Multi-terminal interconnection flexible micro-grid performance evaluation method, system and medium

Abstract

The invention provides a multi-terminal interconnection flexible micro-grid performance evaluation method and system and a medium, and belongs to the technical field of micro-grids, and the method comprises the steps: building a training set based on the original parameters of a flexible micro-grid and the evaluation result of the performance of the multi-terminal interconnection flexible micro-grid; an LSTM model is constructed, the hidden layer node number k, the learning rate # imgabs0 # and the Dropout rate # imgabs1 # of the LSTM model are determined as to-be-optimized parameters, the evaluation accuracy of the five-fold cross validation LSTM model of the training set is used as a target function, the to-be-optimized parameters are optimized through an improved snake optimization algorithm, and optimal LSTM model parameters are obtained; and constructing an LSTM model based on the optimal LSTM model parameters, wherein the LSTM model is used for evaluating the performance of the multi-terminal interconnection flexible micro-grid. According to the method, the accuracy of performance evaluation of the multi-terminal interconnection flexible micro-grid can be effectively improved, and an ideal evaluation effect is achieved.

Description

Technical Field

[0001] The present invention relates to the field of microgrid technology, and in particular to a multi-terminal interconnected flexible microgrid performance evaluation method, system and medium. Background Art

[0002] Multi-terminal interconnected flexible microgrids are one of the key technologies for future land warfare platforms to win. In-depth research on the technical characteristics of multi-terminal interconnected flexible microgrids and evaluation of their performance are the basic work for further design and optimization of multi-terminal interconnected flexible microgrids. LSTM (Long Short-Term Memory, LSTM) is a special recurrent neural network (Recurrent Neural Network, RNN), which belongs to a neural network model in the field of deep learning and has been applied in performance evaluation in the power field.

[0003] During LSTM training, the number of hidden layer nodes of LSTM k , learning rate , Dropout rate The quality of selection directly affects the accuracy of the final unmanned cluster performance evaluation results. Intelligent optimization algorithm is an effective method to optimize the parameters of LSTM model. Snake optimization algorithm (SOA) is a new meta-heuristic algorithm. This algorithm is a new intelligent optimization algorithm that imitates the special mating behavior of snakes. It can also be applied to the performance evaluation of multi-terminal interconnected flexible microgrids. However, there are still some defects in the snake optimization algorithm, which makes the algorithm easy to fall into local optimality and low convergence accuracy. When evaluating the performance of multi-terminal interconnected flexible microgrids, it often fails to achieve the ideal evaluation effect. Summary of the invention

[0004] The present invention provides a multi-terminal interconnected flexible microgrid performance evaluation method, which can effectively improve the accuracy of multi-terminal interconnected flexible microgrid performance evaluation and achieve an ideal evaluation effect. The method specifically includes the following steps: Based on the original parameters of the flexible microgrid and the evaluation results of the performance of the multi-terminal interconnected flexible microgrid, a training set is constructed; Build an LSTM model and determine the number of hidden layer nodes k and learning rate of the LSTM model , Dropout rate As the parameters to be optimized, the evaluation accuracy of the 5-fold cross-validation LSTM model of the training set is used as the objective function, and the parameters to be optimized are optimized by the improved snake optimization algorithm to obtain the optimal LSTM model parameters; wherein, the improved snake optimization algorithm introduces the position update mechanism of the crayfish optimization algorithm and the gradient optimization algorithm into the mating mode of the exploration stage and the development stage of the original snake optimization algorithm, and selects different position update modes, the current optimal position of the snake individual, the optimal position of the previous generation of the snake individual, the cave of the snake individual, the decreasing curve of the snake individual, and the adaptive parameters of the gradient of the snake individual to update the position of the next generation according to the switching probability; An LSTM model is constructed based on the optimal LSTM model parameters to evaluate the performance of multi-terminal interconnected flexible microgrids.

[0005] Preferably, the position update formula of the exploration phase of the improved snake optimization algorithm is: ; ; In the formula, For the t +1 iteration of male position; The randomly selected t The position of the male in the iteration; rand is a random number in the range [0,1]; the ability to find food for males; For the t +1 iteration of female position; The randomly selected t The position of the females in the iteration; the ability to find food for females; For the t The position of the individual snakes at the iteration; , is a random number in the range [0,1]; ; ; ; ; In the formula, Indicates t The best position of the individual snake in the iteration; Indicates t - The best position of the individual snakes in 1 iteration; Indicates t The burrow of the snake individual of the iteration; is a decreasing curve; , is the adaptive parameter of the gradient of the individual snake, , They are 1.2 and 0.2 respectively.

[0006] Preferably, the position update formula in the mating mode of the development stage of the improved snake optimization algorithm is: ; ; Where: For the t +1 iteration i The position of the male; For the t The iteration i The position of the male; For the t +1 iteration i The position of the females; For the t The iteration i The position of the females; rand is a random number in the range [0,1]; and Separate into male and female mating abilities; , is a random number in the range [0,1].

[0007] Preferably, the evaluation indicators of the performance of the multi-terminal interconnected flexible microgrid include reliability, coordination, safety, efficiency, quality and scalability; The flexible microgrid original parameters include power supply output power, output voltage, frequency, load power, system normal operation time, total operation time, current total power of the microgrid and maximum power of the microgrid.

[0008] Preferably, the reliability evaluation value is the proportion of the system normal operation time to the total operation time; The evaluation value of the coordination is the power matching degree; the power matching degree is the matching degree between the power supply output power and the load power; The safety assessment value is the ratio of the current power supply output power to the maximum power of the microgrid; The evaluation value of the efficiency is the ratio of the current load power to the total power of the microgrid; The evaluation values ​​of the quality are voltage and frequency fluctuation rate; The evaluation value of the scalability is the ratio of the current load power to the maximum power of the microgrid.

[0009] Preferably, the formula for the evaluation result of the multi-terminal interconnected flexible microgrid performance is: ; In the formula, is the weight of each evaluation indicator, is the evaluation value of each evaluation indicator after normalization.

[0010] The present invention also proposes a multi-terminal interconnected flexible microgrid performance evaluation system, the system comprising: processor; a memory having stored thereon a computer program executable on the processor; Among them, when the computer program is executed by the processor, the steps of the multi-terminal interconnected flexible microgrid performance evaluation method are implemented.

[0011] The present invention also proposes a computer-readable storage medium, on which a data processing program is stored. When the data processing program is executed by a processor, the steps of the multi-terminal interconnected flexible microgrid performance evaluation method are implemented.

[0012] Beneficial effects of the present invention: The present invention proposes a performance evaluation method for a multi-terminal interconnected flexible microgrid. The method adopts an optimized snake optimization algorithm to optimize the parameters of an LSTM model, and constructs an evaluation model for the performance of a multi-terminal interconnected flexible microgrid based on the LSTM model after parameter optimization. The evaluation model for the performance of a multi-terminal interconnected flexible microgrid can obtain a more accurate and objective evaluation result. Among them, the present invention introduces a position update mechanism of a crayfish optimization algorithm and a gradient optimization algorithm into the exploration phase and mating mode of the snake optimization algorithm, comprehensively considers factors such as selecting different position update modes according to the switching probability, the current optimal position of the individual snake, the optimal position of the previous generation of the individual snake, the individual snake's cave, the individual snake's decreasing curve, and the adaptive parameters of the individual snake's gradient to update the individual snake position, thereby improving the diversity of the algorithm's search direction, avoiding the occurrence of local optimality in each iteration, and obtaining the optimal model parameters through the algorithm to further effectively improve the performance of the LSTM model, thereby improving the performance evaluation effect of the multi-terminal interconnected flexible microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of a multi-terminal interconnected flexible microgrid performance evaluation method according to an embodiment of the present invention; Figure 2 It is an execution flow chart of the improved snake optimization algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0015] Example 1 Snake optimization algorithm (SOA) is a new meta-heuristic algorithm proposed in 2022. This algorithm is a new intelligent optimization algorithm that imitates the special mating behavior of snakes, and can also be applied to the performance evaluation of multi-terminal interconnected flexible microgrids. However, the existing snake optimization algorithm still has some defects: (1) The position update method of male (female) snake individuals in the exploration stage of the snake optimization algorithm only relies on the position of randomly selected males (males and females) to update, and the search direction lacks diversity, and cannot effectively cover the entire problem search space, which may cause the algorithm to fall into a local optimum. (2) In the mating mode of the snake optimization algorithm development stage, parents are selected only based on fitness values, ignoring the diverse distribution of the population, which may lead to excessive concentration of high-quality individuals, reduce the global search potential, and easily fall into local optimality. The above two key deficiencies result in the failure to achieve the best design effect when using the snake optimization algorithm for multi-terminal interconnected flexible microgrid performance evaluation. To this end, the present invention proposes a multi-terminal interconnected flexible microgrid performance evaluation method, and its flow chart is as follows Figure 1 As shown, the specific steps are as follows:

[0016] S1: Construct a training set based on the original parameters of the flexible microgrid and the evaluation results of the performance of the multi-terminal interconnected flexible microgrid.

[0017] S2: Build an LSTM model and determine the number of hidden layer nodes k and learning rate of the LSTM model , Dropout rate As the parameters to be optimized, the evaluation accuracy of the 5-fold cross-validation LSTM model of the training set is used as the objective function, and the parameters to be optimized are optimized by the improved snake optimization algorithm to obtain the optimal LSTM model parameters; wherein, the improved snake optimization algorithm introduces the position update mechanism of the crayfish optimization algorithm and the gradient optimization algorithm into the mating mode of the exploration stage and the development stage of the original snake optimization algorithm, and selects different position update modes, the current optimal position of the snake individual, the optimal position of the previous generation of the snake individual, the cave of the snake individual, the decreasing curve of the snake individual, and the adaptive parameters of the gradient of the snake individual according to the switching probability to update the position of the next generation. S3: Build an LSTM model based on the optimal LSTM model parameters to evaluate the performance of multi-terminal interconnected flexible microgrids.

[0018] The construction of the evaluation model includes: The evaluation indicators for determining the performance of a multi-terminal interconnected flexible microgrid include reliability, coordination, safety, efficiency, quality and scalability; the original parameters of the flexible microgrid include power supply output power, output voltage, frequency, load power, system normal operation time, total operation time, the current total power of the microgrid and the maximum power of the microgrid.

[0019] Calculate the evaluation value of each evaluation index: the evaluation value of reliability is the proportion of the system's normal operating time in the total operating time; the evaluation value of coordination is the power matching degree; the power matching degree is the matching degree between the power supply output power and the load power; the evaluation value of safety is the ratio of the current power supply output power to the maximum power of the microgrid; the evaluation value of efficiency is the ratio of the current load power to the total power of the microgrid; the evaluation value of quality is the voltage and frequency fluctuation rate; the evaluation value of scalability is the ratio of the current load power to the maximum power of the microgrid.

[0020] Further: The formula for the evaluation result of the multi-terminal interconnected flexible microgrid performance is: ; In the formula, is the weight of each evaluation indicator, is the evaluation value of each evaluation indicator after normalization.

[0021] Specifically, Figure 2 As shown in the figure, the improved snake optimization algorithm includes the following steps to optimize the optimization parameters: S2.1: Determine the number of hidden layer nodes of the LSTM model k , learning rate , Dropout rate As the parameter to be optimized, the evaluation accuracy of the 5-fold cross-validation LSTM model of the training set is used as the objective function, that is, the fitness function; at the same time, the corresponding constraints are set: the number of hidden layer nodes k of LSTM, the learning rate , Dropout rate The upper and lower limits of .

[0022] S2.2: Parameter settings, mainly including: population size (i.e. the number of snake individuals) N; maximum number of iterations (i.e. the condition for stopping iteration) T ; Problem optimization lower bound ; Problem optimization upper bound .

[0023] S2.3: Initialize the snake population position: (1); Where: is the position of the i-th snake; r is a random number in the range [0,1]; and are the upper and lower bounds of the problem to be solved.

[0024] S2.4: Divide the population into two groups: females and males Assume that the number of males is 50% and the number of females is 50%. The population is divided into two groups: male group and female group. The following two formulas are used to divide the population: (2); (3); Where: N The size of the snake population; is the number of males; The number of females.

[0025] S2.5: Evaluate each group and define the temperature and amount of food According to the objective function, find the best individual in each group and get the best male And the best female and food location .

[0026] Temperature can be defined by the following formula: (4); Where: t is the current iteration number; T is the maximum number of iterations.

[0027] Food quantity It can be defined by the following formula: (5); Where: is a constant, take 0.5.

[0028] S2.6: If (Threshold ), enter the exploration phase and calculate the positions of male and female individuals respectively, otherwise go to S2.7.

[0029] S2.7: If Temp >0.6, enter the exploration phase and calculate the position of individual snakes, otherwise enter S2.8.

[0030] S2.8: If rand >0.6, enter the combat mode of the development stage and calculate the positions of male and female individuals respectively, otherwise enter S2.9.

[0031] S2.9: Enter the mating mode of the development stage, calculate the positions of male and female individuals, and replace the positions of the worst male and the worst female individuals respectively.

[0032] Specifically, the exploration and development phases include the following steps: Exploration phase (no food) if (Threshold ), the snakes search for food by choosing any random location and update their positions.

[0033] In the SOA exploration phase, the position update method of male (female) snakes in the snake optimization algorithm only relies on the position of randomly selected males (males and females) to update, and the search direction lacks diversity, which cannot effectively cover the entire problem search space, and may cause the algorithm to fall into a local optimum. In order to more effectively improve the performance of the algorithm, the position update mechanism of the crayfish optimization algorithm and the gradient-based optimizer (GBO) algorithm is introduced to improve the position update method of the snake individuals. The snake individual position is updated by comprehensively considering factors such as selecting different position update modes according to the switching probability, the current optimal position of the snake individual, the optimal position of the previous generation of the snake individual, the snake individual's cave, the snake individual's decreasing curve, and the adaptive parameters of the snake individual's gradient, etc., to improve the diversity of the algorithm's search direction, avoid the local optimum in each iteration, and thus improve the performance of the algorithm.

[0034] In the exploration phase of SOA, the crayfish optimization algorithm and gradient optimization algorithm were introduced. The improved position update formulas of male and female snake individuals are: (6); (7); In the formula, For the t +1 iteration of male position; The randomly selected t The position of the male in the iteration; rand is a random number in the range [0,1]; The ability to find food for males, , The position of the randomly selected male The fitness value of For male position The fitness value of is a constant, taking 0.05; For the t +1 iteration of female position; The randomly selected t The position of the females in the iteration; The ability to find food for females, , The position of the randomly selected male The fitness value of For male position The fitness value of For the t The position of the individual snakes at the iteration; , is a random number in the range [0,1]; (8); (9); (10); (11); In the formula, Indicates t The best position of the individual snake in the iteration; Indicates t - The best position of the individual snakes in 1 iteration; Indicates t The burrow of the snake individual of the iteration; is a decreasing curve; , is the adaptive parameter of the gradient of the individual snake, , They are 1.2 and 0.2 respectively.

[0035] Development stage (with food) exist Under the condition of , then the temperature is hot. The snake will only look for food, and the position update formula is as follows:

[0036] (12); Where: is the position of the individual snake (male or female) at the t+1th iteration; is the optimal position of the snake individual in the tth iteration, that is, the food position; rand is a random number in the range of [0,1]; is a constant, take 2.

[0037] exist Under the condition of , then the temperature is cold. The snake will be in fighting mode or mating mode.

[0038] (a) Combat Mode (13); Where: is the position of the i-th male in the t+1-th iteration; is the position of the i-th male in the t-th iteration; is the best position in the female snake group; rand is a random number in the range of [0,1]; For male fighting ability.

[0039] (14); Where: is the position of the i-th female in the t+1-th iteration; is the position of the i-th female in the t-th iteration; is the best position in the male snake group; rand is a random number in the range of [0,1]; For female fighting ability.

[0040] and It can be calculated by the following formula: (15); (16); Where: Best position for female snakes The fitness value of Best position for male snakes The fitness value of is the fitness value of the individual snake.

[0041] (b) Mating mode In the development stage of SOA, in the mating mode of the snake optimization algorithm, parents are selected only based on fitness values, ignoring the diversity distribution of the population, which may lead to excessive concentration of high-quality individuals, reduce the global search potential, and easily fall into the local optimum. In order to more effectively improve the performance of the algorithm, the position update mechanism of the crayfish optimization algorithm and the gradient optimization algorithm (GBO) is introduced to improve the position update method of individual snakes. The individual snake positions are updated by comprehensively considering factors such as selecting different position update modes according to the switching probability, the current optimal position of the individual snake, the optimal position of the previous generation of the individual snake, the individual snake's cave, the individual snake's decreasing curve, and the adaptive parameters of the individual snake's gradient, so as to avoid the rapid loss of population diversity and cause the algorithm to converge to the local optimal solution prematurely, thereby improving the performance of the algorithm.

[0042] In the mating mode of the development stage of SOA, the crayfish optimization algorithm and the gradient optimization algorithm are introduced. The improved position update formulas of male and female snake individuals are: (17); (18); Where: is the position of the i-th male in the t+1-th iteration; is the position of the i-th male in the t-th iteration; is the position of the i-th female in the t+1-th iteration; is the position of the ith female in the tth iteration; rand is a random number in the range [0,1]; , is a random number in the range [0,1]; and The mating capacity of males and females can be calculated by the following formula: (19); (20); Where: is the fitness value of the ith male position; is the fitness value of the ith female position.

[0043] If the eggs hatch, select the worst male and female and replace them.

[0044] (twenty one); (twenty two); Where: It is the worst position in the male snake group; This is the worst position among the female snakes.

[0045] S2.10: Determine whether the stopping condition is met. If not, repeat S2.5-S2.10. Otherwise, output the optimal snake individual, that is, the number of hidden layer nodes with the optimal parameters of LSTM. , learning rate , Dropout rate .

[0046] S2.11: A multi-terminal interconnected flexible microgrid performance evaluation model is established with the output optimal parameters as LSTM parameters, and the test data set is input into the model to obtain the evaluation results and evaluation accuracy of the multi-terminal interconnected flexible microgrid performance.

[0047] In this embodiment: 1000 groups of samples of multi-terminal interconnected flexible microgrids were selected, and 800 of them were randomly selected as training samples, and the remaining 200 groups were used as test samples. SOA-LSTM and ISOA-LSTM were used to evaluate the performance of multi-terminal interconnected flexible microgrids. MATLAB was used as the simulation platform, and the parameters in the SOA algorithm were: N=50, Maxiter=200, the number of hidden layer nodes k, and the learning rate , Dropout rate The search range is between 0 and 100; the parameters in the ISOA algorithm are: N=50, Maxiter=200, the number of hidden layer nodes k, and the learning rate , Dropout rate The search range is between 0-100.

[0048] The evaluation indicators of SOA-LSTM model and ISOA-LSTM model can be selected as: mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE). As shown in Table 1, compared with SOA-LSTM, ISOA-LSTM has a higher accuracy in evaluating the performance of multi-terminal interconnected flexible microgrids, that is, the LSTM parameters searched by ISOA are better than those searched by SOA. The simulation results show that the ISOA algorithm has a stronger search capability than the SOA algorithm, and the ISOA-LSTM has a higher evaluation accuracy than the SOA-LSTM, which verifies the effectiveness of the method.

[0049] Table 1 Comparison of evaluation methods

[0050] The above is a multi-terminal interconnected flexible microgrid performance evaluation method provided by an embodiment of this embodiment. Based on the same idea, this embodiment also provides a corresponding multi-terminal interconnected flexible microgrid performance evaluation system. For the specific definition of the multi-terminal interconnected flexible microgrid performance evaluation system, please refer to the definition of the multi-terminal interconnected flexible microgrid performance evaluation method in the above text, which will not be repeated here. Each module in the above-mentioned multi-terminal interconnected flexible microgrid performance evaluation system can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0051] This embodiment also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A performance evaluation method for a multi-terminal interconnected flexible microgrid is provided.

[0052] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0053] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-terminal interconnected flexible microgrid performance evaluation method, characterized in that: The following steps are involved: Based on the original parameters of the flexible microgrid and the evaluation results of the performance of the multi-terminal interconnected flexible microgrid, a training set is constructed; Build an LSTM model and determine the number of hidden layer nodes k and learning rate of the LSTM model , Dropout rate As the parameters to be optimized, the evaluation accuracy of the 5-fold cross-validation LSTM model of the training set is used as the objective function, and the parameters to be optimized are optimized by the improved snake optimization algorithm to obtain the optimal LSTM model parameters; wherein, the improved snake optimization algorithm introduces the position update mechanism of the crayfish optimization algorithm and the gradient optimization algorithm into the mating mode of the exploration stage and the development stage of the original snake optimization algorithm, and selects different position update modes, the current optimal position of the snake individual, the optimal position of the previous generation of the snake individual, the cave of the snake individual, the decreasing curve of the snake individual, and the adaptive parameters of the gradient of the snake individual to update the position of the next generation according to the switching probability; An LSTM model is constructed based on the optimal LSTM model parameters to evaluate the performance of multi-terminal interconnected flexible microgrids.

2. The multi-terminal interconnected flexible microgrid performance evaluation method according to claim 1 is characterized in that: The position update formula of the exploration phase of the improved snake optimization algorithm is: ； ； In the formula, For the t +1 iteration of male position; The randomly selected t The position of the male in the iteration; rand is a random number in the range [0,1]; the ability to find food for males; For the t +1 iteration of female position; The randomly selected t The position of the females in the iteration; the ability to find food for females; For the t The position of the individual snakes at the iteration; , is a random number in the range [0,1]; ； ； ； ； In the formula, Indicates t The best position of the individual snake in the iteration; Indicates t - The best position of the individual snakes in 1 iteration; Indicates t The burrow of the snake individual of the iteration; is a decreasing curve; , is the adaptive parameter of the gradient of the individual snake, , They are 1.2 and 0.2 respectively.

3. The multi-terminal interconnected flexible microgrid performance evaluation method according to claim 2 is characterized in that: The position update formula in the mating mode of the development stage of the improved snake optimization algorithm is: ； ； Where: For the t +1 iteration i The position of the male; For the t The iteration i The position of the male; For the t +1 iteration i The position of the females; For the t The iteration i The position of the females; rand is a random number in the range [0,1]; and Separate into male and female mating abilities; , is a random number in the range [0,1].

4. The multi-terminal interconnected flexible microgrid performance evaluation method according to claim 1 is characterized in that: The evaluation indicators of the performance of the multi-terminal interconnected flexible microgrid include reliability, coordination, safety, efficiency, quality and scalability; The flexible microgrid original parameters include power supply output power, output voltage, frequency, load power, system normal operation time, total operation time, current total power of the microgrid and maximum power of the microgrid.

5. The multi-terminal interconnected flexible microgrid performance evaluation method according to claim 4 is characterized in that: The reliability evaluation value is the proportion of the system's normal operation time to the total operation time; The evaluation value of the coordination is the power matching degree; the power matching degree is the matching degree between the power supply output power and the load power; The safety assessment value is the ratio of the current power supply output power to the maximum power of the microgrid; The evaluation value of the high efficiency is the ratio of the current load power to the total power of the microgrid; The evaluation values ​​of the quality are voltage and frequency fluctuation rate; The evaluation value of the scalability is the ratio of the current load power to the maximum power of the microgrid.

6. The multi-terminal interconnected flexible microgrid performance evaluation method according to claim 5 is characterized in that: The formula for the evaluation result of the multi-terminal interconnected flexible microgrid performance is: ； In the formula, is the weight of each evaluation indicator, is the evaluation value of each evaluation indicator after normalization.

7. A multi-terminal interconnected flexible microgrid performance evaluation system, characterized in that: The system comprises: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the multi-terminal interconnected flexible microgrid performance evaluation method as described in any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data processing program, and when the data processing program is executed by a processor, the steps of the multi-terminal interconnected flexible microgrid performance evaluation method as described in any one of claims 1 to 6 are implemented.

Images