Microgrid-connected multi-dimensional evaluation method, system, equipment, medium and product

Abstract

The invention belongs to the technical field of power systems, and provides a micro-grid grid-connected multi-dimensional evaluation method, system and device, a medium and a product, and the method comprises the steps: determining an optimal scheme for a micro-grid to access a power distribution network; acquiring parameters of key nodes of the power distribution network before and after the microgrid is accessed under the optimal access scheme; calculating a multi-dimensional index based on the parameters of the key nodes; determining a multi-dimensional index weight; and comprehensively evaluating the optimal scheme based on the multi-dimensional index weight. According to the invention, a technical, economic, safe and environmental four-dimensional evaluation system is constructed, so that grid-connected evaluation and operation optimization of the micro-grid are realized.

Description

Technical Field

[0001] This disclosure belongs to the field of power system technology, and in particular relates to a multi-dimensional evaluation method, system, equipment, medium and product for microgrid grid connection. Background Technology

[0002] Driven by the global energy transition, microgrids, as core units integrating distributed power sources, energy storage, and local loads, have seen their large-scale grid connection become a core trend in distribution network development. Traditional distribution networks exhibit radial unidirectional power flow characteristics, while the integration of microgrids disrupts this structure: their bidirectional power flow characteristics and the randomness of distributed power source output lead to a surge in the risks of power flow reversal, voltage exceeding limits, and equipment overload in the distribution network.

[0003] Existing microgrid access evaluation methods have the following problems: 1. The evaluation dimensions are too narrow: it mainly focuses on technical performance and lacks comprehensive consideration of economic efficiency, safety and environmental impact; 2. Insufficient quantification of indicators: The evaluation indicators lack precise mathematical models and quantitative standards; 3. Poor real-time performance: The evaluation process relies on offline calculations, which cannot adapt to the dynamic operating characteristics of the distribution network; 4. Lack of optimization decision-making: The evaluation results are disconnected from the optimization of the access solution, making it difficult to guide actual engineering applications; 5. Subjective weight allocation: The lack of a systematic method for determining weights leads to insufficient scientific rigor in the comprehensive evaluation results. Summary of the Invention

[0004] To address the aforementioned issues, this disclosure provides a multi-dimensional evaluation method for microgrid grid connection. By constructing a four-dimensional evaluation system encompassing technology, economy, safety, and environment, it enables microgrid grid connection assessment and operation optimization.

[0005] Firstly, this disclosure provides a multi-dimensional evaluation method for microgrid grid connection, including: Determine the optimal solution for connecting the microgrid to the distribution network; Obtain the parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme; Calculate multi-dimensional indicators based on the parameters of key nodes; Determine the weights of multi-dimensional indicators; The optimal solution is comprehensively evaluated based on the weights of multiple indicators.

[0006] Furthermore, Determining the optimal solution for microgrid integration into the distribution network includes: constructing a multi-objective optimization model; solving the multi-objective optimization model to obtain the optimal solution.

[0007] Furthermore, Obtain parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme, including: High-precision measurement devices are installed and deployed at key nodes of the distribution network; parameters of key nodes of the distribution network are collected synchronously before and after the microgrid is connected.

[0008] Furthermore, The multiple dimensions include: technical performance, economic benefits, safety and stability, and environmental impact.

[0009] Furthermore, Determine the weights of multi-dimensional indicators, including: Create dimension markers; Construct a judgment matrix based on multi-dimensional indicators; Calculate the multi-dimensional indicator weight vector based on the judgment matrix.

[0010] Furthermore, The optimal solution is comprehensively evaluated based on multi-dimensional indicator weights, including: Calculate the scores of each dimension of the optimal solution; The total score of the optimal solution is calculated based on the scores of each dimension indicator and the corresponding weights of the multi-dimensional indicators.

[0011] Thirdly, based on the same inventive concept, this disclosure also provides a microgrid grid-connected multi-dimensional evaluation system, including an optimal scheme determination module, a distribution network parameter acquisition module, a multi-dimensional index calculation module, a multi-dimensional index weight determination module, and a comprehensive evaluation module; The optimal solution determination module is used to determine the optimal solution for microgrids to connect to the distribution network; The distribution network parameter acquisition module is used to acquire parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme. The multi-dimensional indicator calculation module is used to calculate multi-dimensional indicators based on the parameters of key nodes. The multi-dimensional indicator weight determination module is used to determine the weights of multi-dimensional indicators. The comprehensive evaluation module is used to comprehensively evaluate the optimal solution based on the weights of multi-dimensional indicators.

[0012] Thirdly, based on the same inventive concept, this disclosure also provides an electronic device, including at least one processor and at least one memory electrically connected; The memory is electrically connected to the processor, wherein the memory stores instructions that can be executed by at least one of the processors, the instructions being executed by at least one of the processors to enable at least one of the processors to perform the microgrid grid-connected multidimensional evaluation method as described above.

[0013] Fourthly, based on the same inventive concept, this disclosure also provides a computer storage medium storing a computer program. When the computer program is executed by the processor, it implements the microgrid grid-connected multidimensional evaluation method as described above.

[0014] Fifthly, based on the same inventive concept, this disclosure also provides a computer program product, which is stored in at least one storage medium; The computer program product includes several instructions to cause at least one electronic device to execute the microgrid grid-connected multidimensional evaluation method as described above.

[0015] Compared with existing technologies, this disclosure provides a multi-dimensional evaluation method for microgrid grid connection, which has the following beneficial effects: 1. Comprehensive evaluation system: For the first time, a comprehensive evaluation system covering four dimensions—technology, economy, safety, and environment—is constructed, overcoming the shortcomings of traditional methods that rely on a single evaluation dimension; 2. Significantly improved quantification accuracy: Through precise mathematical models and real-time data acquisition, the quantification accuracy of evaluation indicators reaches over 95%, far exceeding the approximately 80% of traditional methods; 3. Scientific weight allocation: A judgment matrix is ​​used to determine the weight of each dimension, and consistency checks are combined to ensure that the weights are reasonable, thereby improving the objectivity and credibility of the comprehensive evaluation; 4. Strong engineering applicability: The evaluation indicators, target values ​​and optimization constraints are set based on actual engineering needs and can be directly applied to the planning, design and operation evaluation of microgrids connected to the distribution network.

[0016] Other features and advantages of this disclosure will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a multi-dimensional evaluation method for microgrid grid connection according to an embodiment of the present disclosure is shown. Figure 2 A schematic diagram illustrating the structural principle of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0020] Figure 1 A flowchart illustrating a multi-dimensional evaluation method for microgrid grid connection according to an embodiment of the present disclosure is shown.

[0021] A multi-dimensional evaluation method for microgrid grid connection according to an embodiment of this disclosure includes the following steps: S1, determine the optimal scheme for connecting the microgrid to the distribution network.

[0022] S11. Construct a multi-objective optimization model.

[0023] (1) Determine the decision variables.

[0024] Decision variables include access capacity (Unit: kW), Connection Time t 0 (unit: h, 0≤) t 0≤24).

[0025] (2) Establish the objective function.

[0026] ① Minimize network loss: , in, n Indicates the total number of branches in the distribution network. express t Time of the first k Branch current, Indicates the branch resistance. This indicates the optimization time step.

[0027] ② Minimal voltage fluctuation: , , , Where T represents the continuous operating time of the microgrid. For the measured voltage at key nodes of the distribution network, Contribute to new energy sources Indicates the rated voltage of key nodes in the distribution network. This represents the voltage power sensitivity coefficient.

[0028] ③ Highest equipment utilization rate: , , in, This represents the real-time load power of the distribution network at time t. This indicates the rated total power of key equipment in the power distribution network.

[0029] (3) Determine the constraints.

[0030] ①Access capacity constraints: , in, For the minimum output of the microgrid, This represents the maximum capacity a node can handle.

[0031] ②Access timing constraints: , in, It indicates the start or end time of the first peak load period of the day in the distribution network (usually the morning peak electricity consumption period, such as 8:00). This indicates the start or end time of the second peak load period in a day for the power distribution network (usually the evening peak, such as 20:00). and They jointly defined the "peak load intervals that should be avoided".

[0032] In practice, t 0 should avoid peak load times (8-10 am, 6-8 pm) and prioritize the flat period (11-5 pm).

[0033] ③ Safety constraints: , , in, This indicates the rated current of the branch circuit.

[0034] S12. Solve the multi-objective optimization model to obtain the optimal solution.

[0035] Solving multi-objective optimization models using an improved particle swarm optimization algorithm includes: (1) Initialize the particle swarm.

[0036] Particle coding: Each particle has a dimension of 2, that is ( n =1,2,...,N, where N is the number of particles (30-50). Value range: ; Initial velocity: .

[0037] (2) Calculation of fitness function.

[0038] The weighted sum method is used to integrate the three objectives (weights: Network loss 0.4 Voltage fluctuation 0.3 Equipment utilization rate 0.3): , fitness value The larger the value, the better the solution.

[0039] (3) Particle update.

[0040] Update position and velocity using the individual optimal solution Pbest and the global optimal solution Gbest: , , , in, Indicates adaptive weights, h This represents the current iteration number. The maximum number of iterations, It is a random number between 0 and 1.

[0041] The update process employs a strategy of global search in the early stages and local optimization in the later stages.

[0042] (4) Constraint handling.

[0043] If a particle violates the constraints, the "boundary mapping method" is used to correct it, such as: .

[0044] (5) Termination conditions and optimal solution output In the embodiments of this disclosure, it is set that when the number of iterations reaches... (100-200 iterations) or 10 consecutive iterations of global optimal fitness change At that time, output the optimal access scheme. .

[0045] S2, obtain the parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme.

[0046] Based on the distribution network topology, high-precision measurement devices are installed and deployed at key nodes such as busbars, branch interfaces, and load concentration points.

[0047] With a sampling accuracy of ≥0.2 and a sampling interval of ≤100ms, voltage data of key nodes in the distribution network are synchronously collected before and after microgrid integration. Branch power Load power and distributed power output Data such as...

[0048] Outliers are removed by data preprocessing algorithm (3σ principle), and data is smoothed by moving average method to ensure that data quality meets evaluation requirements.

[0049] S3 calculates multi-dimensional metrics based on parameters of key nodes.

[0050] The multi-dimensional indicators cover at least four categories: technical performance, economic benefits, safety and stability, and environmental impact.

[0051] 1. Technical performance dimension, which should include at least: (1) Loss measurement accuracy index, , Among them, the accuracy of loss measurement The target value is ≤5%. This represents the measured loss value. This represents the theoretically calculated loss value.

[0052] (2) Accuracy of microgrid optimal access point location index, , Among them, the accuracy of microgrid optimal access point location The target value is ≥90%. This indicates the number of correctly located sensitive nodes. This represents the total number of sensitive nodes.

[0053] (3) Voltage stability after connection index, , Among them, voltage stability after microgrid connection The target value is ≤5%. This indicates the measured voltage of the distribution network after the microgrid is connected. Indicates the rated voltage of the power distribution network; (4) Response timeliness indicators , Access scheme adjustment instruction generation and issuance time ≤3 seconds.

[0054] 2. Economic benefits, including at least: (1) Rate of reduction in line loss costs index, , Among them, the reduction rate of line loss costs The target value is ≥8%. This represents the line loss cost before the microgrid is connected. This represents the line loss cost after a microgrid is connected.

[0055] (2) Equipment operation and maintenance cost savings rate index, , Among them, the equipment operation and maintenance cost saving rate The target value is ≥10%. This represents the equipment operation and maintenance costs before the microgrid is connected. This indicates the equipment operation and maintenance costs after the microgrid is connected.

[0056] (3) Investment recovery period index, , Among them, the investment recovery period The target value is ≤5 years. This represents the total investment in connecting a microgrid to the distribution network. This represents the annualized return on investment for microgrids connected to the distribution network.

[0057] 3. Safety and stability dimensions, including at least: (1) Failure risk reduction rate , , Among them, the failure risk reduction rate The target value is ≥40%. This indicates the number of fault risks before the microgrid is connected. This indicates the number of fault risks after the microgrid is connected.

[0058] (2) System compatibility, The target value is ≥95% of the mainstream microgrid types that can be connected to the grid.

[0059] (3) Data security compliance rate The percentage of functional modules that meet the requirements of the Power Information Security Protection Scheme 2.0 is calculated, with a target value of 100%.

[0060] 4. Environmental impact dimension, which should include at least: (1) Clean energy consumption rate , , Among them, clean energy consumption rate The target value is ≥92%. This represents the amount of renewable energy consumed by the distribution network after the microgrid is connected. This represents the total power generation from new energy sources in the microgrid area.

[0061] (2) Carbon emission reduction , , Among them, carbon emission reduction The target value is ≥50 tons / year (10kV line). This represents the difference in power loss of the distribution network before and after the microgrid is connected. This indicates the total annual reduction in carbon emissions of the distribution network after the microgrid is connected. This indicates the increase in renewable energy consumption in the distribution network area after the microgrid is connected. This represents the average carbon emission factor of the distribution network area.

[0062] S4, determine the weights of multi-dimensional indicators.

[0063] S41, Create dimension markers.

[0064] Technical performance dimension marking a 1. Marking the economic benefit dimension a 2. Safety and stability dimension marking a 3. Environmental impact dimension labeling a 4.

[0065] S42, construct a judgment matrix based on multi-dimensional indicators.

[0066] Based on the 1-9 scale (1 = equally important, 3 = slightly important, 5 = important, 7 = very important, 9 = extremely important, 2 / 4 / 6 / 8 are median values, and the reciprocal is the opposite of the comparison), the importance of the multi-dimensional indicators in the four dimensions is compared pairwise to form a 4×4 judgment matrix. A : , in, .

[0067] S43, Calculate the multi-dimensional indicator weight vector based on the judgment matrix.

[0068] (1) Normalize the elements of each column of the judgment matrix: , in, p Represents the judgment matrix of the first... p Row number, p =1,2,3,4.

[0069] (2) Sum the normalized matrix row by row to obtain the row sum vector. : , in, q The normalized judgment matrix represents the first... q Column number, q =1,2,3,4.

[0070] (3) Normalize the rows and vectors to obtain the final weight vector. : , .

[0071] S44, Examine the effectiveness of the multi-dimensional indicator weights.

[0072] (1) Calculate the largest eigenvalue of the judgment matrix.

[0073] , Where A·W is the product of the judgment matrix and the weight vector. The first of the product vectors i Each element.

[0074] (2) Calculate the consistency index CI and random consistency ratio CR for the weights: , , For a 4th-order matrix, RI = 0.90. If CR ≤ 0.1, the weights are valid; otherwise, the judgment matrix is ​​regenerated and corrected. A .

[0075] S5 comprehensively evaluates the optimal solution based on multi-dimensional indicator weights.

[0076] S51, calculate the scores of each dimension of the optimal solution.

[0077] For each secondary indicator under each dimension, scores are assigned based on the degree of fit between the quantified value and the target value (0-100 points), and the weighted average score s is taken. i .

[0078] The quantified values ​​are real-time parameters of key nodes in the distribution network before and after microgrid integration, or values ​​calculated from real-time parameters. The target value is a preset standard value. For example, in this implementation, scoring is based on the following fit rules: Quantitative value = standard value, score 80 points; The quantitative value is better than the standard value, higher than 80 points, and not exceeding 100 points; The quantitative value is lower than the standard value, below 80 points, but not lower than 60 points; The quantitative value is significantly lower than the standard value, below 60 points.

[0079] S52, calculate the total score of the optimal solution based on the scores of each dimension indicator and the corresponding weights of the multi-dimensional indicators.

[0080] .

[0081] Evaluation results can be graded according to the total score, for example: ≥90 points is "excellent", 80-89 points is "good", 60-79 points is "average", and <60 points is "poor".

[0082] The embodiments disclosed herein construct a four-dimensional evaluation system covering technology, economy, safety, and environment. Through a four-stage process of "optimization decision-making - data collection - index calculation - comprehensive evaluation", the microgrid access scheme is accurately designed, evaluated in real time, and dynamically optimized, thereby ensuring the safe and stable operation of the distribution network and maximizing its comprehensive benefits.

[0083] Based on the same inventive concept as the method disclosed above, this disclosure also provides a microgrid grid-connected multi-dimensional evaluation system, including an optimal scheme determination module, a distribution network parameter acquisition module, a multi-dimensional index calculation module, a multi-dimensional index weight determination module, and a comprehensive evaluation module; The optimal solution determination module is used to determine the optimal solution for microgrids to connect to the distribution network; The distribution network parameter acquisition module is used to acquire parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme. The multi-dimensional indicator calculation module is used to calculate multi-dimensional indicators based on the parameters of key nodes. The multi-dimensional indicator weight determination module is used to determine the weights of multi-dimensional indicators. The comprehensive evaluation module is used to comprehensively evaluate the optimal solution based on the weights of multi-dimensional indicators.

[0084] This disclosure applies to distribution networks with voltage levels of 10kV and below, and is compatible with various microgrid types such as photovoltaic, wind power, and energy storage. It can be directly applied to engineering scenarios such as distribution network planning and design, microgrid grid connection evaluation, and operation optimization.

[0085] Based on the same inventive concept as the above-disclosed content, this disclosure also provides an electronic device. For example... Figure 2As shown, the electronic device of this disclosure includes at least one processor and at least one memory electrically connected to the processor. The memory is electrically connected to the processor, wherein the memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the microgrid grid-connected multidimensional evaluation method as described above.

[0086] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. The indirect connection method can be applied to the embodiments of this disclosure as long as it achieves the purpose of this disclosure.

[0087] Based on the same inventive concept, this disclosure also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the microgrid grid-connected multidimensional evaluation method as described above.

[0088] Based on the same inventive concept, this disclosure also provides a computer program product, which is stored in at least one storage medium; the computer program product includes several instructions to cause at least one computer device to execute the microgrid grid-connected multidimensional evaluation method as described above.

[0089] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A multi-dimensional evaluation method for microgrid grid connection, characterized in that, The method includes, Determine the optimal solution for connecting the microgrid to the distribution network; Obtain the parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme; Calculate multi-dimensional indicators based on the parameters of key nodes; Determine the weights of multi-dimensional indicators; The optimal solution is comprehensively evaluated based on the weights of multiple indicators.

2. The method according to claim 1, characterized in that, Determining the optimal solution for microgrid integration into the distribution network includes: constructing a multi-objective optimization model; solving the multi-objective optimization model to obtain the optimal solution.

3. The method according to claim 1, characterized in that, Obtain parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme, including: High-precision measurement devices are installed and deployed at key nodes of the distribution network; parameters of key nodes of the distribution network are collected synchronously before and after the microgrid is connected.

4. The method according to any one of claims 1-3, characterized in that, The multiple dimensions include: technical performance, economic benefits, safety and stability, and environmental impact.

5. The method according to any one of claims 4, characterized in that, Determine the weights of multi-dimensional indicators, including: Create dimension markers; Construct a judgment matrix based on multi-dimensional indicators; Calculate the multi-dimensional indicator weight vector based on the judgment matrix.

6. The method according to claim 5, characterized in that, The optimal solution is comprehensively evaluated based on multi-dimensional indicator weights, including: Calculate the scores of each dimension of the optimal solution; The total score of the optimal solution is calculated based on the scores of each dimension indicator and the corresponding weights of the multi-dimensional indicators.

7. A multi-dimensional evaluation system for microgrid grid connection, characterized in that, The system includes an optimal solution determination module, a distribution network parameter acquisition module, a multi-dimensional index calculation module, a multi-dimensional index weight determination module, and a comprehensive evaluation module. The optimal solution determination module is used to determine the optimal solution for microgrids to connect to the distribution network; The distribution network parameter acquisition module is used to acquire parameters of key nodes in the distribution network before and after microgrid access under the optimal access scheme. The multi-dimensional indicator calculation module is used to calculate multi-dimensional indicators based on the parameters of key nodes. The multi-dimensional indicator weight determination module is used to determine the weights of multi-dimensional indicators. The comprehensive evaluation module is used to comprehensively evaluate the optimal solution based on the weights of multi-dimensional indicators.

8. An electronic device, characterized in that, Includes at least one processor and at least one memory electrically connected; The memory is electrically connected to the processor, wherein the memory stores instructions that can be executed by at least one of the processors, the instructions being executed by at least one of the processors to enable at least one of the processors to perform the microgrid grid-connected multidimensional evaluation method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores a computer program. When the computer program is executed by the processor, it implements the microgrid grid-connected multidimensional evaluation method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product is stored in at least one storage medium; The computer program product includes several instructions to cause at least one electronic device to execute the microgrid grid-connected multidimensional evaluation method according to any one of claims 1-6.

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