Method and system for evaluating and optimizing micro-grid planning scheme

Abstract

The invention provides an evaluation and optimization method and system for a micro-grid planning scheme. The method comprises the steps of obtaining planning information under a to-be-evaluated scheme; obtaining a weight vector of the expert, obtaining a first comparison vector and a second comparison vector about each planning parameter name according to the weight vector, constructing a minimum-maximum weight model, and obtaining a first evaluation weight based on the minimum-maximum weight model; constructing an original data matrix according to the planning parameter value, and calculating a second evaluation weight; obtaining a combination weight of each planning parameter name, and obtaining an evaluation value of the to-be-evaluated scheme according to the combination weight under the cost category and the combination weight under the environmental protection category; and judging whether the evaluation value is greater than a preset threshold value, if not, optimizing the planning information under the to-be-evaluated scheme, and obtaining the evaluation value again until the re-obtained evaluation value is greater than the preset threshold value. According to the method, a very reliable basis can be provided for optimization of a micro-grid design stage.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid evaluation, and in particular to a method and system for evaluating and optimizing a microgrid planning scheme. Background Art

[0002] With the continuous growth of global energy demand and increasing awareness of environmental protection, microgrids, as a key form of grid integration for distributed and renewable energy, are becoming a research hotspot and development trend in the energy sector. By integrating multiple distributed energy resources (such as solar, wind, and energy storage devices), microgrids enable localized energy production, storage, and distribution, effectively improving energy efficiency, reducing dependence on traditional power grids, and enhancing the flexibility and reliability of power systems. Microgrids also play a vital role in promoting the integration of new energy, reducing carbon emissions, and promoting the development of green energy.

[0003] In the field of microgrid design evaluation, a variety of assessment methods and planning parameter naming systems have been proposed and applied. These evaluation methods typically cover multiple dimensions, including economic, technical, and environmental considerations, aiming to provide a comprehensive and objective assessment of microgrid design proposals. Economic evaluations primarily focus on planning parameters such as construction and operating costs; technical evaluations emphasize technical planning parameters such as energy efficiency and system stability; and environmental evaluations focus on environmental planning parameters such as renewable energy consumption and carbon emission reduction. These evaluation methods and planning parameter naming systems provide a reference basis for optimizing microgrid design and decision-making.

[0004] However, due to the complexity and diversity of microgrid technologies, the selection of planning parameter names and weight allocation are often highly subjective and uncertain, which affects the accuracy and comparability of the evaluation results, making it difficult to provide a very reliable basis for the optimization of the microgrid design stage. Summary of the Invention

[0005] The purpose of the present invention is to provide a microgrid planning scheme evaluation and optimization method and system, aiming to comprehensively and objectively evaluate the microgrid planning scheme, thereby improving the evaluation accuracy and providing a reliable technical reference for the optimization of the microgrid design stage.

[0006] In a first aspect, the present invention provides a method for evaluating and optimizing a microgrid planning scheme, the method comprising:

[0007] Obtain planning information under the scheme to be evaluated, the planning information including planning parameter names and corresponding planning parameter values, and divide the planning parameter names into cost categories and environmental protection categories. The cost categories include construction costs and operating costs, and the environmental protection categories include new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, and NOx emission reduction;

[0008] Constructing a distributed language trust social matrix expressing the degree of expert preference, and obtaining a weight vector of the expert according to the distributed language social matrix, and obtaining a first comparison vector and a second comparison vector for each planning parameter name according to the weight vector, and constructing a minimum-maximum weight model according to the first comparison vector and the second comparison vector, and obtaining a first evaluation weight for each planning parameter name based on the minimum-maximum weight model;

[0009] Constructing an original data matrix according to the planning parameter values, and calculating a second evaluation weight of each planning parameter name according to the original data matrix;

[0010] Obtaining a combined weight of each planning parameter name according to the first evaluation weight and the second evaluation weight, and obtaining an evaluation value of the to-be-evaluated scheme according to the combined weight under the cost category and the combined weight under the environmental protection category;

[0011] It is determined whether the evaluation value is greater than a preset threshold. If not, the planning information under the to-be-evaluated scheme is optimized, and the evaluation value is re-acquired until the re-acquired evaluation value is greater than the preset threshold.

[0012] In a second aspect, the present invention provides a system for evaluating and optimizing a microgrid planning scheme, the system comprising:

[0013] A planning information acquisition module is used to obtain planning information under the scheme to be evaluated, the planning information including planning parameter names and corresponding planning parameter values, and to divide the planning parameter names into cost categories and environmental protection categories. The cost categories include construction costs and operating costs, and the environmental protection categories include new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, and NOx emission reduction.

[0014] a trust matrix construction module, configured to construct a distributed language trust social matrix expressing the degree of expert preference, obtain an expert weight vector based on the distributed language trust social matrix, obtain a first comparison vector and a second comparison vector for each planning parameter name based on the weight vector, construct a minimum-maximum weight model based on the first comparison vector and the second comparison vector, and obtain a first evaluation weight for each planning parameter name based on the minimum-maximum weight model;

[0015] A parameter matrix construction module is used to construct an original data matrix according to the planning parameter values, and calculate the second evaluation weight of each planning parameter name according to the original data matrix;

[0016] An evaluation module, configured to obtain a combined weight of each planning parameter name according to the first evaluation weight and the second evaluation weight, and obtain an evaluation value of the to-be-evaluated scheme according to the combined weight under the cost category and the combined weight under the environmental protection category;

[0017] The optimization module is used to determine whether the evaluation value is greater than a preset threshold. If not, the planning information under the evaluation scheme is optimized and the evaluation value is re-acquired until the re-acquired evaluation value is greater than the preset threshold.

[0018] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned method for evaluating and optimizing the microgrid planning scheme.

[0019] In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein:

[0020] The memory is used to store computer programs;

[0021] When the processor is used to execute the computer program stored in the memory, the above-mentioned microgrid planning scheme evaluation and optimization method is implemented.

[0022] Compared with the prior art, the present invention has the following advantages:

[0023] 1. The present invention comprehensively considers planning parameters such as microgrid construction cost, operation cost, new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, NOx emission reduction, etc. to conduct a comprehensive evaluation of microgrid planning schemes in terms of cost and energy conservation and environmental protection, thereby more accurately reflecting the advantages and disadvantages of current microgrid design schemes, and providing a reliable technical reference for microgrid design optimization during the design stage.

[0024] 2. Traditional microgrid evaluation methods often ignore the subjective preferences of decision-makers (experts) during the evaluation process and set the expert weights (weight vectors) to be the same, which leads to deviations between the evaluation results and the actual decision-making needs. The evaluation method of this invention incorporates mathematical and statistical methods such as the distributed language trust social matrix and the minimum-maximum weight model, considers the subjective risk preferences of experts, and integrates the impact of each planning parameter value, making the evaluation results more reliable.

[0025] 3. By introducing a second evaluation weight to determine the importance of each planning parameter value in the overall evaluation system, the reliability of the evaluation is further improved. The evaluation value is then calculated. For the microgrid design scheme, the larger the evaluation value, the more energy-saving and environmentally friendly it is while taking into account the lower cost, thereby achieving the benign optimization of the microgrid design scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flowchart of a method for evaluating and optimizing a microgrid planning scheme according to an embodiment of the present invention;

[0027] Figure 2 A trust relationship diagram between experts according to an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of the structure of a microgrid planning scheme evaluation and optimization system proposed in one embodiment of the present invention.

[0029] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0031] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating and optimizing a microgrid planning scheme, the method comprising steps S101 to S105, wherein:

[0032] Step S101: Acquire planning information under the scheme to be evaluated, the planning information including planning parameter names and corresponding planning parameter values, and divide the planning parameter names into cost categories and environmental protection categories. The cost categories include construction costs and operating costs, and the environmental protection categories include new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, and NOx emission reduction.

[0033] It should be noted that in the initial stage of microgrid design, designers will come up with a rough microgrid design plan based on actual needs. For example, the initially designed microgrid plan consists of a power grid, electric air conditioners, and gas boilers. The planning information is shown in Table 1 below:

[0034] Table 1

[0035] Planning parameter name Planning parameter values Construction costs 289 Running costs 182 New energy consumption 85 Energy efficiency 68 Energy saving rate 17 Carbon emissions reduction 56 NOx emission reduction 1.2

[0036] Step S102: constructing a distributed language trust social matrix expressing the degree of expert preference, obtaining the expert's weight vector based on the distributed language trust social matrix, obtaining a first comparison vector and a second comparison vector for each planning parameter name based on the weight vector, constructing a minimum-maximum weight model based on the first comparison vector and the second comparison vector, and obtaining a first evaluation weight for each planning parameter name based on the minimum-maximum weight model;

[0037] It should be noted that in traditional decision-making, the weight of experts (evaluators) is generally assumed to be known, which is not practical in some decision-making situations. Therefore, building distributed trust relationships in a social network environment and using this as a basis for assigning expert weights is a reliable approach.

[0038] In some embodiments, each expert is defined as a node, G = (E, L, ω) is a directed graph, E = {e1, e2, ..., em} is a node set, L = {l1, l2, ..., l q} is a directed link between nodes, and the distributed language trust evaluation set is the additional information of the connection, SL=(T sk ) m×m is a distributed language trust social matrix associated with the directed graph G, and the centrality of each node is calculated according to the following formula;

[0039]

[0040] in, Represents node e k The centrality, T sk represents the value of the sth row and kth column in the distributed language trust social matrix;

[0041] The weight vector is calculated according to the following formula:

[0042]

[0043] Among them, ω k Represents the node e k The corresponding expert weight vector, Represents node e k The expectation of the distributed trust language trust function, m represents the total number of experts;

[0044] The expectation of the distributed trust language trust function is calculated according to the following formula:

[0045]

[0046] Among them, H α Indicates trust, It represents the membership of the corresponding trust level, and α represents the trust level.

[0047] Specifically, when evaluating microgrid design proposals, five experts (evaluators) in the field of microgrid design first evaluate the proposals to determine the weights of the planning parameters. In traditional group decision-making, the weights of the experts are generally assumed to be known and equal, which is impractical in some decision-making scenarios. Therefore, a distributed linguistic trust model is used to calculate the weights of different experts.

[0048] Furthermore, based on the mutual evaluation among the five evaluators in the social network, we can get the following Figure 2 The trust relationship diagram shown in the figure sets a three-level language scale H = {H1 = low, H2 = general, H3 = high}. Based on the above trust relationship diagram and the distributed language trust function, the following distributed language trust social matrix can be constructed:

[0049]

[0050] Based on the above distributed language trust social matrix, the centrality of each expert or node can be obtained:

[0051]

[0052] Then, using the trust ratio calculation formula, we can get the weight vector of each evaluator:

[0053]

[0054] Then, define a set of decision criteria, such as a 1 to 9 point system, to determine the optimal planning parameter name C under each expert evaluation. B and the worst planning parameter name C W , where the highest score corresponds to the optimal planning parameter name, and the lowest score corresponds to the worst planning parameter name. B Paired comparisons are performed between the optimal planning parameter name and other planning parameter names to construct the first initial comparison vector matrix A of the optimal planning parameter name to other planning parameter names B =(a B1 , a B2 , a B3 ,...,a Bj ,...,a Bn );

[0055] In the worst planning parameter name C W Pairwise comparison is performed with other planning parameter names to construct the worst planning parameter name C W The second initial comparison vector matrix B for other evaluation planning parameter names W =(a1W , a 2W , a 3W ,...,a jW ,...,a nW ) T , where a Bj 、a Bn Respectively represent the first initial comparison vector of the jth and nth planning parameter names, a jW 、a nW Respectively represent the second initial comparison vectors of the j-th and n-th planning parameter names, T represents the transpose of the matrix, and n represents the total number of planning parameter names;

[0056] The first comparison vector and the second comparison vector are calculated according to the following formula:

[0057]

[0058] Among them, A B ′ represents the first comparison vector, B W 'Second comparison vector, A Bk 、B Wk They respectively represent the first initial comparison vector matrix and the second initial comparison vector matrix corresponding to the k-th expert.

[0059] Finally, the first evaluation weight is calculated according to the following formula:

[0060] minmax{||ω B / W j1 -a Bj |,|W j1 / ω W -a jW |}

[0061]

[0062] Among them, ω B represents the weight vector representing the optimal planning parameter name, ω W The weight vector representing the worst planning parameter name, W j1 Indicates the first evaluation weight of the j-th planning parameter name.

[0063] For example, the above steps are used to compare and score each planning information of the microgrid design scheme. According to the opinions of experts, the optimal planning parameter name and the worst planning parameter name are identified and determined, and a first comparison vector of the optimal planning parameter name against other planning parameter names and a second comparison vector of the worst planning parameter name against other planning parameter names are established, as shown in Table 2 below. B The planning parameter name with the number 6.5 in ′ is the optimal planning parameter name, and the comparison vector BW The planning parameter with the number 6.4 in ′ is the worst planning parameter. The vector in Table 2 is the weighted average of the scoring results of the five experts. It can be seen that the best planning parameter is the construction cost, and the worst planning parameter is the operating cost:

[0064] Table 2

[0065] Planning parameter name <![CDATA[First comparison vector A B ′]]> <![CDATA[Second comparison vector B W ′]]> Construction costs 2.2 6.4 Running costs 6.5 2.5 New energy consumption 2.4 5.5 Energy efficiency 3.8 3.6 Energy saving rate 4.2 3.2 Carbon emissions reduction 3.5 4.3 NOx emission reduction 5.6 2.9

[0066] A minimum-maximum model was then established to calculate the final first evaluation weights. The weight calculation results are shown in Table 3. The results show that the optimal planning parameter name has the largest weight, 0.107, while the operating cost has the smallest weight, 0.045. Based on this, the second evaluation weights can be combined to conduct a comprehensive evaluation of the microgrid design scheme.

[0067] Table 3

[0068] Planning parameter name First evaluation weight Construction costs 0.107 Running costs 0.045 New energy consumption 0.104 Energy efficiency 0.094 Energy saving rate 0.085 Carbon emissions reduction 0.097 NOx emission reduction 0.063

[0069] Step S103: constructing an original data matrix according to the planning parameter values, and calculating a second evaluation weight of each planning parameter name according to the original data matrix;

[0070] It should be pointed out that the original data matrix is constructed according to the following formula:

[0071] X=[x 11 ,...,x 1m ];

[0072] Where X represents the original data matrix, x 11 、x 1m Indicates the planning parameter values of the first and mth planning parameter names under the scheme to be evaluated;

[0073] The variability of each planning parameter name is calculated according to the following formula:

[0074]

[0075] The conflict between planning parameter names is calculated using the following formula:

[0076]

[0077] The information content of each planning parameter name is calculated according to the following formula:

[0078] C j =S j ×R j ;

[0079] The second evaluation weight is calculated according to the following formula:

[0080]

[0081] Among them, S j represents the variability of the j-th planning parameter name, Indicates the average value of the planning parameter name, R j Indicates the conflict between planning parameter names, r ij Indicates the correlation between the name of the i-th planning parameter and the name of the j-th planning parameter, C j represents the information content of the j-th planning parameter name, W j2 represents the second evaluation weight of the j-th planning parameter name, and m represents the total number of planning parameter names.

[0082] For example, based on the planning parameter values in Table 1, and considering the second evaluation weights of the planning parameter names calculated in the above steps, the calculation results are shown in Table 4 below:

[0083] Table 4

[0084] Planning parameter name Construction costs Running costs New energy consumption Energy efficiency Energy saving rate Carbon emissions reduction NOx emission reduction Second evaluation weight 0.191 0.054 0.08 0.078 0.071 0.065 0.06

[0085] It can be seen from Table 4 that the second evaluation weight of operating cost is the smallest relative to other planning parameter names, and the second evaluation weight of construction cost is the largest.

[0086] Step S104: obtaining a combined weight of each planning parameter name according to the first evaluation weight and the second evaluation weight, and obtaining an evaluation value of the to-be-evaluated scheme according to the combined weight under the cost category and the combined weight under the environmental protection category;

[0087] It should be pointed out that the purpose of calculating the combined weight in this step is to optimize the target affected by multiple factors. The main idea is to find a balance between different weighting methods, reduce the deviation of each weight as much as possible, and find the optimal combined weight value. The specific process is as follows:

[0088] Optimize the linear combination of the first evaluation weight and the second evaluation weight to obtain the minimum optimization value:

[0089]

[0090] Among them, α j Represents the linear coefficient to be optimized, W1 and W2 represent the first evaluation weight matrix and the second evaluation weight matrix respectively, W1=(W 11 ,...,W j1 ,...,W n1 ), W2=(W 12 ,...,W j2 ,...,W n2 );

[0091] The linear coefficient is calculated according to the following formula:

[0092]

[0093] The normalized linear coefficient is calculated according to the following formula:

[0094]

[0095] The combination weight is calculated according to the following formula:

[0096]

[0097] Among them, α1 represents the linear coefficient corresponding to the first evaluation weight matrix, α2 represents the linear coefficient corresponding to the second evaluation weight matrix, and α * represents the normalized linear coefficient, W′ j Indicates the combined weight of the j-th planning parameter name.

[0098] The evaluation value of the scheme to be evaluated is calculated according to the following formula:

[0099]

[0100] in, represents the evaluation value of the scheme to be evaluated, g represents the number of environmental protection categories for the planning parameter name, mg represents the number of cost categories for the planning parameter name, and x′ 1j Indicates the final value of the j-th planning parameter name;

[0101] The final value of the j-th planning parameter name is calculated according to the following formula:

[0102]

[0103] It should also be noted that if the data value attribute of the planning parameter name is as large as possible, it is a forward planning parameter name. If the data value attribute of the planning parameter name is as small as possible, it is a reverse planning parameter name.

[0104] It should be noted that all planning parameter names are divided into evaluation values of cost category and environmental protection category to determine the evaluation value of the microgrid design scheme. The result may be positive or negative. In order to avoid this situation, it is required to construct a decision matrix for each planning parameter name in the microgrid design scheme. In addition, due to the dimensional differences between the planning parameter names, the decision matrix needs to be normalized. Therefore, different types of planning parameter names are processed separately. At the same time, since the amount of information reflected by the numerical variation degree of the planning parameter names is different, the present invention introduces a second evaluation weight to determine the importance of each planning parameter value in the overall evaluation system to calculate the evaluation value. For the microgrid design scheme, The larger it is, the more energy-saving and environmentally friendly it is while taking into account lower costs.

[0105] For example, by combining the first evaluation weight and the second evaluation weight, the deviation between different weighting methods can be reduced as much as possible. The first evaluation weight and the second evaluation weight are combined and weighted based on game theory. The comprehensive weighting results are shown in Table 5 below:

[0106] Table 5

[0107] Planning parameter name First evaluation weight Second evaluation weight Portfolio Weight Construction costs 0.107 0.191 0.141 Running costs 0.045 0.054 0.049 New energy consumption 0.104 0.08 0.094 Energy efficiency 0.094 0.078 0.088 Energy saving rate 0.085 0.071 0.079 Carbon emissions reduction 0.097 0.065 0.084 NOx emission reduction 0.063 0.06 0.062

[0108] Finally, the evaluation value of the scheme to be evaluated is 1.33.

[0109] Step S105: determining whether the evaluation value is greater than a preset threshold; if not, optimizing the planning information under the to-be-evaluated scheme and re-obtaining the evaluation value until the re-obtained evaluation value is greater than the preset threshold.

[0110] It should be noted that since the preset threshold is related to the actual scenario, it is not limited in detail in this embodiment. For example, if the preset threshold is set to 1.5, the microgrid design scheme obtained according to the above steps does not meet the requirements, that is, it does not achieve a better balance between cost and energy conservation and environmental protection. Based on this, the designer needs to optimize the design scheme according to the evaluation value. The optimization scheme includes optimizing the structural composition of the design scheme and the parameters under each structure. Since the optimization process is a conventional technology, it is not introduced in detail in this embodiment. After the optimization is completed, a new planning parameter value and a new first evaluation weight will be obtained, thereby obtaining a new evaluation value, and then the new evaluation value will be repeatedly judged. This cycle continues until the evaluation value is greater than the preset threshold, which indicates that the microgrid design scheme at this time can better balance cost and energy conservation and environmental protection.

[0111] In summary, the above-mentioned evaluation and optimization method for microgrid planning has the following advantages:

[0112] 1. The present invention comprehensively considers planning parameters such as microgrid construction cost, operation cost, new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, NOx emission reduction, etc. to conduct a comprehensive evaluation of microgrid planning schemes in terms of cost and energy conservation and environmental protection, thereby more accurately reflecting the advantages and disadvantages of current microgrid design schemes, and providing a reliable technical reference for microgrid design optimization during the design stage.

[0113] 2. Traditional microgrid evaluation methods often ignore the subjective preferences of decision-makers (experts) during the evaluation process and set the expert weights (weight vectors) to be the same, which leads to deviations between the evaluation results and the actual decision-making needs. The evaluation method of this invention incorporates mathematical and statistical methods such as the distributed language trust social matrix and the minimum-maximum weight model, considers the subjective risk preferences of experts, and integrates the impact of each planning parameter value, making the evaluation results more reliable.

[0114] 3. By introducing a second evaluation weight to determine the importance of each planning parameter value in the overall evaluation system, the reliability of the evaluation is further improved. The evaluation value is then calculated. For the microgrid design scheme, the larger the evaluation value, the more energy-saving and environmentally friendly it is while taking into account the lower cost, thereby achieving the benign optimization of the microgrid design scheme.

[0115] like Figure 3 As shown, an embodiment of the present invention further provides a microgrid planning scheme evaluation and optimization system, the system comprising:

[0116] The planning information acquisition module 10 is used to obtain planning information under the scheme to be evaluated, wherein the planning information includes planning parameter names and corresponding planning parameter values, and the planning parameter names are divided into cost categories and environmental protection categories. The cost categories include construction costs and operating costs, and the environmental protection categories include new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, and NOx emission reduction.

[0117] A trust matrix construction module 20 is configured to construct a distributed language trust social matrix expressing the degree of expert preference, obtain an expert weight vector based on the distributed language trust social matrix, obtain a first comparison vector and a second comparison vector for each planning parameter name based on the weight vector, construct a minimum-maximum weight model based on the first comparison vector and the second comparison vector, and obtain a first evaluation weight for each planning parameter name based on the minimum-maximum weight model;

[0118] A parameter matrix construction module 30 is configured to construct an original data matrix according to the planning parameter values, and calculate a second evaluation weight of each planning parameter name according to the original data matrix;

[0119] An evaluation module 40 is configured to obtain a combined weight of each planning parameter name based on the first evaluation weight and the second evaluation weight, and obtain an evaluation value of the to-be-evaluated scheme based on the combined weight under the cost category and the combined weight under the environmental protection category;

[0120] The optimization module 50 is configured to determine whether the evaluation value is greater than a preset threshold value. If not, the planning information under the to-be-evaluated scheme is optimized and the evaluation value is re-acquired until the re-acquired evaluation value is greater than the preset threshold value.

[0121] Another aspect of the present invention further provides a storage medium having one or more programs stored thereon, which, when executed by a processor, implement the above-mentioned method for evaluating and optimizing the microgrid planning scheme.

[0122] On the other hand, the present invention also proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned microgrid planning scheme evaluation and optimization method.

[0123] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0124] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0125] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement the hardware: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0126] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A method for evaluating and optimizing a microgrid planning scheme, characterized in that: The method comprises: Obtain planning information under the scheme to be evaluated, the planning information including planning parameter names and corresponding planning parameter values, and divide the planning parameter names into cost categories and environmental protection categories. The cost categories include construction costs and operating costs, and the environmental protection categories include new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, and NOx emission reduction; Constructing a distributed language trust social matrix expressing the degree of expert preference, and obtaining a weight vector of the expert according to the distributed language social matrix, and obtaining a first comparison vector and a second comparison vector for each planning parameter name according to the weight vector, and constructing a minimum-maximum weight model according to the first comparison vector and the second comparison vector, and obtaining a first evaluation weight for each planning parameter name based on the minimum-maximum weight model; Constructing an original data matrix according to the planning parameter values, and calculating a second evaluation weight of each planning parameter name according to the original data matrix; Obtaining a combined weight of each planning parameter name according to the first evaluation weight and the second evaluation weight, and obtaining an evaluation value of the to-be-evaluated scheme according to the combined weight under the cost category and the combined weight under the environmental protection category; It is determined whether the evaluation value is greater than a preset threshold. If not, the planning information under the to-be-evaluated scheme is optimized, and the evaluation value is re-acquired until the re-acquired evaluation value is greater than the preset threshold.

2. The method for evaluating and optimizing a microgrid planning scheme according to claim 1, wherein: The steps of constructing a distributed language trust social matrix expressing the expert preference degree and obtaining the expert's weight vector according to the distributed language trust social matrix include: Define each expert as a node, G = (E, L, ω) as a directed graph, E = {e1, e2, ..., em} as a node set, L = {l1, l2, ..., lq} as a directed link between nodes, and the distributed language trust evaluation set is the additional information of the connection, SL=(T sk ) m×m is a distributed language trust social matrix associated with the directed graph G, and the centrality of each node is calculated according to the following formula; in, Represents node e k The centrality, T sk represents the value of the sth row and kth column in the distributed language trust social matrix; The weight vector is calculated according to the following formula: Among them, ω k Represents the node e k The corresponding expert weight vector, Represents node e k The expectation of the distributed trust language trust function, m represents the total number of experts; The expectation of the distributed trust language trust function is calculated according to the following formula: Among them, H α Indicates trust, It represents the membership of the corresponding trust level, and α represents the trust level.

3. The method for evaluating and optimizing a microgrid planning scheme according to claim 2, wherein: The step of obtaining a first comparison vector and a second comparison vector for each planning parameter name according to the weight vector comprises: Determine the optimal planning parameter name C under each expert evaluation B and the worst planning parameter name C W , in the optimal planning parameter name C B Paired comparisons are performed between the optimal planning parameter name and other planning parameter names to construct the first initial comparison vector matrix A of the optimal planning parameter name to other planning parameter names B =(a B1 , a B2 , a B3 ,...,a Bj ,...,a Bn ); In the worst planning parameter name C W Pairwise comparison is performed with other planning parameter names to construct the worst planning parameter name C W The second initial comparison vector matrix B for other evaluation planning parameter names W =(a 1W , a 2W , a 3W ,...,a jW ,...,a nW ) T , where a Bj 、a Bn Respectively represent the first initial comparison vector of the jth and nth planning parameter names, a jW 、a nW Respectively represent the second initial comparison vectors of the j-th and n-th planning parameter names, T represents the transpose of the matrix, and n represents the total number of planning parameter names; The first comparison vector and the second comparison vector are calculated according to the following formula: Among them, A B ′ represents the first comparison vector, B W 'Second comparison vector, A Bk 、B Wk They respectively represent the first initial comparison vector matrix and the second initial comparison vector matrix corresponding to the k-th expert.

4. The method for evaluating and optimizing a microgrid planning scheme according to claim 3, wherein: The step of constructing a minimum-maximum weight model according to the first comparison vector and the second comparison vector, and obtaining a first evaluation weight of each planning parameter name based on the minimum-maximum weight model includes: The first evaluation weight is calculated according to the following formula: minmax{|ω B / W j1 -a Bj |,|W j1 / h W -a jW |} Among them, ω B represents the weight vector representing the optimal planning parameter name, ω W The weight vector representing the worst planning parameter name, W j1 Indicates the first evaluation weight of the j-th planning parameter name.

5. The method for evaluating and optimizing a microgrid planning scheme according to claim 4, wherein: The step of constructing an original data matrix according to the planning parameter values and calculating the second evaluation weight of each planning parameter name according to the original data matrix includes: The original data matrix is constructed according to the following formula: X=[x 11 、...、x 1m ]; Where X represents the original data matrix, x 11 、x 1m Indicates the planning parameter values of the first and mth planning parameter names under the scheme to be evaluated; The variability of each planning parameter name is calculated according to the following formula: The conflict between planning parameter names is calculated using the following formula: The information content of each planning parameter name is calculated according to the following formula: C j =S j ×R j ; The second evaluation weight is calculated according to the following formula: Among them, S j represents the variability of the j-th planning parameter name, Indicates the average value of the planning parameter name, R j Indicates the conflict between planning parameter names, r ij Indicates the correlation between the name of the i-th planning parameter and the name of the j-th planning parameter, C j represents the information content of the j-th planning parameter name, W j2 represents the second evaluation weight of the j-th planning parameter name, and m represents the total number of planning parameter names.

6. The method for evaluating and optimizing a microgrid planning scheme according to claim 5, wherein: The step of obtaining the combined weight of each planning parameter name according to the first evaluation weight and the second evaluation weight comprises: Optimize the linear combination of the first evaluation weight and the second evaluation weight to obtain the minimum optimization value: Among them, α j Represents the linear coefficient to be optimized, W1 and W2 represent the first evaluation weight matrix and the second evaluation weight matrix respectively, W1=(W 11 ,...,W j1 ,...,W n1 ), W2=(W 12 ,...,W j2 ,...,W n2 ); The linear coefficient is calculated according to the following formula: The normalized linear coefficient is calculated according to the following formula: The combination weight is calculated according to the following formula: Among them, α1 represents the linear coefficient corresponding to the first evaluation weight matrix, α2 represents the linear coefficient corresponding to the second evaluation weight matrix, and α * represents the normalized linear coefficient, W′ j Indicates the combined weight of the j-th planning parameter name.

7. The method for evaluating and optimizing a microgrid planning scheme according to claim 5, wherein: The step of obtaining the evaluation value of the scheme to be evaluated according to the combined weight under the cost category and the combined weight under the environmental protection category includes: The evaluation value of the scheme to be evaluated is calculated according to the following formula: in, represents the evaluation value of the scheme to be evaluated, g represents the number of environmental protection categories for planning parameter names, mg represents the number of cost categories for planning parameter names, and x 1j Indicates the final value of the j-th planning parameter name; The final value of the j-th planning parameter name is calculated according to the following formula:

8. A microgrid planning scheme evaluation and optimization system, characterized in that: The system comprises: A planning information acquisition module is used to obtain planning information under the scheme to be evaluated, the planning information including planning parameter names and corresponding planning parameter values, and to divide the planning parameter names into cost categories and environmental protection categories. The cost categories include construction costs and operating costs, and the environmental protection categories include new energy consumption, energy efficiency, energy saving rate, carbon emission reduction, and NOx emission reduction. a trust matrix construction module, configured to construct a distributed language trust social matrix expressing the degree of expert preference, obtain an expert weight vector based on the distributed language trust social matrix, obtain a first comparison vector and a second comparison vector for each planning parameter name based on the weight vector, construct a minimum-maximum weight model based on the first comparison vector and the second comparison vector, and obtain a first evaluation weight for each planning parameter name based on the minimum-maximum weight model; A parameter matrix construction module is used to construct an original data matrix according to the planning parameter values, and calculate the second evaluation weight of each planning parameter name according to the original data matrix; An evaluation module, configured to obtain a combined weight of each planning parameter name according to the first evaluation weight and the second evaluation weight, and obtain an evaluation value of the to-be-evaluated scheme according to the combined weight under the cost category and the combined weight under the environmental protection category; The optimization module is used to determine whether the evaluation value is greater than a preset threshold. If not, the planning information under the evaluation scheme is optimized and the evaluation value is re-acquired until the re-acquired evaluation value is greater than the preset threshold.

9. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by a processor, implement the evaluation and optimization method of the microgrid planning scheme according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the evaluation and optimization method of the microgrid planning scheme as described in any one of claims 1 to 7.

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