CN116780638A - Snowflake power distribution network operation optimization method and device with soft switch and distributed energy storage - Google Patents

Abstract

The invention proposes a snowflake distribution network operation optimization method and device including soft switching and distributed energy storage, which is suitable for the technical field of distribution network regulation. The method includes: establishing a snowflake power grid operation optimization model containing soft switching and distributed energy storage with the comprehensive objective function of reducing system network losses and improving voltage levels, and introducing active and reactive power including energy storage charging and discharging power at each time, SOP injection Power is used as a decision variable, and system power flow constraints, node power balance constraints, operating voltage and branch current constraints, SOP operation constraints, energy storage operation constraints, etc. are used as constraints to fully cope with the uncertainty of distributed power output and load demand. It can improve the voltage fluctuation level and reduce network losses. The second-order cone model is introduced to reduce the difficulty of solving and improve the solving speed of the distribution network optimization dispatch model.

Description

Snowflake distribution network operation optimization method and device containing soft switches and distributed energy storage

Technical Field

The present invention belongs to the technical field of power distribution system operation planning, and in particular relates to a method and device for optimizing the operation of a snowflake distribution network containing soft switches and distributed energy storage.

Background Art

At present, the construction of new power systems with new energy as the main body is accelerating, the penetration rate of distributed power sources in the distribution system is rapidly increasing, the random volatility of the source and load side is increasing, and a series of problems such as bidirectional power flow, voltage limit, network congestion and network loss are becoming more and more serious. The traditional distribution system has limited control methods and lacks flexibility, and cannot effectively cope with the challenges brought by high-penetration distributed power sources. Soft open point (SOP) and distributed energy storage system (DESS) can realize flexible adjustment of power flow in two dimensions of space and time, which can effectively alleviate the power space-time fluctuation caused by the source and load fluctuation of the distribution network, reduce the network loss of the distribution network and improve the voltage fluctuation level, thereby improving the operation stability of the distribution network and enhancing the absorption capacity of distributed photovoltaics.

As the penetration rate of distributed generation in distribution networks continues to increase, the operation and control problems become more and more complex. However, there are few studies on the combined optimization operation of soft switching and distributed energy storage for distribution networks with snowflake distribution network structures.

Summary of the invention

The present invention proposes a snowflake distribution network operation optimization method and device containing soft switches and distributed energy storage, which can fully cope with the uncertainty of distributed power output and load demand, improve the voltage fluctuation level, reduce network losses, and introduce a second-order cone model to reduce the difficulty of solution and improve the solution speed of the distribution network optimization scheduling model.

In view of the above problems, the present invention adopts the following technical solutions:

In a first aspect, a method for optimizing the operation of a snowflake distribution network including soft switches and distributed energy storage is provided. The method comprises:

Obtain basic data, including the components, structure, equipment parameters, and economic parameters of the Snowflake distribution network;

Establish a snowflake distribution network operation optimization model with soft switches and distributed energy storage. The objective function of the snowflake distribution network operation optimization model is to reduce the network loss of the snowflake distribution network and improve the voltage fluctuation level.

Determine the constraints of the snowflake distribution network operation optimization model, including system flow constraints, operating voltage and current constraints, node power balance constraints, soft switch operation constraints, and energy storage operation constraints;

Solve the Snowflake distribution network operation optimization model based on CPLEX solver;

The solution results are output and used to formulate a day-ahead dispatching strategy that minimizes the network loss and voltage deviation of the Snowflake distribution network. The day-ahead dispatching strategy includes the interactive power between the city power network, the distribution of the lowest and highest node voltages in each period of the Snowflake distribution network, and the objective function value.

Optionally, the objective function is:

(1)

(2)

(3)

Where n is the total number of nodes in the Snowflake distribution network; is the set of all branches in the snowflake distribution network, T is the total number of time periods; is the current amplitude of branch ij at time t ; is the resistance of branch ij ; is the voltage amplitude of node i at time t ; is the weight coefficient of the network loss of the snowflake distribution network, is the weight coefficient of voltage deviation of the snowflake distribution network, + =1; Network losses for the Snowflake distribution network; The voltage deviation of the snowflake distribution network; is the minimum value of the objective function.

Optionally, at any time, for any node j in the snowflake distribution network, the system flow constraint condition satisfies:

(4)

(5)

(6)

in, represents the set of head nodes of the branch with node j as the terminal node, represents the set of end nodes of the branch with node j as the head node, and are the active power and reactive power flowing from node i to node j , and are the resistance and reactance of branch ij respectively; is the current amplitude of branch ij ; is the voltage amplitude at node i , is the active power of node j , is the active power flowing from node j to node k , is the reactive power of node j , is the reactive power flowing from node j to node k , is the voltage amplitude at node j , is the kth end node in the end node set of the branch with node j as the head node.

Optionally, the node power balance constraint satisfies:

(7)

in, and are the net injected values of active power and reactive power of node j at time t respectively; is the active power of the distributed photovoltaic connected to node j at time t ; and is the active power and reactive power of the load connected to node j at time t ; and is the active power and reactive power transmitted by the soft switch connected to node j at time t ; and is the discharge power and charging power of the distributed energy storage connected to node j at time t .

Optionally, the operating voltage and current constraints meet:

(8)

in, and are the upper and lower voltage limits allowed for node i respectively; is the maximum current allowed in branch ij.

Optionally, the energy storage operation constraints meet:

(12)

in, , , They represent the power, charging power, and discharging power of ESS at node i at time t , respectively. and They are the charging efficiency and discharging efficiency of ESS respectively; is the rated power of energy storage at node i , is the rated capacity of energy storage at node i , is the charging state of ESS at time t , is the discharge state of ESS at time t , and the charging state When 1, discharge is 1, and is 0 when idle. and are the maximum values of the ESS state of charge at time t respectively.

Optionally, the soft switching operation constraints meet:

(13)

(14)

in, and are the active powers injected by the soft switches at nodes i and j at time t respectively; and are the reactive powers injected by the soft switches at nodes i and j at time t respectively; is the construction capacity of the soft switch between nodes i and j .

Optionally, the snowflake distribution network operation optimization model is solved based on the CPLEX solver, including:

Transform formula (14) into the following second-order cone constraint formula:

(15)

Based on formula (15), the optimization model of the snowflake distribution network operation is solved.

In the second aspect, a snowflake distribution network operation optimization device including soft switches and distributed energy storage is provided. The device comprises:

Obtain basic data, including the components, structure, equipment parameters, and economic parameters of the Snowflake distribution network;

Establish a snowflake distribution network operation optimization model with soft switches and distributed energy storage. The objective function of the snowflake distribution network operation optimization model is to reduce the network loss of the snowflake distribution network and improve the voltage fluctuation level.

Determine the constraints of the snowflake distribution network operation optimization model, including system flow constraints, operating voltage and current constraints, node power balance constraints, soft switch operation constraints, and energy storage operation constraints;

Solve the Snowflake distribution network operation optimization model based on CPLEX solver;

The solution results are output and used to formulate a day-ahead dispatching strategy that minimizes the network loss and voltage deviation of the Snowflake distribution network. The day-ahead dispatching strategy includes the interactive power between the city power network, the minimum and maximum node voltage distribution of the Snowflake distribution network in each period, and the objective function value.

Optionally, the objective function is:

(1)

(2)

(3)

Where n is the total number of nodes in the Snowflake distribution network; is the set of all branches in the snowflake distribution network, T is the total number of time periods; is the current amplitude of branch ij at time t ; is the resistance of branch ij ; is the voltage amplitude of node i at time t ; is the weight coefficient of the network loss of the snowflake distribution network, is the weight coefficient of voltage deviation of the snowflake distribution network, + =1; Network losses for the Snowflake distribution network; The voltage deviation of the snowflake distribution network; is the minimum value of the objective function.

Optionally, at any time, for any node j in the snowflake distribution network, the system flow constraint condition satisfies:

(4)

(5)

(6)

in, represents the set of head nodes of the branch with node j as the terminal node, represents the set of end nodes of the branch with node j as the head node, and are the active power and reactive power flowing from node i to node j , and are the resistance and reactance of branch ij respectively; is the current amplitude of branch ij ; is the voltage amplitude at node i , is the active power of node j , is the active power flowing from node j to node k , is the reactive power of node j , is the reactive power flowing from node j to node k , is the voltage amplitude at node j , is the kth end node in the end node set of the branch with node j as the head node.

Optionally, the node power balance constraint satisfies:

(7)

in, and are the net injected values of active power and reactive power of node j at time t respectively; is the active power of the distributed photovoltaic connected to node j at time t ; and is the active power and reactive power of the load connected to node j at time t ; and is the active power and reactive power transmitted by the soft switch connected to node j at time t ; and is the discharge power and charging power of the distributed energy storage connected to node j at time t .

Optionally, the operating voltage and current constraints meet:

(8)

in, and are the upper and lower voltage limits allowed for node i respectively; is the maximum current allowed in branch ij.

Optionally, the energy storage operation constraints meet:

(12)

in, , , They represent the power, charging power, and discharging power of ESS at node i at time t , respectively. and They are the charging efficiency and discharging efficiency of ESS respectively; is the rated power of energy storage at node i , is the rated capacity of energy storage at node i , is the charging state of ESS at time t , is the discharge state of ESS at time t , and the charging state When 1, discharge is 1, and is 0 when idle. and are the maximum values of the ESS state of charge at time t respectively.

Optionally, the soft switching operation constraints meet:

(13)

(14)

in, and are the active powers injected by the soft switches at nodes i and j at time t respectively; and are the reactive powers injected by the soft switches at nodes i and j at time t respectively; is the construction capacity of the soft switch between nodes i and j .

Optionally, the solving module is also used to transform formula (14) into the following second-order cone constraint formula, and solve the snowflake distribution network operation optimization model based on formula (15):

(15).

In a third aspect, another snowflake distribution network operation optimization device including soft switches and distributed energy storage is provided, comprising: a processor,

The processor is coupled to the memory;

Among them, the processor is used to read and execute the program or instructions stored in the memory, so that the device executes the snowflake distribution network operation optimization method containing soft switches and distributed energy storage described in the first aspect.

In a fourth aspect, a computer-readable storage medium is provided, which stores a program or instruction. When a computer reads and executes the program or instruction, the computer executes the snowflake distribution network operation optimization method containing soft switches and distributed energy storage described in the first aspect.

Based on the snowflake distribution network operation optimization method and device containing soft switches and distributed energy storage provided by the present invention, a snowflake power grid operation optimization model containing soft switches and distributed energy storage can be established with the comprehensive objective function of reducing system network losses and improving voltage levels. The energy storage charging and discharging power at each moment, and the active and reactive power injected by the SOP are introduced as decision variables, and the system flow constraints, node power balance constraints, operating voltage and branch current constraints, SOP operation constraints, energy storage operation constraints, etc. are used as constraints to solve the problems of network losses and voltage over-limit caused by the rapid development of distributed power sources.

Furthermore, the snowflake power grid operation optimization model containing soft switches and distributed energy storage is mathematically a mixed integer nonlinear programming problem. The model can be converted into a convex programming model with second-order cone constraints, such as using mature mathematical software such as Gurobi and CPLEX to directly solve it, so as to reduce the difficulty of solution and improve the efficiency of solution.

Compared with the prior art, the present invention has the following beneficial effects: the snowflake power grid operation optimization method and device proposed in the present invention can fully cope with the uncertainty of distributed power output and load demand, and play a role in improving the voltage fluctuation level of the snowflake distribution network and reducing system network losses. Furthermore, the Distflow second-order cone model can be used to model the regional power system, and the mathematical programming method can be used to solve the model to reduce the difficulty of solving and improve the speed of solving the regional distribution system's day-ahead optimization scheduling model.

Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a schematic diagram of a hand-in-hand structure distribution network consisting of two branches in a snowflake distribution network provided by an embodiment of the present invention;

FIG2 is a flow chart of a method for optimizing the operation of a snowflake distribution network including soft switches and distributed energy storage provided by an embodiment of the present invention;

FIG3 is a schematic diagram of a typical daily load and photovoltaic output provided by an embodiment of the present invention;

FIG4 is a schematic diagram of the interactive power between a substation A and a city power network provided by an embodiment of the present invention;

FIG5 is a schematic diagram of the power interaction between the substation B and the mains network provided by an embodiment of the present invention;

FIG6 is a schematic diagram of maximum and minimum voltages of scenario 1 and scenario 2 provided by an embodiment of the present invention;

FIG7 is a schematic diagram of maximum and minimum voltages of scenario 2 and scenario 3 provided by an embodiment of the present invention;

FIG8 is a schematic diagram of the structure of another snowflake distribution network operation optimization device including soft switches and distributed energy storage provided by an embodiment of the present invention;

FIG9 is a schematic diagram of the structure of another snowflake distribution network operation optimization device including soft switches and distributed energy storage provided in an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

First, in conjunction with FIG1 , a snowflake distribution network including soft switches and distributed energy storage provided by an embodiment of the present invention is described in detail.

For example, Fig. 1 is a schematic diagram of a hand-in-hand structure power distribution network composed of two branches in a snowflake power distribution network provided by an embodiment of the present invention. As shown in Fig. 1, considering symmetry, a soft switch can be used instead of a tie switch to connect the two branches.

2-7 , a method for optimizing the operation of a snowflake distribution network including soft switches and distributed energy storage provided by an embodiment of the present invention is described in detail below.

For example, FIG2 is a flow chart of a method for optimizing the operation of a snowflake distribution network including soft switches and distributed energy storage provided by an embodiment of the present invention. As shown in FIG2 , the method includes:

S201, obtaining basic data.

Among them, the basic data include the components, structure, equipment parameters and economic parameters of the Snowflake distribution network.

For specific examples of active power and reactive power of node loads in the Snowflake distribution network, the maximum and minimum values allowed for node voltages, and branch impedance values, please refer to Tables 1 and 2 below; for detailed parameters of photovoltaic, soft switches, and distributed energy storage devices, please refer to Table 3.

For example, Fig. 3 is a schematic diagram of a typical daily load and photovoltaic output provided by an embodiment of the present invention. As shown in the figure, the load forecasting method is used to simulate the load curve at a time interval of 1 hour, and the photovoltaic output curve is processed in a similar manner. The current limit of each branch is 500A; the reference voltage of the distribution network is set to 12.66kV and the reference power is set to 10MVA.

Table 1

Node Number Active load (kW) Reactive load (kVar) Maximum allowable voltage (p.u.) Minimum allowable voltage (p.u.) 1 0 0 1.1 0.9 2 100 60 1.1 0.9 3 90 40 1.1 0.9 4 120 80 1.1 0.9 5 60 30 1.1 0.9 6 60 20 1.1 0.9 7 200 100 1.1 0.9 8 200 100 1.1 0.9 9 60 20 1.1 0.9 10 60 20 1.1 0.9 11 45 30 1.1 0.9 12 60 35 1.1 0.9 13 60 35 1.1 0.9 14 120 80 1.1 0.9 15 60 10 1.1 0.9 16 60 20 1.1 0.9 17 60 20 1.1 0.9 18 90 40 1.1 0.9 19 0 0 1.1 0.9 20 100 60 1.1 0.9 twenty one 90 40 1.1 0.9 twenty two 120 80 1.1 0.9 twenty three 60 30 1.1 0.9 twenty four 60 20 1.1 0.9 25 200 100 1.1 0.9 26 200 100 1.1 0.9 27 60 20 1.1 0.9 28 60 20 1.1 0.9 29 45 30 1.1 0.9 30 60 35 1.1 0.9 31 60 35 1.1 0.9 32 120 80 1.1 0.9

Table 2

First Node End Node Resistance (Ω) Reactance(Ω) 1 2 0.0922 0.0470 2 3 0.4930 0.2511 3 4 0.3660 0.1864 4 5 0.3811 0.1941 5 6 0.8190 0.7070 6 7 0.1872 0.6188 7 8 0.7114 0.2351 8 9 1.0300 0.7400 9 10 1.0440 0.7400 10 11 0.1966 0.0650 11 12 0.3744 0.1238 12 13 1.4680 1.1550 13 14 0.5416 0.7129 14 15 0.5910 0.5260 15 16 0.7463 0.5450 16 17 1.2890 1.7210 17 18 0.7320 0.5740 19 20 0.0922 0.0470 20 twenty one 0.4930 0.2511 twenty one twenty two 0.3660 0.1864 twenty two twenty three 0.3811 0.1941 twenty three twenty four 0.8190 0.7070 twenty four 25 0.1872 0.6188 25 26 0.7114 0.2351 26 27 1.0300 0.7400 27 28 1.0440 0.7400 28 29 0.1966 0.0650 29 30 0.3744 0.1238 30 31 1.4680 1.1550 31 32 0.5416 0.7129

Table 3

S202, establish a snowflake distribution network operation optimization model containing soft switches and distributed energy storage. The snowflake distribution network operation optimization model has the objective function of reducing the network loss of the snowflake distribution network and improving the voltage fluctuation level.

Optionally, the objective function is:

(1)

(2)

(3)

Where n is the total number of nodes in the Snowflake distribution network; is the set of all branches in the snowflake distribution network, T is the total number of time periods; is the current amplitude of branch ij at time t ; is the resistance of branch ij ; is the voltage amplitude of node i at time t ; is the weight coefficient of the network loss of the snowflake distribution network, is the weight coefficient of voltage deviation of the snowflake distribution network, + =1; Network losses for the Snowflake distribution network; The voltage deviation of the snowflake distribution network; is the minimum value of the objective function. For example, in the embodiment of the present invention, the weight coefficients ω ₁ and ω ₂ may be 0.8333 and 0.167 respectively.

S203, determining the constraint conditions of the snowflake distribution network operation optimization model.

Among them, the constraints include system flow constraints, operating voltage and current constraints, node power balance constraints, soft switch operation constraints, and energy storage operation constraints.

Optionally, at any time, for any node j in the snowflake distribution network, the system flow constraint condition satisfies:

(4)

(5)

(6)

in, represents the set of head nodes of the branch with node j as the terminal node, represents the set of end nodes of the branch with node j as the head node, and are the active power and reactive power flowing from node i to node j , and are the resistance and reactance of branch ij respectively; is the current amplitude of branch ij ; is the voltage amplitude at node i , is the active power of node j , is the active power flowing from node j to node k , is the reactive power of node j , is the reactive power flowing from node j to node k , is the voltage amplitude at node j , is the kth end node in the end node set of the branch with node j as the head node.

Optionally, the node power balance constraint satisfies:

(7)

in, and are the net injected values of active power and reactive power of node j at time t respectively; is the active power of the distributed photovoltaic connected to node j at time t ; and is the active power and reactive power of the load connected to node j at time t ; and is the active power and reactive power transmitted by the soft switch connected to node j at time t ; and is the discharge power and charging power of the distributed energy storage connected to node j at time t .

Optionally, the operating voltage and current constraints meet:

(8)

in, and are the upper and lower voltage limits allowed for node i respectively; is the maximum current allowed in branch ij.

When the objective function is satisfied Under the conditions of a strictly increasing function, equation (6) can be transformed as follows:

(9)

make , Relax the branch apparent power quadratic constraint shown in formula (5) to a conical constraint:

(10)

The constraints on node voltage and branch current can be expressed as:

(11)

Optionally, the energy storage operation constraints meet:

(12)

in, , , They represent the power, charging power, and discharging power of ESS at node i at time t , respectively. and are the charging efficiency and discharging efficiency of ESS respectively, both of which are 90% in this embodiment; is the rated power of energy storage at node i , is the rated capacity of energy storage at node i , is the charging state of ESS at time t , is the discharge state of ESS at time t , and the charging state When 1, discharge is 1, and is 0 when idle. and are the maximum values of the ESS state of charge at time t respectively.

Optionally, the soft switching operation constraints meet:

(13)

(14)

in, and are the active powers injected by the soft switches at nodes i and j at time t respectively; and are the reactive powers injected by the soft switches at nodes i and j at time t respectively; is the construction capacity of the soft switch between nodes i and j .

S204, solving the snowflake distribution network operation optimization model based on CPLEX solver;

Optionally, the snowflake distribution network operation optimization model is solved based on the CPLEX solver, including:

Transform formula (14) into the following second-order cone constraint formula:

(15)

Based on formula (15), the optimization model of the snowflake distribution network operation is solved.

Specifically, the established snowflake distribution network operation optimization model based on the combination of soft switches and distributed energy storage systems can be programmed through the YALMIP optimization toolbox on the MATLAB 2022a platform, and the IBM ILOG CPLEX algorithm package can be called for solving.

For this embodiment, the following three scenarios can be selected for comparative analysis: scenario 1 (case 1) is not connected to SOP and ESS; scenario 2 (case 2) is only connected to ESS; scenario 3 (case 3) is connected to ESS and SOP.

S205, output the solution result.

Among them, the solution results include system network loss and voltage deviation under different scenarios, the interaction power between the two substations and the municipal power network, the daily operation strategies of SOP and ESS, etc., which can be used to formulate the day-ahead dispatching strategy that minimizes the network loss and voltage deviation of the Snowflake distribution network. The day-ahead dispatching strategy includes the interaction power between the municipal power network, the distribution of the lowest and highest node voltages in each period of the Snowflake distribution network, and the objective function value.

The network loss and voltage deviation under the above three scenarios are shown in Table 4.

Table 4

scene System network loss (kW·h) Voltage deviation (p.u.) case1 748 22.97 Case 2 568 20.11 Case 3 390 10.41

It can be seen from Table 4 that the network loss and voltage deviation of scenarios 1, 2, and 3 gradually decrease, indicating that connecting to SOP and ESS at the same time is more conducive to reducing network loss and voltage deviation, thereby improving the operating efficiency and stability of the distribution network.

The interaction power between the two substations (substation A and substation B) and the mains network in different scenarios is shown in Figures 4 and 5. Compared with scenario 1, in scenario 2, since distributed energy storage is connected to node 12, peak load shaving and valley filling can be achieved, thereby effectively reducing the return power of substation A; on the contrary, since there is no energy storage connected to the feeder of substation B, the interaction power with the mains network does not change.

Furthermore, compared with Scenario 2, based on the configuration of SOP and distributed energy storage, in Scenario 3, since a SOP soft switch is configured between node 18 and node 32, the two feeders can achieve power interaction. During 10:00-14:00, the photovoltaic output is large, and the electric energy generated by distributed photovoltaics can be absorbed through the feeder of substation A and the feeder of substation B. Therefore, substation A does not need to return power to the mains network, that is, it can achieve full absorption, and the power purchased by substation B from the mains network is reduced to a certain extent.

The distribution of the lowest and highest node voltages of the entire distribution network in different scenarios in each period is shown in Figures 6 and 7. Compared with Scenario 1, the optimized operation of ESS in Scenario 2 achieves peak shaving and valley filling by coordinating the output of distributed photovoltaics in each period and the power demand of the load, which helps to reduce network losses; the node voltage variation range is significantly reduced, which effectively improves the power supply quality of the entire distribution network, and can effectively alleviate the problem of low voltage when the load is heavy and the problem of node voltage increase after distributed photovoltaics are connected, thereby further improving the distribution network's acceptance of distributed photovoltaics. Compared with Scenario 2, the SOP in Scenario 3 can alleviate the unbalanced feeder load conditions, reduce network losses, and effectively alleviate the deviation between the voltage and the standard value, improving the voltage level curve.

Based on the snowflake distribution network operation optimization method containing soft switches and distributed energy storage provided by the present invention, a snowflake power grid operation optimization model containing soft switches and distributed energy storage can be established with the comprehensive objective function of reducing system network losses and improving voltage levels. The energy storage charging and discharging power at each moment, and the active and reactive power injected by the SOP are introduced as decision variables, and the system flow constraints, node power balance constraints, operating voltage and branch current constraints, SOP operation constraints, energy storage operation constraints, etc. are used as constraints to solve the problems of network losses and voltage over-limits caused by the rapid development of distributed power sources.

Furthermore, the snowflake power grid operation optimization model containing soft switches and distributed energy storage is mathematically a mixed integer nonlinear programming problem. The model can be converted into a convex programming model with second-order cone constraints, such as using mature mathematical software such as Gurobi and CPLEX to directly solve it, so as to reduce the difficulty of solution and improve the efficiency of solution.

Compared with the prior art, the snowflake distribution network operation optimization method with soft switches and distributed energy storage provided by the present invention has the following beneficial effects:

The snowflake distribution network operation optimization method with soft switches and distributed energy storage provided by the present invention can fully cope with the uncertainty of distributed power output and load demand, and play a role in improving the voltage fluctuation level of the snowflake distribution network and reducing system network losses. Furthermore, the Distflow second-order cone model is used to model the regional power system, and the mathematical programming method is used to solve the model to reduce the difficulty of solving and improve the speed of solving the regional distribution system's day-ahead optimization scheduling model.

The above is a detailed description of the snowflake distribution network operation optimization method containing soft switches and distributed energy storage provided by an embodiment of the present invention in combination with Figures 2 to 7. The following is a description of the snowflake distribution network operation optimization device containing soft switches and distributed energy storage provided by an embodiment of the present invention in combination with Figures 8 and 9.

For example, Fig. 8 is a schematic diagram of the structure of a snowflake distribution network operation optimization device including soft switches and distributed energy storage provided by an embodiment of the present invention. The device can execute the snowflake distribution network operation optimization method including soft switches and distributed energy storage provided by the above method embodiment.

As shown in FIG8 , the device 800 includes: an acquisition module 801, an establishment module 802, a determination module 803, and a solution module 804; wherein,

The acquisition module 801 is used to acquire basic data, which includes components, structure, equipment parameters, and economic parameters of the snowflake distribution network;

Establishing module 802, for establishing a snowflake distribution network operation optimization model including soft switches and distributed energy storage, the snowflake distribution network operation optimization model takes reducing the network loss of the snowflake distribution network and improving the voltage fluctuation level as the objective function;

Determination module 803, used to determine the constraints of the snowflake distribution network operation optimization model, the constraints include system flow constraints, operating voltage and current constraints, node power balance constraints, soft switch operation constraints, and energy storage operation constraints;

A solution module 804 is used to solve the snowflake distribution network operation optimization model based on a CPLEX solver;

The solution module 804 is also used to output the solution results, which are used to formulate a day-ahead scheduling strategy that minimizes the network loss and voltage deviation of the Snowflake distribution network. The day-ahead scheduling strategy includes the interactive power between the municipal power network, the minimum and maximum node voltage distribution of the Snowflake distribution network in each time period, and the objective function value.

Optionally, the objective function is:

(1)

(2)

(3)

Where n is the total number of nodes in the Snowflake distribution network; is the set of all branches in the snowflake distribution network, T is the total number of time periods; is the current amplitude of branch ij at time t ; is the resistance of branch ij ; is the voltage amplitude of node i at time t ; is the weight coefficient of the network loss of the snowflake distribution network, is the weight coefficient of voltage deviation of the snowflake distribution network, + =1; Network losses for the Snowflake distribution network; The voltage deviation of the snowflake distribution network; is the minimum value of the objective function.

Optionally, at any time, for any node j in the snowflake distribution network, the system flow constraint condition satisfies:

(4)

(5)

(6)

in, represents the set of head nodes of the branch with node j as the terminal node, represents the set of end nodes of the branch with node j as the head node, and are the active power and reactive power flowing from node i to node j , and are the resistance and reactance of branch ij respectively; is the current amplitude of branch ij ; is the voltage amplitude at node i , is the active power of node j , is the active power flowing from node j to node k , is the reactive power of node j , is the reactive power flowing from node j to node k , is the voltage amplitude at node j , is the kth end node in the end node set of the branch with node j as the head node.

Optionally, the node power balance constraint satisfies:

(7)

in, and are the net injected values of active power and reactive power of node j at time t respectively; is the active power of the distributed photovoltaic connected to node j at time t ; and is the active power and reactive power of the load connected to node j at time t ; and is the active power and reactive power transmitted by the soft switch connected to node j at time t ; and is the discharge power and charging power of the distributed energy storage connected to node j at time t .

Optionally, the operating voltage and current constraints meet:

(8)

in, and are the upper and lower voltage limits allowed for node i respectively; is the maximum current allowed in branch ij.

Optionally, the energy storage operation constraints meet:

(12)

in, , , They represent the power, charging power, and discharging power of ESS at node i at time t , respectively. and They are the charging efficiency and discharging efficiency of ESS respectively; is the rated power of energy storage at node i , is the rated capacity of energy storage at node i , is the charging state of ESS at time t , is the discharge state of ESS at time t , and the charging state When 1, discharge is 1, and is 0 when idle. and are the maximum values of ESS charge state at time t respectively.

Optionally, the soft switching operation constraints meet:

(13)

(14)

in, and are the active powers injected by the soft switches at nodes i and j at time t respectively; and are the reactive powers injected by the soft switches at nodes i and j at time t respectively; is the construction capacity of the soft switch between nodes i and j .

Optionally, the solving module 804 is further used to convert formula (14) into the following second-order cone constraint formula, and solve the snowflake distribution network operation optimization model based on formula (15):

(15).

Exemplarily, FIG9 is a schematic diagram of the structure of another snowflake distribution network operation optimization device including soft switches and distributed energy storage provided in an embodiment of the present invention.

As shown in FIG9 , the device 900 includes: a processor 901 , the processor 901 is coupled to a memory 902 ;

Among them, the processor 901 is used to read and execute the program or instructions stored in the memory 902, so that the device 900 executes the snowflake distribution network operation optimization method containing soft switches and distributed energy storage described in the above method embodiment.

Optionally, the device 900 may further include a transceiver 903 for the device 900 to communicate with other devices.

It should be noted that, for the sake of convenience, Figures 8 and 9 only show the main components of the snowflake distribution network operation optimization device containing soft switches and distributed energy storage. In actual applications, the snowflake distribution network operation optimization device containing soft switches and distributed energy storage may also include components or assemblies not shown in the figures.

An embodiment of the present invention also provides a computer-readable storage medium, which stores a program or instruction. When a computer reads and executes the program or instruction, the computer executes the snowflake distribution network operation optimization method containing soft switches and distributed energy storage described in the above method embodiment.

Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (18)

1. A method for optimizing the operation of a snowflake distribution network containing soft switches and distributed energy storage, characterized in that the method comprises: Acquire basic data, wherein the basic data includes components, structure, equipment parameters, and economic parameters of the snowflake distribution network; Establishing a snowflake distribution network operation optimization model containing soft switches and distributed energy storage, wherein the snowflake distribution network operation optimization model has an objective function of reducing the network loss of the snowflake distribution network and improving the voltage fluctuation level; Determine the constraints of the snowflake distribution network operation optimization model, wherein the constraints include system flow constraints, operating voltage and current constraints, node power balance constraints, soft switch operation constraints, and energy storage operation constraints; Solve the Snowflake distribution network operation optimization model based on CPLEX solver; Output the solution result, and the solution result is used to formulate a day-ahead scheduling strategy that minimizes the network loss and voltage deviation of the Snowflake distribution network. The day-ahead scheduling strategy includes the interactive power between the mains network, the minimum and maximum node voltage distribution of the Snowflake distribution network in each time period, and the objective function value. 2. The snowflake distribution network operation optimization method containing soft switches and distributed energy storage according to claim 1 is characterized in that the objective function is: (1) (2) (3) Wherein, n is the total number of nodes of the Snowflake distribution network; is the set of all branches in the snowflake distribution network, T is the total number of time periods; is the current amplitude of branch ij at time t ; is the resistance of branch ij ; is the voltage amplitude of node i at time t ; is the weight coefficient of the network loss of the Snowflake distribution network, is the weight coefficient of the voltage deviation of the snowflake distribution network, + =1; is the network loss of the Snowflake distribution network; is the voltage deviation of the snowflake distribution network; is the minimum value of the objective function. 3. The operation optimization method of the snowflake distribution network with soft switches and distributed energy storage according to claim 1 is characterized in that, at any time, for any node j in the snowflake distribution network, the system flow constraint condition satisfies: (4) (5) (6) in, represents the set of head nodes of the branch with node j as the terminal node, represents the set of end nodes of the branch with node j as the head node, and are the active power and reactive power flowing from node i to node j , and are the resistance and reactance of branch ij respectively; is the current amplitude of branch ij ; is the voltage amplitude at node i , is the active power of node j , is the active power flowing from node j to node k , is the reactive power of node j , is the reactive power flowing from node j to node k , is the voltage amplitude at node j , is the kth end node in the end node set of the branch with node j as the head node. 4. The snowflake distribution network operation optimization method with soft switches and distributed energy storage according to claim 1 is characterized in that the node power balance constraint condition satisfies: (7) in, and are the net injected values of active power and reactive power of node j at time t respectively; is the active power of the distributed photovoltaic connected to node j at time t ; and is the active power and reactive power of the load connected to node j at time t ; and is the active power and reactive power transmitted by the soft switch connected to node j at time t ; and is the discharge power and charging power of the distributed energy storage connected to node j at time t . 5. The snowflake distribution network operation optimization method with soft switches and distributed energy storage according to claim 1 is characterized in that the operating voltage and current constraint conditions satisfy: (8) in, and are the upper and lower voltage limits allowed for node i respectively; is the maximum current allowed in branch ij. 6. The snowflake distribution network operation optimization method containing soft switches and distributed energy storage according to claim 1 is characterized in that the energy storage operation constraint condition satisfies: (12) in, , , They represent the power, charging power, and discharging power of ESS at node i at time t , respectively. and They are the charging efficiency and discharging efficiency of ESS respectively; is the rated power of energy storage at node i , is the rated capacity of energy storage at node i , is the charging state of ESS at time t , is the discharge state of ESS at time t , and the charging state When 1, discharge is 1, and is 0 when idle. and are the maximum values of the ESS state of charge at time t respectively. 7. The snowflake distribution network operation optimization method containing soft switches and distributed energy storage according to claim 1 is characterized in that the soft switch operation constraint condition satisfies: (13) (14) in, and are the active powers injected by the soft switches at nodes i and j at time t respectively; and are the reactive powers injected by the soft switches at nodes i and j at time t respectively; is the construction capacity of the soft switch between nodes i and j . 8. The snowflake distribution network operation optimization method containing soft switches and distributed energy storage according to any one of claims 1 to 7, characterized in that the snowflake distribution network operation optimization model is solved based on the CPLEX solver, comprising: Transform formula (14) into the following second-order cone constraint formula: (15) The snowflake distribution network operation optimization model is solved based on formula (15). 9. A snowflake distribution network operation optimization device containing soft switches and distributed energy storage, characterized in that the device includes: an acquisition module, an establishment module, a determination module, and a solution module; wherein, The acquisition module is used to acquire basic data, and the basic data includes components, composition structure, equipment parameters, and economic parameters of the snowflake distribution network; The establishment module is used to establish a snowflake distribution network operation optimization model containing soft switches and distributed energy storage, and the snowflake distribution network operation optimization model has an objective function of reducing the network loss of the snowflake distribution network and improving the voltage fluctuation level; The determination module is used to determine the constraint conditions of the snowflake distribution network operation optimization model, wherein the constraint conditions include system flow constraint conditions, operation voltage and current constraint conditions, node power balance constraint conditions, soft switch operation constraint conditions, and energy storage operation constraint conditions; The solving module is used to solve the snowflake distribution network operation optimization model based on the CPLEX solver; The solution module is also used to output the solution results, and the solution results are used to formulate a day-ahead scheduling strategy that minimizes the network loss and voltage deviation of the Snowflake distribution network. The day-ahead scheduling strategy includes the interactive power between the municipal power network, the minimum and maximum node voltage distribution of the Snowflake distribution network in each time period, and the objective function value. 10. The snowflake distribution network operation optimization device containing soft switches and distributed energy storage according to claim 9, characterized in that the objective function is: (1) (2) (3) Wherein, n is the total number of nodes of the Snowflake distribution network; is the set of all branches in the snowflake distribution network, T is the total number of time periods; is the current amplitude of branch ij at time t ; is the resistance of branch ij ; is the voltage amplitude of node i at time t ; is the weight coefficient of the network loss of the Snowflake distribution network, is the weight coefficient of the voltage deviation of the snowflake distribution network, + =1; is the network loss of the Snowflake distribution network; is the voltage deviation of the snowflake distribution network; is the minimum value of the objective function. 11. The snowflake distribution network operation optimization device with soft switches and distributed energy storage according to claim 9 is characterized in that, at any time, for any node j in the snowflake distribution network, the system flow constraint condition satisfies: (4) (5) (6) in, represents the set of head nodes of the branch with node j as the terminal node, represents the set of end nodes of the branch with node j as the head node, and are the active power and reactive power flowing from node i to node j , and are the resistance and reactance of branch ij respectively; is the current amplitude of branch ij ; is the voltage amplitude at node i , is the active power of node j , is the active power flowing from node j to node k , is the reactive power of node j , is the reactive power flowing from node j to node k , is the voltage amplitude at node j , is the kth end node in the end node set of the branch with node j as the head node. 12. The snowflake distribution network operation optimization device with soft switches and distributed energy storage according to claim 9, characterized in that the node power balance constraint condition satisfies: (7) in, and are the net injected values of active power and reactive power of node j at time t respectively; is the active power of the distributed photovoltaic connected to node j at time t ; and is the active power and reactive power of the load connected to node j at time t ; and is the active power and reactive power transmitted by the soft switch connected to node j at time t ; and is the discharge power and charging power of the distributed energy storage connected to node j at time t . 13. The snowflake distribution network operation optimization device with soft switches and distributed energy storage according to claim 9 is characterized in that the operating voltage and current constraint conditions satisfy: (8) in, and are the upper and lower voltage limits allowed for node i respectively; is the maximum current allowed in branch ij. 14. The snowflake distribution network operation optimization device with soft switches and distributed energy storage according to claim 9, characterized in that the energy storage operation constraint condition satisfies: (12) in, , , They represent the power, charging power, and discharging power of ESS at node i at time t , respectively. and They are the charging efficiency and discharging efficiency of ESS respectively; is the rated power of energy storage at node i , is the rated capacity of energy storage at node i , is the charging state of ESS at time t , is the discharge state of ESS at time t , and the charging state When 1, discharge is 1, and is 0 when idle. and are the maximum values of the ESS state of charge at time t respectively. 15. The snowflake distribution network operation optimization device containing soft switches and distributed energy storage according to claim 9, characterized in that the soft switch operation constraint condition satisfies: (13) (14) in, and are the active powers injected by the soft switches at nodes i and j at time t respectively; and are the reactive powers injected by the soft switches at nodes i and j at time t respectively; is the construction capacity of the soft switch between nodes i and j . 16. The snowflake distribution network operation optimization device containing soft switches and distributed energy storage according to any one of claims 9 to 15, characterized in that: The solving module is also used to transform formula (14) into the following second-order cone constraint formula: (15) The solution module is also used to solve the snowflake distribution network operation optimization model based on formula (15). 17. A snowflake distribution network operation optimization device containing soft switches and distributed energy storage, characterized by comprising: a processor, The processor is coupled to a memory; Wherein, the processor is used to read and execute the program or instructions stored in the memory, so that the device executes the snowflake distribution network operation optimization method containing soft switches and distributed energy storage as described in any one of claims 1-8. 18. A computer-readable storage medium, characterized in that it stores a program or instruction, and when a computer reads and executes the program or instruction, the computer executes the snowflake distribution network operation optimization method containing soft switches and distributed energy storage as described in any one of claims 1-8.