Online deployment method and system based on household optical storage all-in-one machine cluster

Abstract

The invention discloses an online deployment method and system based on a household optical storage all-in-one machine cluster. The method comprises the following steps: acquiring basic data of a low-voltage power distribution network; the uncertain factors are preprocessed; establishing a power distribution network model; calculating internal schedulable power or vacant power of the node, and performing energy allocation of the optical storage all-in-one machine in the node; establishing upper and lower layer models of an online deployment strategy, and optimizing input information of upper and lower layers of the power distribution network model; and integrating the optimized input information to obtain the input power of the reference node of the power distribution network, the energy allocation condition between the optical storage all-in-one machines and the reactive power output by the optical storage node. According to the method, a double-layer household optical storage all-in-one machine cluster optimization model is adopted, local electric energy quality optimization and internal mutual support are achieved, the method adapts to the characteristics of multiple points and wide faces of distributed photovoltaic resources, photovoltaic in-situ elimination is achieved, optical storage nodes can be helped to participate in operation of a low-voltage power distribution network, and the stability of the power distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the field of energy consumption in the electric power industry, and in particular relates to an online allocation method and system based on a household photovoltaic storage integrated machine cluster. Background Art

[0003] In the low-voltage distribution network of rural towns and villages, it is appropriate to adopt the form of photovoltaic energy storage access on the user side. Household photovoltaic storage integrated machine is a device built by residents at home, which can be connected to the photovoltaic storage charging port at the same time, flexibly store energy through energy storage, and respond to the active and reactive power regulation of the power grid at the same time. Through effective operation mode and large-scale access of photovoltaic storage integrated machine, market benefits of multiple entities can be realized, and at the same time, the power grid company can carry out refined management and control of large-scale rural distributed photovoltaic.

[0004] However, household photovoltaic storage devices still have significant shortcomings such as lack of mature operation mechanism, high cost, large size and low operation efficiency. Large-scale distributed photovoltaics are not connected to the low-voltage distribution network in the substation in a disorderly manner, resulting in poor power supply quality and low photovoltaic utilization, making it difficult to form an effective aggregation area to cooperate with upper-level control.

[0005] Therefore, the online flexible allocation technology of household photovoltaic and energy storage cluster resources that takes into account the differences in power generation and energy storage has a high application value. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide an online allocation method and system based on a household photovoltaic storage integrated cluster, which can achieve real-time balance of energy storage and controllable capacity, respond to upper-level management and control strategies, make full use of the characteristics of energy storage, participate in grid frequency regulation, peak-valley filling and other services, and provide flexible support for the distribution network system.

[0007] Technical solution: The present invention provides an online deployment method based on a household photovoltaic storage integrated machine cluster, comprising:

[0008] Obtain basic data of low-voltage distribution network;

[0009] Pre-process the uncertainties in the operation of the distribution network in the basic data;

[0010] According to the power flow constraints and safe operation constraints of the distribution network, the basic distribution network model is established in combination with the pre-processed basic data;

[0011] According to the calculated dispatchable power or insufficient power inside the node to which the photovoltaic and storage integrated device is connected, it is judged whether the node to which the photovoltaic and storage integrated device is connected needs to absorb power from the distribution network, and the energy of the photovoltaic and storage integrated device in the node is allocated according to the judgment result, and the dispatchable power constraint of the photovoltaic and storage node is formed and added to the basic distribution network model to obtain the distribution network model;

[0012] Establish an upper model of the online dispatching strategy, which uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model;

[0013] Establish the lower model of the online dispatching strategy, and use the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model;

[0014] By integrating the input information of the upper layer of the optimized distribution network model and the input information of the lower layer of the distribution network model, the input power of the reference node of the distribution network, the energy allocation between the photovoltaic and storage integrated machines, and the reactive power output of the photovoltaic and storage nodes are obtained.

[0015] Furthermore, the basic distribution network model includes:

[0016] Power flow constraints of distribution network:

[0017] The node voltage relationship between nodes i and j is expressed as:

[0018]

[0019] Among them, V j represents the actual voltage at node j, V i Represents the actual voltage of node i; I ij Represents the current in branch ij; R ij With X ij are the impedance and inductive reactance on branch ij respectively; P ij With Q ij Represents the active power and reactive power on branch ij; P jl With Q jl represents the active power and reactive power on branch jl; P j and Q j represents the active power and reactive power injected by node j; the constraints are as follows:

[0020]

[0021] π(j) and σ(j) are the sets of parent nodes and child nodes of node j, and they need to satisfy the following calculation constraints of active power and reactive power:

[0022]

[0023] Constraints on safe operation of distribution networks:

[0024] The current in branch ij and the voltage at node j need to be within the safe operating range:

[0025] V min ≤V j ≤V max

[0026]

[0027] Further, according to the calculated dispatchable power or insufficient power inside the node to which the integrated photovoltaic and storage device is connected, it is determined whether the node to which the integrated photovoltaic and storage device is connected needs to absorb power from the distribution network, and the energy of the integrated photovoltaic and storage device in the node is allocated according to the determination result, including:

[0028] Traverse each user. When the user's photovoltaic power generation is greater than the user's load, calculate the user's excess power and store the user's excess power in the user's energy storage or distribute it to other users at the same node. When the user's photovoltaic power generation is less than the user's load, calculate the user's power shortage. If the user's energy storage has enough surplus, the power shortage will be compensated from the user's energy storage first. Otherwise, the power shortage will be compensated from other users at the same node. Until the power calculation of all photovoltaic and energy storage users in the node is completed, the maximum dispatchable power output of the node or the shortage power that needs to be absorbed from other nodes is obtained, and the energy allocation of the photovoltaic and energy storage integrated machine in the node is completed.

[0029] Furthermore, the objective function F of the upper model of the online deployment strategy u for:

[0030]

[0031] Among them, β and γ are the weights of active power input and user energy storage capacity difference respectively; P ref Indicates the active power input to node 1; It represents the difference between the energy storage capacity of the u-th user at node j and the average energy storage capacity of all users in the distribution network.

[0032] Furthermore, the upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model, including:

[0033] The Benders decomposition algorithm is used to optimize the active power of the photovoltaic and storage nodes, including the input power of the reference node and the input power between the photovoltaic and storage nodes. The input power of the reference node and the input power between the photovoltaic and storage nodes are decomposed into a main problem and sub-problems. The input power of the reference node is obtained by solving the main problem, and the input and output of the power between the photovoltaic and storage nodes are obtained by solving the sub-problems, thereby balancing the energy storage capacity among users.

[0034] Furthermore, the input power of the reference node is obtained by solving the main problem, and the input and output of power between the photovoltaic and energy storage nodes are obtained by solving the sub-problems, so as to balance the energy storage capacity between users, including:

[0035] Initialize algorithm parameters, initial upper and lower bounds;

[0036] Initialize the main problem: randomly generate a solution to the main problem within the operating range, , that is, the total input power of the reference node, F m =P ref ;

[0037] Solve the sub-problem: Substitute the solution of the main problem into the node information of the distribution network model, and calculate the power flow distribution of the distribution network while satisfying the constraints, so as to obtain the solution of the sub-problem and the corresponding target value. The solution of the sub-problem is the input and output of user power between the photovoltaic and storage nodes, and the corresponding objective function is the energy storage difference. According to the direction of minimizing the target value, the input and output of user power between the optical storage nodes are optimized through iteration, so as to obtain the optimal solution of the sub-problem and the minimized target function value;

[0038] Update the main problem. According to the cutting plane fed back by the sub-problem, add the generated Benders cut to the constraints of the main problem and adjust the solution of the main problem, including:

[0039] Calculate the shadow price of the target solution and obtain it through the Lagrange multiplier method of linear programming. The shadow price is usually expressed as the Lagrange multiplier λ corresponding to the constraint condition at each node. j , represents the sensitivity to the solution of the main problem, that is, the rate at which the objective function of the sub-problem changes when the solution of the main problem changes slightly. The calculation formula is:

[0040]

[0041] P j is the power of node j;

[0042] The Benders cut is calculated by the shadow price to approximate the solution space of the main problem. Assume that the solution of the current subproblem is Then the Benders cut is:

[0043]

[0044] This inequality will be added to the main problem to constrain the solution of the main problem, making future solutions closer and closer to the global optimal solution;

[0045] Take the value in the newly generated solution space of the main problem and get the solution of the new main problem Among them, the updated main problems include the new Benders cut;

[0046] Substitute the solution of the new main problem into the subproblem to solve it, and repeat the above steps until the solutions of the main problem and the subproblem converge.

[0047] Furthermore, the objective function F of the lower model of the online deployment strategy is lfor:

[0048] F l =min∑ j (V j -V j_ref ) 2

[0049] Among them, V j and V j_ref are the actual voltage and reference voltage of node j respectively.

[0050] Furthermore, the lower model of the online dispatching strategy uses the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model, including:

[0051] Initialize the number of particles N in the particle swarm; each particle represents a possible solution; the dimension of the particle is equal to the number of variables to be optimized, that is, the number of photovoltaic storage nodes; the position of each particle represents its output reactive power Q j ; Initialize the position of the particles within the feasible range of reactive power; assign an initial velocity v to each particle j ; Take the initial position of the particle as its initial individual optimal solution p j , select a particle with the smallest objective function value in the initial particle swarm as the global optimal solution g j ; In the tth iteration, the particle velocity is updated according to the formula:

[0052]

[0053] Among them, ω is the inertia weight; c1 and c2 are acceleration constants, ranging from 0.1 to 0.3; r1 and r2 are random numbers; the updated velocity is used to adjust the position of the particle If it exceeds the boundary, constrain the position to the boundary;

[0054] In the tth iteration, the objective function value is calculated for the new position of each particle, that is, the impact of reactive power distribution on system voltage, and the objective function value corresponding to the new position of the particle and the individual optimal position in history is compared. The new position of the particle and the better position in the historical optimal position are determined according to the objective function value of the new position of the particle and the objective function value of the historical optimal position of the particle. If the new position of the particle is better, the new position of the particle is used to update the individual optimal solution of the particle.

[0055] Compare the individual optimal solutions of all particles, determine the global optimal solution in the current iteration from the individual optimal solutions of all particles, and update the global optimal solution based on the global optimal solution in the current iteration and the global optimal solution in the previous iteration. Check the convergence conditions: If the number of iterations reaches the maximum value, or the global optimal solution does not change in multiple iterations, the algorithm is considered to have converged; if not, return to continue iterating; after the algorithm converges, the global optimal solution is output, which is the optimal distribution of reactive power.

[0056] Based on the same inventive concept, an online deployment system based on a household photovoltaic storage integrated machine cluster of the present invention comprises:

[0057] Data acquisition module, used to obtain basic data of low-voltage distribution network;

[0058] Data preprocessing module, used to preprocess the uncertain factors in the operation process of the distribution network in the basic data;

[0059] The distribution network model building module is used to build a basic distribution network model based on the power flow constraints and safe operation constraints of the distribution network and combined with the pre-processed basic data;

[0060] The energy allocation module is used to determine whether the node to which the photovoltaic and storage integrated device is connected needs to absorb power from the distribution network based on the calculated dispatchable power or shortfall power inside the node to which the photovoltaic and storage integrated device is connected, and to allocate the energy of the photovoltaic and storage integrated device in the node based on the judgment result, and to form a dispatchable power constraint of the photovoltaic and storage node and add it to the basic distribution network model to obtain a distribution network model;

[0061] The model optimization module is used to establish the upper model of the online dispatching strategy. The upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model.

[0062] The model optimization module is used to establish a lower-level model of the online dispatching strategy, and the lower-level model of the online dispatching strategy uses a particle swarm optimization algorithm to optimize the input information of the lower-level distribution network model;

[0063] The information integration module is used to integrate the input information of the upper layer of the optimized distribution network model and the input information of the lower layer of the distribution network model to obtain the input power of the reference node of the distribution network, the energy allocation between the photovoltaic and storage integrated machines, and the reactive power output of the photovoltaic and storage nodes.

[0064] Furthermore, the basic distribution network model includes:

[0065] Power flow constraints of distribution network:

[0066] The node voltage relationship between nodes i and j is expressed as:

[0067]

[0068] Among them, V j represents the actual voltage at node j, V iRepresents the actual voltage of node i; I ij Represents the current in branch ij; R ij With X ij are the impedance and inductive reactance on branch ij respectively; P ij With Q ij Represents the active power and reactive power on branch ij; P jl With Q jl represents the active power and reactive power on branch jl; P j and Q j represents the active power and reactive power injected by node j; the constraints are as follows:

[0069]

[0070] π(j) and σ(j) are the sets of parent nodes and child nodes of node j, and they need to satisfy the following calculation constraints of active power and reactive power:

[0071]

[0072] Constraints on safe operation of distribution networks:

[0073] The current in branch ij and the voltage at node j need to be within the safe operating range:

[0074] V min ≤V j ≤V max

[0075]

[0076] Further, according to the calculated dispatchable power or insufficient power inside the node to which the integrated photovoltaic and storage device is connected, it is determined whether the node to which the integrated photovoltaic and storage device is connected needs to absorb power from the distribution network, and the energy of the integrated photovoltaic and storage device in the node is allocated according to the determination result, including:

[0077] Traverse each user. When the user's photovoltaic power generation is greater than the user's load, calculate the user's excess power and store the user's excess power in the user's energy storage or distribute it to other users at the same node. When the user's photovoltaic power generation is less than the user's load, calculate the user's power shortage. If the user's energy storage has enough surplus, the power shortage will be compensated from the user's energy storage first. Otherwise, the power shortage will be compensated from other users at the same node. Until the power calculation of all photovoltaic and energy storage users in the node is completed, the maximum dispatchable power output of the node or the shortage power that needs to be absorbed from other nodes is obtained, and the energy allocation of the photovoltaic and energy storage integrated machine in the node is completed.

[0078] Furthermore, the objective function F of the upper model of the online deployment strategy u for:

[0079]

[0080] Among them, β and γ are the weights of active power input and user energy storage capacity difference respectively; P ref Indicates the active power input to node 1; It represents the difference between the energy storage capacity of the u-th user at node j and the average energy storage capacity of all users in the distribution network.

[0081] Furthermore, the upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model, including:

[0082] The Benders decomposition algorithm is used to optimize the active power of the photovoltaic and storage nodes, including the input power of the reference node and the input power between the photovoltaic and storage nodes. The input power of the reference node and the input power between the photovoltaic and storage nodes are decomposed into a main problem and sub-problems. The input power of the reference node is obtained by solving the main problem, and the input and output of the power between the photovoltaic and storage nodes are obtained by solving the sub-problems, thereby balancing the energy storage capacity among users.

[0083] Furthermore, the input power of the reference node is obtained by solving the main problem, and the input and output of power between the photovoltaic and energy storage nodes are obtained by solving the sub-problems, so as to balance the energy storage capacity between users, including:

[0084] Initialize algorithm parameters, initial upper and lower bounds;

[0085] Initialize the main problem: randomly generate a solution to the main problem within the operating range, , that is, the total input power of the reference node, F m =P ref ;

[0086] Solve the sub-problem: Substitute the solution of the main problem into the node information of the distribution network model, and calculate the power flow distribution of the distribution network while satisfying the constraints, so as to obtain the solution of the sub-problem and the corresponding target value. The solution of the sub-problem is the input and output of user power between the photovoltaic and storage nodes, and the corresponding objective function is the energy storage difference. According to the direction of minimizing the target value, the input and output of user power between the optical storage nodes are optimized through iteration, so as to obtain the optimal solution of the sub-problem and the minimized target function value;

[0087] Update the main problem. According to the cutting plane fed back by the sub-problem, add the generated Benders cut to the constraints of the main problem and adjust the solution of the main problem, including:

[0088] Calculate the shadow price of the target solution and obtain it through the Lagrange multiplier method of linear programming. The shadow price is usually expressed as the Lagrange multiplier λ corresponding to the constraint condition at each node.j , represents the sensitivity to the solution of the main problem, that is, the rate at which the objective function of the sub-problem changes when the solution of the main problem changes slightly. The calculation formula is:

[0089]

[0090] P j is the power of node j;

[0091] The Benders cut is calculated by the shadow price to approximate the solution space of the main problem. Assume that the solution of the current subproblem is Then the Benders cut is:

[0092]

[0093] This inequality will be added to the main problem to constrain the solution of the main problem, making future solutions closer and closer to the global optimal solution;

[0094] Take the value in the newly generated solution space of the main problem and get the solution of the new main problem Among them, the updated main problems include the new Benders cut;

[0095] Substitute the solution of the new main problem into the subproblem to solve it, and repeat the above steps until the solutions of the main problem and the subproblem converge.

[0096] Furthermore, the objective function F of the lower model of the online deployment strategy is l for:

[0097] F l =min∑ j (V j -V j_ref ) 2

[0098] Among them, V j and V j_ref are the actual voltage and reference voltage of node j respectively.

[0099] Furthermore, the lower model of the online dispatching strategy uses the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model, including:

[0100] Initialize the number of particles N in the particle swarm; each particle represents a possible solution; the dimension of the particle is equal to the number of variables to be optimized, that is, the number of photovoltaic storage nodes; the position of each particle represents its output reactive power Q j ; Initialize the position of the particles within the feasible range of reactive power; assign an initial velocity v to each particle j ; Take the initial position of the particle as its initial individual optimal solution p j, select a particle with the smallest objective function value in the initial particle swarm as the global optimal solution g j ; In the tth iteration, the particle velocity is updated according to the formula:

[0101]

[0102] Where ω is the inertia weight; c1 and c2 are acceleration constants; the selection range is 0.1 to 0.3; r1 and r2 are random numbers; the updated velocity is used to adjust the particle's position If it exceeds the boundary, constrain the position to the boundary;

[0103] In the tth iteration, the objective function value is calculated for the new position of each particle, that is, the impact of reactive power distribution on system voltage, and the objective function value corresponding to the new position of the particle and the individual optimal position in history is compared. The new position of the particle and the optimal position in the historical optimal position are determined according to the objective function value of the new position of the particle and the objective function value of the historical optimal position of the particle. If the new position of the particle is optimal, the individual optimal solution of the particle is updated using the new position of the particle.

[0104] Compare the individual optimal solutions of all particles, determine the global optimal solution in the current iteration from the individual optimal solutions of all particles, and update the global optimal solution based on the global optimal solution in the current iteration and the global optimal solution in the previous iteration. Check the convergence conditions: If the number of iterations reaches the maximum value, or the global optimal solution does not change in multiple iterations, the algorithm is considered to have converged; if not, return to continue iterating; after the algorithm converges, the global optimal solution is output, which is the optimal distribution of reactive power.

[0105] Based on the same inventive concept, the present invention provides an online allocation device based on a household photovoltaic and storage integrated machine cluster, including a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the above-mentioned online allocation method based on a household photovoltaic and storage integrated machine cluster.

[0106] Based on the same inventive concept, a computer-readable storage medium of the present invention is characterized in that the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned online deployment method based on a household optical storage integrated machine cluster.

[0107] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are:

[0108] The present invention can form a cluster of household photovoltaic and storage integrated machines in the substation area, and first realize distribution within the cluster. When the photovoltaic and storage power of some users is insufficient to support the user's load, other photovoltaic and storage integrated machines are used to meet the power load in priority. While improving the photovoltaic utilization rate of villages and towns, the reactive power and harmonic wave management of the village and town distribution network is achieved, the power quality of villages and towns is improved, and user satisfaction is enhanced.

[0109] The present invention can realize an optimal configuration model for photovoltaic storage resource scheduling with the goal of economic efficiency. Through a double-layer model formed based on the characteristics of the algorithm, in the upper model, because two goals need to be optimized at the same time, the Benders algorithm for decomposing primary and secondary problems in the algorithm is selected, which improves the optimization efficiency of the algorithm, adds the energy allocation process inside the household photovoltaic storage integrated machine, improves the consumption efficiency of distributed photovoltaic energy, balances the differences in energy storage capacity among users, enhances the stability of weak power grids in coping with extreme weather, and can suppress voltage fluctuations in low-voltage distribution networks.

[0110] The present invention balances the differences in energy storage capacity of users in each node, thereby reducing the waste of photovoltaic power generation energy on the one hand, and increasing the overall capacity of energy storage in the distribution network on the other hand, thereby improving the stability of the low-voltage distribution network.

[0111] The present invention forms a photovoltaic storage integrated machine cluster in a low-voltage distribution network into an effective whole, which facilitates the response to the deployment instructions of the upper level. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 It is a flow chart of an online deployment method based on a household photovoltaic storage integrated machine cluster disclosed in an embodiment of the present invention;

[0113] Figure 2 It is a flowchart of a Benders algorithm disclosed in an embodiment of the present invention;

[0114] Figure 3 It is a flow chart of a PSO algorithm disclosed in an embodiment of the present invention;

[0115] Figure 4 It is a structural schematic diagram of an online deployment system based on a household photovoltaic storage integrated machine cluster disclosed in an embodiment of the present invention;

[0116] Figure 5 It is a structural schematic diagram of an online allocation device based on a household photovoltaic storage integrated machine cluster disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0117] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It will be understood by those skilled in the art that the purposes and advantages that can be achieved with the present invention are not limited to the specific description of the above beneficial effects, and the above and other purposes that can be achieved by the present invention will be more clearly understood based on the following detailed description.

[0118] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software or a combination of the two. Whether it is implemented in hardware or software depends on the specific application and design of the technical solution. Professional and technical personnel can use different methods to implement the functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0119] Reference to "embodiments" in the present invention means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0120] Example 1

[0121] See also Figure 1 , Figure 1 This is a flow chart of an online deployment method based on a household photovoltaic storage integrated machine cluster disclosed in an embodiment of the present invention. Figure 1 The online allocation method described is applied to the power system, such as online flexible allocation of household photovoltaic storage integrated machine cluster resources used by residents, etc., which is not limited in the embodiments of the present invention. Figure 1 As shown, the online deployment method based on the household photovoltaic storage integrated machine cluster may include the following operations:

[0122] S1. Obtain basic data of low-voltage distribution network.

[0123] Input basic data of distribution network, such as network structure, branch impedance, line capacity, etc.

[0124] S2. Preprocess the uncertainties in the operation of the distribution network in the basic data, eliminate abnormal values ​​in the distribution network data, and fill in missing values;

[0125] S3. According to the power flow constraints and safe operation constraints of the distribution network, a basic distribution network model is established in combination with the pre-processed basic data.

[0126] Among them, the basic distribution network model includes:

[0127] Power flow constraints of distribution network:

[0128] The node voltage relationship between nodes i and j is expressed as:

[0129]

[0130] Among them, V j represents the actual voltage at node j, V i Represents the actual voltage of node i; I ij Represents the current in branch ij; R ij With X ij are the impedance and inductive reactance on branch ij respectively; P ij With Q ij Represents the active power and reactive power on branch ij; P jl With Q jl represents the active power and reactive power on branch jl; P j and Q j represents the active power and reactive power injected by node j; the constraints are as follows:

[0131]

[0132] π(j) and σ(j) are the sets of parent nodes and child nodes of node j, and they need to satisfy the following calculation constraints of active power and reactive power:

[0133]

[0134] The safe operation constraints of the distribution network are:

[0135] The current in branch ij and the voltage at node j need to be within the safe operating range:

[0136] V min ≤V j ≤V max

[0137]

[0138] S4. Based on the calculated dispatchable power or deficit power inside the node to which the integrated PV and storage device is connected, determine whether the node to which the integrated PV and storage device is connected needs to absorb power from the distribution network, allocate energy of the integrated PV and storage device in the node based on the determination result, and form a dispatchable power constraint for the PV and storage node and add it to the basic distribution network model to obtain a distribution network model.

[0139] Traverse each user. When the user's photovoltaic power generation is greater than the user's load, calculate the user's excess power and store the user's excess power in the user's energy storage or distribute it to other users at the same node. When the user's photovoltaic power generation is less than the user's load, calculate the user's power shortage. If the user's energy storage has enough surplus, the power shortage will be compensated from the user's energy storage first. Otherwise, the power shortage will be compensated from other users at the same node. Until the power calculation of all photovoltaic and energy storage users in the node is completed, the maximum dispatchable power that the node can output or the shortage power that needs to be absorbed from other nodes is obtained, and the energy allocation of the photovoltaic and energy storage integrated machine in the node is completed.

[0140] S5, such as Figure 2 As shown, an upper-level model of the online dispatching strategy is established, and the upper-level model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper-level distribution network model.

[0141] In this embodiment, the objective function F of the upper model of the online deployment strategy u for:

[0142]

[0143] Among them, β and γ are the weights of active power input and user energy storage capacity difference respectively; P ref Indicates the active power input to node 1; It represents the difference between the energy storage capacity of the u-th user at node j and the average energy storage capacity of all users in the distribution network.

[0144] It should be explained that when the algorithm optimizes the problem, it will make the upper model objective function F of the online deployment strategy u The calculation result of becomes smaller and smaller, minimizing this value. This value is a weighted value of the difference between the reference power and the energy storage capacity. Node 1 represents the first node of the distribution network, which is generally called the reference node.

[0145] Among them, the upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model, as follows:

[0146] The Benders decomposition algorithm is used to optimize the active power of the photovoltaic and storage nodes, including the input power of the reference node and the input power between the photovoltaic and storage nodes. The input power of the reference node and the input power between the photovoltaic and storage nodes are decomposed into a main problem and sub-problems. The input power of the reference node is obtained by solving the main problem, and the input and output of the power between the photovoltaic and storage nodes are obtained by solving the sub-problems, thereby balancing the energy storage capacity among users.

[0147] In this embodiment, the input power of the reference node is obtained by solving the main problem, and the input and output of power between the photovoltaic and energy storage nodes are obtained by solving the sub-problems, so as to balance the energy storage capacity between users, including the following contents:

[0148] Initialize algorithm parameters, initial upper and lower bounds;

[0149] Initialize the main problem: randomly generate a solution to the main problem within the operating range (i.e., the total input power of the reference node, F m =P ref );

[0150] Solve the sub-problem: Substitute the solution of the main problem into the node information of the distribution network model, and calculate the power flow distribution of the distribution network while satisfying the constraints, so as to obtain the solution of the sub-problem and the corresponding target value. The solution of the sub-problem is the input and output of user power between the photovoltaic and storage nodes, and the corresponding objective function is the energy storage difference. According to the direction of minimizing the target value, the input and output of user power between the optical storage nodes are optimized through iteration, so as to obtain the optimal solution of the sub-problem and the minimized target function value;

[0151] Update the main problem. According to the cutting plane fed back by the sub-problem, add the generated Benders cut to the constraints of the main problem and adjust the solution of the main problem, including:

[0152] Calculate the shadow price of the target solution and obtain it through the Lagrange multiplier method of linear programming. The shadow price is usually expressed as the Lagrange multiplier λ corresponding to the constraint condition at each node. j , represents the sensitivity to the solution of the main problem, that is, the rate at which the objective function of the sub-problem changes when the solution of the main problem changes slightly. The calculation formula is:

[0153]

[0154] P j is the power of node j;

[0155] The Benders cut is calculated by the shadow price to approximate the solution space of the main problem. Assume that the solution of the current subproblem is Then the Benders cut is:

[0156]

[0157] This inequality will be added to the main problem to constrain the solution of the main problem, making future solutions closer and closer to the global optimal solution;

[0158] Take the value in the newly generated solution space of the main problem and get the solution of the new main problem Among them, the updated main problems include the new Benders cut;

[0159] Substitute the solution of the new main problem into the subproblem to solve it, and repeat the above steps until the solutions of the main problem and the subproblem converge.

[0160] In this scheme, the objective function of the Benders algorithm consists of two parts, the former is defined as the main problem, and the latter is defined as the sub-problem. Under other operating conditions of the distribution network, the power of the reference node of the distribution network is obtained by solving the main problem. Then, according to the solution of the main problem, the sub-problem is solved to obtain the active output power of the photovoltaic storage device and the value of the objective function. Determine whether the solution of the sub-problem meets the constraints. If not, generate a cut function and add it to the main problem. The cut function is to gradually approximate the feasible domain range of the main problem variables. If the constraints are met, update the upper and lower bounds, and the expressions of the upper and lower bounds are:

[0161] U B =min(U B ,F u )

[0162] L B =max(L B ,F u )

[0163] S6, such as Figure 3 As shown, a lower-level model of the online dispatching strategy is established, and the lower-level model of the online dispatching strategy uses a particle swarm optimization algorithm to optimize the input information of the lower-level distribution network model.

[0164] In this embodiment, the objective function F of the lower model of the online deployment strategy is l for:

[0165] F l =minΣ j (V j -V j_ref ) 2

[0166] Among them, V j and V j_ref are the actual voltage and reference voltage of node j respectively.

[0167] It needs to be explained that the objective function F l This means minimizing the node voltage deviation.

[0168] Among them, the lower model of the online dispatching strategy uses the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model, as follows:

[0169] The particle swarm optimization algorithm is used to optimize the reactive power of the photovoltaic storage integrated machine nodes and reduce the voltage fluctuations of each node in the distribution network.

[0170] Initialize the number of particles N in the particle swarm; each particle represents a possible solution; the dimension of the particle is equal to the number of variables to be optimized, that is, the number of photovoltaic storage nodes; the position of each particle represents its output reactive power Q j ; Initialize the position of the particles within the feasible range of reactive power; assign an initial velocity v to each particle j ; Take the initial position of the particle as its initial individual optimal solution p j , select a particle with the smallest objective function value in the initial particle swarm as the global optimal solution g j ; In the tth iteration, the particle velocity is updated according to the formula:

[0171]

[0172] Where ω is the inertia weight; c1 and c2 are acceleration constants; the selection range is 0.1 to 0.3; r1 and r2 are random numbers (between 0 and 1). Use the updated velocity to adjust the particle's position If it exceeds the boundary, constrain the position to the boundary;

[0173] In the tth iteration, the objective function value is calculated for the new position of each particle, that is, the impact of reactive power distribution on system voltage (the sum of squares of voltage fluctuations), and the objective function value corresponding to the new position of the particle and the individual optimal position in history is compared (individual optimal). The new position of the particle and the optimal position in the historical optimal position are determined according to the objective function value of the new position of the particle and the objective function value of the historical optimal position of the particle. If the new position of the particle is optimal, the individual optimal solution of the particle is updated using the new position of the particle.

[0174] Compare the individual optimal solutions of all particles, determine the global optimal solution in the current iteration from the individual optimal solutions of all particles, and update the global optimal solution based on the global optimal solution in the current iteration and the global optimal solution in the previous iteration. Check the convergence condition: If the number of iterations reaches the maximum value, or the global optimal solution does not change in multiple iterations (the amount of change is less than the set threshold), the algorithm is considered to have converged. If it has not converged, it returns to continue iterating. After the algorithm converges, it outputs the global optimal solution, which is the optimal distribution of reactive power.

[0175] The present invention adopts a two-layer optimization model. The upper layer uses the Benders algorithm to optimize the balance between the active input of the distribution network and the user's energy storage capacity to obtain the output active power of the household photovoltaic and energy storage integrated machine cluster; the lower layer uses the PSO algorithm to optimize the voltage deviation of the distribution network nodes to obtain the output reactive power of the household photovoltaic and energy storage integrated machine cluster.

[0176] In this scheme, the initial position of the particle swarm is the reactive power output by the PESIE node, and the initial speed of the particle swarm is set. For each particle, its fitness, i.e., the voltage deviation of the distribution network, is calculated, thereby updating the speed and position of the particle.

[0177] S7. Integrate the input information of the upper layer of the optimized distribution network model and the input information of the lower layer of the distribution network model to obtain the input power of the reference node of the distribution network, the energy allocation between the photovoltaic and storage integrated devices, and the reactive power output of the photovoltaic and storage nodes.

[0178] It should be noted that the household photovoltaic storage integrated machine is a user in the node that has three characteristics: photovoltaic power generation, energy storage and user load. The difference between the three determines the user's external performance. The household photovoltaic storage integrated machine cluster needs to meet the following constraints:

[0179]

[0180] in and are respectively the minimum and maximum values ​​allowed for the energy storage capacity of the u-th user at node j during operation. is the power allowed to be output by user u, P j,u,PV and P j,u,load represents the photovoltaic power generation and load of the user. s is the state function. When the photovoltaic power generation of the user is greater than the load required by itself, its value is 1, otherwise it is 0. The total output power of node j is Depend on And the shortfall of the user's photovoltaic power generation capacity Decide.

[0181] The photovoltaic storage integrated cluster can also achieve reactive power output Constrained by active power output and inverter capacity:

[0182]

[0183] Among them, α is the maximum power factor angle output by the integrated photovoltaic and storage device.

[0184] The external manifestation of the photovoltaic and storage machine is: when the user's photovoltaic power generation is greater than the user's load, the user will not absorb power from the distribution network, and the excess power will be absorbed by the energy storage or transmitted to other photovoltaic and storage machines; when the user's photovoltaic power generation is less than the user's load, the shortfall will be provided by other photovoltaic and storage machines or energy storage.

[0185] The photovoltaic and energy storage integrated machines in the same node give priority to supporting each other. When the photovoltaic power generation of a user is lower than the power consumption, the excess power will be absorbed from the users in the same node whose photovoltaic power generation is greater than the power consumption and whose energy storage capacity is high. After calculating the power situation in the same node, the power will be allocated between different nodes, with priority given to supplying power to the insufficient nodes from nodes with high energy storage capacity and whose overall photovoltaic power generation is greater than the power consumption.

[0186] The present invention adopts a double-layer household photovoltaic storage integrated machine cluster optimization model, which can not only realize local power quality optimization and internal mutual support, adapt to the characteristics of distributed photovoltaic resources with many points and wide areas, realize on-site photovoltaic consumption, but also help photovoltaic storage nodes participate in the operation of low-voltage distribution network and improve the stability of distribution network.

[0187] Example 2

[0188] See also Figure 4 , Figure 4 The structure diagram of an online allocation system based on a household photovoltaic storage integrated machine cluster disclosed in an embodiment of the present invention can realize online flexible allocation of household photovoltaic storage integrated machine cluster resources used by residents, specifically including:

[0189] Data acquisition module, used to obtain basic data of low-voltage distribution network;

[0190] Data preprocessing is used to preprocess the uncertainties in the operation of the distribution network in the basic data, eliminate abnormal values ​​in the distribution network data, and fill in missing values;

[0191] The distribution network model building module builds the distribution network model based on the distribution network flow constraints and safety operation constraints combined with the pre-processed data;

[0192] The energy allocation module is used to calculate the dispatchable power or power shortage within the node, allocate the energy of the photovoltaic and storage integrated devices in the node, and form the dispatchable power constraints of the photovoltaic and storage nodes and add them to the distribution network model;

[0193] The model optimization module is used to establish the upper model of the online dispatching strategy. The upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model.

[0194] The model optimization module is used to establish a lower-level model of the online dispatching strategy, and the lower-level model of the online dispatching strategy uses a particle swarm optimization algorithm to optimize the input information of the lower-level distribution network model;

[0195] The information integration module is used to integrate the input information of the upper layer and the lower layer of the optimized distribution network model to obtain the input power of the reference node of the distribution network, the energy allocation between the photovoltaic and storage integrated machines, and the reactive power output of the photovoltaic and storage nodes.

[0196] In an optional embodiment, an online dispatching method based on a cluster of household photovoltaic and storage devices includes: a) obtaining basic data of a low-voltage distribution network; b) preprocessing uncertain factors, eliminating abnormal values ​​in the distribution network data, and filling missing values; c) establishing a distribution network model based on the distribution network flow constraints and safe operation constraints in combination with the preprocessed data; d) calculating the dispatchable power or insufficient power within the node, and performing energy dispatch of the photovoltaic and storage devices within the node; e) establishing an upper model of an online dispatching strategy, and the upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model; f) establishing a lower model of an online dispatching strategy, and the lower model of the online dispatching strategy uses the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model; g) integrating the upper and lower input information of the optimized distribution network model to obtain the input power of the reference node of the distribution network, the energy dispatch situation between the photovoltaic and storage devices, and the reactive power output by the photovoltaic and storage nodes.

[0197] In one embodiment, the basic distribution network model includes:

[0198] Power flow constraints of distribution network:

[0199] The node voltage relationship between nodes i and j is expressed as:

[0200]

[0201] Among them, V j represents the actual voltage at node j, V i Represents the actual voltage of node i; I ij Represents the current in branch ij; R ij With X ij are the impedance and inductive reactance on branch ij respectively; P ij With Q ij Represents the active power and reactive power on branch ij; P jl With Q jl represents the active power and reactive power on branch jl; P j and Q j represents the active power and reactive power injected by node j; the constraints are as follows:

[0202]

[0203] π(j) and σ(j) are the sets of parent nodes and child nodes of node j, and they need to satisfy the following calculation constraints of active power and reactive power:

[0204]

[0205] Constraints on safe operation of distribution networks:

[0206] The current in branch ij and the voltage at node j need to be within the safe operating range:

[0207] V min ≤V j ≤V max

[0208]

[0209] In one embodiment, according to the calculated dispatchable power or insufficient power inside the node to which the integrated photovoltaic and storage device is connected, it is determined whether the node to which the integrated photovoltaic and storage device is connected needs to absorb power from the power distribution network, and according to the determination result, energy allocation of the integrated photovoltaic and storage device in the node is performed, including:

[0210] Traverse each user. When the user's photovoltaic power generation is greater than the user's load, calculate the user's excess power and store the user's excess power in the user's energy storage or distribute it to other users at the same node. When the user's photovoltaic power generation is less than the user's load, calculate the user's power shortage. If the user's energy storage has enough surplus, the power shortage will be compensated from the user's energy storage first. Otherwise, the power shortage will be compensated from other users at the same node. Until the power calculation of all photovoltaic and energy storage users in the node is completed, the maximum dispatchable power output of the node or the shortage power that needs to be absorbed from other nodes is obtained, and the energy allocation of the photovoltaic and energy storage integrated machine in the node is completed.

[0211] In one embodiment, the objective function F of the upper model of the online deployment strategy is u for:

[0212]

[0213] Among them, β and γ are the weights of active power input and user energy storage capacity difference respectively; P ref Indicates the active power input to node 1; It represents the difference between the energy storage capacity of the u-th user at node j and the average energy storage capacity of all users in the distribution network.

[0214] In one embodiment, the upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model, including:

[0215] The Benders decomposition algorithm is used to optimize the active power of the photovoltaic and storage nodes, including the input power of the reference node and the input power between the photovoltaic and storage nodes. The input power of the reference node and the input power between the photovoltaic and storage nodes are decomposed into a main problem and sub-problems. The input power of the reference node is obtained by solving the main problem, and the input and output of the power between the photovoltaic and storage nodes are obtained by solving the sub-problems, thereby balancing the energy storage capacity among users.

[0216] In one embodiment, the input power of the reference node is obtained by solving the main problem, and the input and output of power between the photovoltaic and energy storage nodes are obtained by solving the sub-problems, so as to balance the energy storage capacity between users, including:

[0217] Initialize algorithm parameters, initial upper and lower bounds;

[0218] Initialize the main problem: randomly generate a solution to the main problem within the operating range, that is, the total input power of the reference node, F m =P ref ;

[0219] Solve the sub-problem: Substitute the solution of the main problem into the node information of the distribution network model, and calculate the power flow distribution of the distribution network while satisfying the constraints, so as to obtain the solution of the sub-problem and the corresponding target value. The solution of the sub-problem is the input and output of user power between the photovoltaic and storage nodes, and the corresponding objective function is the energy storage difference. According to the direction of minimizing the target value, the input and output of user power between the optical storage nodes are optimized through iteration, so as to obtain the optimal solution of the sub-problem and the minimized target function value;

[0220] Update the main problem. According to the cutting plane fed back by the sub-problem, add the generated Benders cut to the constraints of the main problem and adjust the solution of the main problem, including:

[0221] Calculate the shadow price of the target solution and obtain it through the Lagrange multiplier method of linear programming. The shadow price is usually expressed as the Lagrange multiplier λ corresponding to the constraint condition at each node. j , represents the sensitivity to the solution of the main problem, that is, the rate at which the objective function of the sub-problem changes when the solution of the main problem changes slightly. The calculation formula is:

[0222]

[0223] P j is the power of node j;

[0224] The Benders cut is calculated by the shadow price to approximate the solution space of the main problem. Assume that the solution of the current subproblem is Then the Benders cut is:

[0225]

[0226] This inequality will be added to the main problem to constrain the solution of the main problem, making future solutions closer and closer to the global optimal solution;

[0227] Take the value in the newly generated solution space of the main problem and get the solution of the new main problem Among them, the updated main problems include the new Benders cut;

[0228] Substitute the solution of the new main problem into the subproblem to solve it, and repeat the above steps until the solutions of the main problem and the subproblem converge.

[0229] In one embodiment, the objective function F of the lower model of the online deployment strategy is l for:

[0230] F l =min∑ j (V j -V j_ref ) 2

[0231] Among them, V j and V j_ref are the actual voltage and reference voltage of node j respectively.

[0232] In one embodiment, the lower model of the online dispatching strategy uses a particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model, including:

[0233] Initialize the number of particles N in the particle swarm; each particle represents a possible solution; the dimension of the particle is equal to the number of variables to be optimized, that is, the number of photovoltaic storage nodes; the position of each particle represents its output reactive power Q j ; Initialize the position of the particles within the feasible range of reactive power; assign an initial velocity v to each particle j ; Take the initial position of the particle as its initial individual optimal solution p j , select a particle with the smallest objective function value in the initial particle swarm as the global optimal solution g j ; In the tth iteration, the particle velocity is updated according to the formula:

[0234]

[0235] Where ω is the inertia weight; c1 and c2 are acceleration constants; the selection range is 0.1 to 0.3; r1 and r2 are random numbers; the updated velocity is used to adjust the particle's position If it exceeds the boundary, constrain the position to the boundary;

[0236] In the tth iteration, the objective function value is calculated for the new position of each particle, that is, the impact of reactive power distribution on system voltage, and the objective function value corresponding to the new position of the particle and the individual optimal position in history is compared. The new position of the particle and the optimal position in the historical optimal position are determined according to the objective function value of the new position of the particle and the objective function value of the historical optimal position of the particle. If the new position of the particle is optimal, the individual optimal solution of the particle is updated using the new position of the particle.

[0237] Compare the individual optimal solutions of all particles, determine the global optimal solution in the current iteration from the individual optimal solutions of all particles, and update the global optimal solution based on the global optimal solution in the current iteration and the global optimal solution in the previous iteration. Check the convergence conditions: If the number of iterations reaches the maximum value, or the global optimal solution does not change in multiple iterations, the algorithm is considered to have converged; if not, return to continue iterating; after the algorithm converges, the global optimal solution is output, which is the optimal distribution of reactive power.

[0238] Example 3

[0239] See also Figure 5 , Figure 5 : is a structural diagram of an online deployment device based on a household photovoltaic storage integrated machine cluster disclosed in an embodiment of the present invention. Figure 5 The described device can be applied to power systems, such as for online flexible allocation of household photovoltaic and storage integrated machine cluster resources used by residents, etc., and the embodiments of the present invention are not limited thereto.

[0240] like Figure 5 As shown, the device may include a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the method described in the above embodiment and can achieve the technical effect consistent with the above method.

[0241] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The program modules typically perform the functions and / or methods in the embodiments described herein.

[0242] The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention.

[0243] Example 4

[0244] Embodiment 4 of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method described in the above embodiment are implemented and the technical effect consistent with the above method can be achieved.

[0245] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0246] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0247] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0248] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0249] Of course, the storage medium containing computer executable instructions provided by an embodiment of the present invention, whose computer executable instructions are not limited to the above method operations, can also execute related operations in the method provided by any embodiment of the present invention.

[0250] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An online deployment method based on a household photovoltaic storage integrated machine cluster, characterized in that: include: Obtain basic data of low-voltage distribution network; Pre-process the uncertain factors in the operation of the distribution network in the basic data; According to the power flow constraints and safe operation constraints of the distribution network, the basic distribution network model is established in combination with the pre-processed basic data; According to the calculated dispatchable power or insufficient power inside the node to which the photovoltaic and storage integrated device is connected, it is judged whether the node to which the photovoltaic and storage integrated device is connected needs to absorb power from the distribution network, and the energy of the photovoltaic and storage integrated device in the node is allocated according to the judgment result, and the dispatchable power constraint of the photovoltaic and storage node is formed and added to the basic distribution network model to obtain the distribution network model; Establish an upper model of the online dispatching strategy, which uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model; Establish the lower model of the online dispatching strategy, and use the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model; By integrating the input information of the upper layer of the optimized distribution network model and the input information of the lower layer of the distribution network model, the input power of the reference node of the distribution network, the energy allocation between the photovoltaic and storage integrated machines, and the reactive power output of the photovoltaic and storage nodes are obtained.

2. The online deployment method based on household photovoltaic storage integrated machine cluster according to claim 1 is characterized in that: The basic distribution network model includes: Power flow constraints of distribution network: The node voltage relationship between nodes i and , is expressed as: Among them, V j represents the actual voltage at node j, V i Represents the actual voltage of node i; I ij Represents the current in branch ij; R ij With X ij are the impedance and inductive reactance on branch ij respectively; P ij With Q ij Represents the active power and reactive power on branch ij; P jl With Q jl represents the active power and reactive power on branch jl; P j and Q j represents the active power and reactive power injected by node j; the constraints are as follows: π(j) and σ(j) are the sets of parent nodes and child nodes of node j, and they need to satisfy the following calculation constraints of active power and reactive power: Constraints on safe operation of distribution networks: The current in branch ij and the voltage at node j need to be within the safe operating range: In min ≤V j ≤V max 3. The online deployment method based on household photovoltaic and storage integrated machine cluster according to claim 1 is characterized in that: According to the calculated dispatchable power or insufficient power inside the node to which the PV-storage integrated device is connected, it is determined whether the node to which the PV-storage integrated device is connected needs to absorb power from the distribution network, and the energy of the PV-storage integrated device in the node is allocated according to the determination result, including: Traverse each user. When the user's photovoltaic power generation is greater than the user's load, calculate the user's excess power and store the user's excess power in the user's energy storage or distribute it to other users at the same node. When the user's photovoltaic power generation is less than the user's load, calculate the user's power shortage. If the user's energy storage has enough surplus, the power shortage will be compensated from the user's energy storage first. Otherwise, the power shortage will be compensated from other users at the same node. Until the power calculation of all photovoltaic and energy storage users in the node is completed, the maximum dispatchable power output of the node or the shortage power that needs to be absorbed from other nodes is obtained, and the energy allocation of the photovoltaic and energy storage integrated machine in the node is completed.

4. The online deployment method based on household photovoltaic and storage integrated machine cluster according to claim 1 is characterized in that: The objective function F of the upper model of the online deployment strategy is u for: Among them, β and γ are the weights of active power input and user energy storage capacity difference respectively; P ref Indicates the active power input to node 1; Represents the difference between the energy storage capacity of the u-th user at node and the average energy storage capacity of all users in the distribution network.

5. The online deployment method based on household photovoltaic and storage integrated machine cluster according to claim 4 is characterized in that: The upper model of the online dispatch strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model, including: The Benders decomposition algorithm is used to optimize the active power of the photovoltaic and storage nodes, including the input power of the reference node and the input power between the photovoltaic and storage nodes. The input power of the reference node and the input power between the photovoltaic and storage nodes are decomposed into a main problem and sub-problems. The input power of the reference node is obtained by solving the main problem, and the input and output of the power between the photovoltaic and storage nodes are obtained by solving the sub-problems, thereby balancing the energy storage capacity among users.

6. The online deployment method based on household photovoltaic storage integrated machine cluster according to claim 5 is characterized in that: The input power of the reference node is obtained by solving the main problem, and the input and output of power between the photovoltaic and energy storage nodes are obtained by solving the sub-problems, so as to balance the energy storage capacity between users, including: Initialize algorithm parameters, initial upper and lower bounds; Initialize the main problem: randomly generate a solution to the main problem within the operating range, , that is, the total input power of the reference node, F m =P ref ; Solve the sub-problem: Substitute the solution of the main problem into the node information of the distribution network model, and calculate the power flow distribution of the distribution network while satisfying the constraints, so as to obtain the solution of the sub-problem and the corresponding target value. The solution of the sub-problem is the input and output of user power between the photovoltaic and storage nodes, and the corresponding objective function is the energy storage difference. According to the direction of minimizing the target value, the input and output of user power between the optical storage nodes are optimized through iteration, so as to obtain the optimal solution of the sub-problem and the minimized target function value; Update the main problem. According to the cutting plane fed back by the sub-problem, add the generated Benders cut to the constraints of the main problem and adjust the solution of the main problem, including: Calculate the shadow price of the target solution and obtain it through the Lagrange multiplier method of linear programming. The shadow price is usually expressed as the Lagrange multiplier λ corresponding to the constraint condition at each node. j , represents the sensitivity to the solution of the main problem, that is, the rate at which the objective function of the sub-problem changes when the solution of the main problem changes slightly. The calculation formula is: P j is the power of node j; The Benders cut is calculated by the shadow price to approximate the solution space of the main problem. Assume that the solution of the current subproblem is Then the Benders cut is: This inequality will be added to the main problem to constrain the solution of the main problem, making future solutions closer and closer to the global optimal solution; Take the value in the newly generated solution space of the main problem and get the solution of the new main problem Among them, the updated main problems include the new Benders cut; Substitute the solution of the new main problem into the subproblem to solve it, and repeat the above steps until the solutions of the main problem and the subproblem converge.

7. The online deployment method based on household photovoltaic and storage integrated machine cluster according to claim 1 is characterized in that: The objective function F of the lower model of the online deployment strategy is l for: F l =min∑ j (V j -V j_ref ) 2 Among them, V j and V j_ref are the actual voltage and reference voltage of node j respectively.

8. The online deployment method based on household photovoltaic and storage integrated machine cluster according to claim 7 is characterized in that: The lower model of the online dispatching strategy uses the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model, including: Initialize the number of particles N in the particle swarm; each particle represents a possible solution; the dimension of the particle is equal to the number of variables to be optimized, that is, the number of photovoltaic storage nodes; the position of each particle represents its output reactive power Q j ; Initialize the position of the particles within the feasible range of reactive power; assign an initial velocity v to each particle j ; Take the initial position of the particle as its initial individual optimal solution p j , select a particle with the smallest objective function value in the initial particle swarm as the global optimal solution g j ; In the tth iteration, the particle velocity is updated according to the formula: Where ω is the inertia weight; c1 and c2 are acceleration constants; the selection range is 0.1 to 0.3; r1 and r2 are random numbers; the updated velocity is used to adjust the particle's position If it exceeds the boundary, constrain the position to the boundary; In the tth iteration, the objective function value is calculated for the new position of each particle, that is, the impact of reactive power distribution on system voltage, and the objective function value corresponding to the new position of the particle and the individual optimal position in history is compared. The better position of the particle between the new position and the historical optimal position is determined according to the objective function value of the new position of the particle and the objective function value of the historical optimal position of the particle. If the new position of the particle is better, the new position of the particle is used to update the individual optimal solution of the particle. Compare the individual optimal solutions of all particles, determine the global optimal solution in the current iteration from the individual optimal solutions of all particles, and update the global optimal solution based on the global optimal solution in the current iteration and the global optimal solution in the previous iteration. Check the convergence conditions: If the number of iterations reaches the maximum value, or the global optimal solution does not change in multiple iterations, the algorithm is considered to have converged; if not, return to continue iterating; after the algorithm converges, the global optimal solution is output, which is the optimal distribution of reactive power.

9. An online deployment system based on a household photovoltaic storage integrated machine cluster, characterized in that: include: Data acquisition module, used to obtain basic data of low-voltage distribution network; Data preprocessing module, used to preprocess the uncertain factors in the operation process of the distribution network in the basic data; The distribution network model building module is used to build a basic distribution network model based on the power flow constraints and safe operation constraints of the distribution network and combined with the pre-processed basic data; The energy allocation module is used to determine whether the node to which the photovoltaic and storage integrated device is connected needs to absorb power from the distribution network based on the calculated dispatchable power or shortfall power inside the node to which the photovoltaic and storage integrated device is connected, and to allocate the energy of the photovoltaic and storage integrated device in the node based on the judgment result, and to form a dispatchable power constraint of the photovoltaic and storage node and add it to the basic distribution network model to obtain a distribution network model; The model optimization module is used to establish the upper model of the online dispatching strategy. The upper model of the online dispatching strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model. The model optimization module is used to establish a lower-level model of the online dispatching strategy, and the lower-level model of the online dispatching strategy uses a particle swarm optimization algorithm to optimize the input information of the lower-level distribution network model; The information integration module is used to integrate the input information of the upper layer of the optimized distribution network model and the input information of the lower layer of the distribution network model to obtain the input power of the reference node of the distribution network, the energy allocation between the photovoltaic and storage integrated machines, and the reactive power output of the photovoltaic and storage nodes.

10. The online deployment system based on household photovoltaic and storage integrated machine cluster according to claim 9 is characterized in that: The basic distribution network model includes: Power flow constraints of distribution network: The node voltage relationship between nodes i and , is expressed as: Among them, V j represents the actual voltage at node j, V i Represents the actual voltage of node i; I ij Represents the current in branch ij; R ij With X ij are the impedance and inductive reactance on branch ij respectively; P ij With Q ij Represents the active power and reactive power on branch ij; P jl With Q jl represents the active power and reactive power on branch jl; P j and Q j represents the active power and reactive power injected by node j; the constraints are as follows: π(j) and σ(j) are the sets of parent nodes and child nodes of node j, and they need to satisfy the following calculation constraints of active power and reactive power: Constraints on safe operation of distribution networks: The current in branch ij and the voltage at node j need to be within the safe operating range: In min ≤V j ≤V max 11. The online deployment system based on household photovoltaic and storage integrated machine cluster according to claim 9 is characterized in that: According to the calculated dispatchable power or insufficient power inside the node to which the PV-storage integrated device is connected, it is determined whether the node to which the PV-storage integrated device is connected needs to absorb power from the distribution network, and the energy of the PV-storage integrated device in the node is allocated according to the determination result, including: Traverse each user. When the user's photovoltaic power generation is greater than the user's load, calculate the user's excess power and store the user's excess power in the user's energy storage or distribute it to other users at the same node. When the user's photovoltaic power generation is less than the user's load, calculate the user's power shortage. If the user's energy storage has enough surplus, the power shortage will be compensated from the user's energy storage first. Otherwise, the power shortage will be compensated from other users at the same node. Until the power calculation of all photovoltaic and energy storage users in the node is completed, the maximum dispatchable power output of the node or the shortage power that needs to be absorbed from other nodes is obtained, and the energy allocation of the photovoltaic and energy storage integrated machine in the node is completed.

12. The online deployment system based on household photovoltaic and storage integrated machine cluster according to claim 9, characterized in that: The objective function F of the upper model of the online deployment strategy is u for: Among them, β and γ are the weights of active power input and user energy storage capacity difference respectively; P ref Indicates the active power input to node 1; Represents the difference between the energy storage capacity of the u-th user at node and the average energy storage capacity of all users in the distribution network.

13. The online deployment system based on household photovoltaic and storage integrated machine cluster according to claim 12 is characterized in that: The upper model of the online dispatch strategy uses the Benders decomposition algorithm to optimize the input information of the upper layer of the distribution network model, including: The Benders decomposition algorithm is used to optimize the active power of the photovoltaic and storage nodes, including the input power of the reference node and the input power between the photovoltaic and storage nodes. The input power of the reference node and the input power between the photovoltaic and storage nodes are decomposed into a main problem and sub-problems. The input power of the reference node is obtained by solving the main problem, and the input and output of the power between the photovoltaic and storage nodes are obtained by solving the sub-problems, thereby balancing the energy storage capacity among users.

14. The online deployment system based on household photovoltaic and storage integrated machine cluster according to claim 13, characterized in that: The input power of the reference node is obtained by solving the main problem, and the input and output of power between the photovoltaic and energy storage nodes are obtained by solving the sub-problems, so as to balance the energy storage capacity between users, including: Initialize algorithm parameters, initial upper and lower bounds; Initialize the main problem: randomly generate a solution to the main problem within the operating range, that is, the total input power of the reference node, F m =P ref ; Solve the sub-problem: Substitute the solution of the main problem into the node information of the distribution network model, and calculate the power flow distribution of the distribution network while satisfying the constraints, so as to obtain the solution of the sub-problem and the corresponding target value. The solution of the sub-problem is the input and output of user power between the photovoltaic and storage nodes, and the corresponding objective function is the energy storage difference. According to the direction of minimizing the target value, the input and output of user power between the optical storage nodes are optimized through iteration, so as to obtain the optimal solution of the sub-problem and the minimized target function value; Update the main problem. According to the cutting plane fed back by the sub-problem, add the generated Benders cut to the constraints of the main problem and adjust the solution of the main problem, including: Calculate the shadow price of the target solution and obtain it through the Lagrange multiplier method of linear programming. The shadow price is usually expressed as the Lagrange multiplier λ corresponding to the constraint condition at each node. j , represents the sensitivity to the solution of the main problem, that is, the rate at which the objective function of the sub-problem changes when the solution of the main problem changes slightly. The calculation formula is: P j is the power of node j; The Benders cut is calculated by the shadow price to approximate the solution space of the main problem. Assume that the solution of the current subproblem is Then the Benders cut is: This inequality will be added to the main problem to constrain the solution of the main problem, making future solutions closer and closer to the global optimal solution; Take the value in the newly generated solution space of the main problem and get the solution of the new main problem Among them, the updated main problems include the new Benders cut; Substitute the solution of the new main problem into the subproblem to solve it, and repeat the above steps until the solutions of the main problem and the subproblem converge.

15. The online deployment system based on household photovoltaic and storage integrated machine cluster according to claim 9 is characterized in that: The objective function F of the lower model of the online deployment strategy is l for: F l =min∑ j (V j -V j_ref ) 2 Among them, V j and V j_ref are the actual voltage and reference voltage of node j respectively.

16. The online deployment system based on household photovoltaic and storage integrated machine cluster according to claim 15, characterized in that: The lower model of the online dispatching strategy uses the particle swarm optimization algorithm to optimize the input information of the lower layer of the distribution network model, including: Initialize the number of particles N in the particle swarm; each particle represents a possible solution; the dimension of the particle is equal to the number of variables to be optimized, that is, the number of photovoltaic storage nodes; the position of each particle represents its output reactive power Q j ; Initialize the position of the particles within the feasible range of reactive power; assign an initial velocity v to each particle j ; Take the initial position of the particle as its initial individual optimal solution p j , select a particle with the smallest objective function value in the initial particle swarm as the global optimal solution g j ; In the tth iteration, the particle velocity is updated according to the formula: Where ω is the inertia weight; c1 and c2 are acceleration constants; the selection range is 0.1 to 0.3; r1 and r2 are random numbers; the updated velocity is used to adjust the particle's position If it exceeds the boundary, constrain the position to the boundary; In the tth iteration, the objective function value is calculated for the new position of each particle, that is, the impact of reactive power distribution on system voltage, and the objective function value corresponding to the new position of the particle and the individual optimal position in history is compared. The new position of the particle and the optimal position in the historical optimal position are determined according to the objective function value of the new position of the particle and the objective function value of the historical optimal position of the particle. If the new position of the particle is optimal, the individual optimal solution of the particle is updated using the new position of the particle. Compare the individual optimal solutions of all particles, determine the global optimal solution in the current iteration from the individual optimal solutions of all particles, and update the global optimal solution based on the global optimal solution in the current iteration and the global optimal solution in the previous iteration. Check the convergence conditions: If the number of iterations reaches the maximum value, or the global optimal solution does not change in multiple iterations, the algorithm is considered to have converged; if not, return to continue iterating; after the algorithm converges, the global optimal solution is output, which is the optimal distribution of reactive power.

17. An online deployment device based on a household photovoltaic storage integrated machine cluster, characterized in that: The electronic device comprises a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the electronic device implements the steps of the online deployment method based on a household optical storage integrated machine cluster as described in any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the online deployment method based on a household optical-storage integrated machine cluster as described in any one of claims 1 to 8.

Images