soil sensitive area, 、 The formulas are The first coefficient and the second coefficient of .

[0193] The above function represents the impact of engineering construction on soil structure and water pollution risk. It is stated that the larger the installed capacity of new energy, the greater the construction intensity required, and thus the degree of soil and water disturbance increases. However, as the scale expands, the marginal effect of subsequent capacity increase on the additional disturbance decreases. In addition, It indicates that if the construction is far away from water and soil sensitive areas, the impact of construction on the environment will be significantly reduced. item, Take 0.10, Take 0.15.

[0194] In summary, the ecological impact factors can be modeled as:

[0195]

[0196] in, 、 、 They are the first, second and third weight coefficients of the ecological impact factor, respectively, which can be set according to factors such as the unit type, the overlap between the site selection area and the protection area, and in this embodiment are taken as 0.3, 0.4 and 0.3 respectively.

[0197] Second, climate influencing factors:

[0198] The climate impact factor is used to describe the extent to which new energy facilities change the local microclimate, mainly considering the following two aspects:

[0199] 1. Turbulence effect and temperature disturbance caused by fans ( ): Wind turbines disturb the atmospheric boundary layer, causing surface wind speeds to drop and surface temperatures to rise at night, affecting the ecological balance;

[0200]

[0201] in, for The distance calculation boundary, is the distance from the fan, 、 The formulas are The first coefficient and the second coefficient of .

[0202] The above function reflects the impact of large-scale wind farm operation on local atmospheric turbulence. It shows that the operation of wind turbines will cause the turbulence in the wake area to increase and the wind speed to decrease. The intensity of the impact increases with the increase of installed capacity, but the additional reduction of wind speed with each additional capacity becomes relatively small. In addition, It shows that the turbulence effect will decay with distance. In this embodiment, the climate impact factor item, Take 2000m, Take 0.25, Take 0.05.

[0203] 2. Changes in ground albedo caused by photovoltaic panels ( ): Large-scale photovoltaic deployment reduces surface reflectivity, changes heat distribution, and may trigger a "heat island effect";

[0204]

[0205] in, for The distance calculation boundary, is the distance from the photovoltaic panel, 、 The formulas are The first coefficient and the second coefficient of .

[0206] The above function describes the environmental impact of photovoltaic power stations changing the surface albedo. It shows that as the installed capacity of photovoltaic power generation increases, the proportion of ground surface coverage increases, which leads to a greater decrease in the average albedo. When the density of photovoltaic power generation reaches a certain scale, the albedo reduction effect tends to saturate. In addition, It is used to indicate the spatial limitation of the impact. The impact is mainly limited to the surrounding area of ​​the site and has almost no impact on distant areas. item, Take 2000m, Take 0.20, Take 0.05.

[0207] In summary, the climate impact factors can be modeled as:

[0208]

[0209] in, 、 are the first weight coefficient and the second weight coefficient of the climate impact factor, respectively, which can be set according to data such as the scale of the new energy unit and the regional meteorological sensitivity. In this embodiment, they are taken as 0.5 and 0.5 respectively.

[0210] Third, human settlement impact factors:

[0211] The human settlement impact factor is used to quantify the impact of new energy facilities on residents' living environment and psychological feelings, mainly including:

[0212] 1. Noise pollution ( ): The operation of the fan will generate low-frequency noise, which will affect the health and sleep of residents;

[0213]

[0214] in, is the distance between the wind turbine and the residential area, 、 、 The formulas are The first coefficient, the second coefficient, and the third coefficient.

[0215] The above function describes the attenuation law of fan operation noise with installed scale and distance. It shows that the fan noise will increase with the increase of installed capacity, but due to the logarithmic effect of the superposition of noise from multiple fans, the contribution of each additional fan to the total noise tends to decrease. It reflects that the noise decays exponentially with increasing distance during the propagation process. item, Take 0.90, Take 0.35, Take 0.20.

[0216] 2. Visual pollution ( ): Tall wind towers and dense photovoltaic arrays destroy the natural landscape and reduce residential satisfaction.

[0217]

[0218] in, is the distance between the new energy unit and the residential area, 、 The formulas are The first coefficient and the second coefficient of .

[0219] The above function is used to measure the impact of wind turbines or photovoltaic units on the visual landscape. It shows that as the installed capacity increases, the occupied area increases, and the degree of occupation and damage to the landscape becomes more severe, until the visual impact approaches a certain upper limit. This reflects the attenuation of visual impact with the distance from the observer. In this embodiment, the human settlement impact factor item, Take 0.25, Take 0.15.

[0220] In summary, the human settlement impact factor can be modeled as:

[0221]

[0222] in: 、 They are the first weight coefficient and the second weight coefficient of the human settlement impact factor, which can be set based on the distance between the unit and the residential area, the noise simulation results, etc. In this embodiment, they are taken as 0.5 and 0.5 respectively.

[0223] Based on carbon emissions, combined with ecological impact factors, climate impact factors, and human settlement impact factors, the environmental benefit objective function proposed in this invention reflects that when planning microgrids, ecological protection, climate regulation, and human settlement friendliness should be coordinated to achieve higher environmental adaptability.

[0224] The establishment of the reliability objective function includes:

[0225] In microgrid planning, reliability aims to minimize wind and solar power curtailment and load shedding, as expressed as follows:

[0226]

[0227] in, is the reliability objective function; Indicates the amount of wind and solar power curtailment; Indicates load shedding amount; Penalty costs for units that curtail wind and solar power; is the unit load shedding penalty cost; For the new energy unit collection; For load collection.

[0228] The constraints in step S2 include external power purchase constraints, gas turbine constraints, flexible load constraints, energy storage constraints, planning constraints, and environmental benefit constraints, specifically:

[0229] External power purchase constraints: For external power purchases, including upper and lower limits:

[0230]

[0231] in, is the maximum power purchased by the microgrid;

[0232] Gas turbine constraints: For gas turbines, including upper and lower output constraints and ramp constraints:

[0233]

[0234]

[0235]

[0236] in, 、 are the upper and lower limits of the gas turbine active power respectively; is the maximum upward climbing power of the gas turbine; is the maximum downward climbing power of the gas turbine;

[0237] Adjustable load constraints: For adjustable loads, there are constraints on the total load amount and the load translation amount:

[0238]

[0239]

[0240] in, 、 They are the maximum and minimum load power demands respectively;

[0241] Energy storage constraints: The regulation capability of energy storage is modeled as follows:

[0242]

[0243]

[0244] in, and They are Moment and The remaining energy stored at the end of the moment; 、 are the energy storage charging and discharging efficiency respectively; 、 are the energy storage charging and discharging power respectively; is the net charging power of energy storage;

[0245] Energy storage regulation constraints include upper and lower limits on charge and discharge power, and upper and lower limits on storage capacity:

[0246]

[0247]

[0248]

[0249]

[0250] in, 、 are the upper and lower limits of energy storage capacity of the energy storage battery respectively; 、 They are the upper limits of charging and discharging power of the energy storage battery respectively; 、 are the charging and discharging status 0 / 1 variables of the energy storage battery respectively;

[0251] Planning constraints: For planning energy storage:

[0252]

[0253]

[0254]

[0255] in, A 0-1 auxiliary variable for planning energy storage, where 0 means no expansion and 1 means expansion; To plan the maximum capacity of energy storage;

[0256] For planning new energy:

[0257]

[0258] in, It is a 0-1 auxiliary variable for planning new energy, where 0 means no expansion and 1 means expansion; To plan the maximum capacity of new energy;

[0259] Environmental benefit constraints include ecological red line constraints, ecological buffer zone distance constraints, and human settlement environment suitability constraints. Specifically:

[0260] Ecological red line constraints:

[0261]

[0262] in, Indicates the 0 / 1 variable indicating whether the region will build new energy units. Represents the set of ecological red line coverage areas;

[0263] Ecological buffer zone distance constraints:

[0264]

[0265] in, Indicates construction area Ecologically sensitive areas The distance between them is described by Euclidean distance; It is a collection of areas that can be planned for new energy; It is a collection of ecologically sensitive areas; Indicates the distance of the ecological buffer zone, which can be set to 300-500m. In this embodiment, it is set to 400m;

[0266] Constraints on the suitability of the human settlement environment:

[0267]

[0268] in, Building a region for new energy and settlements The distance between them is characterized by Euclidean distance; Gathering for areas where residents gather; Indicates the suitability distance of the human living environment, which can be set to 300m–1000m. In this embodiment, it is set to 500m.

[0269] The compact form of the microgrid planning model in step S3 is:

[0270]

[0271] in, is the decision variable; represents transpose; 、 They are the first coefficient matrix and the second coefficient matrix of the economic cost objective function respectively; is the nonlinear form of the environmental benefit objective function; 、 are the first coefficient matrix and the second coefficient matrix of the reliability objective function respectively; 、 are the coefficient matrix and constant term of the inequality constraints respectively; 、 are the coefficient matrix and constant term of the inequality constraints respectively; 、 The decision variables The lower and upper bound matrices of .

[0272] According to the compact form of the above model, since environmental benefits are taken into account, the constructed model is a nonlinear multi-objective optimization problem.

[0273] Step S4 includes:

[0274] A1: Determine the Pareto frontier boundary points and the Pareto frontier auxiliary surface;

[0275] For the three sets of objective functions in the multi-objective optimization problem, each objective function is optimized separately to obtain the corresponding optimal solution. The three sets of optimal solutions are recorded as Pareto frontier boundary points.

[0276] like Figure 2 As shown, the optimal solution corresponding to the optimization of the economic cost objective function is , then the objective function value is 、 、 ; The optimal solution corresponding to the optimization of the environmental benefit objective function is , then the objective function value is 、 、 ; The optimal solution corresponding to the optimization of the reliability objective function is , then the objective function value is 、 、 ;

[0277] Note that the Pareto frontier point 、 、 The determined triangular plane is the auxiliary surface of the Pareto frontier;

[0278] A2: If Figure 3As shown, the three objective functions in the model are normalized so that the objective function values ​​are all between [0,1], removing the influence of dimension and order of magnitude on the uniformity of the solution:

[0279]

[0280]

[0281]

[0282] in, is the normalized economic cost objective function; 、 are the maximum and minimum values ​​of the economic cost objective function respectively; is the normalized environmental benefit objective function; 、 are the maximum and minimum values ​​of the environmental benefit objective function respectively; is the normalized reliability objective function; 、 are the maximum and minimum values ​​of the reliability objective function respectively;

[0283] After normalization, the Pareto frontier boundary points become normalized Pareto frontier boundary points, and the Pareto frontier auxiliary surface becomes normalized Pareto frontier auxiliary surface.

[0284] A3: Obtain the normalized Pareto frontier point set;

[0285] like Figure 4 As shown, on the normalized Pareto front auxiliary surface, a set of evenly distributed points are selected , represented as the normalized Pareto frontier point 、 、 Three-point linear combination:

[0286]

[0287]

[0288] in, 、 、 They are the normalized Pareto front auxiliary surface and the normalized Pareto front boundary points. 、 、 The weight coefficient of

[0289] Pass every point , make a perpendicular line to the normalized Pareto front auxiliary surface, calculate the following optimization model, and get the point set projected to the normalized Pareto front surface :

[0290]

[0291] in, express and The distance is shown;

[0292] A4: Adaptive adjustment of point set uniformity based on Euclidean distance correction

[0293] Due to the introduction of mathematical modeling of environmental benefits, the normalized Pareto frontier has nonlinear properties, that is, it is a curved surface. The normalized Pareto front auxiliary surface is uniformly selected, but due to the nonlinear nature of the normalized Pareto front surface, the point set projected onto the normalized Pareto front surface is The distribution is uneven. This conclusion can be seen from Figure 4 Reflected in: The points selected on the normalized Pareto front auxiliary surface is uniformly distributed, but the projected points on the normalized Pareto frontier are is non-uniformly distributed;

[0294] Step A4 includes the following steps:

[0295] A4-1: Calculate the set of adjacent points on the normalized Pareto frontier 、 Euclidean distance between :

[0296]

[0297] in, express The values ​​of the axis coordinates; express The values ​​of the axis coordinates; express The values ​​of the axis coordinates;

[0298] A4-2: Calculate the average Euclidean distance between all adjacent point sets :

[0299]

[0300] in, represents the total number of point sets;

[0301] A4-3: Setting the adjustment threshold ;

[0302] like , then it indicates that the point with dot The spacing between points is too small; therefore, on the normalized Pareto front auxiliary surface, the points The three-dimensional coordinate position of is updated as follows:

[0303]

[0304] in, It is a parameter that can be set dynamically, and its value range is between [0,1];

[0305] For updated position , make a perpendicular line to the normalized Pareto front auxiliary surface, and get the point set projected onto the normalized Pareto front surface ; Recalculate at this time :like , then increase appropriately ;like , then appropriately reduce ;like , then fix the current Location;

[0306] like , then it indicates that the point with dot The spacing is too large; therefore, the normalized Pareto front auxiliary surface is The three-dimensional coordinate position of is updated as follows:

[0307]

[0308] For updated position , make a perpendicular line to the normalized Pareto front auxiliary surface, and get the point set projected onto the normalized Pareto front surface ; Recalculate at this time :like , then appropriately reduce ;like , then increase appropriately ;like , then fix the current Location;

[0309] like , then it indicates that the point with dot If the spacing is within a reasonable range, no adjustment is made.

[0310] In summary, through the adaptive adjustment of the uniformity of the point set based on the Euclidean distance correction, the point set on the normalized Pareto frontier surface is more uniform, and the Euclidean distances between adjacent point sets are all within , thereby improving the uniformity of the solution results.

[0311] The specific method of selecting the optimal compromise solution from the Pareto front in step S5 includes:

[0312] For the normalized economic cost objective function , normalized environmental benefit objective function , normalized reliability objective function , and solve their satisfaction through the following formulas:

[0313]

[0314] in, Indicates the The satisfaction of the normalized objective function is between [0,1]; For the A normalized objective function, For the The maximum value of the normalized objective function, For the The minimum value of the normalized objective function;

[0315] After calculating the satisfaction of each normalized objective function, the comprehensive satisfaction of the solution is determined by the following formula:

[0316]

[0317] in, is the number of normalized objective functions, is the comprehensive satisfaction value;

[0318] Among all the solutions on the Pareto frontier, the one with the highest overall satisfaction is the optimal compromise solution. In addition, the microgrid planning expansion plan includes the expansion results of energy storage and new energy units.

[0319] Example 2:

[0320] Based on the method of Example 1, this embodiment provides a microgrid planning system based on an improved normal boundary intersection method, including:

[0321] The objective function construction module is used to construct the objective function of the microgrid planning model taking into account the impact of ecology, climate, and human settlement;

[0322] A constraint condition construction module is used to construct the constraint conditions of the microgrid planning model taking into account environmental benefit constraints;

[0323] a model optimization module for building a compact form of the microgrid planning model;

[0324] The model solving module is used to solve the Pareto frontier based on the improved normal boundary intersection method corrected by Euclidean distance, select the optimal compromise solution from the Pareto frontier, and obtain the microgrid expansion planning scheme.

[0325] Example 3:

[0326] This example uses a structure where three microgrids are connected to the same distribution network to verify the effectiveness of the proposed model and solution algorithm. The capacity parameter settings of each device are shown in Table 1.

[0327] Table 1 Capacity parameter settings

[0328]

[0329] Other parameter settings of the microgrid planning model are shown in Table 2:

[0330] Table 2 Other parameter settings

[0331]

[0332] For the new energy-load scenario, the present invention uses Latin hypercube sampling to generate 100 daily scenarios, and uses the K-means algorithm to reduce the scenarios, and finally obtains 15 typical daily scenarios for the microgrid planning model. There are 15 typical day scenes in total. In each typical day scene, the total time period is Contains 24 moments.

[0333] First, the improved normal boundary intersection method proposed in this invention is used to solve the problem and obtain 20 Pareto solutions on the normalized Pareto frontier, such as Figure 5 shown.

[0334] Depend on Figure 5 It can be seen that, on the one hand, the Pareto frontier set obtained using the improved normal boundary intersection method can obtain a relatively evenly distributed solution set, with good results. On the other hand, observing the Pareto frontier shows that as the value of one objective function increases, the values ​​of the other two objective functions decrease. This shows that when planning microgrids, there is a certain conflict between the three objective functions of economic cost, environmental benefit, and reliability, and a compromise is required among these three objective functions.

[0335] Secondly, for the 20 solutions in the normalized Pareto frontier, three compromise solutions P1, P2, and P3 are selected, among which P2 is the optimal compromise solution. The results are shown in Table 3:

[0336] Table 3 Optimization results of different compromise solutions

[0337]

[0338] As can be seen from Table 3, P1 performs better in terms of environmental benefits, but has the highest economic cost and poor reliability; P3 has the lowest economic cost and good reliability, but performs weaker in terms of environmental benefits; P2, as the most balanced solution, achieves good results on all three types of objective functions, reflecting reasonable compromise performance.

[0339] The results of the extended planning scheme corresponding to the optimal compromise solution P2 are shown in Table 4:

[0340] Table 4 Planning results corresponding to P2

[0341]

[0342] In contrast, in the environmental benefit modeling, only carbon emissions + ecological factors + climate factors (comparison plan 1), carbon emissions + ecological factors + human settlement factors (comparison plan 2), and carbon emissions + climate factors + human settlement factors (comparison plan 3) are considered, while other conditions remain unchanged. The comparison of planning scheme results is shown in Table 5:

[0343] Table 5 Comparison of planning schemes obtained from different environmental benefit modeling

[0344]

[0345] Comparing the results in Tables 4 and 5, we can see that compared to the planning scheme of the present invention, which comprehensively considers ecological, climatic, and human settlement factors, the planned capacity of new energy units (photovoltaic and wind turbines) in all three comparison schemes has increased. This demonstrates that, by comprehensively considering various environmental benefit indicators, the present invention can account for the limitations on new energy capacity imposed by factors such as ecological buffer zones and human settlement adaptability, thereby rationally planning new energy capacity and improving the environmental adaptability of microgrid planning.

[0346] In order to verify the effectiveness and rationality of the improved normal boundary intersection method proposed in this invention, the following comparison algorithm is designed to conduct a comparative analysis on the Pareto frontier of the obtained multi-objective optimization problem:

[0347] Algorithm 1: The improved normal boundary intersection method proposed in this invention.

[0348] Algorithm 2: Existing normal boundary intersection method.

[0349] Algorithm 3: NSGA-II algorithm.

[0350] Under the unified model and parameter settings, based on the three different algorithms mentioned above, 20 different Pareto frontier solutions in the normalized space are obtained, such as Figure 6 shown.

[0351] In addition, the relevant statistical indicators of the 20 Pareto front solutions of the three algorithms are shown in Table 6:

[0352] Table 6 Statistical indicators of Pareto frontier solutions of three algorithms

[0353]

[0354] according to Figure 6 From the results in Table 6, we can see that the standard deviation of Algorithm 1 is only 0.012, and the coefficient of variation is only 0.014, indicating that its solution set distribution is concentrated and more stable. Compared with Algorithms 2 and 3, the solution set generated by Algorithm 1 is evenly distributed on the entire frontier, avoiding local aggregation or sparse areas. Therefore, the uniformity adaptive adjustment mechanism based on Euclidean distance proposed in the present invention effectively overcomes the problem of uneven solution set distribution in traditional algorithms in nonlinear Pareto frontiers. The method of the present invention is particularly suitable for microgrid planning problems that have high requirements on solution set quality and diversity, and provides decision makers with a solution set with more reference value.

Claims

1. A microgrid planning method based on an improved normal boundary intersection method, characterized in that: The steps include: S1: Construct the objective function of the microgrid planning model taking into account the impact of ecology, climate, and human settlement; S2: Constraints for constructing a microgrid planning model that takes environmental benefit constraints into account; S3: constructing a compact form of the microgrid planning model based on the objective function constructed in step S1 and the constraints constructed in step S2; S4: For the compact microgrid planning model, the Pareto frontier is obtained by using the improved normal boundary intersection method based on Euclidean distance correction; S5: Select the optimal compromise solution from the Pareto frontier to obtain the microgrid expansion planning scheme.

2. A microgrid planning method based on an improved normal boundary intersection method according to claim 1, characterized in that: The objective functions of the microgrid planning model taking into account the impact of ecology, climate, and human settlement in step S1 include an economic cost objective function, an environmental benefit objective function, and a reliability objective function.

3. A microgrid planning method based on improved normal boundary intersection method according to claim 2, characterized in that: The establishment of the economic cost objective function in step S1 includes: Microgrid planning aims to achieve the minimum total economic cost, that is, the minimum sum of planning and operating costs: ； Where: is the economic cost objective function; To plan costs; is the total scene set; is the total time period set; variable subscript Indicates the Scenario; variable subscript Indicates time period ; The cost of purchasing electricity for the microgrid; is the gas turbine fuel cost; is the incentive cost for adjustable load; The cost of charging and discharging energy storage; For planning costs : ； Where: is the cost of expanding the unit energy storage capacity; Expand capacity for energy storage; The cost of expanding the capacity of new energy units per unit; Expand capacity for new energy units; is a candidate energy storage set; A collection of candidate new energy units; Regarding running costs: ； ； ； ； Where: The electricity purchase price; The power of purchased electricity; is the gas turbine fuel cost coefficient; Output power for the gas turbine; is the load incentive cost coefficient; is the flexible load power; is the load demand value under no-load excitation; 、 are the energy storage charging and discharging cost coefficients respectively; 、 are the energy storage charging efficiency and energy storage discharging efficiency respectively; and are the energy storage charging power and energy storage discharging power respectively; Among them, the adjustable load incentive cost For nonlinear terms containing absolute values, the large M method is used for linearization: ； ； ； ； ； in, is the introduced continuous auxiliary variable; is the introduced 0-1 auxiliary variable; Is a positive number.

4. A microgrid planning method based on an improved normal boundary intersection method according to claim 2, characterized in that: The establishment of the environmental benefit objective function in step S1 includes: On the basis of carbon emissions, we further introduce three dimensions: ecological impact, climate impact, and human settlement impact to construct an environmental benefit objective function: ； in, is the environmental benefit objective function; The environmental benefit objective function in the existing microgrid planning; 、 、 are the weight factors for ecological impact, climate impact, and human settlement impact, respectively; is the ecological impact factor; is the climate influencing factor; The impact factor of human settlement.

5. The microgrid planning method based on the improved normal boundary intersection method according to claim 2 is characterized in that: The establishment of the reliability objective function in step S1 includes: In microgrid planning, reliability aims to minimize wind and solar power curtailment and load shedding, as expressed as follows: ； in, is the reliability objective function; Indicates the amount of wind and solar power curtailment; Indicates load shedding amount; Penalty costs for units that curtail wind and solar power; is the unit load shedding penalty cost; For the new energy unit collection; For load collection.

6. A microgrid planning method based on improved normal boundary intersection method according to claim 2, characterized in that: The constraints in step S2 include external power purchase constraints, gas turbine constraints, flexible load constraints, energy storage constraints, planning constraints, and environmental benefit constraints, specifically: External power purchase constraints: For external power purchases, including upper and lower limits: ； in, is the maximum power purchased by the microgrid; Gas turbine constraints: For gas turbines, including upper and lower output constraints and ramp constraints: ； ； ； in, 、 are the upper and lower limits of the gas turbine active power respectively; is the maximum upward climbing power of the gas turbine; is the maximum downward climbing power of the gas turbine; Adjustable load constraints: For adjustable loads, there are constraints on the total load amount and the load translation amount: ； ； in, 、 They are the maximum and minimum load power demands respectively; Energy storage constraints: The regulation capability of energy storage is modeled as follows: ； ； in, and They are Moment and The remaining energy stored at the end of the moment; 、 are the energy storage charging and discharging efficiency respectively; 、 are the energy storage charging and discharging power respectively; is the net charging power of energy storage; Energy storage regulation constraints include upper and lower limits on charge and discharge power, and upper and lower limits on storage capacity: ； ； ； ； in, 、 are the upper and lower limits of energy storage capacity of the energy storage battery respectively; 、 They are the upper limits of charging and discharging power of the energy storage battery respectively; 、 are the charging and discharging status 0 / 1 variables of the energy storage battery respectively; Planning constraints: For planning energy storage: ； ； ； in, A 0-1 auxiliary variable for planning energy storage, where 0 means no expansion and 1 means expansion; To plan the maximum capacity of energy storage; For planning new energy: ； in, It is a 0-1 auxiliary variable for planning new energy, where 0 means no expansion and 1 means expansion; To plan the maximum capacity of new energy; Environmental benefit constraints include ecological red line constraints, ecological buffer zone distance constraints, and human settlement environment suitability constraints. Specifically: Ecological red line constraints: ； in, Indicates the 0 / 1 variable indicating whether the region will build new energy units. Represents the set of ecological red line coverage areas; Ecological buffer zone distance constraints: ； in, Indicates construction area Ecologically sensitive areas the distance between them; It is a collection of areas that can be planned for new energy; It is a collection of ecologically sensitive areas; represents the distance of ecological buffer zone; Constraints on the suitability of the human settlement environment: ； in, Building a region for new energy and settlements the distance between them; Gathering for areas where residents gather; It represents the suitability distance of human settlement environment.

7. A microgrid planning method based on improved normal boundary intersection method according to claim 6, characterized in that: The compact form of the microgrid planning model in step S3 is: ； in, is the decision variable; represents transpose; 、 are the first coefficient matrix and the second coefficient matrix of the economic cost objective function respectively; is the nonlinear form of the environmental benefit objective function; 、 are the first coefficient matrix and the second coefficient matrix of the reliability objective function respectively; 、 are the coefficient matrix and constant term of the inequality constraints respectively; 、 are the coefficient matrix and constant term of the inequality constraints respectively; 、 The decision variables The lower and upper bound matrices of .

8. A microgrid planning method based on improved normal boundary intersection method according to claim 7, characterized in that: The step S4 comprises: A1: Determine the Pareto frontier boundary points and the Pareto frontier auxiliary surface; For the three sets of objective functions in the multi-objective optimization problem, each objective function is optimized separately to obtain the corresponding optimal solution. The three sets of optimal solutions are recorded as Pareto frontier boundary points. The optimal solution corresponding to the optimization of the economic cost objective function is , then the objective function value is 、 、 ; The optimal solution corresponding to the optimization of the environmental benefit objective function is , then the objective function value is 、 、 ; The optimal solution corresponding to the optimization of the reliability objective function is , then the objective function value is 、 、 ; Note that the Pareto frontier point 、 、 The determined triangular plane is the auxiliary surface of the Pareto frontier; A2: Normalize the three objective functions in the model so that their values ​​are all between [0, 1], eliminating the effects of dimension and magnitude on the uniformity of the solution: ； ； ； in, is the normalized economic cost objective function; 、 are the maximum and minimum values ​​of the economic cost objective function respectively; is the normalized environmental benefit objective function; 、 are the maximum and minimum values ​​of the environmental benefit objective function respectively; is the normalized reliability objective function; 、 are the maximum and minimum values ​​of the reliability objective function respectively; After normalization, the Pareto frontier boundary points become normalized Pareto frontier boundary points, and the Pareto frontier auxiliary surface becomes normalized Pareto frontier auxiliary surface. A3: Obtain the normalized Pareto frontier point set; On the normalized Pareto front auxiliary surface, select a set of evenly distributed points , represented as the normalized Pareto frontier point 、 、 Three-point linear combination: ； ； in, 、 、 They are the normalized Pareto front auxiliary surface and the normalized Pareto front boundary points. 、 、 The weight coefficient of Pass every point , make a perpendicular line to the normalized Pareto front auxiliary surface, calculate the following optimization model, and get the point set projected to the normalized Pareto front surface : ； in, express and distance; A4: Adaptive adjustment of point set uniformity based on Euclidean distance correction, including the following steps: A4-1: Calculate the set of adjacent points on the normalized Pareto frontier 、 Euclidean distance between : ； in, express The values ​​of the axis coordinates; express The values ​​of the axis coordinates; express The values ​​of the axis coordinates; A4-2: Calculate the average Euclidean distance between all adjacent point sets : ； in, represents the total number of point sets; A4-3: Setting the adjustment threshold ; like , on the normalized Pareto front auxiliary surface, point The three-dimensional coordinate position of is updated as follows: ； in, It is a parameter that can be set dynamically, and its value range is between [0,1]; For updated position , make a perpendicular line to the normalized Pareto front auxiliary surface, and get the point set projected onto the normalized Pareto front surface ; Recalculate at this time :like , then increase ;like , then reduce ;like , then fix the current Location; like , put the point The three-dimensional coordinate position of is updated as follows: ； For updated position , make a perpendicular line to the normalized Pareto front auxiliary surface, and get the point set projected onto the normalized Pareto front surface ; Recalculate at this time :like , then reduce ;like , then increase ;like , then fix the current Location; like , then it indicates that the point with dot If the spacing is within a reasonable range, no adjustment is made.

9. A microgrid planning method based on improved normal boundary intersection method according to claim 8, characterized in that: The specific method of selecting the optimal compromise solution from the Pareto front in step S5 includes: For the normalized economic cost objective function , normalized environmental benefit objective function , normalized reliability objective function , and solve their satisfaction through the following formulas: ； in, Indicates the The satisfaction of the normalized objective function is between [0,1]; For the A normalized objective function, For the The maximum value of the normalized objective function, For the The minimum value of the normalized objective function; After calculating the satisfaction of each normalized objective function, the comprehensive satisfaction of the solution is determined by the following formula: ； in, is the number of normalized objective functions, is the comprehensive satisfaction value; Among all the solutions on the Pareto frontier, the solution with the highest overall satisfaction is the optimal compromise solution.

10. A microgrid planning system based on an improved normal boundary intersection method, characterized in that: For implementing the method described in claim 1, the system includes: The objective function construction module is used to construct the objective function of the microgrid planning model taking into account the impact of ecology, climate, and human settlement; A constraint condition construction module is used to construct the constraint conditions of the microgrid planning model taking into account environmental benefit constraints; a model optimization module for building a compact form of the microgrid planning model; The model solving module is used to solve the Pareto frontier based on the improved normal boundary intersection method corrected by Euclidean distance, select the optimal compromise solution from the Pareto frontier, and obtain the microgrid expansion planning scheme.

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