Integrated emergency control method for coupling load correlation factor and low-frequency load shedding single-three-phase series-parallel micro-grid group

Abstract

An integrated emergency control method for a coupling load correlation factor and low-frequency load shedding single-three-phase series-parallel micro-grid group comprises the following steps: taking the sum of economic loss of load shedding during low-frequency load shedding of the micro-grid group, frequency change offset loss and network loss caused by three-phase imbalance as a minimum objective function; establishing a micro-grid group integrated low-frequency load shedding model of the coupling load correlation factors; constructing an integrated low-frequency load shedding meta-model; correcting and dynamically updating the integrated low-frequency load shedding meta-model by adopting an MSALO to obtain an optimal dynamic meta-model and a corresponding low-frequency load shedding optimal target function value; and generating an optimal integrated emergency control strategy through the optimal dynamic meta-model. According to the method provided by the invention, the total economic loss of load shedding and the three-phase unbalance degree can be comprehensively reduced on the premise of ensuring the safety and stability of the system frequency, and the decision response speed is improved.

Description

Technical Field

[0001] The present invention relates to the field of microgrid emergency control, and in particular to an integrated emergency control method for a single-phase and three-phase hybrid microgrid group coupling load-related factors and low-frequency load reduction. Background Art

[0002] As an effective form of energy management for distributed energy generation, microgrids can effectively improve the comprehensive utilization rate of energy, and improve power supply reliability and power quality. When microgrids switch from grid-connected operation to island operation, transient instability of load-side voltage and frequency is prone to occur, resulting in power shortages in the system, which ultimately affects the normal operation of the system. Therefore, how to maintain system frequency stability and power balance while comprehensively reducing three-phase imbalance and response time has become an urgent problem to be solved in current research.

[0003] In the prior art, the document [1]: "Adaptive wide-area load shedding scheme based on the sink and source concept to preserve power system stability" (R. Bekhradian, M. Sanaye-Pasand, and A. Mahari, "Adaptive wide-area load shedding scheme based on the sink and source concept to preserve power system stability," IEEE Syst. J., vol. 17, no. 1, pp. 503-512, Mar. 2023.) proposed a new wide-area load shedding algorithm based on phasor measurement units, which can adaptively determine the load shedding amount of each area according to the interaction between load buses. The low-frequency load shedding scheme proposed in this document has achieved good results in frequency regulation, but does not consider the economic loss of load shedding.

[0004] Reference [2]: "Optimizing the post-disaster control of islanded microgrid: a multi-agent deep reinforcement learning approach" (H. Nie, Y. Chen, Y. Xia, et al., "Optimizing the post-disaster control of islanded microgrid: a multi-agent deep reinforcement learning approach," IEEE Access, vol. 8, pp. 153455-153469, Aug. 2020.) proposed a multi-agent based low-frequency load reduction strategy. This strategy takes into account the load shedding cost factor, achieves dual optimal control on the power supply side and the load side, and maximizes the economic benefits of the microgrid. However, the proposed scheme has a high computational complexity and does not involve the study of low-frequency load reduction for single-phase three-phase hybrid microgrid groups. Single-phase three-phase hybrid microgrid groups are one of the mainstream modes of microgrid group system development, but they are prone to three-phase imbalance problems in unplanned island switching scenarios, which further leads to increased line voltage drop and power loss and reduced power supply efficiency.

[0005] To address this problem, the paper [3]: "Coordinated control of passive transition from grid-connected to islanded operation for three / single-phase hybrid multimicrogrids considering speed and smoothness" (C. Wang, X. Li, T. Tian, ​​et al., "Coordinated control of passive transition from grid-connected to islanded operation for three / single-phase hybrid multimicrogrids considering speed and smoothness," IEEE Trans. Ind. Electron., vol. 67, no. 3, pp. 1921–1931, Mar. 2020.) proposed a low-frequency load reduction strategy based on implicit enumeration in the passive grid-connected and off-grid switching process. This strategy can quickly achieve grid-connected and off-grid switching and significantly reduce the three-phase imbalance of the system. However, it requires a large amount of complicated sampling and calling of the low-frequency load reduction model and constraints, which will significantly increase the response time.

[0006] To this end, the literature [4]: ​​"Control strategy of unintentional islanding transition with high adaptability for three / single-phase hybrid multimicrogrids" (C. Wang, S. Chu, H. Yu, et al., "Control strategy of unintentional islanding transition with high adaptability for three / single-phase hybrid multimicrogrids," Int. J. Electr. Power Energy Syst., vol. 136, pp. 107724, 2022.) considers the three-phase unbalance factor and proposes a virtual combination method based on insertion sorting. According to the three-phase unbalance index, the components in the single-phase sub-microgrid are virtually combined into a virtual three-phase load, which together with the three-phase load constitutes the candidate for load reduction action. This virtual three-phase combination method reduces the low-frequency load reduction decision time and the three-phase imbalance of the system to a certain extent, but its essence is a step-by-step form of load evaluation and virtual three-phase combination, and the calculation complexity and load reduction response time are still high.

[0007] In addition, when formulating low-frequency load reduction plans, the above-mentioned literature did not take into account the impact of various load-related factors such as load economic losses, three-phase imbalance, and load frequency adjustment coefficient on load reduction economy, frequency recovery, and system three-phase imbalance correction effects. Summary of the invention

[0008] In view of the deficiencies of the prior art literature, the present invention provides an integrated emergency control method for a single-three-phase hybrid microgrid group that couples load-related factors with low-frequency load shedding. First, in order to reduce the complexity of the model and shorten the response time of the load shedding decision, an integrated low-frequency load shedding strategy framework that couples virtual three-phase combination-load evaluation-load shedding action is constructed based on the metamodel; secondly, the economic loss of load removal during low-frequency load shedding of the microgrid group, the frequency change offset loss, and the sum of network losses caused by three-phase imbalance are used as the minimum objective function to establish an integrated low-frequency load shedding model for microgrid groups with coupled load-related factors; finally, an integrated low-frequency load shedding metamodel is constructed and the MSALO algorithm is used to revise and dynamically update it, and the optimal load shedding decision is generated through the obtained optimal dynamic metamodel. The method proposed in the present invention can comprehensively reduce the total economic loss of load shedding and the three-phase imbalance while ensuring the safety and stability of the system frequency, and improve the decision-making response speed.

[0009] The technical solution adopted by the present invention is:

[0010] An integrated emergency control method for a single-phase and three-phase hybrid microgrid group coupling load correlation factors and low-frequency load reduction includes the following steps:

[0011] Step 1: Taking the economic loss of load shedding during low-frequency load shedding of microgrid group, the frequency change offset loss and the sum of network losses caused by three-phase imbalance as the minimum objective function, an integrated low-frequency load shedding model of microgrid group with coupled load correlation factors is established;

[0012] Step 2: Construct an integrated low-frequency load reduction meta-model;

[0013] Step 3: Use MSALO to modify and dynamically update the integrated under-frequency load reduction meta-model to obtain the optimal dynamic meta-model and the corresponding optimal objective function value of under-frequency load reduction;

[0014] Step 4: Generate the optimal integrated emergency control strategy through the optimal dynamic meta-model.

[0015] In step 1, the frequency regulation effect of the load describes the response characteristics of the active load to the frequency change. When the influence of the node voltage is ignored and the frequency regulation effect of the load is taken into account, a polynomial is used to represent the load mathematical model, as shown below:

[0016]

[0017] Where P L is the active load corresponding to the system frequency f; f N is the rated frequency; P LN is the rated active power of the load; a n is the proportional coefficient of each type of load in the rated load,

[0018] In the steady-state load model of the actual power system, the load that is cubic or above the frequency is ignored, the above formula is simplified and expressed in per-unit value, and the various load models can be obtained by derivation of the frequency as follows:

[0019]

[0020] In the formula, K L P is the frequency regulation effect coefficient of the load, which indicates the load's ability to regulate the frequency; L* is the per unit value of system active power; f * K is the per unit value of the system frequency. L A larger value means that the load has a greater impact on the frequency regulation while absorbing the same power. When the system frequency decreases, K should be removed first. L For smaller loads, keep K L The load with a larger value can fully utilize this effect to reduce the amount of low-frequency load reduction, thereby contributing to the rapid and stable recovery of system frequency.

[0021] There are both single-phase load and three-phase load in the single-phase three-phase hybrid microgrid, so it is easy to cause the three-phase imbalance problem of the system due to the unbalanced characteristics of the single-phase load. The three-phase imbalance represented by the load power is calculated as follows:

[0022]

[0023] Where, ε is the three-phase unbalance; I1 is the positive sequence component of the three-phase current; I2 is the negative sequence component of the three-phase current; S L , S L2 are the positive sequence apparent power and the negative sequence apparent power respectively; P L2 , Q L2 are negative sequence active power and reactive power respectively; P A , P B , P C and Q A , Q B , Q C They are the three-phase active power and reactive power respectively.

[0024] In step one, the existing research premise on single-phase and three-phase hybrid microgrids is that the initial three-phase imbalance of the system is 0. Load shedding decisions are made based on the sorting results to correct the three-phase imbalance of the microgrid after the unplanned island, but there are still deficiencies when applied to microgrids with initial three-phase imbalance. To this end, the present invention considers the scenario where the microgrid has initial three-phase imbalance, and directly considers the three-phase imbalance factor into the objective function of low-frequency load shedding, with the goal of minimizing the three-phase imbalance of the system after the load shedding is completed to achieve the same effect of correcting the three-phase imbalance of the system as the virtual three-phase combination. In order to comprehensively consider the influence of various load factors such as load frequency adjustment coefficient, load three-phase imbalance, load power, load importance level, etc. on the economic loss of low-frequency load shedding, frequency recovery effect, and correction of system three-phase imbalance, the present invention takes the sum of the economic loss of load removal, frequency change offset loss, and network loss caused by three-phase imbalance when the microgrid is low-frequency load shedding as the minimum objective function, and establishes an integrated low-frequency load shedding model for microgrids as follows:

[0025]

[0026] In the formula, C loss is the total economic loss of load shedding in the microgrid group; n is the total amount of load shedding; c i is the cost coefficient of the i-th load cut; α i is the load reduction ratio at node i; P i is the load power at node i; β i is the frequency offset penalty coefficient at node i; t0 is the starting integration time of the frequency curve; t 1iis the end time of frequency curve integration; △f(t) is the frequency deviation curve at time t; It is the network loss penalty coefficient when there is three-phase imbalance in the system after load shedding action; ε is the degree of three-phase imbalance in the system.

[0027] The relevant constraints in the whole process of low-frequency load reduction include power balance constraints, load node flow constraints, three-phase imbalance constraints and bus frequency constraints, as follows:

[0028]

[0029] ε≤ε0

[0030] f min ≤f≤f max

[0031]

[0032] In the above formula, m1, m2 and m3 are the total number of buses for energy storage, photovoltaic and load respectively; P BS,i is the active power output by the energy storage device; P PV,i is the active power output of photovoltaic power generation; P LD,i is the load active power; P i , Q i are the active power and reactive power of node i respectively; U i , U j are the voltage amplitudes of nodes i and j respectively; G ij , B ij are the conductance and susceptance between nodes i and j respectively; θ ij is the voltage phase difference between nodes i and j; ε0 is the maximum allowable value of three-phase unbalance, which is 15% in the present invention; f is the system bus frequency, f min 、f max are the minimum and maximum values ​​of the system bus frequency, which are 49.5Hz and 50.5Hz respectively; P ij is the line flow value, are the minimum and maximum values ​​of the line power flow respectively; U i is the voltage amplitude at node i, are the minimum and maximum voltage amplitudes at node i, respectively.

[0033] In the step 2, the integrated emergency control model established involves multiple load-related factors such as load frequency regulation coefficient, load three-phase imbalance, load power, load importance level, etc. There is a complex nonlinear relationship between the control decision and different load-related factors. If the intelligent algorithm is directly used to iterate the model to generate the optimal emergency control strategy, there will be shortcomings such as complicated calculations and low efficiency.

[0034] The essence of the meta-model is to use the collected samples to extract the mapping relationship between the input parameters and the output parameters. It can reduce the amount of calculation while ensuring reasonable accuracy, and is one of the effective ways to solve complex nonlinear problems. Therefore, the present invention proposes a method for generating an optimal integrated emergency control strategy for a single-phase and three-phase hybrid microgrid group based on a dynamic meta-model. The integrated model composed of load evaluation, virtual three-phase combination, and load reduction action in the emergency control process of the microgrid group is replaced by a high-computational efficiency and high-precision RBF meta-model fitting, and the mapping relationship between the input load-related factors and the output control decision is simply visualized, so as to realize the efficient generation of the optimal control strategy of the integrated emergency control model;

[0035] C loss =F(X1,X2,…,X n )

[0036] Where F(i) represents the implicit relationship between load correlation factors and emergency control decisions; X n It is the nth type of load association factor.

[0037] In step two, before generating the optimal control strategy of the integrated emergency control model, it is necessary to initialize the parameters of the microgrid group system, including all energy storage, photovoltaic power, island switching time, low-frequency load reduction delay time, load-related factors, etc. in the single-phase sub-microgrid and the three-phase sub-microgrid. When generating a set containing the above-mentioned emergency control parameters, if the sampling method is to be optimal, the number of sampling points should be minimized, the sampling time should be minimized, and the uniform distribution should be high while satisfying the sampling accuracy. Compared with pseudo-random number sequences, Halton sequences, and Hammersley sequences, the Sobol sequence has better uniformity and does not have the clustering effect in high-dimensional sampling. Therefore, the present invention selects the Sobol sequence with better uniform distribution characteristics to generate a set of emergency control parameters. The specific steps are as follows:

[0038] (1): Construct the primitive polynomial H of the emergency control parameter set:

[0039] The primitive polynomial H corresponding to the emergency control parameter set cannot be factorized and can divide x p +1, its minimum power is p=2 q +1, specifically:

[0040] H(z)=z q +d1z q-1 +…+d q-1 z+d q

[0041] Where d1, d2, …, d q ={0,1}.

[0042] (2) Construct the direction number v of the emergency control parameter set i , :

[0043]

[0044] In the formula, i is the number of directions. i The equivalent recursive formula and the relationship between the decimal integer n and the base b = 2 are shown below:

[0045] g i =2d1g i-1 ⊕2 2 d2g i-2 ⊕…⊕2 q d q g i-q ⊕g i-q

[0046]

[0047] In the formula, a j ={0,1}; k is the smallest integer greater than or equal to log2n. When the number of directions in the parameter set i>q, v i satisfy:

[0048] v i =d1g i-1 ⊕d2g i-2 ⊕…⊕d q g i-q ⊕[v i-q / 2 q ]

[0049] (3) Generate the Sobol sequence of the emergency control parameter set:

[0050] The Sobol sequence of the emergency control parameter set is as follows:

[0051] m i =a1v1⊕a2v2⊕…⊕a k v k

[0052] In the formula, ⊕ represents the XOR operation in binary; m i are the control variables for emergency control; a1, a2, …, a k ={0,1}.

[0053] In step 2, the metamodel can be divided into two categories: static and dynamic metamodels according to the construction method. Among them, the dynamic metamodel can selectively add new sample points to update the model, thereby improving the approximation accuracy. The present invention selects the RBF dynamic metamodel with more outstanding comprehensive performance such as fitting accuracy and computational efficiency to fit the integrated emergency control model of the microgrid group. Its basic expression and matrix form are:

[0054]

[0055] Aλ=f

[0056] Where f(x) is the basic expression of the RBF metamodel; x is the current sampling point; x i is the initial sample point; ||xx i || is the Euclidean norm, which represents the Euclidean distance between the current sampling point and the sample point; λ i is the weight coefficient of the i-th basis function; A is the matrix form of the basis function of the RBF dynamic metamodel; λ represents the eigenvector of the basis function weight coefficient; m is the number of sampling points; f is the matrix form of the basic expression of the RBF metamodel; φ is the basis function, and the expression is:

[0057]

[0058] Where c is a positive real number; σ c is the expansion factor.

[0059] Based on the above, the specific steps for building an integrated emergency control metamodel are summarized as follows:

[0060] (1) Generate m emergency control parameter sampling points through the Sobol sequence, and calculate the objective function value of each emergency control parameter sampling point. The function value is the corresponding actual response value of the emergency control.

[0061] (2) Substitute the emergency control parameter sampling points and their corresponding response values ​​into the linear equations of the meta-model to obtain the linear equations of the integrated emergency control meta-model.

[0062] (3) Solve the linear equations of the meta-model and obtain the weight coefficients λ0=[λ1,λ2,…,λ m ] T .

[0063] (4) Set the sampling point x i and λ i Substituting it into the basic expression of the metamodel, we can get the expression of the integrated emergency control metamodel.

[0064] In the step three, the present invention uses the MSALO algorithm to correct the integrated low-frequency load reduction meta-model to obtain the optimal dynamic meta-model and the corresponding optimal objective function value of the low-frequency load reduction.

[0065] The MSALO algorithm uses the mutation mechanism of Levy flight and the golden sine algorithm to update the original algorithm based on the Ant Lion Optimization Algorithm (ALO). At the same time, it introduces nonlinear convergence factors and dynamic proportional coefficients to improve the global optimization ability and local development ability of the algorithm, and avoid premature convergence of the algorithm during the iteration process. The position update formulas based on the Levy flight mechanism and the golden sine algorithm are:

[0066]

[0067] in,

[0068]

[0069] x1=-π+2π(1-τ)

[0070] x2=-π+2πτ

[0071] In the formula, are the positions of the i-th individual in the t-th and t+1-th iterations respectively; α is the nonlinear convergence factor; * is the dot product;

[0072] L(i) is a random search path that conforms to the Levy distribution and satisfies the constraint in Γ is the gamma function, u and v obey the standard normal distribution; R1∈[0,2π] and R2∈[0,π] are random numbers, which respectively determine the moving distance and position update direction of the next iteration; T is the maximum number of iterations; x1 and x2 are the coefficients obtained by the golden section; τ is the golden section number.

[0073] The MSALO algorithm specifically includes the following steps:

[0074] Step 1: Set the basic parameters of the ALO algorithm: maximum number of iterations T, proportional coefficient I, number of ants and ant lions numB, numY.

[0075] Step 2: Randomly initialize the positions of ants and antlions, and calculate the optimal values ​​of their fitness functions.

[0076]

[0077]

[0078] In the formula, is the position of the i-th ant after the t-th iteration; a i 、b i are the minimum and maximum values ​​of the random walk of the ith variable, respectively; are the minimum and maximum values ​​of the i-th variable after the t-th iteration respectively; is the position of the i-th antlion after the t-th iteration; f is the fitness function; represents the position of the j-th antlion after the t-th iteration; represents the fitness function value of the position of the i-th ant after the t-th iteration; Represents the fitness function value of the position of the i-th antlion after the t-th iteration.

[0079] Step 3: Use the roulette method to optimize the selection of antlions and update them using the above formula and the position of the ants, where the dynamic proportional coefficient is used to adjust the ant position model in the original ALO algorithm as shown in the following formula.

[0080] Ant(t)=[0,cums(2r(t1)-1),cums(2r(t2)-1),

[0081] …,cums(2r(t T )-1)]

[0082]

[0083] Where Ant(t) is the random walk model of the ant position; cums is the cumulative sum of the ant's random walk steps; t is the current iteration number; r(t) is the random function; I is the proportional coefficient; v is a fixed value; is the position of the ant after a random walk around the antlion selected by the roulette wheel in the tth iteration; is the position of the ants after they wander around the elite antlion in the tth iteration.

[0084] Step 4: Compare the optimal position of the current elite ant lion. If the conditions are met, update the optimal values ​​of the ant and ant lion, otherwise increase the value of the proportional coefficient I by 1.

[0085] Step 5: If the optimal values ​​of two adjacent iterations are almost equal, it is judged that the algorithm is trapped in a local optimum. The position update formula of the Levy flight mechanism is used to replace the position update formula of the i-th ant after the t-th iteration in the original algorithm to further update the position and optimal value of the ant colony.

[0086] Step 6: Introduce the golden section coefficient in the trap reconstruction stage, and update the elite ant lion position and optimal value according to the position update formula of the golden sine algorithm.

[0087] Step7: Repeat Step1 to Step6 until the maximum number of iterations is reached and output the optimal value.

[0088] In step 3, set the metamodel performance evaluation indicators:

[0089] The present invention selects the mean absolute percentage error (MAPE), the root mean square error (RMSE) and the complex correlation coefficient as the fitting accuracy evaluation index of the integrated low-frequency load reduction element model. The formula is as follows:

[0090]

[0091] In step 4, generating an optimal integrated emergency control strategy includes the following steps:

[0092] Step 1: Initialize system parameters, including system parameters of single-phase and three-phase hybrid microgrids, variable constraint range, maximum number of iterations, convergence factor, etc.

[0093] Step 2: Based on the initialization system parameters and constraints, use the Sobol sequence to generate m groups of initial low-frequency load shedding parameter sampling point sets including n variables, calculate the actual response values ​​of the load shedding objective function corresponding to the sampling points, and use the sampling points and their actual response values ​​to generate an initial sampling point database.

[0094] Step 3: Extract the initial low-frequency load reduction parameter set and the corresponding actual response value, and fit and generate the meta-model corresponding to the current integrated low-frequency load reduction model.

[0095] Step 4: Use the MSALO algorithm to perform global optimization on the meta-model obtained in step 3 to obtain the region that may contain the optimal solution and the corresponding local optimal response value.

[0096] Step 5: In the region that may contain the optimal solution in step 4, a new set of low-frequency load shedding parameter sampling points is generated again based on sampling, and the meta-model is used to continue estimating the load shedding objective function response value of the new sampling point set.

[0097] Step 6: Select the best value from the estimated values ​​of the integrated low-frequency load reduction metamodel obtained in step 5, bring the selected excellent point set into the real original model to calculate the actual response value of its load reduction objective function, and compare it with the current local optimal response values.

[0098] Step 7: If the calculated actual response value is better than the current optimal solution, update the local optimal solution, add the excellent point set to the initial low-frequency load reduction parameter sampling point set, reconstruct the integrated low-frequency load reduction meta-model and update the sampling point database. Otherwise, keep the current sampling point set unchanged and repeat step 5.

[0099] Step 8: Move to the next area that may contain the optimal solution and obtain the global optimal solution of the current iteration by comparing all current local optimal solutions.

[0100] Step 9: Determine whether the integrated low-frequency load reduction meta-model corresponding to the sampling point set at this time meets the convergence condition. If so, output the optimal meta-model and the corresponding global optimal solution; otherwise, return to step 3.

[0101] The present invention provides an integrated emergency control method for a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction, and the technical effects are as follows:

[0102] 1) Aiming at the emergency low-frequency load shedding control problem of single-phase and three-phase hybrid microgrids, the present invention proposes an integrated emergency control strategy for single-phase and three-phase hybrid microgrids that couples load-related factors with low-frequency load shedding. The strategy takes into account multiple load-related factors, realizes the integrated emergency control of "virtual three-phase combination, load evaluation, and load shedding action" based on the dynamic metamodel, and reduces the complexity of the emergency control model. In addition, the strategy uses the MSALO algorithm to correct and dynamically update the integrated emergency control metamodel, and generates the optimal emergency control decision by fitting the optimal dynamic metamodel. The feasibility and superiority of the proposed strategy are verified in the scenarios of unplanned islanding and photovoltaic failure in single-phase and three-phase hybrid microgrids. The simulation results show that compared with the virtual combination control strategy based on insertion sort and the comprehensive planning control strategy based on two stages, the strategy proposed in the present invention can shorten the frequency fluctuation range and frequency recovery stabilization time under the premise of ensuring the safety and stability of the system frequency, comprehensively reduce the total economic loss of emergency control and the three-phase imbalance of the system, and improve the response speed of emergency control decisions.

[0103] 2) The present invention proposes an integrated emergency control strategy that couples virtual three-phase combination-load evaluation-load reduction action. This strategy couples the execution process of virtual three-phase combination, load evaluation and load reduction action of single-phase and three-phase hybrid microgrid groups, which can not only reduce the overall economic loss and three-phase imbalance during emergency control, but also shorten the response time of emergency control strategy decision-making.

[0104] 3) The present invention proposes a method for generating an integrated emergency control strategy based on a dynamic metamodel. The method uses the metamodel to fit the integrated emergency control model of a microgrid group to generate an optimal integrated emergency control strategy, thereby realizing the mapping of the relationship between input load correlation factors and output control decisions and the efficient generation of the optimal control strategy.

[0105] 4) The present invention proposes a metamodel correction and dynamic update method based on the fusion of multi-strategy antlion optimization algorithm. The method corrects and dynamically updates the metamodel based on the fusion of multi-strategy antlion optimization algorithm. The proposed algorithm can improve the speed and efficiency of correction and dynamic update of the metamodel, and has more advantages in various performance aspects such as the fitting accuracy of the metamodel obtained after correction and update. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0107] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0108] Figure 1 It is an integrated emergency control strategy framework for single-phase and three-phase hybrid microgrids.

[0109] Figure 2 A flow chart is generated for the integrated emergency control strategy of the single-phase three-phase hybrid microgrid group proposed in the present invention.

[0110] Figure 3 This is the simulation result diagram of load node load reduction in the unplanned island scenario.

[0111] Figure 4 This is the frequency recovery curve in an unplanned islanding scenario.

[0112] Figure 5 To improve the single-phase three-phase hybrid microgrid topology of IEEE-37 nodes. DETAILED DESCRIPTION

[0113] The present invention will be further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0114] The present invention proposes an integrated emergency control strategy for a single-phase and three-phase hybrid microgrid group that couples load-related factors and low-frequency load shedding. First, in order to reduce the complexity of the model and shorten the response time of the load shedding decision, an integrated low-frequency load shedding strategy framework of coupled virtual three-phase combination-load evaluation-load shedding action is constructed based on the metamodel; secondly, the economic loss of load shedding, frequency change offset loss, and the sum of network losses caused by three-phase imbalance during low-frequency load shedding of the microgrid group are taken as the minimum objective function to establish an integrated low-frequency load shedding model for the microgrid group with coupled load-related factors; finally, an integrated low-frequency load shedding metamodel is constructed and the Ant Lion Optimization Algorithm Integrating with Multi-strategy (MSALO) is used to revise and dynamically update it, and the optimal load shedding decision is generated through the obtained optimal dynamic metamodel.

[0115] An integrated low-frequency load reduction strategy framework based on the meta-model is proposed, which is coupled with virtual three-phase combination, load evaluation and load reduction action. The details are as follows:

[0116] The types of loads that can be connected to a single-phase and three-phase hybrid microgrid group are different, and the associated factors such as the power, importance level, and frequency regulation coefficient of each load are also different. How to coordinate the removal of load objects and the corresponding load reduction ratio will have a significant impact on the system stability after low-frequency load reduction and the overall economy of load reduction. In view of the low-frequency load reduction problem of a single-phase and three-phase hybrid microgrid group after an unplanned island event, existing research results obtain the optimal load reduction decision by executing a step-by-step form of virtual three-phase combination-load evaluation-load reduction action. However, the model complexity and response time are high. To this end, the present invention proposes an integrated low-frequency load reduction strategy framework, which uses a dynamic metamodel to couple the virtual three-phase combination of a single-phase and three-phase hybrid microgrid group, the load evaluation problem, and the response execution process of the load reduction action into one, and then directly outputs the optimal load reduction decision by inputting the system parameter information in the microgrid group into the metamodel, and finally achieves the purpose of correcting the three-phase imbalance and making a load reduction decision quickly.

[0117] Figure 1 The framework diagram of the integrated low-frequency load shedding strategy of the single-phase and three-phase hybrid microgrid group of the method proposed in the present invention is shown in Figure 1. The virtual three-phase combination of the single-phase and three-phase hybrid microgrid group, the load evaluation problem, and the response execution process of the load shedding action are coupled together by using the dynamic metamodel, and then the optimal load shedding decision is directly output by inputting the system parameter information in the microgrid group, as shown in Table 1, into the metamodel, and finally the purpose of correcting the three-phase imbalance and making a load shedding decision quickly is achieved.

[0118] The present invention is based on Figure 5 The single-phase and three-phase hybrid microgrid group topology structure based on the improved IEEE-37 node is shown. A single-phase and three-phase hybrid microgrid group model is built in MATLAB R2018b / Simulink software for simulation testing. Among them, 3P1, 3P2, and 3P3 are three-phase sub-microgrids, and A, B, and C are single-phase sub-microgrids. Its source-load-storage configuration information is shown in the following table. BES1 in the three-phase sub-microgrid 1 is set as the main energy storage. When the distributed power sources in the system cannot meet the power required by the system, it is necessary to eliminate the power shortage by cutting off part of the load. The present invention considers that the communication transmission delay time of low-frequency load shedding is 10ms, the delay time between the start-up of the relay and the low-frequency load shedding is 10ms, the rated frequency is 50Hz, and the frequency threshold for triggering the low-frequency load shedding action is 49.5Hz.

[0119] Table 1 Source-load-storage configuration information of each sub-microgrid in the single-phase three-phase hybrid microgrid group

[0120]

[0121] Figure 2A flow chart for generating an integrated emergency control strategy for a single-phase and three-phase hybrid microgrid group of the method proposed in the present invention. First, when determining the low-frequency load reduction model, the load-related factors such as system frequency stability, load economic losses, and system three-phase imbalance are taken into account; secondly, after determining the set of low-frequency load reduction parameter sampling points, the corresponding integrated low-frequency load reduction metamodel is generated, and the metamodel is corrected using the MSALO algorithm to obtain the optimal dynamic metamodel of integrated low-frequency load reduction and the corresponding optimal objective function value of low-frequency load reduction. When the integrated low-frequency load reduction metamodel does not meet the convergence conditions, valid sample points close to the true response value of the optimal solution are screened out based on the current sample points, and added to the current sample library. Finally, the current integrated low-frequency load reduction metamodel is dynamically updated based on the updated sample library until the optimal metamodel and the optimal solution are obtained. At this time, the optimal solution obtained is the optimal decision for low-frequency load reduction of the microgrid group.

[0122] Figure 3 The load shedding simulation result diagram of the load node in the unplanned island scenario of the method proposed in the present invention is shown in FIG. The load shedding strategy proposed in the present invention is set as strategy 1, the virtual combination load shedding strategy based on insertion sort is set as strategy 2, and the load shedding strategy based on two-stage comprehensive planning is set as strategy 3.

[0123] Table 2 Under-frequency load shedding results in unplanned islanding scenarios

[0124]

[0125] Table 2 shows the low-frequency load shedding results of the proposed method in the unplanned island scenario. It can be concluded from Table 2 that the total economic loss of load shedding of strategy 1 is $3421.83, and the three-phase imbalance ε is 5.86%, which is 10.79% and 42.16% less than that of strategy 2 and strategy 3 in terms of total economic loss of load shedding, and 20.91% and 42.67% less than that of strategy 2 and strategy 3, respectively. The reason why the three-phase imbalance of strategy 1 is reduced is that it takes into account the scenario of initial three-phase imbalance in the system, fits the virtual three-phase combination into the meta-model, and implements the optimal load shedding strategy. The three-phase imbalance of the system is reduced compared with the initial value of 12.04%. Although strategy 2 incorporates the three-phase imbalance into the load shedding factor, its applicable scenario is the initial three-phase balance of the system, which is not applicable to the situation where the initial three-phase imbalance exists, and the three-phase imbalance eventually increases. Strategy 3 does not consider the impact of three-phase imbalance on the load shedding effect, resulting in a higher total load shedding amount, total economic loss, and system three-phase imbalance. It can be seen that strategy 1 has better advantages in reducing the total economic loss of load shedding and the three-phase imbalance of the system after load shedding.

[0126] Figure 4This is the frequency recovery curve of the method proposed in the present invention in the unplanned island scenario. The maximum frequency fluctuation of strategy 1 is 50.191Hz, the minimum frequency fluctuation is 49.718Hz, the frequency overshoot is 0.38%, and the frequency recovery time is 0.358s. Compared with strategies 2 and 3, the maximum frequency fluctuation is reduced by 0.051Hz and 0.089Hz, respectively, and the minimum frequency fluctuation is increased by 0.062Hz and 0.097Hz, respectively; the frequency overshoot is reduced by 21.12% and 31.72%, respectively; the frequency recovery time is reduced by 8.67% and 14.96%, respectively. Strategy 2 reduces the load after the virtual three-phase combination is completed, and the decision response time increases. Strategy 3 is more complicated when sampling and calling system data. Strategy 1 adopts an integrated approach to load reduction. By fitting the comprehensive load evaluation, virtual three-phase combination and load reduction action with the same dynamic metamodel, the optimal load reduction strategy is generated, which reduces the load reduction decision response time and reduces the complexity of the model. Therefore, strategy 1 has better frequency recovery effect and shorter load shedding decision response time.

[0127] The present invention discloses an integrated emergency control method for a single-phase hybrid microgrid group coupling load-related factors and low-frequency load shedding. First, in order to reduce the complexity of the model and shorten the response time of the load shedding decision, an integrated low-frequency load shedding strategy framework of coupling virtual three-phase combination-load evaluation-load shedding action is constructed based on a metamodel. Secondly, the economic loss of load shedding, frequency change offset loss, and the sum of network losses caused by three-phase imbalance during low-frequency load shedding of the microgrid group are taken as the minimum objective function to establish an integrated low-frequency load shedding model of the microgrid group coupling load-related factors. Finally, an integrated low-frequency load shedding metamodel is constructed and the Ant Lion Optimization Algorithm Integrating with Multi-strategy (MSALO) is used to correct and dynamically update it, and the optimal load shedding decision is generated by the obtained optimal dynamic metamodel. The strategy proposed in the present invention can comprehensively reduce the total economic loss of load shedding and the three-phase imbalance under the premise of ensuring the safety and stability of the system frequency, and improve the decision response speed.

Claims

1. An integrated emergency control method for single-phase and three-phase hybrid microgrids with coupled load correlation factors and low-frequency load reduction, characterized in that The following steps are involved: Step 1: Taking the economic loss of load shedding during low-frequency load shedding of microgrid group, the frequency change offset loss and the sum of network losses caused by three-phase imbalance as the minimum objective function, an integrated low-frequency load shedding model of microgrid group with coupled load correlation factors is established; Step 2: Construct an integrated low-frequency load reduction meta-model; Step 3: Use MSALO to modify and dynamically update the integrated under-frequency load reduction meta-model to obtain the optimal dynamic meta-model and the corresponding optimal objective function value of under-frequency load reduction; Step 4: Generate the optimal integrated emergency control strategy through the optimal dynamic meta-model.

2. According to claim 1, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction, characterized in that: In the step 1, the frequency regulation effect of the load describes the response characteristics of the active load to the frequency change; when the frequency regulation effect of the load is taken into account, a polynomial is used to represent the load mathematical model, as shown below: Where P L is the active load corresponding to the system frequency f; f N is the rated frequency; P LN is the rated active power of the load; a n is the proportional coefficient of each type of load in the rated load, In the steady-state load model, the above formula is simplified and expressed in per-unit value, and the various load models are derived by frequency differentiation as follows: In the formula, K L P is the frequency regulation effect coefficient of the load, which indicates the load's ability to regulate the frequency; L* is the per unit value of system active power; f * K is the per unit value of system frequency; L A larger value means that the load has a greater impact on the frequency regulation while absorbing the same power; when the system frequency decreases, K is removed first. L For smaller loads, keep K L Larger load value.

3. According to claim 2, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction is characterized by: There are both single-phase load and three-phase load in the single-phase three-phase hybrid microgrid, so it is easy to cause the three-phase imbalance problem of the system due to the unbalanced characteristics of the single-phase load; the three-phase imbalance represented by the load power is calculated as follows: Where, ε is the three-phase unbalance; I1 is the positive sequence component of the three-phase current; I2 is the negative sequence component of the three-phase current; S L , S L2 are the positive sequence apparent power and the negative sequence apparent power respectively; P L2 , Q L2 are negative sequence active power and reactive power respectively; P A , P B , P C and Q A , Q B , Q C They are the three-phase active power and reactive power respectively.

4. According to claim 1, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction is characterized by: In step 1, the economic loss of load shedding during low-frequency load shedding of the microgrid group, the frequency change offset loss, and the network loss caused by three-phase imbalance are taken as the minimum objective function, and the integrated low-frequency load shedding model of the microgrid group is established as follows: In the formula, C loss is the total economic loss of load shedding in the microgrid group; n is the total amount of load shedding; c i is the cost coefficient of the i-th load cut; α i is the load reduction ratio at node i; P i is the load power at node i; β i is the frequency offset penalty coefficient at node i; t0 is the starting integration time of the frequency curve; t 1i is the end time of frequency curve integration; △f(t) is the frequency deviation curve at time t; θ is the network loss penalty coefficient when the system has three-phase imbalance after load shedding action; ε is the three-phase imbalance degree of the system; The relevant constraints in the whole process of low-frequency load reduction include power balance constraints, load node flow constraints, three-phase imbalance constraints and bus frequency constraints, as follows: ε≤ε0 f min ≤f≤f max In the above formula, m1, m2 and m3 are the total number of buses for energy storage, photovoltaic and load respectively; P BS,i is the active power output by the energy storage device; P PV,i is the active power output of photovoltaic power generation; P LD,i is the load active power; P i , Q i are the active power and reactive power of node i respectively; U i , U j are the voltage amplitudes of nodes i and j respectively; G ij , B ij are the conductance and susceptance between nodes i and j respectively; θ ij is the voltage phase difference between nodes i and j; ε0 is the maximum allowable three-phase unbalance; f is the system bus frequency, f min 、f max are the minimum and maximum values ​​of the system bus frequency respectively; P ij is the line flow value, are the minimum and maximum values ​​of the line power flow respectively; U i is the voltage amplitude at node i, are the minimum and maximum voltage amplitudes at node i, respectively.

5. According to claim 1, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction, characterized in that: In the step 2, the RBF metamodel is used to fit the integrated model consisting of load evaluation, virtual three-phase combination and load reduction action in the emergency control process of the microgrid group, and the mapping relationship between the input load correlation factors and the output control decision is simply displayed; C loss =F(X1,X2,…,X n ) Where F(i) represents the implicit relationship between load correlation factors and emergency control decisions; X n It is the nth type of load association factor.

6. According to claim 1, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction, characterized in that: In step 2, the parameters of the microgrid group system are initialized, including all energy storage, photovoltaic power, island switching time, low-frequency load reduction delay time, and load correlation factors in the single-phase sub-microgrid and the three-phase sub-microgrid; the Sobol sequence is selected to generate the emergency control parameter set. The specific steps are as follows: (1): Construct the primitive polynomial H of the emergency control parameter set: The primitive polynomial H corresponding to the emergency control parameter set cannot be factorized and can divide x p +1, its minimum power is p=2 q +1, specifically: H(z)=z q +d1z q-1 +…+d q-1 z+d q Where d1, d2, …, d q ={0,1}; (2) Construct the direction number v of the emergency control parameter set i , : In the formula, i is the number of directions; the sequence g i The equivalent recursive formula and the relationship between the decimal integer n and the base b = 2 are shown below: In the formula, a j ={0,1}; k is the smallest integer greater than or equal to log2n; when the number of directions in the parameter set i>q, v i satisfy: (3) Generate the Sobol sequence of the emergency control parameter set: The Sobol sequence of the emergency control parameter set is as follows: In the formula, Represents the XOR operation in binary; m i are the control variables for emergency control; a1, a2, …, a k ={0,1}.

7. According to claim 1, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction is characterized by: In step 2, the RBF dynamic meta-model is selected to fit the integrated emergency control model of the microgrid group. Its basic expressions and matrix forms are: Aλ=f Where f(x) is the basic expression of the RBF metamodel; x is the current sampling point; x i is the initial sample point; ||xx i || is the Euclidean norm, which represents the Euclidean distance between the current sampling point and the sample point; λ i is the weight coefficient of the i-th basis function; A is the matrix form of the basis function of the RBF dynamic metamodel; λ represents the eigenvector of the basis function weight coefficient; m is the number of sampling points; f is the matrix form of the basic expression of the RBF metamodel; φ is the basis function, and the expression is: Where c is a positive real number; σ c is the expansion factor.

8. According to claim 1, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group for coupling load correlation factors and low-frequency load reduction is characterized by: In the step 3, the integrated low-frequency load reduction metamodel is modified by using the MSALO algorithm to obtain the optimal dynamic metamodel and the corresponding optimal objective function value of the low-frequency load reduction; The MSALO algorithm uses the mutation mechanism of Levy flight and the golden sine algorithm to update the original algorithm based on the Ant Lion Optimization Algorithm (ALO). At the same time, it introduces nonlinear convergence factors and dynamic proportional coefficients. The position update formulas based on the Levy flight mechanism and the golden sine algorithm are: in, x1=-π+2π(1-τ) x2=-π+2πτ In the formula, are the positions of the i-th individual in the t-th and t+1-th iterations respectively; α is the nonlinear convergence factor; * is the dot product; L(i) is a random search path that conforms to the Levy distribution and satisfies the constraint in Γ is the gamma function, u and v obey the standard normal distribution; R1∈[0,2π] and R2∈[0,π] are random numbers, which respectively determine the moving distance and position update direction of the next iteration; T is the maximum number of iterations; x1 and x2 are the coefficients obtained by the golden section; τ is the golden section number.

9. According to claim 8, a method for integrated emergency control of a single-phase and three-phase hybrid microgrid group coupling load correlation factors and low-frequency load reduction is characterized by: The MSALO algorithm specifically includes the following steps: Step 1: Set the basic parameters of the ALO algorithm: maximum number of iterations T, proportional coefficient I, number of ants and ant lions numB, numY; Step 2: Randomly initialize the positions of ants and antlions, and calculate the optimal values ​​of their fitness functions; In the formula, is the position of the i-th ant after the t-th iteration; a i 、b i are the minimum and maximum values ​​of the random walk of the ith variable, respectively; are the minimum and maximum values ​​of the i-th variable after the t-th iteration respectively; is the position of the i-th antlion after the t-th iteration; f is the fitness function; represents the position of the j-th antlion after the t-th iteration; represents the fitness function value of the position of the i-th ant after the t-th iteration; represents the fitness function value of the position of the i-th antlion after the t-th iteration; Step 3: Use the roulette method to optimize the selection of antlions and update them using the above formula and ant positions, wherein the ant position model in the original ALO algorithm is adjusted using a dynamic proportional coefficient as shown below; Ant(t)=[0,cums(2r(t1)-1),cums(2r(t2)-1),…,cums(2r(t T )-1)] Where Ant(t) is the random walk model of the ant position; cums is the cumulative sum of the ant's random walk steps; t is the current iteration number; r(t) is the random function; I is the proportional coefficient; v is a fixed value; is the position of the ant after a random walk around the antlion selected by the roulette wheel in the tth iteration; is the position of the ants after they wander around the elite antlion in the tth iteration; Step 4: Compare the optimal position of the current elite ant lion. If the conditions are met, update the optimal values ​​of the ant and ant lion, otherwise increase the value of the proportional coefficient I by 1; Step 5: If the optimal values ​​of two adjacent iterations are almost equal, it is judged that the algorithm is trapped in a local optimum. The position update formula of the Levy flight mechanism is used to replace the position update formula of the i-th ant after the t-th iteration in the original algorithm to further update the position and optimal value of the ant colony. Step 6: Introduce the golden section coefficient in the trap reconstruction stage, and update the elite ant lion position and optimal value according to the position update formula of the golden sine algorithm; Step7: Repeat Step1 to Step6 until the maximum number of iterations is reached and output the optimal value.

10. An integrated emergency control method for a single-phase and three-phase hybrid microgrid group coupling load correlation factors and low-frequency load reduction according to claim 9, characterized in that: In the step 4, generating the optimal integrated emergency control strategy includes the following steps: Step 1: Initialize system parameters, including system parameters of single-phase and three-phase hybrid microgrids, variable constraint range, maximum number of iterations, and convergence factor; Step 2: Based on the initial system parameters and constraints, use the Sobol sequence to generate m groups of initial low-frequency load shedding parameter sampling points including n variables, calculate the actual response values ​​of the load shedding objective function corresponding to the sampling points, and use the sampling points and their actual response values ​​to generate an initial sampling point database; Step 3: Extract the initial low-frequency load reduction parameter set and the corresponding actual response value, and fit and generate the meta-model corresponding to the current integrated low-frequency load reduction model; Step 4: Use the MSALO algorithm to perform global optimization on the meta-model obtained in step 3 to obtain the region that may contain the optimal solution and the corresponding local optimal response value; Step 5: In the region that may contain the optimal solution in step 4, a new set of low-frequency load shedding parameter sampling points is generated again based on sampling, and the meta-model is used to continue estimating the load shedding objective function response value of the new sampling point set; Step 6: Select the best among the estimated values ​​of the integrated low-frequency load reduction metamodel obtained in step 5, bring the selected excellent point set into the real original model to calculate the actual response value of its load reduction objective function, and compare it with the current local optimal response values; Step 7: If the calculated actual response value is better than the current optimal solution, the local optimal solution is updated, and the excellent point set is added to the initial low-frequency load reduction parameter sampling point set, the integrated low-frequency load reduction meta-model is reconstructed and the sampling point database is updated; otherwise, the current sampling point set is kept unchanged and step 5 is repeated; Step 8: Turn to the next area that may contain the optimal solution, and obtain the global optimal solution of the current iteration by comparing all current local optimal solutions; Step 9: Determine whether the integrated low-frequency load reduction meta-model corresponding to the sampling point set at this time meets the convergence condition. If so, output the optimal meta-model and the corresponding global optimal solution; otherwise, return to step 3.

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