Distributed optimal power flow calculation method and system for power system diagram data

Abstract

The invention provides a distributed optimal power flow calculation method and system for power system diagram data. The method comprises the following steps: acquiring running diagram data of a distributed power supply connected to a direct-current power system; based on the running diagram data, solving a pre-constructed distributed power flow calculation model by using an alternating direction multiplier method to obtain the optimal power flow of the direct-current power system; wherein the distributed load flow calculation model is constructed by taking power generation cost minimization as a target; according to the invention, the alternating direction multiplier method is adopted to solve the pre-constructed distributed power flow calculation model, so that the optimal power flow of the direct current power system has the advantages of low communication traffic, dispersed calculation amount and high calculation efficiency in the calculation process.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly relates to a distributed optimal power flow calculation method and system for power system graph data. Background Art

[0002] At present, with the wide application of power electronic devices and the large-scale grid connection of distributed power sources, the distributed power sources based on new energy have developed rapidly, gradually showing the trend of "many points, wide area, and local high-density grid connection", and also making the development of future power systems show variable operation scenarios and complex operation characteristics. In addition, due to the complex graph data structure generated by the access of distributed power sources to the power system, which contains a large amount of node and edge information, the complexity of optimal power flow calculation is high, and a large amount of computing resources and time are required. Therefore, higher requirements are put forward for power flow calculation of graph data in power systems.

[0003] As the basis for the reliable operation of power systems, the optimal power flow problem is widely applied in fields such as economic dispatch, power grid planning, and power system stability assessment. The optimal power flow problem is one of the most common optimization problems in power systems, which means that under the conditions of meeting physical constraints such as power stable operation and safety constraints, the objective functions such as the total power operation cost and the total network loss are optimized. However, in the prior art, when performing optimal power flow calculation on the graph data during the operation of a DC power system, most of them adopt a centralized optimization method. Using a centralized optimization method for optimal power flow calculation requires modeling and calculation of the entire power system, involving more variables and constraint conditions, and also having relatively high requirements for communication and computing resources. Therefore, it will lead to problems of high calculation complexity and low calculation efficiency. Summary of the Invention

[0004] In order to solve the problem that in the prior art, when performing optimal power flow calculation on the graph data during the operation of a DC power system, most of them adopt a centralized optimization method, resulting in a large amount of calculation dispersion and low calculation efficiency of power flow calculation, the present invention proposes a distributed optimal power flow calculation method for power system graph data, including:

[0005] Obtain the operation graph data of distributed power sources accessing the DC power system;

[0006] Based on the operation graph data, use the alternating direction multiplier method to solve the pre-constructed distributed power flow calculation model to obtain the optimal power flow of the DC power system;

[0007] Wherein, the distributed power flow calculation model is constructed with the goal of minimizing the power generation cost.

[0008] Optionally, the construction process of the distributed power flow calculation model includes the following:

[0009] Taking the minimum power generation cost in the DC power system as the goal, construct the objective function;

[0010] According to the objective function, formulate the corresponding constraint conditions;

[0011] Based on the objective function and the constraint conditions, obtain the distributed power flow calculation model.

[0012] Optionally, the expression corresponding to the objective function is as follows:

[0013]

[0014] Among them, C represents the objective function; n represents the number of distributed power sources; α1 represents the first coefficient value; α2 represents the second coefficient value; α3 represents the constant term coefficient value; Q i represents the active power output data of the i-th distributed power source; i = 1…n.

[0015] Optionally, the constraint conditions include: power flow constraint, stability constraint, energy constraint, node voltage safety constraint, and line transmission power constraint.

[0016] Optionally, the expressions corresponding to the constraint conditions are as follows:

[0017]

[0018] Among them, V p represents the voltage of node p in the distributed power source; V q represents the voltage of node q in the distributed power source; r pq represents the impedance of the line composed of node p and node q; I pq represents the current of the line composed of node p and node q; l p represents the injected active power of distributed power source node p; L pq represents the active power of the line composed of distributed power source node p and node q; E represents the set of nodes; V min represents the lower limit value of the node voltage; V max represents the upper limit value of the node voltage; Q i，p represents the output data of the i-th distributed power source node p; Q min represents the lower limit value of the output data; Q max represents the upper limit value of the output data.

[0019] Optionally, the use of the alternating direction multiplier method to solve the pre-constructed distributed power flow calculation model to obtain the optimal power flow of the DC power system includes:

[0020] According to the objective function and the constraint conditions, construct the corresponding augmented Lagrangian function;

[0021] Taking the operation diagram data as the input data of the augmented Lagrangian function to obtain each Lagrange multiplier in the augmented Lagrangian function;

[0022] Forming the power flow optimization variables with each Lagrange multiplier in the augmented Lagrangian function, and performing separate alternating iterations on each variable in the power flow optimization variables in a set order until each variable in the power flow optimization variables is in a converged state, so as to obtain the updated power flow optimization variables as the optimal power flow of the DC power system.

[0023] Based on the same inventive concept, the present invention also provides a distributed optimal power flow calculation system for power system diagram data, including:

[0024] A data acquisition module: used to acquire the operation diagram data of distributed power sources accessing the DC power system;

[0025] A parallel computing module: used to solve a pre-constructed distributed power flow calculation model based on the operation diagram data by using the alternating direction multiplier method to obtain the optimal power flow of the DC power system;

[0026] Wherein, the distributed power flow calculation model is constructed with the goal of minimizing the generation cost.

[0027] Optionally, the construction process of the distributed power flow calculation model in the parallel computing module is as follows:

[0028] Constructing an objective function with the goal of the lowest generation cost in the DC power system;

[0029] Formulating corresponding constraint conditions according to the objective function;

[0030] Based on the objective function and the constraint conditions, obtaining a distributed power flow calculation model.

[0031] Optionally, the expression corresponding to the objective function in the parallel computing module is as follows:

[0032]

[0033] Wherein, C represents the objective function; n represents the number of distributed power sources; α1 represents the first coefficient value; α2 represents the second coefficient value; α3 represents the constant term coefficient value; Q i represents the active power output data of the i-th distributed power source; i = 1...n.

[0034] Optionally, the constraint conditions in the parallel computing module include: power flow constraint, stability constraint, energy constraint, node voltage safety constraint, and line transmission power constraint.

[0035] Optionally, the expressions corresponding to the constraint conditions in the parallel computing module are as follows:

[0036]

[0037] Wherein, V p represents the voltage of node p in the distributed power source; V q represents the voltage of node q in the distributed power source; r pq represents the impedance of the line formed by node p and node q; I pq represents the current of the line formed by node p and node q; l p represents the injected active power of distributed power source node p; L pq represents the active power of the line formed by distributed power source nodes p and q; E represents the set of nodes; V min represents the lower limit value of the node voltage; V max represents the upper limit value of the node voltage; Q i，p represents the output data of the i-th distributed power source node p; Q min represents the lower limit value of the output data; Q max represents the upper limit value of the output data.

[0038] Optionally, the parallel computing module uses the alternating direction multiplier method to solve the pre-constructed distributed power flow calculation model to obtain the optimal power flow of the DC power system, including:

[0039] Construct a corresponding augmented Lagrangian function according to the objective function and the constraint conditions;

[0040] Use the operation diagram data as the input data of the augmented Lagrangian function to obtain each Lagrange multiplier in the augmented Lagrangian function;

[0041] Form the power flow optimization variables with each Lagrange multiplier in the augmented Lagrangian function, and perform separate alternating iterations on each variable in the power flow optimization variables in a set order until each variable in the power flow optimization variables is in a converged state, and obtain the updated power flow optimization variables as the optimal power flow of the DC power system.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] The present invention provides a distributed optimal power flow calculation method and system for power system graph data, including: obtaining the operation graph data of a DC power system with distributed power sources connected; based on the operation graph data, using the alternating direction multiplier method to solve a pre-constructed distributed power flow calculation model to obtain the optimal power flow of the DC power system; wherein, the distributed power flow calculation model is constructed with the goal of minimizing the power generation cost; by using the alternating direction multiplier method to solve the pre-constructed distributed power flow calculation model, the present invention has the advantages of low communication volume, dispersed calculation amount, and high calculation efficiency in the calculation of the optimal power flow for the graph data in the DC power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic flow chart of a distributed optimal power flow calculation method for power system graph data provided by the present invention;

[0045] Figure 2 It is a schematic flow chart of using the alternating direction multiplier method to solve the distributed power flow calculation model in the distributed optimal power flow calculation method for power system graph data provided by the present invention;

[0046] Figure 3 It is a schematic structural composition diagram of a distributed optimal power flow calculation system for power system graph data provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] The present invention proposes a distributed optimal power flow calculation method and system for power system graph data. The following further details the specific embodiments of the present invention with reference to the accompanying drawings.

[0048] Embodiment 1:

[0049] A distributed optimal power flow calculation method for power system graph data, the schematic flow chart is as Figure 1 shown, including:

[0050] Step 1: Obtain the operation graph data of a DC power system with distributed power sources connected;

[0051] Step 2: Based on the operation graph data, use the alternating direction multiplier method to solve a pre-constructed distributed power flow calculation model to obtain the optimal power flow of the DC power system;

[0052] Wherein, the distributed power flow calculation model is constructed with the goal of minimizing the power generation cost.

[0053] Specifically, the operation graph data of the DC power system with distributed power sources connected in Step 1 includes one or more of the following: topological graph data, distributed power source information graph data, power flow distribution graph data, voltage distribution data, power balance graph data, and voltage distribution graph data.

[0054] The distributed power flow calculation model in Step 2 includes the following construction process:

[0055] Taking the lowest power generation cost in the DC power system as the goal, construct the objective function;

[0056] According to the objective function, formulate the corresponding constraint conditions;

[0057] Based on the objective function and the constraint conditions, obtain the distributed power flow calculation model.

[0058] The expression corresponding to the objective function is as follows:

[0059]

[0060] Among them, C represents the objective function; n represents the number of distributed power sources; α1 represents the first coefficient value; α2 represents the second coefficient value; α3 represents the constant term coefficient value; Q i represents the active power output data of the i-th distributed power source; i = 1…n; the first coefficient value, the second coefficient value, and the constant term coefficient value are all non-negative constants, and are all determined by the power generation characteristics of the distributed power source.

[0061] The constraint conditions include: power flow constraint, stability constraint, energy constraint, node voltage security constraint, and line transmission power constraint.

[0062] The expressions corresponding to the constraint conditions are as follows:

[0063]

[0064] Among them, V p represents the voltage of node p in the distributed power source; V q represents the voltage of node q in the distributed power source; r pq represents the impedance of the line composed of node p and node q; I pq represents the current of the line composed of node p and node q; l p represents the injected active power of distributed power source node p; L pq represents the active power of the line composed of distributed power source node p and node q; E represents the set of nodes; V min represents the lower limit value of the node voltage; V max represents the upper limit value of the node voltage; Q i，p represents the output data of the i-th distributed power source node p; Q min represents the lower limit value of the output data; Q max represents the upper limit value of the output data;

[0065] When constructing the distributed power flow calculation model of the present invention, considerations are made from three aspects: the objective function, equality constraints, and inequality constraints, and the active power loss of the line is reduced to optimize the power generation cost.

[0066] As Figure 2 shown, solving the pre-constructed distributed power flow calculation model by using the alternating direction multiplier method to obtain the optimal power flow of the DC power system includes:

[0067] Step S1: Construct a corresponding augmented Lagrangian function according to the objective function and the constraint conditions;

[0068] Step S2: Use the operation diagram data as the input data of the augmented Lagrangian function to obtain each Lagrange multiplier in the augmented Lagrangian function;

[0069] Step S3: Combine each Lagrange multiplier in the augmented Lagrangian function into a power flow optimization variable, and perform separate alternating iterations on each variable in the power flow optimization variable in a set order until each variable in the power flow optimization variable is in a converged state, and obtain the updated power flow optimization variable as the optimal power flow of the DC power system.

[0070] A distributed optimal power flow calculation method for power system diagram data provided by the present invention is beneficial to improving the optimization efficiency of the algorithm and shortening the algorithm solving time by using the distributed optimization method - alternating direction multiplier method to solve the distributed power flow calculation model, and then obtaining the global optimization strategy of the DC power system, getting rid of the dependence on the central controller, and truly achieving decentralized processing.

[0071] Embodiment 2:

[0072] Based on the same inventive concept, the present invention also provides a distributed optimal power flow calculation system for power system diagram data. The structural composition schematic diagram is as Figure 3 shown, including:

[0073] Data acquisition module: used to acquire the operation diagram data of distributed power sources accessing the DC power system;

[0074] Parallel computing module: used to solve the pre-constructed distributed power flow calculation model based on the operation diagram data by using the alternating direction multiplier method to obtain the optimal power flow of the DC power system;

[0075] Among them, the distributed power flow calculation model is constructed with the goal of minimizing the power generation cost.

[0076] The construction process of the distributed power flow calculation model in the parallel computing module is as follows:

[0077] Construct an objective function with the lowest power generation cost in the DC power system as the goal;

[0078] Formulate corresponding constraint conditions according to the objective function;

[0079] Based on the objective function and the constraint conditions, obtain a distributed power flow calculation model.

[0080] The expression corresponding to the objective function in the parallel computing module is as follows:

[0081]

[0082] Among them, C represents the objective function; n represents the number of distributed power sources; α1 represents the first coefficient value; α2 represents the second coefficient value; α3 represents the constant term coefficient value; Q i represents the active power output data of the i-th distributed power source; i = 1…n.

[0083] The constraint conditions in the parallel computing module include: power flow constraint, stability constraint, energy constraint, node voltage security constraint, and line transmission power constraint.

[0084] The expressions corresponding to the constraint conditions in the parallel computing module are as follows:

[0085]

[0086] Among them, V p represents the voltage of node p in the distributed power source; V q represents the voltage of node q in the distributed power source; r pq represents the impedance of the line composed of node p and node q; I pq represents the current of the line composed of node p and node q; l p represents the injected active power of distributed power source node p; L pq represents the active power of the line composed of distributed power source node p and node q; E represents the set of nodes; V min represents the upper limit value of the node voltage; V max represents the lower limit value of the node voltage; Q i，p represents the output data of the i-th distributed power source node p; Q min represents the lower limit value of the output data; Q max represents the upper limit value of the output data.

[0087] In the parallel computing module, the alternating direction multiplier method is used to solve the pre-constructed distributed power flow calculation model to obtain the optimal power flow of the DC power system, including:

[0088] Construct a corresponding augmented Lagrangian function according to the objective function and the constraint conditions;

[0089] Using the running chart data as the input data of the augmented Lagrangian function to obtain each Lagrange multiplier in the augmented Lagrangian function;

[0090] Combining each Lagrange multiplier in the augmented Lagrangian function to form a power flow optimization variable, and performing separate alternating iterations on each variable in the power flow optimization variable in a set order until each variable in the power flow optimization variable is in a convergent state, obtaining the updated power flow optimization variable as the optimal power flow of the DC power system.

[0091] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can 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 code.

[0092] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0093] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than limit the scope of its protection. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present invention, various changes, modifications or equivalent replacements can still be made to the specific implementation manners of the application. However, these changes, modifications or equivalent replacements are all within the scope of protection of the claims pending approval of the application.

Claims

1. A distributed optimal power flow calculation method for power system diagram data, characterized in that, Including: Obtain the operation diagram data of the distributed power source accessing the DC power system; Based on the operation diagram data, use the alternating direction multiplier method to solve the pre-constructed distributed power flow calculation model, and obtain the optimal power flow of the DC power system; Wherein, the distributed power flow calculation model is constructed with the goal of minimizing the power generation cost.

2. The method according to claim 1, characterized in that The construction process of the distributed power flow calculation model includes the following: Construct an objective function with the goal of minimizing the power generation cost in the DC power system; According to the objective function, formulate corresponding constraint conditions; Based on the objective function and the constraint conditions, obtain the distributed power flow calculation model.

3. The method according to claim 2, wherein The expression corresponding to the objective function is as follows: Among them, C represents the objective function; n represents the number of distributed power sources; α1 represents the first coefficient value; α2 represents the second coefficient value; α3 represents the constant term coefficient value; Q i represents the active power output data of the i-th distributed power source; i = 1…n.

4. The method according to claim 2, characterized in that The constraint conditions include: power flow constraint, stability constraint, energy constraint, node voltage safety constraint, and line transmission capacity constraint.

5. The method according to claim 4, wherein The expressions corresponding to the constraint conditions are as follows: Among them, V p represents the voltage of node p in the distributed power source; V q represents the voltage of node q in the distributed power source; r pq represents the impedance of the line formed by node p and node q; I pq represents the current of the line formed by node p and node q; l p represents the injected active power of node p of the distributed power source node; L pq represents the active power of the line formed by node p and node q of the distributed power source node; E represents the set of nodes; V min represents the lower limit value of the node voltage; V max represents the upper limit value of the node voltage; Q i,p represents the output data of node p of the i-th distributed power source node; Q min represents the lower limit value of the output data; Q max represents the upper limit value of the output data.

6. The method according to claim 2, wherein, The process of using the alternating direction multiplier method to solve the pre-constructed distributed power flow calculation model and obtain the optimal power flow of the DC power system includes: According to the objective function and the constraint conditions, construct a corresponding augmented Lagrangian function; Use the operation diagram data as the input data of the augmented Lagrangian function to obtain each Lagrange multiplier in the augmented Lagrangian function; Form the power flow optimization variables with each Lagrange multiplier in the augmented Lagrangian function, and perform separate alternating iterations on each variable in the power flow optimization variables in a set order until each variable in the power flow optimization variables is in a convergent state, and obtain the updated power flow optimization variables as the optimal power flow of the DC power system.

7. A distributed optimal power flow calculation system for power system diagram data, characterized in that, Including: Data acquisition module: used to obtain the operation diagram data of the distributed power source accessing the DC power system; Parallel computing module: used to solve the pre-constructed distributed power flow calculation model based on the operation diagram data by using the alternating direction multiplier method, and obtain the optimal power flow of the DC power system; Wherein, the distributed power flow calculation model in the distributed computing module is constructed with the goal of minimizing the power generation cost.

8. The system according to claim 7, wherein The construction process of the distributed power flow calculation model in the parallel computing module includes the following: Construct an objective function with the goal of minimizing the power generation cost in the DC power system; According to the objective function, formulate corresponding constraint conditions; Based on the objective function and the constraint conditions, obtain the distributed power flow calculation model.

9. The system according to claim 8, wherein, The process of using the alternating direction multiplier method to solve the pre-constructed distributed power flow calculation model in the parallel computing module and obtain the optimal power flow of the DC power system includes: According to the objective function and the constraint conditions, construct a corresponding augmented Lagrangian function; Use the operation diagram data as the input data of the augmented Lagrangian function to obtain each Lagrange multiplier in the augmented Lagrangian function; Form the power flow optimization variables with each Lagrange multiplier in the augmented Lagrangian function, and perform separate alternating iterations on each variable in the power flow optimization variables in a set order until each variable in the power flow optimization variables is in a convergent state, and obtain the updated power flow optimization variables as the optimal power flow of the DC power system.

10. The system according to claim 8, wherein The expression corresponding to the objective function in the parallel computing module is as follows: Among them, C represents the objective function; n represents the number of distributed power sources; α1 represents the first coefficient value; α2 represents the second coefficient value; α3 represents the constant term coefficient value; Q i represents the active power output data of the i-th distributed power source; i = 1...n.

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