An alternating current-direct current hybrid microgrid group control method, device, equipment and medium

Abstract

Embodiments of the present application disclose an AC-DC hybrid micro-grid group control method, device, equipment and medium. The method comprises: constructing a power generation cost function and a load constraint condition through the output power of a distributed power source, determining a micro-increase rate corresponding to the distributed power source based on the power generation cost function and the load constraint condition; constructing a frequency and active power droop control expression through the frequency and active power output by the distributed power source; transforming the frequency and active power droop control expression to obtain a frequency and active power control expression; constructing a cost scheduling function and an overload constraint condition through the output power of a sub-grid, determining a group layer cost optimization control expression of distributed secondary control based on the frequency and active power control expression, the cost scheduling function and the overload constraint condition; solving the group layer cost optimization control expression by adjusting the micro-increase rate, and effectively controlling the AC-DC hybrid micro-grid group by using a target solution, so as to ensure the group control effect while effectively reducing the control cost.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of energy, in particular to a control method, device, equipment and medium for AC / DC hybrid micro-grid group. BACKGROUND

[0002] With the development of technology and the promotion of clean energy, the micro-grid technology is applied to the power industry.

[0003] At present, the existing micro-grid (MG) control strategy includes: a distributed voltage droop control strategy, an interlinking converter (IC) power distribution optimization control strategy, a flexible control strategy for power flow of the interlinking converter of the hybrid micro-grid, and an interlinking converter control strategy of adaptive droop control.

[0004] However, the distributed voltage droop control strategy is usually used to control a single DC micro-grid, but cannot be applied to the AC / DC hybrid micro-grid group which has a more complex topology and more control targets. The interlinking converter power distribution optimization control strategy is usually used for power balance in the AC micro-grid (AC MG) and the DC micro-grid (DC MG) and autonomous distribution control strategy between the ICs, but cannot take into account the economic benefits. The flexible control strategy for power flow of the interlinking converter of the hybrid micro-grid is usually used to adjust the inertia reduction in the hybrid MG, but this strategy only analyzes the control strategy of the interlinking converter and has certain limitations. The interlinking converter control strategy of adaptive droop control usually introduces the multiple of the frequency of the AC micro-grid and the differential of the voltage difference of the DC micro-grid, thereby improving the performance of the micro-grid, but cannot take into account the economic benefits. SUMMARY

[0005] The embodiment of the present application provides a control method, device, equipment and medium for AC / DC hybrid micro-grid group, so as to accurately and conveniently determine the target solution and realize effective control of the AC / DC hybrid micro-grid group by using the target solution, thereby guaranteeing the control effect of the AC / DC hybrid micro-grid group and effectively reducing the control cost.

[0006] In a first aspect, the embodiment of the present application provides a control method for AC / DC hybrid micro-grid group, comprising:

[0007] A power generation cost function corresponding to the distributed power supply and a load constraint condition are constructed by the output power of the distributed power supply in the AC / DC micro-grid group, and a micro-increment rate corresponding to the distributed power supply is determined based on the power generation cost function and the load constraint condition;

[0008] A frequency active droop control expression corresponding to the distributed power supply is constructed by the frequency and the active power output by the distributed power supply;

[0009] transforming the frequency active droop control expression based on the consistency theory, to obtain a frequency active control expression based on the consistency algorithm considering the generation cost;

[0010] constructing a cost scheduling function and an overload constraint condition corresponding to the sub-network by the output power of the sub-network in the AC / DC micro-grid group, and determining a distributed secondary control group layer cost optimization control expression based on the frequency active control expression, the cost scheduling function and the overload constraint condition;

[0011] solving the distributed secondary control group layer cost optimization control expression by adjusting the micro-increment rate, to obtain a target solution, and controlling the AC / DC hybrid micro-grid group based on the target solution.

[0012] Optionally, the method further comprises: determining a minimum generation cost function in the micro-grid group based on the generation cost function and the load constraint condition; and solving the minimum generation cost function based on the Lagrange multiplier method to obtain a micro-increment rate corresponding to the distributed power supply.

[0013] Optionally, the method further comprises: the reference frequency factor in the frequency active droop control expression is adjustable; the reference frequency factor comprises: a rated frequency, a frequency compensation factor and an active compensation factor; the frequency compensation factor is used to eliminate the frequency deviation generated by the droop control; and the active compensation factor is used to eliminate the active deviation generated by the droop control.

[0014] Optionally, the method further comprises: determining a dynamic solving expression of the frequency compensation factor based on the consistency theory; determining a dynamic solving expression of the active compensation factor based on the consistency theory; and transforming the frequency active droop control expression based on the dynamic solving expression of the frequency compensation factor and the dynamic solving expression of the active compensation factor, to obtain a frequency active control expression based on the consistency algorithm considering the generation cost.

[0015] Optionally, the method further comprises: iteratively solving the distributed secondary control group layer cost optimization control expression based on the improved artificial bee colony algorithm, to obtain a target solution of the sub-network corresponding to the equal micro-increment rate.

[0016] Optionally, the method further comprises: the improved artificial bee colony algorithm comprises: a traditional artificial bee colony algorithm, a Gaussian mutation and a chaotic disturbance.

[0017] In a second aspect, the embodiments of the present application further provide an AC / DC hybrid micro-grid group control device, which comprises:

[0018] The micro-increment rate determination module is configured to construct a power generation cost function corresponding to the distributed power supply and a load constraint condition by output power of the distributed power supply in the AC / DC micro-grid group, and determine a micro-increment rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition.

[0019] The first expression determination module is configured to construct a frequency and active power droop control expression corresponding to the distributed power supply by frequency and active power output by the distributed power supply.

[0020] The second expression determination module is configured to transform the frequency and active power droop control expression based on a consensus consistency theory to obtain a frequency and active power control expression based on a consensus algorithm considering power generation cost.

[0021] The third expression determination module is configured to construct a cost scheduling function corresponding to the sub-grid and an overload constraint condition by output power of the sub-grid in the AC / DC micro-grid group, and determine a distributed secondary control grid group layer cost optimization control expression based on the frequency and active power control expression, the cost scheduling function and the overload constraint condition.

[0022] The AC / DC hybrid micro-grid group control module is configured to solve the distributed secondary control grid group layer cost optimization control expression by adjusting the micro-increment rate to obtain a target solution, and control the AC / DC hybrid micro-grid group based on the target solution.

[0023] In a third aspect, an electronic device is provided, and the electronic device includes:

[0024] One or more processors;

[0025] A memory configured to store one or more programs;

[0026] When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the AC / DC hybrid micro-grid group control method provided in any of the embodiments of the present application.

[0027] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the AC / DC hybrid micro-grid group control method provided in any of the embodiments of the present application.

[0028] In a fifth aspect, a computer program product is provided, and the computer program product includes a computer program, and the computer program is executed by a processor to implement the AC / DC hybrid micro-grid group control method provided in any of the embodiments of the present application.

[0029] The technical scheme of the embodiment of the present application constructs the power generation cost function corresponding to the distributed power supply and the load constraint condition through the output power of the distributed power supply in the AC / DC micro-grid group, and determines the micro-increment rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition; constructs the frequency and active power droop control expression corresponding to the distributed power supply through the frequency and active power output by the distributed power supply; transforms the frequency and active power droop control expression based on the consistency theory to obtain the frequency and active power control expression based on the consistency algorithm considering the power generation cost; constructs the cost scheduling function corresponding to the sub-grid and the overload constraint condition through the output power of the sub-grid in the AC / DC micro-grid group, and determines the distributed secondary control grid group layer cost optimization control expression based on the frequency and active power control expression, the cost scheduling function and the overload constraint condition; solves the distributed secondary control grid group layer cost optimization control expression by adjusting the micro-increment rate, so as to accurately and conveniently determine the target solution and realize effective control of the AC / DC hybrid micro-grid group by using the target solution, thereby ensuring the control effect of the AC / DC hybrid micro-grid group while effectively reducing the control cost.

[0030] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0032] Figure 1 is a flow chart of an AC / DC hybrid micro-grid group control method provided by the first embodiment of the present application;

[0033] Figure 2 is a flow chart of an improved artificial bee colony algorithm related to the first embodiment of the present application;

[0034] Figure 3 is a structural schematic diagram of an AC / DC hybrid micro-grid group control device provided by the second embodiment of the present application;

[0035] Figure 4 is a structural schematic diagram of an electronic device for implementing the AC / DC hybrid micro-grid group control method of the present application. DETAILED DESCRIPTION

[0036] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application, so that those skilled in the art can better understand the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of the present application.

[0037] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0038] Embodiment one

[0039] Figure 1 A flowchart of a control method for an AC-DC hybrid micro-grid group is provided for the first embodiment of the present application. The present embodiment can be applicable to the effective control of an AC-DC hybrid micro-grid group. The method can be executed by an AC-DC hybrid micro-grid group control device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in Figure 1 , the method comprises:

[0040] S110, constructing a power generation cost function corresponding to the distributed power supply and a load constraint condition by the output power of the distributed power supply in the AC-DC micro-grid group, and determining a micro-increment rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition.

[0041] The AC-DC micro-grid group can refer to a micro-grid group composed of an AC sub-network and a DC sub-network. The distributed power supply can refer to an independent power supply. The distributed power supply can be a power supply of 35kV and below voltage level not directly connected to a centralized power transmission system. The distributed power supply can include power generation equipment and energy storage devices. The power generation cost function can refer to a function that changes the power generation cost driven by the output power of the distributed power supply. The load constraint condition can refer to a constraint condition for the output power of the distributed power supply. The micro-increment rate can refer to the ratio of the input consumption micro-increment to the output power micro-increment.

[0042] Specifically, different capacity and different types of distributed power in the micro-grid exist different power generation cost functions. The power generation cost function can be represented by a quadratic function related to active power. In a micro-grid system, it is assumed that the number of distributed power is n, and the power generation cost function C i (P Gi ) of each distributed power can be represented as:

[0043] where P Gi is the output power of the i-th distributed power, α i , β i , γ i are the operating cost coefficients of the i-th distributed power. Assuming that the total load demand of the micro-grid group is P D , the load constraint condition is obtained as The micro-increment rate corresponding to the distributed power is determined based on the power generation cost function and the load constraint condition.

[0044] On the basis of the above technical solutions, the step of determining the micro-increment rate corresponding to the distributed power based on the power generation cost function and the load constraint condition can include: determining the minimum power generation cost function in the micro-grid group based on the power generation cost function and the load constraint condition; and solving the minimum power generation cost function based on the Lagrange multiplier method to obtain the micro-increment rate corresponding to the distributed power.

[0045] where the minimum power generation cost function can refer to the minimum power generation cost function that meets the power generation constraint condition.

[0046] Specifically, to meet the load constraint condition, the minimum power generation cost function of the micro-grid system is obtained as Solving the minimum value of the total cost of the micro-grid system can be converted into solving the extreme value of a multivariate function under the power generation constraint condition. The Lagrange multiplier method can be used to solve the extreme value of the function, that is,

[0047] where λ is the Lagrange multiplier, and L is the Lagrange function. If the function L has an extreme value, the following condition needs to be met: where λ i is the micro-increment rate of the i-th distributed power.

[0048] S120, constructing a frequency active droop control expression corresponding to the distributed power through the frequency and active power output by the distributed power.

[0049] Specifically, the phase difference is adjusted by the active power p output by the distributed power supply to avoid the formation of active circulating current. Therefore, the droop control model of P-ω can be defined as: ω = ω0-mp. Among them, ω is the frequency output by the distributed power supply, ω0 is the rated frequency of the distributed power supply, m is the droop coefficient of the active power of the distributed power supply, and p is the active power output by the distributed power supply. The frequency active power droop control expression corresponding to each distributed power supply is obtained: ω i =ω ni -m i p i Among them, ω i is the frequency output by the i-th inverter, m i is the active power droop coefficient of the i-th inverter, p i is the active power of the i-th inverter, ω ni is a variable reference frequency factor.

[0050] It should be noted that the voltage amplitude difference is adjusted by the reactive power q output by the distributed generation to avoid the formation of reactive circulating current. Therefore, the droop control model of QE can be defined as: E = v0 - nq. Where E is the voltage output by the distributed generation, v0 is the rated voltage of the distributed generation, n is the droop coefficient of the distributed generation reactive power, and q is the reactive power output by the distributed generation.

[0051] Based on the above technical solution, the reference frequency factor in the frequency active power droop control expression is adjustable; the reference frequency factor includes: rated frequency, frequency compensation factor and active power compensation factor; the frequency compensation factor is used to eliminate the frequency deviation caused by the droop control; the active power compensation factor is used to eliminate the active power deviation caused by the droop control.

[0052] Specifically, the expression of the reference frequency factor is: ω ni =ω ref +λ ωi +λ pi Among them, ω ref is the rated frequency of the microgrid system, λ ωi is the frequency compensation factor, λ pi is the active power compensation factor.

[0053] S130. Based on the pinning consistency theory, the frequency active power droop control expression is transformed to obtain a frequency active power control expression based on a consistency algorithm that takes power generation cost into consideration.

[0054] Specifically, based on the pinning consistency theory, the frequency compensation factor and the active power compensation factor in the frequency droop control expression are transformed to obtain the frequency active power control expression based on the consistency algorithm and considering the power generation cost after parameter transformation.

[0055] On the basis of the above technical solutions, the "transformation of the frequency active droop control expression based on the consensus theory to obtain a frequency active control expression based on the consensus algorithm considering the generation cost" can include: determining the dynamic solving expression of the frequency compensation factor based on the consensus theory; determining the dynamic solving expression of the active compensation factor based on the consensus theory; transforming the frequency active droop control expression based on the dynamic solving expression of the frequency compensation factor and the dynamic solving expression of the active compensation factor to obtain the frequency active control expression based on the consensus algorithm considering the generation cost.

[0056] Specifically, λ ωi is mainly used to eliminate the frequency deviation caused by droop control, and its dynamic solving equation can be obtained according to the consensus theory as follows: wherein c wi is the coupling weight of the consensus algorithm, a ij is the communication weight coefficient, g i is the communication weight coefficient between the follower and the leader, and ω ref is the reference frequency of the micro-grid system. By taking the reference frequency of the micro-grid system as a virtual leader, the frequency ω i of all inverters can be tracked through a sparse communication network, and finally the frequency is restored.

[0057] However, in the frequency restoration process, the compensation value of each distributed power supply is generally different each time, which will cause differences in the transient frequency of each node, which will destroy the active power distribution achieved based on the droop coefficient in the primary control. In order to ensure that the active power distribution is not affected, that is, to ensure that the equation m1p1=m2p2=…=m i p i still holds, a power compensation factor λ pi is introduced, and its dynamic solving equation can be obtained according to the consensus theory as follows: wherein c pi is the coupling weight of the consensus algorithm. The m i p i difference between different inverters is calculated by the consensus algorithm to compensate for the active deviation, so as to restore the active power distribution in the droop control.

[0058] Let the active power be combined with the droop formula, and the droop control equation containing the generation cost of the distributed power supply can be obtained as follows: ω i =ω ni -k i η i (P Gi ). Wherein k i is a positive proportional coefficient, and ηi (P Gi ) is the micro-increment rate of the distributed power. The droop control equation of the above power generation cost is substituted into the dynamic solving expression of the active compensation factor to obtain: Based on the dynamic solving expression of the frequency compensation factor and the dynamic solving expression of the active compensation factor, a frequency and active power control expression based on the consensus algorithm considering the power generation cost is finally obtained: When the micro-grid system is stable, the micro-increment rates of different distributed powers can be consistent under the action of the consensus algorithm, that is, the active power of the system can be reasonably distributed according to the power generation cost.

[0059] It should be noted that on the basis of ensuring power balance and safe power transmission, the production efficiency of electric energy is increased and the power transmission loss in the distribution process is reduced as much as possible, so that the operation cost can be greatly reduced. In an electric power system, if it is necessary to ensure that the operation is in the most economical condition, the most basic criterion to be achieved between various distributed powers is the "equal micro-increment rate criterion".

[0060] S140, constructing a cost scheduling function and an overload constraint condition corresponding to the sub-network through the output power of the sub-network in the AC / DC micro-grid group, and determining a group layer cost optimization control expression of the distributed secondary control based on the frequency and active power control expression, the cost scheduling function and the overload constraint condition.

[0061] The sub-network contains distributed power. The overload constraint condition can be used to determine that when the distributed power and the sub-network are not overloaded, the sub-network is independently operated and the economic optimal control method is adopted; when the distributed power is overloaded and the sub-network is not overloaded, the sub-network is still independently operated and the control method is switched to the droop control based on the equal distribution of the capacity of the distributed power; when the distributed power and the sub-network are overloaded, all the sub-networks are coordinately operated in parallel and the droop control based on the equal distribution of the capacity of the distributed power is adopted, so as to realize the power sharing between the sub-networks.

[0062] Specifically, the cost scheduling function corresponding to the sub-network constructed through the output power of the sub-network in the AC / DC micro-grid group is as follows:

[0063]

[0064] Wherein, minC cost is the minimum cost model function, C i (P red.i ) is the cost function of the i-th sub-network at the rated power, n MG is the number of sub-networks of the micro-grid group, a i , b i , c i are the power generation cost coefficients of the i-th sub-network, and λ iThe incremental rate of the ith sub-network is obtained by derivation of the generation cost. The overload constraint condition is as follows:

[0065]

[0066] where λ opt is the global optimal incremental rate. In order to make the virtual synchronous generator (VSG) control and economic optimization compatible, and at the same time restore the frequency deviation caused by the VSG control, a control strategy of distributed economic optimization and frequency restoration at the grid group level is proposed based on distributed consensus in the embodiment.

[0067] Considering that the economic optimization needs to be achieved at the grid group level, according to the frequency active control expression, the cost scheduling function and the overload constraint condition, the steady-state incremental rates of each VSG are equal. Therefore, the control expression of the distributed quadratic control at the grid group level is as follows:

[0068]

[0069] where k MG is the gain coefficient of the distributed control at the grid group level, ω ij is the weight coefficient of the distributed communication, and has In order to explore the steady-state performance of the distributed control strategy corresponding to the control expression of the distributed quadratic control at the grid group level, the distributed control term of the above formula can be rewritten in the form of a vector, that is: where L is the Laplace matrix of the undirected communication link, and W is the weight coefficient matrix. At the steady state, Multiplying the above formula by the all-1 vector on the left side, we have: T k MG (P-P ref )=1 T WLλ=0.

[0070] According to the VSG control term of the economic optimization control at the grid group level, the dynamic characteristic of inertia simulation is usually faster than that of the distributed control. It is generally considered that the system frequency ω is in quasi-steady state under the dynamic adjustment of the distributed control, and therefore the above formula can be rewritten as: T k MG (ω-ω0)=0. At the same time, when the system is in the steady state, the output frequencies of all VSGs are the same, and have Therefore, the above proposed algorithm can restore the frequency to the rated value, and at this time, P=P ref Therefore, we have: At the steady state of the microgrid system, according to the convergence of the distributed consensus control, we have λ1=λ2=…=λ n , which proves that the above control algorithm can achieve the system frequency correction of multiple VSGs and the economic optimization scheduling at the grid group level at the same time.

[0071] S150, solve the grid group layer cost optimization control expression of the distributed secondary control by adjusting the micro-increment rate to obtain a target solution, and control the AC / DC hybrid micro-grid group based on the target solution.

[0072] The target solution can be a global optimal solution.

[0073] Specifically, since the artificial bee colony algorithm is suitable for solving multivariable optimization problems and can realize global search, the algorithm is used to solve the grid group layer cost optimization control expression of the distributed secondary control to obtain a target solution, and the AC / DC hybrid micro-grid group is controlled based on the target solution.

[0074] It should be noted that the target solution in the embodiment of the application corresponds to a distributed hierarchical control strategy, and the distributed hierarchical control strategy is used to improve the stability and operation economy of the AC / DC hybrid micro-grid group. First, for the primary control, it can be understood as S110-S130, and the droop control method is used to realize real-time power distribution of the AC distributed power supply, the DC distributed power supply and the interconnected converter. By distributing the unbalanced disturbance power among the distributed power supplies in real time and using the interconnected converter to realize power mutual aid of the AC / DC hybrid micro-grid, the stability of the system voltage and frequency can be improved. Secondly, for the secondary control, it can be understood as S140-S150, and the corresponding distributed control strategy is designed to realize economic scheduling of the AC / DC micro-grid group subsystem (sub-grid) and between systems in a small time scale while realizing voltage and frequency recovery control of the AC / DC hybrid micro-grid group, thereby improving the system operation economy. In addition, the stability of the designed distributed control strategy can be analyzed by using the Lyapunov method.

[0075] The technical scheme of the embodiment of the application constructs a power generation cost function corresponding to the distributed power supply and a load constraint condition through the output power of the distributed power supply in the AC / DC micro-grid group, and determines the micro-increment rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition. A frequency and active power droop control expression corresponding to the distributed power supply is constructed through the frequency and active power output by the distributed power supply. A frequency and active power control expression based on the consistency algorithm considering the power generation cost is obtained by transforming the frequency and active power droop control expression based on the consistency theory. A cost scheduling function corresponding to the sub-grid and an overload constraint condition are constructed through the output power of the sub-grid in the AC / DC micro-grid group, and a grid group layer cost optimization control expression of the distributed secondary control is determined based on the frequency and active power control expression, the cost scheduling function and the overload constraint condition. The grid group layer cost optimization control expression of the distributed secondary control is solved by adjusting the micro-increment rate to accurately and conveniently determine the target solution and realize effective control of the AC / DC hybrid micro-grid group by using the target solution, thereby ensuring the control effect of the AC / DC hybrid micro-grid group while effectively reducing the control cost.

[0076] On the basis of the above technical solutions, the "solving the network group layer cost optimization control expression of the distributed quadratic control by adjusting the micro-increment rate to obtain a target solution" can include: based on the improved artificial bee colony algorithm, the network group layer cost optimization control expression of the distributed quadratic control is iteratively solved to obtain a target solution corresponding to the micro-increment rate of the subnetwork.

[0077] The improved artificial bee colony algorithm includes: a traditional artificial bee colony algorithm, a Gaussian mutation, and a chaotic disturbance.

[0078] Specifically, in order to avoid the problems of weak local search ability and early maturity, the Gaussian mutation and the chaotic disturbance are introduced based on the traditional artificial bee colony algorithm to improve the optimization performance of the algorithm. In actual engineering application and real life, many problems are composed of multiple objectives that conflict and contradict each other. Usually, more than one optimization objective is needed, and the problem of simultaneously solving multiple objectives in a given region is called a multi-objective optimization problem. The mathematical expression is as follows:

[0079] Min F = [f1(X), f2(X),..., f n (X)] T ;

[0080] s.t.g i (X) < 0 i = 1, 2,..., p

[0081] h i (X) = 0 i = 1, 2,..., q

[0082] Where X is the control variable, f i (X) is the i-th objective function, n is the number of objective functions, h j (X) and g i (X) are the equality and inequality constraints, respectively.

[0083] Usually, the solutions X and Y of the multi-objective problem have the following relationship: one dominates the other, or they are not dominated by each other. If the objective function values corresponding to the solutions X and Y satisfy the following formula, then X is called a non-dominated solution, and the set of non-dominated solutions is called a Pareto optimal set.

[0084] During the optimization process, the Pareto optimal solution found needs to be stored. Due to the limited capacity of the storage library, the number of Pareto optimal solutions should not exceed the limit. In addition, due to the influence of the objective function, the optimal value of the objective function obtained by each iteration may have a large difference, affecting the selection of the optimal solution. Therefore, fuzzy clustering is needed to control the size of the storage library while comparing the optimal values of all objective functions under the same standard. The expression of the membership function is as follows:

[0085]

[0086] where, and are the maximum and minimum of the ith objective function. The membership function is normalized as follows:

[0087]

[0088] where n is the number of objective functions, m is the number of Pareto solutions, ω i is the weight factor. In the process of N μ (j) After all the Pareto solutions are evaluated, the repository is sorted in descending order, and the solution corresponding to the maximum N μ (j) is selected as the optimal solution.

[0089] The artificial bee colony algorithm simulates the foraging behavior of bees to optimize, which is described as follows: First, the concept of honey source is introduced, which represents each solution of the objective function, and the fitness function is used to measure the size of the honey source nectar, that is, the quality of the solution. Then, according to the role of each bee in the foraging behavior, the bee colony is divided into foraging bees, observation bees and scouts. The main role of foraging bees is to search for honey sources and determine the amount of nectar in specific honey sources, and pass this information to other bees to achieve data sharing. Observation bees collect foraging bee information and make choices about honey sources. Scouts search for new honey sources by receiving information from the other two types of bees and performing local searches at the location of the optimal honey source. The roles of the three types of bees can be switched according to actual conditions. When observation bees and scouts find the location of a honey source, they are converted to foraging bees; if foraging bees and observation bees abandon a honey source because its nectar is too small, they are converted to scouts. In the optimization process, the probability of a honey source being selected by an observation bee is represented by the following formula:

[0090]

[0091] where fit i is the fitness function value corresponding to the ith solution, representing the quality of the honey source; N is the number of honey sources, also representing the number of foraging bees. In the artificial bee colony algorithm, the optimization problem is solved by the following formula to obtain a new solution:

[0092] where i, k ∈ {1, 2, …, N} and j ∈ {1, 2, …, D} are randomly selected, but with the constraint that k ≠ i, D is the number of optimization problem parameters, and rand() has a value range of [-1, 1]. After each new solution is generated, it is compared with Compare. If the new solution is better than the current solution or the same, replace the current solution with the new solution; otherwise, the current solution remains unchanged.

[0093] As a new modern intelligent optimization algorithm, artificial bee colony algorithm is widely used in solving multivariable optimization problems, which can greatly improve the probability of finding the optimal solution. However, the algorithm also has some shortcomings such as easy to be premature, local convergence, and the like. In addition, when approaching the global optimal position, the iteration speed slows down, and even the algorithm search stagnates. Therefore, Gaussian mutation and chaos disturbance are introduced to improve the artificial bee colony algorithm, enhance the local search ability of the algorithm, improve the convergence accuracy, and make the individual trapped in the local optimum jump out of the limit to continue searching. Moreover, compared with other intelligent optimization algorithms, the improved artificial bee colony algorithm can convert among the three kinds of bees according to the specific search strategy and requirements, and cooperate to find the optimal solution.

[0094] The artificial bee colony algorithm is improved, Gaussian mutation is adopted, and a random number of normal distribution with mean μ and variance σ 2 is used to replace the previous solution. The mutation formula is: M(x) = x · (1 + N(0, 1)).

[0095] Wherein, x is the obtained existing solution, N(0, 1) is a random number of normal distribution, and M(x) is the value after Gaussian mutation. The local search ability of Gaussian mutation is strong, and it is suitable for optimization problems with multiple minimum values. By introducing Gaussian mutation into the algorithm, the search ability of the artificial bee colony algorithm is greatly improved, and then the algorithm accuracy can be improved.

[0096] Random behavior is added, and when the above algorithm enters a local optimal solution, a chaotic sequence is generated based on the local optimal solution to disturb the individual causing the algorithm to stagnate, promote the algorithm to jump out of the limit and continue searching. In chaos disturbance, chaos mapping needs to be used to generate chaotic variables. The present application uses Tent mapping which has a more flat and uniform distribution:

[0097]

[0098] Wherein, X d is the d-dimensional vector of the chaotic sequence X, x d is a random number subject to uniform distribution, and x d ∈[0,1].

[0099] The basic process of chaos disturbance using Tent mapping is as follows: (1) generate chaotic variables X d based on Tent mapping; (2) map the variables X d to the solution space of the objective function using the following formula: newX d =min d +(maxd -min d )X d ; (3) the swarm individual is disturbed by chaos as follows: newX'=(X'+newX) / 2.

[0100] Wherein, X' is the individual before chaos disturbance, newX is the chaos disturbance quantity generated, and newX' is the individual after chaos disturbance.

[0101] Figure 2 A flow chart of an improved artificial bee colony algorithm is given, referring to Figure 2 The flow of the improved artificial bee colony algorithm is as follows: (1) bee colony initialization. Set algorithm parameters: the bee swarm size is N, the local search number is L, and the maximum iteration number is M. (2) randomly generate initial solutions, and calculate the fitness function values of the individuals corresponding to the initial solutions. (3) classify the bee swarm according to the fitness function values. The nectar bees update the search of new nectar sources. (4) the onlooker bees select the nectar sources with larger nectar amounts according to the fitness calculation probability P, and convert into the role of nectar bees to continue searching in the nectar source neighborhood. (5) if the number of searching for new solutions reaches the maximum value, compare and select the current optimal solution, and determine the position of the corresponding individual. (6) calculate the fitness values and the average fitness value of the individuals in the bee colony. For the individual with a fitness value greater than the average fitness value, Gaussian mutation is performed; otherwise, chaos disturbance is performed. (7) after the maximum iteration number is reached, terminate the program, and output the result; otherwise, return to step three and continue to execute.

[0102] The technical scheme further provides a priority embodiment, including: establishing a power generation cost function through an equal micro-increment rate criterion, and solving the function by using a Lagrange multiplier method, so that a frequency active control expression based on a consistency algorithm considering power generation cost can be finally obtained. In a micro-grid group, a distributed quadratic control group layer cost optimization control expression is established by considering the influence of system frequency recovery on economic dispatch, and a distributed quadratic control group layer economic dispatch optimization control strategy is solved, so that economic control and reactive power sharing of a sub-micro-grid are realized, and the frequency and voltage of the sub-micro-grid are recovered to a reference level. An improved artificial bee colony algorithm based on Gaussian mutation and chaos disturbance is provided. On the basis of a traditional artificial bee colony algorithm, Gaussian mutation is introduced, so that the local search capability of the algorithm is strengthened, and the robustness of the algorithm is improved; chaos disturbance is added, and the ergodicity of a chaos variable is utilized to disturb an individual falling into a local optimum, so that the stagnated algorithm jumps out of the limitation and continues to search.

[0103] The power generation cost is taken as a cost scheduling function, and an overload constraint condition is established, and aiming at the problems of the overload of the distributed power supply under the action of the cost optimal controller and the overload of the sub-network when the sub-network is independently operated, a coordinated operation control strategy is provided. For the secondary control, a corresponding distributed control strategy is designed, which realizes the voltage and frequency recovery control of the AC / DC hybrid micro-grid group, realizes the economic scheduling of the AC / DC micro-grid group subsystem and the system in a small time scale, and improves the economic efficiency of the system operation. The improved artificial bee colony algorithm is used to solve the function model, so as to achieve a solution that meets the required conditions, speed up the solving process, reduce the instability factors in the general solving process, and further improve the solving accuracy and efficiency.

[0104] The following is an embodiment of the AC / DC hybrid micro-grid group control device provided by the embodiment of the application. The device and the AC / DC hybrid micro-grid group control method of each embodiment described above belong to the same inventive concept. Details not described in the embodiment of the AC / DC hybrid micro-grid group control device can be referred to the embodiment of the AC / DC hybrid micro-grid group control method.

[0105] Embodiment two

[0106] Figure 3 A structure diagram of an AC / DC hybrid micro-grid group control device provided by the embodiment two of the application is shown in the figure. Figure 3 As shown in the figure, the device comprises a micro-increase rate determination module 310, a first expression determination module 320, a second expression determination module 330, a third expression determination module 340 and an AC / DC hybrid micro-grid group control module 350.

[0107] The micro-increase rate determination module 310 is configured to construct a power generation cost function corresponding to the distributed power supply and a load constraint condition by using the output power of the distributed power supply in the AC / DC micro-grid group, and determine the micro-increase rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition.

[0108] The technical scheme of the embodiment of the present application constructs the power generation cost function corresponding to the distributed power supply and the load constraint condition by using the output power of the distributed power supply in the AC / DC micro-grid group, and determines the micro-increase rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition.

[0109] On the basis of the above technical scheme, the micro-increase rate determination module 310 is configured to determine the minimum power generation cost function in the micro-grid group based on the power generation cost function and the load constraint condition, and solve the minimum power generation cost function based on the Lagrange multiplier method to obtain the micro-increase rate corresponding to the distributed power supply.

[0110] On the basis of the above technical scheme, the reference frequency factor in the frequency active droop control expression is adjustable;The reference frequency factor includes: rated frequency, frequency compensation factor and active compensation factor;The frequency compensation factor is used to eliminate the frequency deviation caused by the droop control;The active compensation factor is used to eliminate the active deviation caused by the droop control.

[0111] On the basis of the above technical scheme, the second expression determination module 330 is specifically used for: determining the dynamic solving expression of the frequency compensation factor based on the consistency theory;Determine the dynamic solving expression of the active compensation factor based on the consistency theory;Based on the dynamic solving expression of the frequency compensation factor and the dynamic solving expression of the active compensation factor, the frequency active droop control expression is transformed to obtain the frequency active control expression based on the consistency algorithm considering the generation cost.

[0112] On the basis of the above technical scheme, the "solving the distributed secondary control network group layer cost optimization control expression by adjusting the micro-increment rate, obtaining the target solution" in the AC / DC hybrid microgrid group control module 350 is specifically used for: based on the improved artificial bee colony algorithm, the distributed secondary control network group layer cost optimization control expression is iteratively solved, and the target solution corresponding to the subnet with equal micro-increment rate is obtained.

[0113] On the basis of the above technical scheme, the improved artificial bee colony algorithm includes: traditional artificial bee colony algorithm, Gaussian mutation and chaos disturbance.

[0114] The AC / DC hybrid microgrid group control device provided by the embodiment of the application can execute the AC / DC hybrid microgrid group control method provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of executing the AC / DC hybrid microgrid group control method.

[0115] It is worth noting that in the above-mentioned embodiment of the AC / DC hybrid microgrid group control, each unit and module included is only divided according to the function logic, but is not limited to the above-mentioned division, as long as the corresponding function can be realized;In addition, the specific name of each functional unit is only for easy mutual distinction, and does not limit the protection scope of the application.

[0116] Embodiment three

[0117] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0118] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0119] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0120] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the AC / DC hybrid microgrid group control method.

[0121] In some embodiments, the AC / DC hybrid microgrid cluster control method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the above-described AC / DC hybrid microgrid cluster control method can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the AC / DC hybrid microgrid cluster control method by way of other any suitable means (e.g., by way of firmware).

[0122] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0123] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program running on the processor implements the functions / operations specified in the flowcharts and / or the block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a standalone software package, or entirely on a remote machine or server.

[0124] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0125] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0126] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0127] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of great management difficulty and weak business scalability in traditional physical hosts and VPS services.

[0128] The embodiment of the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements the AC-DC hybrid microgrid group control method provided by any of the embodiments of the present application.

[0129] The computer program product can be written in one or more programming languages or combinations of languages including object-oriented languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). The program product of the present application and the AC-DC hybrid microgrid group control method disclosed in the embodiments of the present application belong to the same inventive concept, and therefore will not be repeated here.

[0130] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, without departing from the desired results of the technical solutions of the present application, and are not limited herein.

[0131] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A hybrid AC / DC microgrid cluster control method, characterized in that, The application relates to a method for controlling an AC / DC hybrid micro-grid group. The method comprises the following steps: a power generation cost function corresponding to a distributed power supply and a load constraint condition are constructed by output power of the distributed power supply in the AC / DC micro-grid group, and a micro-increment rate corresponding to the distributed power supply is determined based on the power generation cost function and the load constraint condition; a frequency and active power droop control expression corresponding to the distributed power supply is constructed by frequency and active power output by the distributed power supply; a reference frequency factor in the frequency and active power droop control expression is adjustable; the reference frequency factor comprises a rated frequency, a frequency compensation factor and an active compensation factor; the frequency compensation factor is used for eliminating frequency deviation caused by the droop control; and the active compensation factor is used for eliminating active deviation caused by the droop control; a dynamic solving expression of the frequency compensation factor is determined based on a containment consistency theory; a dynamic solving expression of the active compensation factor is determined based on the containment consistency theory; the frequency and active power droop control expression is transformed based on the dynamic solving expression of the frequency compensation factor and the dynamic solving expression of the active compensation factor, so that a frequency and active power control expression based on the consistency algorithm and considering power generation cost is obtained; a cost scheduling function corresponding to a sub-grid and an overload constraint condition are constructed by output power of the sub-grid in the AC / DC micro-grid group, and a distributed secondary control grid group layer cost optimization control expression is determined based on the frequency and active power control expression, the cost scheduling function and the overload constraint condition; the overload constraint condition comprises the following: when neither the distributed power supply nor the sub-grid is overloaded, the sub-grid independently runs and an economic optimal control method is adopted; when the distributed power supply is overloaded and the sub-grid is not overloaded, the sub-grid independently runs and a control method is switched to droop control based on equal distribution of the distributed power supply capacity; and when both the distributed power supply and the sub-grid are overloaded, all the sub-grids run in parallel and the droop control based on equal distribution of the distributed power supply capacity is adopted; 2. The method of claim 1, wherein, the distributed secondary control grid group layer cost optimization control expression is solved by adjusting the micro-increment rate, so that a target solution is obtained, and the AC / DC hybrid micro-grid group is controlled based on the target solution. The method for determining the micro-increment rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition comprises the following steps: a minimum power generation cost function in the micro-grid group is determined based on the power generation cost function and the load constraint condition; 3. The method of claim 1, wherein, the minimum power generation cost function is solved based on a Lagrange multiplier method, so that the micro-increment rate corresponding to the distributed power supply is obtained. The method for solving the distributed secondary control grid group layer cost optimization control expression by adjusting the micro-increment rate, so that the target solution is obtained, comprises the following steps:

4. The method of claim 3, wherein, the distributed secondary control grid group layer cost optimization control expression is iteratively solved based on an improved artificial bee colony algorithm, so that a target solution of the sub-grid corresponding to the equal micro-increment rate is obtained.

5. An AC / DC hybrid microgrid cluster control device, characterized by, The improved artificial bee colony algorithm comprises a traditional artificial bee colony algorithm, a Gaussian mutation and a chaotic disturbance. The device comprises: The micro-increment rate determination module is configured to construct a power generation cost function corresponding to the distributed power supply and a load constraint condition by using output power of the distributed power supply in the AC / DC micro-grid group, and determine a micro-increment rate corresponding to the distributed power supply based on the power generation cost function and the load constraint condition; The first expression determination module is configured to construct a frequency and active power droop control expression corresponding to the distributed power supply by using frequency and active power output by the distributed power supply; The second expression determination module is configured to transform the frequency and active power droop control expression based on a consensus algorithm to obtain a frequency and active power control expression considering power generation cost based on the consensus algorithm; The third expression determination module is configured to construct a cost scheduling function corresponding to the sub-grid and an overload constraint condition by using output power of the sub-grid in the AC / DC micro-grid group, and determine a distributed secondary control grid group layer cost optimization control expression based on the frequency and active power control expression, the cost scheduling function and the overload constraint condition; The AC / DC hybrid micro-grid group control module is configured to solve the distributed secondary control grid group layer cost optimization control expression by adjusting the micro-increment rate, obtain a target solution, and control the AC / DC hybrid micro-grid group based on the target solution. The reference frequency factor in the frequency and active power droop control expression is adjustable; the reference frequency factor includes a rated frequency, a frequency compensation factor and an active compensation factor; the frequency compensation factor is used to eliminate frequency deviation caused by droop control; and the active compensation factor is used to eliminate active deviation caused by droop control. The second expression determination module is specifically configured to determine a dynamic solving expression of the frequency compensation factor based on the consensus algorithm, determine a dynamic solving expression of the active compensation factor based on the consensus algorithm, and transform the frequency and active power droop control expression based on the dynamic solving expression of the frequency compensation factor and the dynamic solving expression of the active compensation factor to obtain the frequency and active power control expression considering power generation cost based on the consensus algorithm. The overload constraint condition includes: when neither the distributed power supply nor the sub-grid is overloaded, the sub-grid is independently operated and an economically optimal control method is adopted; when the distributed power supply is overloaded and the sub-grid is not overloaded, the sub-grid is independently operated and the control method is switched to droop control based on equal distribution of capacity of the distributed power supply; and when both the distributed power supply and the sub-grid are overloaded, all the sub-grids are operated in parallel and droop control based on equal distribution of capacity of the distributed power supply is adopted.

6. An electronic device, comprising: The electronic device includes: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the AC / DC hybrid micro-grid group control method of any one of claims 1-4.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the AC / DC hybrid micro-grid group control method of any one of claims 1-4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the AC / DC hybrid micro-grid group control method of any one of claims 1-4.

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