Power grid side power flow regulation and control method, system and device based on convex optimization and storage medium

Abstract

The invention discloses a convex optimization-based power grid side power flow regulation and control method, system and device and a storage medium, and belongs to the technical field of electric power. The method comprises the steps of obtaining grid structure information of a target power grid; constructing a power flow regulation and control model comprising a target function and constraint conditions according to the network architecture information; solving the power flow regulation and control model by adopting a pre-obtained convex optimization solver to obtain a variable power supply output time sequence and an energy storage power supply output time sequence; and correcting the output time sequence of the energy storage power supply to obtain a corrected output time sequence of the energy storage power supply, and regulating and controlling the power flow value of the target power grid according to the output time sequence of the variable power supply and the corrected output time sequence of the energy storage power supply. Based on the power flow change of the key section of the power grid, the energy storage project near the key section is regulated and controlled, the peak-valley difference is stabilized in cooperation with the load and the power output, the problems of new energy consumption and power supply guarantee are further solved, and the economical efficiency and flexibility of the power system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and specifically relates to a grid-side power flow control method, system, device and storage medium based on convex optimization. Background Art

[0003] With the rapid development of new energy sources such as wind power and photovoltaic power, the output of photovoltaic and wind power has become more random and intermittent. Large-scale grid connection has brought about increased peak-shaving pressure, node voltage offset, load fluctuation, and difficulty in absorbing new energy. In order to promote the solution of system balance problems caused by the randomness, volatility, and seasonal imbalance of new energy generation, multi-time scale energy storage technology has been applied on a large scale, and the form of the power system has gradually changed from the three elements of "source, grid, and load" to the four elements of "source, grid, load, and storage". Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a grid-side flow control method, system, device and storage medium based on convex optimization, so as to reasonably select and configure a batch of energy storage projects, play a role in balancing key flow sections, and contribute to the rational absorption of new energy within the power grid.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, a power grid-side flow control method based on convex optimization is provided, comprising: obtaining grid structure information of a target power grid, which includes information of several branches, fixed power supply information, variable power supply information and energy storage power supply information; constructing a flow control model including an objective function and constraints according to the network architecture information, wherein the objective function takes the minimum amplitude of the power grid flow curve as the optimization target, and the constraints include branch total flow time series constraints, variable power supply output time series constraints, energy storage power supply output time series constraints and energy storage power supply power time series constraints; using a pre-acquired convex optimization solver to solve the flow control model to obtain a variable power supply output time series and an energy storage power supply output time series; correcting the energy storage power supply output time series to obtain a corrected energy storage power supply output time series, and controlling the flow value of the target power grid according to the variable power supply output time series and the corrected energy storage power supply output time series.

[0007] As an optional technical solution of the present invention, the target power grid includes a plurality of branches, a fixed power source, a variable power source and an energy storage power source, and the fixed power source, the variable power source and the energy storage power source are located at the nodes of the branches;

[0008] The i-th branch is expressed as:

[0009] ;

[0010] in, represents the input node of the i-th branch, represents the output node of the i-th branch, represents the time series of the power flow value of the i-th branch, represents the upper limit of the power flow of the ith branch, represents the lower limit of the power flow of the i-th branch;

[0011] The fixed power supply is expressed as:

[0012] ;

[0013] in, Indicates the node where the fixed power source is located. Represents the fixed power source power flow output time series;

[0014] The variable power supply is expressed as:

[0015] ;

[0016] in, Indicates the node where the variable power source is located, Indicates the upper limit of variable power output. Indicates the lower limit of variable power output;

[0017] The energy storage power supply is expressed as:

[0018] ;

[0019] in, Indicates the node where the energy storage power source is located. Indicates the initial capacity of the energy storage power supply, Indicates the maximum capacity of the energy storage power supply. Indicates the minimum capacity of the energy storage power supply, Indicates the maximum power of the energy storage power supply charging, Indicates the maximum power of the energy storage power supply discharge, Indicates the efficiency of charging the energy storage power source.

[0020] As an optional technical solution of the present invention, the minimum amplitude of the power grid flow curve is expressed as:

[0021] ;

[0022] in, represents the norm, =1, 2 or ∞, Indicates The time series of the power flow value after adjustment of the branches, express The mean of Indicates The controllable input power time series of the branches;

[0023] No. Time series of power flow values ​​after adjustment of branches It is expressed as:

[0024] ;

[0025] in, represents the time series of the power flow value of the i-th branch, represents the diversion time series of all fixed power sources to the i-th branch, represents the diversion time series of the lower limit of the output of all variable power sources for the i-th branch;

[0026] No. The controllable input power time series of the branches It is expressed as:

[0027] ;

[0028] in, Indicates that all energy storage power sources are The branch flow time series, Indicates that all variable power supplies are The time series of the branch flow.

[0029] As an optional technical solution of the present invention, the branch total power flow time series constraint is expressed as:

[0030] ;

[0031] in, Indicates The total flow time series of the branches, represents the lower limit of the power flow of the ith branch, represents the upper limit of the power flow of the ith branch;

[0032] The variable power output time series constraint is expressed as:

[0033] ;

[0034] in, Indicates The variable power output time series of each node, Indicates The lower limit of the output of the variable power supply of each node, Indicates The output limit of the variable power supply of each node;

[0035] The energy storage power supply output time series constraint is expressed as:

[0036] ;

[0037] in, Indicates The energy storage power output time series of each node, Indicates The maximum discharge power of the energy storage power supply of each node, Indicates The maximum charging power of the energy storage power supply of each node;

[0038] The energy storage power supply time series constraint is expressed as:

[0039] ;

[0040] in, Indicates The time series of energy storage power supply of each node is expressed as:

[0041] ;

[0042] in, , , represents the initial time, Indicates The initial power of the energy storage power supply of each node, Indicates The efficiency of the energy storage power supply of each node;

[0043] make ,but It is expressed as:

[0044] .

[0045] As an optional technical solution of the present invention, before using the pre-acquired convex optimization solver to solve the power flow control model, it is necessary to establish a functional relationship between the node output and the branch power flow, which is expressed as;

[0046] ;

[0047] Where Z represents the power flow transformation matrix,

[0048] ,

[0049] , ,

[0050] ,

[0051] ,

[0052] ,

[0053] Indicates the total number of nodes, Indicates the total number of branches.

[0054] As an optional technical solution of the present invention, before using the pre-acquired convex optimization solver to solve the power flow control model, it is also necessary to discretize all time series in the power flow control model, including:

[0055] Collect fixed power flow output time series with the same time interval and time span And the time series of power flow value The true value of , where the time interval vector is represented as:

[0056] ;

[0057] in, Indicates the length of the time series of sampling points;

[0058] The power flow control model is removed The time series other than Discrete time series of

[0059] Will The integral in the expression is replaced by a summation, expressed as:

[0060] ;

[0061] Among them, k represents the time;

[0062] In the above formula ,but It is expressed as:

[0063] .

[0064] As an optional technical solution of the present invention, the step of correcting the energy storage power supply output time series to obtain a corrected energy storage power supply output time series includes:

[0065] Traverse all nodes and Initial power of energy storage power supply of each node As the Energy storage power supply capacity of each node ;

[0066] Traverse The energy storage power output time series of each node All the ingredients ,like , then judge Is it less than If so, then If not, then , ;

[0067] like , then let ;

[0068] After the correction is completed, the final As the corrected energy storage power output time series.

[0069] In a second aspect, a power grid side power flow control system based on convex optimization is provided, comprising:

[0070] An information acquisition module is used to acquire the grid structure information of the target power grid, which includes information on several branches, fixed power supply information, variable power supply information and energy storage power supply information;

[0071] A model building module, used to build a power flow control model including an objective function and constraint conditions according to the network architecture information, wherein the objective function takes the minimum amplitude of the power grid power flow curve as the optimization target, and the constraint conditions include the branch total power flow time series constraint, the variable power supply output time series constraint, the energy storage power supply output time series constraint and the energy storage power supply power time series constraint;

[0072] A model solving module, used to solve the power flow control model using a pre-acquired convex optimization solver to obtain a variable power supply output time series and an energy storage power supply output time series;

[0073] The control module is used to correct the energy storage power supply output time sequence to obtain the corrected energy storage power supply output time sequence, and control the power flow value of the target power grid according to the variable power supply output time sequence and the corrected energy storage power supply output time sequence.

[0074] According to a third aspect, a device is provided, comprising: a memory, a processor, and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method as described in the first aspect.

[0075] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0076] Compared with the prior art, the present invention has the following beneficial effects:

[0077] A grid-side power flow control method based on convex optimization provided by the present invention constructs a power flow control model and adopts a convex optimization solver to solve it to obtain a control plan, which solves the system balance problems caused by the randomness, volatility, and seasonal imbalance of renewable energy power generation, plays a role in balancing key power flow sections, is conducive to the rational absorption of renewable energy within the power grid, improves the utilization rate of renewable energy, and promotes the high-quality development of local power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a flow chart of a grid-side power flow control method based on convex optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0080] Example 1

[0081] This embodiment provides a grid-side power flow control method based on convex optimization, such as Figure 1 As shown, the specific steps include:

[0082] Step 1: Obtain the grid structure information of the target power grid, which includes information on several branches, fixed power sources, variable power sources, and energy storage power sources.

[0083] The target power grid includes several branches, fixed power sources, variable power sources and energy storage power sources. The fixed power sources, variable power sources and energy storage power sources are located at the nodes of the branches, but not all nodes contain power sources. Among them, the fixed power source is typically wind power, whose output cannot be controlled artificially and has no power storage capacity. The variable power source is typically thermal power, whose output is controllable, but has no power storage capacity. The energy storage power source is typically chemical energy storage, which has power storage capacity and its output is controllable, but it must meet the restriction conditions with its stored power.

[0084] (1) The i-th branch is expressed as:

[0085] ;

[0086] in, represents the input node of the i-th branch, represents the output node of the i-th branch, represents the time series of the power flow value of the i-th branch, represents the upper limit of the power flow of the ith branch, represents the lower limit of the power flow of the ith branch.

[0087] The branch information includes branch parameter data and branch load data.

[0088] (2) The fixed power supply is expressed as:

[0089] ;

[0090] in, Indicates the node where the fixed power source is located. Represents the fixed power source power flow output time series.

[0091] (3) The variable power supply is expressed as:

[0092] ;

[0093] in, Indicates the node where the variable power source is located, Indicates the upper limit of variable power output. Indicates the lower limit of the variable power output.

[0094] (4) The energy storage power source is expressed as:

[0095] ;

[0096] in, Indicates the node where the energy storage power source is located. Indicates the initial capacity of the energy storage power supply, Indicates the maximum capacity of the energy storage power supply. Indicates the minimum capacity of the energy storage power supply, Indicates the maximum power of the energy storage power supply charging, Indicates the maximum power of the energy storage power supply discharge, Indicates the efficiency of charging the energy storage power source.

[0097] In this embodiment, the target power grid includes 3 units (Gen1~Gen3), 9 nodes (Bus1~Bus9) and 9 branches (Line1~Line9). Among them, Gen1 is an offshore wind turbine connected to Bus1. The 24-hour output data of Gen1 is shown in Table 1.

[0098] Table 1 Gen1 24-hour output data

[0099] Hour 1 2 3 4 5 6 contribute 263.14 105 44.43 34.65 18.66 53.1 Hour 7 8 9 10 11 12 contribute 52.87 44.35 16.01 6.46 1.19 1.75 Hour 13 14 15 16 17 18 contribute 1.56 1.61 1.56 1.53 1.47 1.5 Hour 19 20 21 22 23 24 contribute 1.53 1.53 1.56 1.47 1.56 1.56

[0100] Gen2 is a conventional unit, connected to Bus2, with an installed power of 100MW and an output range of 30MW-100MW. Gen3 is a storage unit, connected to Bus7, with an installed power of 100MW, a charging time of 2 hours, an efficiency of 85%, a minimum capacity of 0, and an initial capacity of 0.

[0101] The parameters of each branch are shown in Table 2, and the load of each branch is shown in Table 2.

[0102] Table 2 Parameters of each branch

[0103] Branch Road Starting Node Endpoint resistance Reactance Trend limit Flow lower limit 1 1 4 0 0.0576 250 0 2 4 5 0.017 0.092 250 0 3 4 6 0.039 0.17 150 0 4 3 9 0 0.0586 300 0 5 5 7 0.0119 0.1008 150 0 6 7 8 0.0085 0.072 250 0 7 2 7 0 0.0625 250 0 8 8 9 0.032 0.161 250 0 9 9 6 0.01 0.085 250 0

[0104] Table 3 Load table of each branch

[0105] Branch 1 Branch 2 Branch 3 Branch 4 Branch 5 Branch 6 Branch 7 Branch 8 Branch 9 1 hour 169.7214 103.0879 69.91762 167.008 73.26181 97.56225 123.2944 122.0817 120.3207 2 hours 93.568 151.7468 95.90119 208.0711 97.40218 91.74553 52.25814 87.94423 163.6949 3 hours 58.21246 102.0657 38.45195 133.9452 39.66187 153.4369 122.2145 133.3002 122.3691 4 hours 115.833 106.1696 82.25643 128.9714 54.46811 150.4859 74.03387 134.179 120.6289 5 hours 121.4528 58.64291 85.29298 122.4693 57.94233 175.9085 154.3677 112.9416 109.3508 6 hours 81.82358 119.7884 69.56738 96.49796 54.48128 93.15563 167.9095 90.24205 95.96792 7 hours 108.0263 95.22032 93.76146 74.86218 56.6867 66.55483 52.24442 113.8879 79.18756 8 hours 106.3422 128.3836 67.22427 127.5833 150.6247 75.41396 127.8812 130.7393 137.467 9 hours 109.7039 158.0442 83.07275 132.0902 107.1113 158.0305 111.4571 122.0548 95.96894 10 hours 116.5075 147.0934 50.65743 130.117 57.88685 122.5953 112.3188 115.9544 127.6163 11 hours 72.64518 22.33255 97.60691 86.32749 27.37092 61.38807 99.66453 107.9205 121.9475 12 hours 83.25332 116.8972 114.8379 138.2948 37.56142 57.77746 105.0835 40.4995 98.53474 13 hours 35.79066 131.1453 3.264771 112.4635 29.84806 129.2971 62.83526 89.85469 65.71131 14 hours 57.09431 55.87555 14.64442 85.45286 45.3479 64.14019 52.81409 55.8765 58.18262 15 hours 98.79542 116.4662 69.96669 117.8323 48.06539 70.38225 90.09403 106.6436 114.343 16 hours 74.89594 88.59279 48.54236 91.36142 7.511302 105.2387 114.0438 100.3653 109.6648 17 hours 124.4155 131.8491 113.0197 190.0623 94.23056 204.66 118.571 148.5072 155.5948 18 hours 170.212 122.4532 88.03701 190.2891 84.96402 123.4952 140.9865 167.2629 201.0169 19 hours 178.283 205.5451 116.6075 229.257 72.19388 151.0137 152.5184 177.7949 128.7534 20 hours 176.8011 189.1625 65.28736 241.2618 124.4457 164.4452 160.6831 208.5841 141.4662 21 hours 151.0871 174.9186 139.8536 226.7865 50.941 162.5535 144.8756 100.595 201.9211 22 hours 122.0377 137.0495 117.2781 162.3842 128.8314 239.7028 97.44585 172.7867 141.7926 23 hours 99.87924 143.4587 113.1198 177.5162 103.4255 157.1064 205.3701 134.4236 145.8732 24 hours 91.49752 126.0933 82.03551 188.4829 105.5325 102.6058 160.9727 120.392 156.1923

[0106] Step 2: Construct a power flow control model including an objective function and constraints based on the network architecture information, wherein the objective function takes the minimum amplitude of the power grid power flow curve as the optimization goal, and the constraints include the branch total power flow time series constraints, the variable power source output time series constraints, the energy storage power source output time series constraints and the energy storage power source power time series constraints.

[0107] (1) Objective function

[0108] The minimum amplitude of the power grid flow curve is expressed as:

[0109] ;

[0110] in, represents the norm, =1, 2 or ∞, Indicates The time series of the power flow value after adjustment of the branches, express The mean of Indicates The controllable input power time series of the branches.

[0111] No. Time series of power flow values ​​after adjustment of branches It is expressed as:

[0112] ;

[0113] in, represents the time series of the power flow value of the i-th branch, represents the diversion time series of all fixed power sources to the i-th branch, Represents the diversion time series of the lower limit of the output of all variable power sources for the i-th branch.

[0114] No. The controllable input power time series of the branches It is expressed as:

[0115] ;

[0116] in, Indicates that all energy storage power sources are The branch flow time series, Indicates that all variable power supplies are The time series of the branch flow.

[0117] (2) Constraints

[0118] The branch total power flow time series constraint is expressed as:

[0119] ;

[0120] in, Indicates The total flow time series of the branches, represents the lower limit of the power flow of the ith branch, represents the upper limit of the power flow of the ith branch.

[0121] The variable power output time series constraint is expressed as:

[0122] ;

[0123] in, Indicates The variable power output time series of each node, Indicates The lower limit of the output of the variable power supply of each node, Indicates The output limit of the variable power supply of each node.

[0124] The energy storage power supply output time series constraint is expressed as:

[0125] ;

[0126] in, Indicates The energy storage power output time series of each node, where a positive value indicates discharge and a negative value indicates charging. Indicates The maximum discharge power of the energy storage power supply of each node, Indicates The maximum charging power of the energy storage power supply of each node.

[0127] The energy storage power supply time series constraint is expressed as:

[0128] ;

[0129] in, Indicates The time series of energy storage power supply of each node is expressed as:

[0130] ;

[0131] in, , , represents the initial time, Indicates The initial power of the energy storage power supply of each node, Indicates The efficiency of the energy storage power supply of each node. The integrability of the integral is given by Intermittent continuity guarantee.

[0132] like When it is close to 1 or needs to be approximated to satisfy the convexity condition, let ,but It is expressed as:

[0133] .

[0134] In this step, the optimization parameter to be sought is Time series of variable power output of node With The energy storage power output time series of each node .

[0135] Step 3: Use the pre-acquired convex optimization solver to solve the power flow control model to obtain the variable power source output time series and the energy storage power source output time series.

[0136] In order to solve the model, it is necessary to establish a functional relationship between the node output and the branch flow. And for the purpose of convex optimization, it is best to have a linear functional relationship. The functional relationship between the node output and the branch flow is expressed as;

[0137] ;

[0138] Where Z represents the power flow transformation matrix,

[0139] ,

[0140] , ,

[0141] ,

[0142] ,

[0143] ,

[0144] Indicates the total number of nodes, Indicates the total number of branches.

[0145] Before using the pre-acquired convex optimization solver to solve the power flow control model, it is also necessary to discretize all time series in the power flow control model, including:

[0146] (1) Collect the fixed power flow output time series with the same time interval and time span And the time series of power flow value The true value of , where the time interval vector is represented as:

[0147] ;

[0148] in, Indicates the length of the time series of sampling points.

[0149] (2) Remove the The time series other than The remaining time series are automatically discretized according to the definition.

[0150] (3) The integral in the expression is replaced by a summation, expressed as:

[0151] ;

[0152] Among them, k represents the time.

[0153] In the above formula ,but It is expressed as:

[0154] .

[0155] In this embodiment, after discretization, the objective function of the optimization problem is the norm sum of linear functions, and the constraints of the optimization problem are all linear inequalities, so this optimization problem is a typical convex optimization problem, which can be solved by using the MATLAB convex optimization solver CVX or the CVXPY library with similar functions on the Python platform. In order to meet the requirements of convexity, this step obtains the first The energy storage power output time series of each node is an approximate form. In the next step, The energy storage power output time series of each node Make corrections.

[0156] Step 4: Correct the energy storage power output time series to obtain a corrected energy storage power output time series, and adjust the power flow value of the target power grid according to the variable power output time series and the corrected energy storage power output time series. Specifically including:

[0157] 4.1. Traverse all nodes and Initial power of energy storage power supply of each node As the Energy storage power supply capacity of each node .

[0158] 4.2, traverse The energy storage power output time series of each node All the ingredients ,like , then judge Is it less than .

[0159] If so, then If not, then , .

[0160] like , then let .

[0161] After the correction is completed, the final As the corrected energy storage power output time series.

[0162] In this embodiment, Bus7 to Bus5 are taken as the optimized branch, and the optimization goal is to stabilize the branch power flow from Bus7 to Bus5. The corrected energy storage power output time series is obtained by solving, that is, the energy storage battery output scheme is shown in Table 4, where negative numbers represent charging and positive numbers represent discharging.

[0163] Table 4 Energy storage battery output table

[0164] Hour 1 2 3 4 5 6 contribute -20.86 20.13 -44.76 -24.1 -19.24 -24.1 Hour 7 8 9 10 11 12 contribute -21 84.41 49.44 0 -40.65 -26.43 Hour 13 14 15 16 17 18 contribute -37.2 -15.57 -11.78 -68.36 25.75 10.91 Hour 19 20 21 22 23 24 contribute -8.49 55.26 -41.77 10.33 27.11 18.86

[0165] Variable power output time series , that is, the thermal power battery output plan is shown in Table 5.

[0166] Table 5 Thermal power battery output table

[0167] Hour 1 2 3 4 5 6 contribute 53.67 46.35 30.7 30.68 30.68 30.68 Hour 7 8 9 10 11 12 contribute 30.66 51.93 30.6 30 30 30 Hour 13 14 15 16 17 18 contribute 30 30 30 30 36.32 38.22 Hour 19 20 21 22 23 24 contribute 39.81 48.96 43.45 56.02 47.78 58.97

[0168] Based on Table 4 and Table 5, the target power grid is regulated.

[0169] Example 2

[0170] This embodiment provides a power grid side power flow control system based on convex optimization, including:

[0171] The information acquisition module is used to obtain the grid structure information of the target power grid, which includes information on several branches, fixed power supply information, variable power supply information and energy storage power supply information.

[0172] A model building module is used to build a power flow control model including an objective function and constraints according to the network architecture information, wherein the objective function takes the minimum amplitude of the power grid power flow curve as the optimization goal, and the constraints include the branch total power flow time series constraints, the variable power supply output time series constraints, the energy storage power supply output time series constraints and the energy storage power supply power time series constraints.

[0173] The model solving module is used to solve the power flow control model using a pre-acquired convex optimization solver to obtain a variable power supply output time series and an energy storage power supply output time series.

[0174] The control module is used to correct the energy storage power supply output time sequence to obtain the corrected energy storage power supply output time sequence, and control the power flow value of the target power grid according to the variable power supply output time sequence and the corrected energy storage power supply output time sequence.

[0175] Example 3

[0176] This embodiment provides a device, including: a memory, a processor, and a computer program;

[0177] The computer program is stored in the memory and is configured to be executed by the processor to implement the method as described in Example 1.

[0178] Example 4

[0179] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described in Embodiment 1 is implemented.

[0180] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0181] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0184] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A grid-side power flow control method based on convex optimization, characterized in that: include: Obtaining the grid structure information of the target power grid, which includes information on several branches, fixed power sources, variable power sources, and energy storage power sources; A power flow control model including an objective function and constraint conditions is constructed according to the network architecture information, wherein the objective function takes the minimum amplitude of the power grid power flow curve as the optimization target, and the constraint conditions include the branch total power flow time series constraint, the variable power supply output time series constraint, the energy storage power supply output time series constraint and the energy storage power supply power time series constraint; The power flow control model is solved by using a pre-acquired convex optimization solver to obtain a variable power source output time series and an energy storage power source output time series; The energy storage power supply output time series is corrected to obtain a corrected energy storage power supply output time series, and the power flow value of the target power grid is regulated according to the variable power supply output time series and the corrected energy storage power supply output time series.

2. The grid-side power flow control method based on convex optimization according to claim 1 is characterized in that: The target power grid includes a plurality of branches, a fixed power source, a variable power source and an energy storage power source, wherein the fixed power source, the variable power source and the energy storage power source are located at nodes of the branches; The i-th branch is expressed as: ； in, represents the input node of the i-th branch, represents the output node of the i-th branch, represents the time series of the power flow value of the i-th branch, represents the upper limit of the power flow of the ith branch, represents the lower limit of the power flow of the i-th branch; The fixed power supply is expressed as: ； in, Indicates the node where the fixed power source is located. Represents the fixed power source power flow output time series; The variable power supply is expressed as: ； in, Indicates the node where the variable power source is located, Indicates the upper limit of variable power output. Indicates the lower limit of variable power output; The energy storage power supply is expressed as: ； in, Indicates the node where the energy storage power source is located. Indicates the initial capacity of the energy storage power supply, Indicates the maximum capacity of the energy storage power supply. Indicates the minimum capacity of the energy storage power supply, Indicates the maximum power of the energy storage power supply charging, Indicates the maximum power of the energy storage power supply discharge, Indicates the efficiency of charging the energy storage power source.

3. The grid-side power flow control method based on convex optimization according to claim 1 is characterized in that: The minimum amplitude of the power grid flow curve is expressed as: ； in, represents the norm, =1, 2 or ∞, Indicates The time series of the power flow value after adjustment of the branches, express The mean of Indicates The controllable input power time series of the branches; No. Time series of power flow values ​​after adjustment of branches It is expressed as: ； in, represents the time series of the power flow value of the i-th branch, represents the diversion time series of all fixed power sources to the i-th branch, represents the diversion time series of the lower limit of the output of all variable power sources for the i-th branch; No. The controllable input power time series of the branches It is expressed as: ； in, Indicates that all energy storage power sources are The branch flow time series, Indicates that all variable power supplies are The time series of the branch flow.

4. The grid-side power flow control method based on convex optimization according to claim 1 is characterized in that: The total flow time series constraint of the branch is expressed as: ； in, Indicates The total flow time series of the branches, represents the lower limit of the power flow of the ith branch, represents the upper limit of the power flow of the ith branch; The variable power output time series constraint is expressed as: ； in, Indicates The variable power output time series of each node, Indicates The lower limit of the output of the variable power supply of each node, Indicates The output limit of the variable power supply of each node; The energy storage power supply output time series constraint is expressed as: ； in, Indicates The energy storage power output time series of each node, Indicates The maximum discharge power of the energy storage power supply of each node, Indicates The maximum charging power of the energy storage power supply of each node; The energy storage power supply time series constraint is expressed as: ； in, Indicates The time series of energy storage power supply of each node is expressed as: ； in, , , represents the initial time, Indicates The initial power of the energy storage power supply of each node, Indicates The efficiency of the energy storage power supply of each node; make ,but It is expressed as: 。 5. The grid-side power flow control method based on convex optimization according to claim 4 is characterized in that: Before using the pre-obtained convex optimization solver to solve the power flow control model, it is necessary to establish a functional relationship between the node output and the branch power flow, which is expressed as; ； Where Z represents the power flow transformation matrix, ， ， ， ， ， ， Indicates the total number of nodes, Indicates the total number of branches.

6. The grid-side power flow control method based on convex optimization according to claim 5 is characterized in that: Before using the pre-acquired convex optimization solver to solve the power flow control model, it is also necessary to discretize all time series in the power flow control model, including: Collect fixed power flow output time series with the same time interval and time span And the time series of power flow value The true value of , where the time interval vector is represented as: ； in, Indicates the length of the time series of sampling points; The power flow control model is The time series other than Discrete time series of Will The integral in the expression is replaced by a summation, expressed as: ； Among them, k represents the time; In the above formula ,but It is expressed as: 。 7. The grid-side power flow control method based on convex optimization according to claim 1 is characterized in that: The step of correcting the energy storage power supply output time series to obtain a corrected energy storage power supply output time series includes: Traverse all nodes and Initial power of energy storage power supply of each node As the Energy storage power supply capacity of each node ; Traverse The energy storage power output time series of each node All the ingredients ,like , then judge Is it less than If so, then If not, then , ; like , then let ; After the correction is completed, the final As the corrected energy storage power output time series.

8. A power grid side power flow control system based on convex optimization, characterized in that: include: An information acquisition module is used to acquire the grid structure information of the target power grid, which includes information on several branches, fixed power supply information, variable power supply information and energy storage power supply information; A model building module, used to build a power flow control model including an objective function and constraint conditions according to the network architecture information, wherein the objective function takes the minimum amplitude of the power grid power flow curve as the optimization target, and the constraint conditions include the branch total power flow time series constraint, the variable power supply output time series constraint, the energy storage power supply output time series constraint and the energy storage power supply power time series constraint; A model solving module, used to solve the power flow control model using a pre-acquired convex optimization solver to obtain a variable power supply output time series and an energy storage power supply output time series; The control module is used to correct the energy storage power supply output time sequence to obtain the corrected energy storage power supply output time sequence, and control the power flow value of the target power grid according to the variable power supply output time sequence and the corrected energy storage power supply output time sequence.

9. A device, characterized in that: include: Memory, processor and computer program; The computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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