Power distribution method and system of energy storage system based on GSA-GWO algorithm

Abstract

The invention provides a power distribution method and system of an energy storage system based on a GSA-GWO algorithm. In the method, a stable control model of the energy storage system is constructed by taking the minimum voltage deviation and voltage distortion rate of all nodes in a target power system as a target; constructing an optimization control model of the energy storage system by taking the minimum active power network loss rate of the target power system and the minimum new energy station loss rate of the node as targets; improving a grey wolf optimization algorithm by using a genetic annealing algorithm, and solving a stability control model and an optimization control model of the energy storage system by using the improved grey wolf optimization algorithm; and performing power distribution on the energy storage system according to the solved stability control model and optimization control model. The control strategy provided by the invention effectively improves the new energy consumption, gives full play to the adjustment effect of the energy storage system, and is beneficial to guaranteeing the flexible and reliable operation of the power distribution network.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution in electric power systems, and in particular relates to a power distribution method and system for an energy storage system based on a GSA-GWO algorithm. Background Art

[0002] With the transformation of the global energy mix and the rapid development of renewable energy technologies, the penetration of renewable energy in power systems continues to increase. However, the volatility and randomness of renewable energy can lead to problems such as voltage fluctuations and power imbalances on the power grid, posing serious challenges to the safe and stable operation of the grid. To alleviate these problems and improve the absorption capacity of renewable energy, energy storage systems (BESS) serve as a key regulation method. They can regulate charge and discharge when there is a mismatch between power supply and demand, thereby smoothing out fluctuations in renewable energy output and enhancing the flexibility and reliability of the distribution network.

[0003] In the area of ​​optimal control of distribution systems with a high proportion of renewable energy, existing research primarily focuses on the optimal scheduling of energy storage systems. A smooth control strategy for energy storage systems has been proposed, comprehensively considering wind power fluctuations and battery state of charge (SOC). This approach uses wavelet analysis to extract the amplitude and frequency characteristics of wind power output fluctuations, providing data support for the control strategy. By acquiring the battery SOC level in real time, it is determined whether the system is within the safe operating range to avoid overcharging and over-discharging. An optimization model is constructed with the goal of minimizing wind power fluctuations, incorporating SOC limits as hard constraints and considering the impact of energy storage charge and discharge power limits, efficiency, and lifespan. Model predictive control is employed to generate dynamically adjusted control instructions for the energy storage system based on the predicted information. However, these studies primarily focus on power allocation and SOC constraint management, with limited research on the economics of system operation and a lack of comprehensive consideration of renewable energy consumption and energy storage benefits. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a power distribution method and system for an energy storage system based on a GSA-GWO algorithm.

[0005] In a first aspect, the present invention provides a power allocation method for an energy storage system based on a GSA-GWO algorithm, comprising:

[0006] A stable control model for the energy storage system is constructed with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system.

[0007] An optimization control model for the energy storage system is constructed with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node;

[0008] The genetic annealing algorithm is used to improve the gray wolf optimization algorithm, so as to use the improved gray wolf optimization algorithm to solve the stability control model and optimization control model of the energy storage system;

[0009] The power of the energy storage system is distributed according to the solved stability control model and optimization control model.

[0010] Optionally, the step of constructing a stable control model for the energy storage system with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system includes:

[0011] The voltage deviation ΔU of all nodes is calculated according to the following formula:

[0012]

[0013] Among them, V n is the actual voltage value of node n in the target power system; V N is the rated voltage of the node; Y is the total number of nodes in the target power system;

[0014] The voltage distortion rate of each node is calculated according to the following formula:

[0015]

[0016] Among them, T n,HD is the voltage distortion rate of node n in the target power system; U n,h is the hth voltage harmonic content of node n in the target power system; U1 is the fundamental voltage;

[0017] Construct the expression of the stable control model F1 of the energy storage system:

[0018] F1=ω1ΔU+ω2T n,HD ;

[0019] Among them, ω1 is the weight of ΔU; ω2 is T n,HD The weight of .

[0020] Optionally, the constructing of an optimization control model for the energy storage system with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node as the goal includes:

[0021] Calculate the target power system active network loss rate p according to the following formula: loss :

[0022]

[0023] in, is the active power of the new energy station at node n in the target power system; P nis the other active power on node n in the target power system; Y is the total number of nodes in the target power system; is the active power of the load on node n; is the active power of the energy storage system at node n' in the target power system; Y is the total number of nodes in the target power system; r is the working state value of the energy storage system, discharging r = 1, charging r = -1; N bess is the total number of energy storage systems in the target power system;

[0024] The loss rate of new energy stations is calculated according to the following formula:

[0025]

[0026] in, is the loss rate of the new energy station at node n” in the target power system; The active power output of the new energy station at node n" in the target power system; is the actual value of the grid-connected active power of the new energy station at node n” in the target power system; is the rated power of node n" in the target power system; N new is the total number of new energy stations in the target power system;

[0027] The expression of the optimal control model F2 of the energy storage system is constructed as follows:

[0028]

[0029] Among them, ω3 is p loss The weight of The weight of .

[0030] In a second aspect, the present invention provides a power distribution system for an energy storage system based on a GSA-GWO algorithm, comprising:

[0031] The first building module is used to build a stable control model of the energy storage system with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system;

[0032] The second construction module is used to construct an optimization control model for the energy storage system with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node;

[0033] A model solving module is used to improve the gray wolf optimization algorithm using a genetic annealing algorithm, so as to solve the stability control model and the optimization control model of the energy storage system using the improved gray wolf optimization algorithm;

[0034] The power distribution module is used to distribute power to the energy storage system according to the solved stable control model and optimized control model.

[0035] Optionally, the first building block includes:

[0036] The first calculation unit is configured to calculate the voltage deviation ΔU of all nodes according to the following formula:

[0037]

[0038] Among them, V n is the actual voltage value of node n in the target power system; V N is the rated voltage of the node; Y is the total number of nodes in the target power system;

[0039] The second calculation unit is used to calculate the voltage distortion rate of each node according to the following formula:

[0040]

[0041] Among them, T n,HD is the voltage distortion rate of node n in the target power system; U n,h is the hth voltage harmonic content of node n in the target power system; U1 is the fundamental voltage;

[0042] The first building block is used to construct the expression of the stability control model F1 of the energy storage system:

[0043] F1=ω1ΔU+ω2T n,HD ;

[0044] Among them, ω1 is the weight of ΔU; ω2 is T n,HD The weight of .

[0045] Optionally, the second building block includes:

[0046] The third calculation unit is used to calculate the target power system active network loss rate p according to the following formula loss :

[0047]

[0048] in, is the active power of the new energy station at node n in the target power system; P n is the other active power on node n in the target power system; Y is the total number of nodes in the target power system; is the active power of the load on node n; is the active power of the energy storage system at node n' in the target power system; Y is the total number of nodes in the target power system; r is the working state value of the energy storage system, discharging r = 1, charging r = -1; N bess is the total number of energy storage systems in the target power system;

[0049] The fourth calculation unit is used to calculate the loss rate of the new energy station according to the following formula:

[0050]

[0051] in, is the loss rate of the new energy station at node n” in the target power system; The active power output of the new energy station at node n" in the target power system; is the actual value of the grid-connected active power of the new energy station at node n” in the target power system; is the rated power of node n" in the target power system; N new is the total number of new energy stations in the target power system;

[0052] The fourth construction unit is used to construct the expression of the optimization control model F2 of the energy storage system:

[0053]

[0054] Among them, ω3 is p loss The weight of The weight of .

[0055] In a third aspect, the present invention provides a computer device comprising a processor and a memory; wherein, when the processor executes a computer program stored in the memory, the steps of the power allocation method for an energy storage system based on the GSA-GWO algorithm described in the first aspect are implemented.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the power allocation method for an energy storage system based on the GSA-GWO algorithm described in the first aspect are implemented.

[0057] In a fifth aspect, the present invention provides a computer program product, comprising computer executable instructions or a computer program. When the computer executable instructions or the computer program are executed by a processor, the steps of the power allocation method of the energy storage system based on the GSA-GWO algorithm described in the first aspect are implemented.

[0058] The present invention provides a power distribution method and system for an energy storage system based on the GSA-GWO algorithm. In the method, a stable control model for the energy storage system is constructed with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system. An optimized control model for the energy storage system is constructed with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the nodes. The genetic annealing algorithm is used to improve the gray wolf optimization algorithm, and the improved gray wolf optimization algorithm is used to solve the stable control model and the optimized control model of the energy storage system. Power is distributed to the energy storage system based on the solved stable control model and the optimized control model. The control strategy proposed in the present invention effectively improves the consumption of new energy, gives full play to the regulatory role of the energy storage system, and is conducive to ensuring the flexible and reliable operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 A schematic diagram of a flow chart of a power distribution method for an energy storage system provided in an embodiment of the present invention;

[0061] Figure 2 A schematic diagram of the GSA-GWO algorithm flow provided in an embodiment of the present invention;

[0062] Figure 3 A schematic diagram of a two-layer optimization control model solution process according to an embodiment of the present invention;

[0063] Figure 4 A schematic structural diagram of a power distribution system of an energy storage system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] Example 1

[0066] like Figure 1 As shown, an embodiment of the present invention provides a power distribution method for an energy storage system, comprising:

[0067] Step 101 : constructing a stable control model for an energy storage system with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system.

[0068] During the charging and discharging process of the energy storage system (BESS), the BESS charging and discharging power constraints are:

[0069] 0≤σ(t)P bess (t)≤P v ;

[0070]

[0071] Wherein, σ(t) represents the charge and discharge state of the energy storage system at time t, when σ(t) = 1, it is charging, and when σ(t) = -1, it is discharging; P bess (t) is the charge and discharge power of the energy storage system at time t; P v is the rated charge and discharge power of the energy storage system.

[0072] The change of energy storage capacity in the time period t to t+1 can be expressed according to the charge and discharge of the energy storage system as follows:

[0073]

[0074] Among them, E e (t+1) is the amount of electricity in the energy storage system during the t+1 period; E e (t) is the amount of electricity in the energy storage system during period t; η ch is the charging efficiency of the energy storage system; η ch is the charging efficiency of the energy storage system; η dis is the discharge efficiency of the energy storage system.

[0075] The cut-off SOC of the energy storage system during the T period is expressed as:

[0076]

[0077] Among them, SOC e (t) is the cut-off SOC of the energy storage system during the T period; SOC s (t) is the initial SOC of the energy storage system on that day; E rate is the rated capacity of the energy storage system.

[0078] This embodiment proposes a two-tiered optimization control strategy to coordinate the operation of distributed energy storage systems, improve the level of renewable energy consumption, and optimize the performance of the distribution network. The upper optimization control layer determines the total active and reactive output of the distributed energy storage system with the goal of minimizing voltage deviation and voltage distortion rate.

[0079] Exemplarily, the voltage deviation ΔU of all nodes is calculated according to the following formula:

[0080]

[0081] Among them, V n is the actual voltage value of node n in the target power system; V N is the rated voltage of the node; Y is the total number of nodes in the target power system.

[0082] The voltage distortion rate of each node is calculated according to the following formula:

[0083]

[0084] Among them, T n,HD is the voltage distortion rate of node n in the target power system; U n,h is the h-th voltage harmonic content of node n in the target power system; U1 is the fundamental voltage.

[0085] Construct the expression of the stable control model F1 of the energy storage system:

[0086] F1=ω1ΔU+ω2T n,HD .

[0087] Among them, ω1 is the weight of ΔU; ω2 is T n,HD The weight of .

[0088] Step 102 : constructing an optimization control model for the energy storage system with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node.

[0089] The lower optimization control layer focuses on optimizing the economic efficiency of the distribution network, comprehensively considering the goals of minimizing system losses and renewable energy consumption, and further optimizing the distributed energy storage power allocation strategy. This two-layer optimization control strategy can effectively smooth load peaks and valleys, improve renewable energy absorption capacity, and enhance the safety and economic efficiency of the distribution network.

[0090] For example, the target power system active network loss rate p is calculated according to the following formula: loss :

[0091]

[0092] in, is the active power of the new energy station at node n in the target power system; P n is the other active power on node n in the target power system; Y is the total number of nodes in the target power system; is the active power of the load on node n; is the active power of the energy storage system at node n' in the target power system; Y is the total number of nodes in the target power system; r is the working state value of the energy storage system, r = 1 when discharging and r = -1 when charging; N bess is the total number of energy storage systems in the target power system.

[0093] The loss rate of new energy stations is calculated according to the following formula:

[0094]

[0095] in, is the loss rate of the new energy station at node n” in the target power system; The active power output of the new energy station at node n" in the target power system; is the actual value of the grid-connected active power of the new energy station at node n” in the target power system; is the rated power of node n" in the target power system; N new is the total number of new energy stations in the target power system.

[0096] The expression of the optimal control model F2 of the energy storage system is constructed as follows:

[0097]

[0098] Among them, ω3 is p loss The weight of The weight of .

[0099] Step 103 : Using a genetic annealing algorithm to improve the Grey Wolf Optimization Algorithm, so as to use the improved Grey Wolf Optimization Algorithm to solve the stability control model and the optimization control model of the energy storage system.

[0100] Step 104 : Distribute power to the energy storage system according to the solved stable control model and the optimized control model.

[0101] The constraints in steps 101-102 include:

[0102]

[0103] Among them, Q i,j is the reactive power of energy storage station i at time j; Q i,max is the maximum reactive power of energy storage station i; S i,max is the maximum apparent power of energy storage station i; P i,j is the active power of energy storage station i at time j.

[0104]

[0105] in, is the minimum charging power of energy storage station i; is the charging power of energy storage station i at time j; is the maximum charging power of energy storage station i; is the minimum discharge power of energy storage station i; is the discharge power of energy storage station i at time j; is the maximum discharge power of energy storage station i; SOC i,min is the minimum state of charge of energy storage system i; SOC i,j is the state of charge of energy storage system i at time j; SOC i,max is the maximum state of charge of energy storage system i.

[0106]

[0107] Among them, SOC i,j is the state of charge of energy storage system i at time j+1; Δj is the time interval from time j to j+1; λ sd is the self-discharge rate of the energy storage power station.

[0108]

[0109] Among them, P m is the other active power at node m in the target power system; is the active power of the new energy station at node m in the target power system; is the active power of the energy storage system at node m in the target power system; is the active power of the load on node m; U m is the voltage of node m in the target power system; U n is the voltage of node n in the target power system; G mn is the real part of the node admittance matrix; θ mn is the phase angle difference between node m and node n; B mn is the imaginary part of the node admittance matrix; Q m is the other reactive power at node m in the target power system; is the reactive power of the new energy station at node m in the target power system; The reactive power of the energy storage system at node m in the target power system; is the reactive power of the load on node m.

[0110] E i,min ≤E i,j ≤E i,max .

[0111] Among them, E i,min is the minimum power of energy storage system i; E i,maxis the maximum amount of electricity of energy storage system i; E i,max is the amount of electricity in energy storage system i at time j.

[0112] like Figure 2 As shown, this embodiment introduces the Grey Wolf Optimization Algorithm to solve the two-layer optimization control model and uses the Genetic Annealing Algorithm (GSA) to improve the Grey Wolf Optimization Algorithm (GWO) to improve the accuracy and speed of solving the two-layer optimization control model.

[0113] The Gray Wolf Optimization (GWO) algorithm is a heuristic mathematical optimization algorithm that simulates the wandering, encirclement, and attack strategies of gray wolves to solve mathematical problems and find optimal solutions. This example uses the GWO algorithm to solve a multi-objective function problem. Through iterative loops, it finds the highest-ranked wolf α in the pack, representing the optimal solution to the objective function. Furthermore, it also derives the suboptimal wolf pack rankings, which are ranked in descending order as β, δ, and η.

[0114] The objective function is solved by simulating the surrounding and predation behaviors of wolves. The mathematical expression of the behavior of surrounding prey is:

[0115]

[0116] Among them, S is the parameter distance; I prey (t) is the current position of the prey; I(t) is the position of the gray wolf after t iterations; A is a random number; C i is the perturbation coefficient parameter, i = 1, 2, 3; a is the convergence factor; r1 is a random number between 0 and 1.

[0117] Led by wolves α, β, and δ, the wolf pack continues to approach its prey. During this process, the wolves' positions change dynamically, and the mathematical model of their behavior is:

[0118]

[0119] Where: S α is the distance between wolf α and other wolves; S β is the distance between wolf β and other wolves; S δ is the distance between wolf δ and other wolves; I1 is the influence between wolf α and other wolves; I2 is the influence between wolf β and other wolves; I3 is the influence between wolf δ and other wolves; I(t+1) is the position of the individual gray wolf after being guided by wolves α, β and δ; I α , I β and I δ denote the positional influence of α, β and δ wolves, respectively; A1, A2 and A3 denote the influence factors of α, β and δ wolves on other wolves, respectively.

[0120] Key operators such as mutation, crossover, and selection from the Genetic Simulated Annealing (GSA) algorithm are introduced into the Gray Wolf Optimization algorithm to update the position of the gray wolf. The method proposed in this example fully leverages the powerful global search capabilities of the genetic algorithm while taking advantage of the local search advantages of the simulated annealing algorithm. This results in an improved GWO algorithm that exhibits superior performance when solving complex optimization problems.

[0121] Mutation operator:

[0122] v b (t) = x s,1 (t)+ω[x s,2 (t)-x s,3 (t)].

[0123] Among them, v b (t) represents the individual variation solution of the gray wolf after t iterations; x s is the current solution of the gray wolf individual, and the subscripts 1, 2, and 3 represent the wolf packs led by α, β, and δ wolves, respectively.

[0124] Crossover operator:

[0125]

[0126] Where: z b (t) is the new generation of gray wolf individuals generated in the t-th iteration; P R is the crossover probability value between new gray wolf individuals; x b (t) represents the individual current solution of the gray wolf after t iterations.

[0127] The selection operator compares the newly generated individuals with the parent individuals through a greedy strategy and selects the best new individuals. In order to enhance the global convergence of the algorithm, the Boltzmann mechanism of the simulated annealing algorithm is introduced into the genetic algorithm to accept individuals after crossover and mutation. Its expression is:

[0128]

[0129] Where f(·) is the fitness function; ξ is the annealing coefficient; and T' is the annealing temperature.

[0130] like Figure 3 As shown in Figure 2, the steps for solving the two-level optimization control model are:

[0131] 1) Input initial conditions and calculate the system node voltages, and determine whether the node voltages exceed the safe operating range;

[0132] 2) If it is detected that some node voltages are outside the allowable range, the upper-level optimization calculation is started, and the GSA-GWO algorithm is used to traverse the feasible solution space to find the optimal solution that minimizes the system node voltage deviation;

[0133] 3) Based on the optimization results of the upper layer, the power of the energy storage system is further optimized and allocated. The optimization function F is used as the fitness function. The specific power allocation scheme of each energy storage system is solved through the GSA-GWO algorithm, and the charging and discharging power of each energy storage is output;

[0134] 4) If the system node voltage does not exceed the safety range, reactive power optimization is not performed, and power allocation calculation is performed directly. The charging and discharging power of the energy storage system is calculated and allocated, and the reactive power regulation of the energy storage system (i.e., the energy storage power station) is ultimately output.

[0135] In summary, the power distribution method for the energy storage system provided in this embodiment analyzes the structure of the energy storage system and constructs a mathematical model of the energy storage system including a power-capacity model, an energy conversion efficiency model, and a state of charge (SOC) model. A two-layer optimization control model for a distributed energy storage system is proposed. The upper layer constructs a stable control model with the goal of minimizing the system voltage deviation and voltage distortion rate, while the lower layer constructs an optimization control model with the goal of new energy consumption and active network loss to perform power distribution for distributed energy storage. The gray wolf optimization algorithm is introduced to solve the optimization control model, and the genetic annealing algorithm (GSA) is used to improve the gray wolf optimization algorithm (GWO) to improve the accuracy and speed of solving the two-layer optimization control model. The control strategy proposed in this embodiment effectively improves the consumption of new energy, gives full play to the regulatory role of the energy storage system, and is conducive to ensuring the flexible and reliable operation of the distribution network.

[0136] Example 2

[0137] Based on the same inventive concept as Example 1, this embodiment also provides a power distribution system for an energy storage system. Since the principle of solving the problem by this system is similar to the power distribution method of the aforementioned energy storage system, the implementation of this system can refer to the implementation of the power distribution method of the energy storage system.

[0138] like Figure 4 As shown, the power distribution system of the energy storage system includes:

[0139] The first building module 10 is used to build a stable control model of the energy storage system with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system.

[0140] The second construction module 20 is used to construct an optimization control model for the energy storage system with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node.

[0141] The model solving module 30 is used to improve the gray wolf optimization algorithm by using the genetic annealing algorithm, so as to solve the stability control model and the optimization control model of the energy storage system by using the improved gray wolf optimization algorithm.

[0142] The power distribution module 40 is used to distribute power to the energy storage system according to the solved stable control model and the optimized control model.

[0143] Exemplarily, the first building block includes:

[0144] The first calculation unit is configured to calculate the voltage deviation ΔU of all nodes according to the following formula:

[0145]

[0146] Among them, V n is the actual voltage value of node n in the target power system; V N is the rated voltage of the node; Y is the total number of nodes in the target power system.

[0147] The second calculation unit is used to calculate the voltage distortion rate of each node according to the following formula:

[0148]

[0149] Among them, T n,HD is the voltage distortion rate of node n in the target power system; U n,h is the h-th voltage harmonic content of node n in the target power system; U1 is the fundamental voltage.

[0150] The first building block is used to construct the expression of the stability control model F1 of the energy storage system:

[0151] F1=ω1ΔU+ω2T n,HD .

[0152] Among them, ω1 is the weight of ΔU; ω2 is T n,HD The weight of .

[0153] Exemplarily, the second building block includes:

[0154] The third calculation unit is used to calculate the target power system active network loss rate p according to the following formula loss :

[0155]

[0156] in, is the active power of the new energy station at node n in the target power system; P n is the other active power on node n in the target power system; Y is the total number of nodes in the target power system; is the active power of the load on node n; is the active power of the energy storage system at node n' in the target power system; Y is the total number of nodes in the target power system; r is the working state value of the energy storage system, discharging r = 1, charging r = -1; N bess is the total number of energy storage systems in the target power system.

[0157] The fourth calculation unit is used to calculate the loss rate of the new energy station according to the following formula:

[0158]

[0159] in, is the loss rate of the new energy station at node n” in the target power system; The active power output of the new energy station at node n" in the target power system; is the actual value of the grid-connected active power of the new energy station at node n” in the target power system; is the rated power of node n" in the target power system; N new is the total number of new energy stations in the target power system.

[0160] The fourth construction unit is used to construct the expression of the optimization control model F2 of the energy storage system:

[0161]

[0162] Among them, ω3 is p loss The weight of The weight of .

[0163] For more specific working processes of the above modules, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0164] Example 3

[0165] This embodiment provides a computer device including a processor and a memory. When the processor executes a computer program stored in the memory, the processor implements the steps of the power allocation method for an energy storage system based on the GSA-GWO algorithm described in Example 1.

[0166] For more specific details about the above method, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0167] Example 4

[0168] This embodiment provides a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the steps of the power allocation method for an energy storage system based on the GSA-GWO algorithm described in Example 1 are implemented.

[0169] For more specific details about the above method, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0170] Example 5

[0171] This embodiment provides a computer program product, including computer-executable instructions or a computer program. When the computer-executable instructions or the computer program are executed by a processor, the steps of the power allocation method for an energy storage system based on the GSA-GWO algorithm described in Example 1 are implemented.

[0172] For more specific details about the above method, please refer to the corresponding content disclosed in Example 1, which will not be repeated here.

[0173] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments will be sufficient. The systems, devices, storage media, and computer program products disclosed in the embodiments correspond to the methods disclosed in the embodiments, so their descriptions are relatively simplified. For relevant details, refer to the method descriptions.

[0174] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus a necessary general-purpose hardware platform. Based on this understanding, the technical solutions in the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention or certain portions of the embodiments.

[0175] In some embodiments, computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0176] As an example, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code portions).

[0177] By way of example, computer-executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0178] The present invention has been described in detail above with reference to specific embodiments and exemplary examples. However, these descriptions should not be construed as limiting the present invention. Those skilled in the art will appreciate that various equivalent substitutions, modifications, or improvements may be made to the technical solutions and implementations of the present invention without departing from the spirit and scope of the present invention, all of which fall within the scope of the present invention. The scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A power allocation method for an energy storage system based on the GSA-GWO algorithm, characterized in that: include: A stable control model for the energy storage system is constructed with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system. An optimization control model for the energy storage system is constructed with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node; The genetic annealing algorithm is used to improve the gray wolf optimization algorithm, so as to use the improved gray wolf optimization algorithm to solve the stability control model and optimization control model of the energy storage system; The power of the energy storage system is distributed according to the solved stability control model and optimization control model.

2. The power distribution method of the energy storage system according to claim 1, characterized in that: The stable control model of the energy storage system is constructed with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system, including: The voltage deviation ΔU of all nodes is calculated according to the following formula: Among them, V n is the actual voltage value of node n in the target power system; V N is the rated voltage of the node; Y is the total number of nodes in the target power system; The voltage distortion rate of each node is calculated according to the following formula: Among them, T n,HD is the voltage distortion rate of node n in the target power system; U n,h is the hth voltage harmonic content of node n in the target power system; U1 is the fundamental voltage; Construct the expression of the stable control model F1 of the energy storage system: F1=ω1ΔU+ω2T n,HD ; Among them, ω1 is the weight of ΔU; ω2 is T n,HD The weight of .

3. The power distribution method of the energy storage system according to claim 1, characterized in that: The optimization control model of the energy storage system is constructed with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node, including: Calculate the target power system active network loss rate p according to the following formula: loss : in, is the active power of the new energy station at node n in the target power system; P n is the other active power on node n in the target power system; Y is the total number of nodes in the target power system; is the active power of the load on node n; is the active power of the energy storage system at node n' in the target power system; Y is the total number of nodes in the target power system; r is the working state value of the energy storage system, discharging r = 1, charging r = -1; N bess is the total number of energy storage systems in the target power system; The loss rate of new energy stations is calculated according to the following formula: in, is the loss rate of the new energy station at node n” in the target power system; The active power output of the new energy station at node n" in the target power system; is the actual value of the grid-connected active power of the new energy station at node n” in the target power system; is the rated power of node n" in the target power system; N new is the total number of new energy stations in the target power system; The expression of the optimal control model F2 of the energy storage system is constructed as follows: Among them, ω3 is p loss The weight of The weight of .

4. A power distribution system for an energy storage system based on the GSA-GWO algorithm, characterized in that: include: The first building module is used to build a stable control model of the energy storage system with the goal of minimizing the voltage deviation and voltage distortion rate of all nodes in the target power system; The second construction module is used to construct an optimization control model for the energy storage system with the goal of minimizing the active network loss rate of the target power system and the loss rate of the new energy station at the node; A model solving module is used to improve the gray wolf optimization algorithm using a genetic annealing algorithm, so as to solve the stability control model and the optimization control model of the energy storage system using the improved gray wolf optimization algorithm; The power distribution module is used to distribute power to the energy storage system according to the solved stable control model and optimized control model.

5. The power distribution system of the energy storage system according to claim 1, characterized in that: The first building block includes: The first calculation unit is configured to calculate the voltage deviation ΔU of all nodes according to the following formula: Among them, V n is the actual voltage value of node n in the target power system; V N is the rated voltage of the node; Y is the total number of nodes in the target power system; The second calculation unit is used to calculate the voltage distortion rate of each node according to the following formula: Among them, T n,HD is the voltage distortion rate of node n in the target power system; U n,h is the hth voltage harmonic content of node n in the target power system; U1 is the fundamental voltage; The first building block is used to construct the expression of the stability control model F1 of the energy storage system: F1=ω1ΔU+ω2T n,HD ; Among them, ω1 is the weight of ΔU; ω2 is T n,HD The weight of .

6. The power distribution system of the energy storage system according to claim 1, characterized in that: The second building block includes: The third calculation unit is used to calculate the target power system active network loss rate p according to the following formula loss : in, is the active power of the new energy station at node n in the target power system; P n is the other active power on node n in the target power system; Y is the total number of nodes in the target power system; is the active power of the load on node n; is the active power of the energy storage system at node n' in the target power system; Y is the total number of nodes in the target power system; r is the working state value of the energy storage system, discharging r = 1, charging r = -1; N bess is the total number of energy storage systems in the target power system; The fourth calculation unit is used to calculate the loss rate of the new energy station according to the following formula: in, is the loss rate of the new energy station at node n” in the target power system; The active power output of the new energy station at node n" in the target power system; is the actual value of the grid-connected active power of the new energy station at node n” in the target power system; is the rated power of node n" in the target power system; N new is the total number of new energy stations in the target power system; The fourth construction unit is used to construct the expression of the optimization control model F2 of the energy storage system: Among them, ω3 is p loss The weight of The weight of .

7. A computer device, characterized in that: The system comprises a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the system implements the steps of the power allocation method of the energy storage system based on the GSA-GWO algorithm as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that Used to store a computer program; when the computer program is executed by a processor, the steps of the power allocation method for an energy storage system based on the GSA-GWO algorithm according to any one of claims 1 to 3 are implemented.

9. A computer program product, characterized in that The method comprises computer executable instructions or a computer program. When the computer executable instructions or the computer program are executed by a processor, the steps of the power distribution method of the energy storage system based on the GSA-GWO algorithm described in claims 1-3 are implemented.

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