A method for planning a large power grid

Abstract

This invention belongs to the field of power grid planning technology and discloses a large-scale power grid planning method, which includes dividing the power grid topology into multiple regions, each region containing non-blocking lines, and retaining blocked lines as inter-regional transmission links; performing equivalent simplification on the network in each region, the equivalent simplification including using a node power redistribution method to simplify the power grid topology; and simulating the scheduling and operation of units based on unit cluster modeling, optimizing through planning objective functions and constraints, and finally achieving efficient power grid planning.

Description

Technical Field

[0001] This invention belongs to the field of power grid planning technology, and more specifically, relates to a method for planning large-scale power grids. Background Technology

[0002] With the continuous expansion of modern power systems, power grid planning and design have become increasingly complex. Traditional power grid planning methods often require consideration of a large number of power grid devices and operational constraints, resulting in massive computational loads and difficulty in obtaining optimal solutions within a reasonable timeframe. This computational complexity typically increases dramatically for large-scale power grid systems, leading to low planning efficiency. While existing technologies include methods based on redundant transmission constraint identification, most cannot simultaneously balance power grid system accuracy and computational efficiency. Therefore, there is an urgent need for a novel power grid planning method that can significantly improve computational efficiency while maintaining planning accuracy. Summary of the Invention

[0003] In view of the above-mentioned defects or improvement needs of existing technologies, this invention provides a large-scale power grid planning method. Its purpose is to achieve efficient planning and optimization of large-scale power grids through regional division and network simplification, as well as scheduling simulation based on unit clusters. This solves the technical problems of high computational complexity, low planning efficiency, and difficulty in balancing accuracy and efficiency in traditional power grid planning methods in large-scale systems.

[0004] To achieve the above objectives, the present invention provides a large-scale power grid planning method, comprising the following steps: The power grid topology is divided into multiple regions, each containing non-blocking lines, while blocking lines are retained as inter-regional transmission links; The network in each region is simplified by equivalent means, including the use of node power redistribution to simplify the topology of the power grid. Based on generator cluster modeling, the scheduling and operation of generators are simulated. Optimization is achieved through planning objective functions and constraints, ultimately realizing efficient power grid planning.

[0005] The present invention also provides an electronic device, comprising: a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the above-described method.

[0006] The present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to perform the above-described method.

[0007] The present invention also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the above-described method.

[0008] In summary, compared with the prior art, the large-scale power grid planning method provided by this invention has the following advantages: (1) Reduce the computational complexity of power grid planning: This invention uses the regional division technique to divide a large and complex power grid into multiple relatively independent regions, reducing the coupling relationship between regions, thereby effectively reducing the overall planning computation scale and improving the solution efficiency.

[0009] (2) Improve planning efficiency: By adopting network simplification technology, the network within the region is equivalently processed, reducing the number of system nodes and lines, thereby significantly shortening the optimization solution time.

[0010] (3) Balancing planning accuracy and operational feasibility: By adopting unit cluster modeling technology, units with similar operating characteristics are aggregated, and capacity constraints and ramp-up capabilities are comprehensively considered to achieve accurate simulation of unit operating status, thereby improving the feasibility and accuracy of planning results.

[0011] (4) Improve the economic efficiency of power grid planning: By constructing a joint optimization objective function of equipment construction cost and system operation cost, the coordinated optimization of power transmission construction and operation scheduling can be achieved, thereby effectively reducing the comprehensive cost of the power grid throughout its entire life cycle.

[0012] (5) Applicable to large-scale complex power systems: This invention can adapt to the power grid planning needs under multi-regional interconnection, large-scale new energy access and complex operation modes, and has strong engineering applicability and promotion value. Attached Figure Description

[0013] Figure 1 This is a general block diagram of the large-scale power grid planning method provided by the present invention.

[0014] Figure 2 This is a comparison of the unit power stacking and thermal power unit operating capacity of the embodiment of the present invention and the traditional planning model on a typical day.

[0015] Figure 3 This is a comparison of the errors and solution times of the embodiments of the present invention and traditional methods under different simulation time scales.

[0016] Figure 4 This is the planning result of the embodiments of the present invention under different renewable energy quotas. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0018] This invention provides a large-scale power grid planning method. Its core logic lies in dividing the power grid topology into regions and simplifying the networks within each region through equivalent methods. Simultaneously, it simulates unit scheduling and operation based on unit cluster modeling, and combines the planning objective function with planning constraints to achieve efficient solutions to large-scale power grid planning problems. The overall block diagram of the proposed planning model is shown below. Figure 1 As shown.

[0019] 1. Equivalent simplification of power grid topology Network simplification is performed in two steps. First, the original system is divided into multiple regions based on key transmission connections; then, network simplification is performed independently within each region, and the parameters of the simplified network are calculated.

[0020] Under normal operating conditions of modern power systems, only a few transmission lines experience congestion. This structural characteristic provides the basis for the region partitioning criterion of this invention: the partitioning aims to ensure that each region contains only non-congested lines, while reserving all potentially congested paths as inter-regional connections. Potentially congested lines are selected based on historical power flow distribution patterns, i.e., lines where the power flow exceeds 90% of their transmission capacity. Based on these potential congested lines, the region partitioning problem is constructed as the following multi-cut model:

[0021]

[0022]

[0023]

[0024] The objective function (1) indicates that the goal of this model is to minimize the number of cross-regional connection lines through regional division. Wherein, Represents a set of transmission lines; Indicates that the two ends are and Transmission lines; Let the decision variable be a Boolean variable, when the node and When they belong to the same area, It is 0 if it is not 1 otherwise; For power transmission lines The weight, when When it is a potentially congested line, =0, when node and When only one node in the network is the end node of a potentially congested line, The value is 1 when the node is 1. and When none of them are end nodes of a potentially congested line, The value is 2.

[0025] Constraint (2) mandates that potentially congested lines must become inter-regional tie lines. Among these, Represents the set of potentially blocked lines; and These represent potentially blocked lines. The first and last nodes.

[0026] Constraint (3) is a triangle inequality, which guarantees the transitivity of the regional subordination relationship between nodes, that is: if node Belonging to the same region, and nodes If they belong to the same region, then the nodes Belonging to the same region; if nodes If they belong to different regions, then the nodes and , or node and They belong to different regions. Represents a set of nodes.

[0027] The multi-cut model is solved using the RAMA algorithm to obtain the region partitioning results.

[0028] After partitioning, network simplification is performed independently for each region.

[0029] Network simplification is based on a mathematical transformation of the DC power flow model, which is as follows:

[0030] in Represents the net injected power matrix at the nodes. The admittance matrix of the power grid is represented. This represents the node voltage phase angle matrix.

[0031] Divide the nodes of the power grid into a set of nodes to be eliminated. and the set of nodes to be retained The set of nodes to be retained The sum of the terminal nodes of the cross-regional connection line, and the set of nodes to be eliminated. For the original system node set With the reserved node set The difference set. (5) It can be further divided into the following forms:

[0032] Eliminate using Gaussian elimination We can obtain:

[0033] Rewrite (7) as follows:

[0034] in, Represents the set of nodes that were eliminated. The net injected power of the internal nodes is allocated to the reserved node set. The proportional relationship of the internal nodes; The admittance matrix represents the simplified network; the calculation formula is as follows:

[0035]

[0036] The nodes in the simplified network are cross-regional tie-line end nodes, and the connection relationship between the lines and nodes is based on... The negative value element is determined, that is, the node corresponding to the row and column of a certain negative value element is the two end nodes of the line, and the absolute value of the value is the susceptance of the line.

[0037] 2. Large-scale power grid planning model based on unit cluster modeling Thermal power units with similar operating parameters within a region are grouped into clusters. The parameters of a unit cluster are typically the capacity-weighted average of the parameters of the units within the cluster.

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] in, For the region Cluster A collection of generating units; For the unit Rated capacity; For the region cluster Rated capacity; , For the region Cluster The ratio of minimum and maximum output to rated capacity; , For the unit The ratio of minimum and maximum output to rated capacity; , For the region Cluster The ratio of maximum power to rated capacity when climbing uphill and downhill; , For the unit Maximum power for climbing uphill and downhill; , For the region Cluster The cost of powering on and off per unit capacity; , For the unit The cost of powering on and off.

[0045] Cluster capacity upper and lower limits constraints:

[0046] in, , , For the region Cluster During the period The operating, startup, and shutdown capacity.

[0047] Time correlation of cluster operation, startup, and shutdown capacity:

[0048] Cluster output power upper and lower limit constraints:

[0049]

[0050] Cluster uphill and downhill constraints:

[0051]

[0052] Minimum startup time constraint for the cluster:

[0053]

[0054]

[0055] in For the region Cluster Minimum boot time.

[0056] Minimum shutdown time constraint for the cluster:

[0057]

[0058]

[0059] in For the region Cluster Minimum shutdown time.

[0060] The cluster output is equal to the sum of the outputs of the units within the cluster:

[0061] in For the unit During the period The output power.

[0062] Upper and lower limits of thermal power unit output constraints:

[0063] Thermal power unit uphill and downhill ramp constraints:

[0064] Upper and lower limits of renewable energy unit output:

[0065] in: , For renewable energy units During the period The actual output and the maximum feasible output.

[0066] Simplify the DC power flow model in the network:

[0067] in, For power transmission lines During the period The power flow rate; For power transmission lines Admittance; , For power transmission lines During the period The voltage phase angles of the sending and receiving nodes.

[0068] Upper and lower limits of power flow constraints on the connection line:

[0069] in, , For power transmission lines The lower and upper limits of transmission capacity; For the collection of connecting lines.

[0070] The objective function of the planning model is to minimize the sum of equipment construction costs and system operating costs.

[0071] in, , For thermal power units to be built The construction cost and the Boolean decision variables for whether or not to construct; , For renewable energy units to be built The unit capacity construction cost and construction capacity decision variables; , For the planned power transmission line The construction cost and the Boolean decision variables for whether or not to construct; For thermal power units to be built The marginal operating cost.

[0072] Upper and lower limits of output constraints for thermal power units under construction:

[0073] Slope climbing constraints for thermal power units under construction:

[0074] Upper and lower limits of output constraints for renewable energy units under construction:

[0075] in, For renewable energy units to be built Actual output For renewable energy units to be built During the period The capacity factor.

[0076] Simplify the DC power flow model of the tie lines to be built in the network:

[0077] Upper and lower limits of power flow constraints for the planned connecting line:

[0078] in, For the planned connection line Current power, , For the planned connection line The transmission capacity is both below and above.

[0079] Renewable energy quota constraints:

[0080] in, For load During the period Power requirements; This refers to the proportion of total renewable energy consumption to total load.

[0081] Considering the cluster's rated capacity after the construction of the planned thermal power units:

[0082] Simplify node power balancing in a network:

[0083] in, , , , , thermal power units Thermal power units to be built Renewable energy units Renewable energy units under construction ,load The node is located on the simplified network node. The power allocation factor.

[0084] System backup constraints:

[0085] in, Reserve rate of load; Reserve capacity for renewable energy.

[0086] The large-scale power system used in this embodiment of the invention comprises 354 nodes, 544 transmission lines, and 162 thermal power units. Seven potentially congested lines were identified based on historical power flow congestion. By solving the multi-cut problem proposed in this invention, the power grid was divided into three regions. Furthermore, the region simplification technique proposed in this invention was applied to remove approximately 96% of the nodes in the system, resulting in a simplified network with only 14 nodes and 33 transmission lines. Table 1 shows the number of constraints and the extent of simplification before and after network simplification. The comparison results indicate that dimensionality reduction significantly reduces the number of transmission constraints in the model, thereby improving the model's solution efficiency.

[0087] Table 1

[0088] Figure 2 This paper presents a comparison of the unit power stacking and thermal power unit operating capacity of the proposed embodiment with those of the traditional planning model on a typical day. The results show that the unit power curve obtained by the proposed model is highly consistent with the traditional model, verifying the high accuracy of the model. Furthermore, the thermal power unit operating capacity calculated by the proposed model is higher than that of the traditional model, indicating that the proposed scheduling strategy is more reliable, can effectively cope with sudden failures, and provides sufficient reserve capacity for the safe operation of the system.

[0089] Figure 3 The results show a comparison of the errors and solution times between the proposed solution and traditional methods at different simulation time scales. Experimental results demonstrate that the proposed model exhibits an exponentially faster solution speed across different simulation scales, with a maximum error not exceeding 0.06%. This proves that the proposed model can significantly improve computational efficiency while maintaining high accuracy, thereby effectively solving the problem of solving large-scale power grid planning models.

[0090] Figure 4 The present invention presents planning results under different renewable energy quotas. The results show that when no renewable energy quota restrictions are imposed, the system only constructs thermal power units to achieve the lowest cost. When the renewable energy quota target is increased, the system prioritizes wind power construction, followed by photovoltaic investment. No inter-regional interconnection investment was observed, indicating that each region has sufficient operational flexibility to accommodate new renewable energy generation, while the investment cost of renewable energy remains lower than other alternatives. All simulation cases converged within 42–58 minutes, verifying the computational feasibility and reliability of the proposed model in large-scale, long-term power grid planning.

[0091] Example 2 The present invention also relates to an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0092] The electronic device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The memory can be used to store computer programs and / or modules. The processor performs various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory.

[0093] Example 3 The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0094] Specifically, the memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0095] Example 4 This invention provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of the method described in the above embodiments of this invention.

[0096] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. It should be noted that the terms "in one embodiment," "for example," and "again" in this invention are intended to illustrate the invention and are not intended to limit the invention.

[0097] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method of planning a large power grid, characterized by, Includes the following steps: The power grid topology is divided into multiple regions, each containing non-blocking lines, while blocking lines are retained as inter-regional transmission links; The network in each region is simplified by equivalent means, including the use of node power redistribution to simplify the topology of the power grid. Based on generator cluster modeling, the scheduling and operation of generators are simulated. Optimization is achieved through planning objective functions and constraints, ultimately realizing efficient power grid planning.

2. The method of claim 1, wherein, The division of the power grid topology into multiple regions, each region containing non-blocking lines and retaining blocked lines as inter-regional transmission links, includes: Potentially congested lines, i.e., lines whose power flow exceeds 90% of their transmission capacity, are screened based on historical power flow distribution patterns. The region partitioning problem is then constructed as a multi-cut model as follows: To minimize the number of inter-zone tie lines: Constraints: in, Represents a set of transmission lines; Indicates that the two ends are and Transmission lines; Let the decision variable be a Boolean variable, when the node and When they belong to the same area, It is 0 if it is not 1 otherwise; For power transmission lines The weight, when When it is a potentially congested line, =0, when node and When only one node in the network is the end node of a potentially congested line, The value is 1 when the node is 1. and When none of them are end nodes of a potentially congested line, It is 2; Represents the set of potentially blocked lines; and These represent potentially blocked lines. The beginning and end nodes; For nodes, Represents a set of nodes; The multi-cut model is solved to obtain the region division results.

3. The large-scale power grid planning method as described in claim 1, characterized in that, The method for node power redistribution simplifies the power grid topology, including: The power of the nodes to be eliminated in the power grid is proportionally allocated to the remaining nodes using the node power allocation ratio formula until the network is simplified to the optimal structure.

4. The method of claim 1, wherein, The objective function of the planning is to minimize the sum of equipment construction costs and system operating costs. in, , For thermal power units to be built The construction cost and the Boolean decision variables for whether or not to construct; , For renewable energy units to be built The unit capacity construction cost and construction capacity decision variables; , For the planned power transmission line The construction cost and the Boolean decision variables for whether or not to construct; For thermal power units to be built The marginal operating cost; For the unit During the period The output power, , For the region Cluster The cost of powering on and off per unit capacity; , For the region Cluster During the period Power-on and power-off capacity; For the collection of connecting lines.

5. The method of claim 1, wherein, The constraints are as follows: Cluster capacity upper and lower limits constraints: wherein is a region cluster operating capacity in a time period ; Cluster output power upper and lower limit constraints: wherein is the area cluster at the time period output power, , is the area cluster minimum and maximum output power fractions of the rated capacity; Cluster uphill and downhill constraints: wherein , is the area cluster the ratio of the maximum ramp-up, ramp-down power to the rated capacity; Minimum startup time constraint for the cluster: wherein is a region cluster minimum boot time; Minimum shutdown time constraint for the cluster: wherein is a region cluster minimum shutdown time; Upper and lower limits of thermal power unit output constraints: Thermal power unit uphill and downhill ramp constraints: in , For the unit Maximum power for climbing uphill and downhill; Upper and lower limits of renewable energy unit output: wherein , is a renewable energy unit the actual output and the maximum feasible output of the renewable energy unit in the time period ; Upper and lower limits of power flow constraints on the connection line: wherein is the transmission line In the time period of the power flow, , is the transmission line upper and lower limits of the transmission capacity Upper and lower limits of output constraints for thermal power units under construction: Slope climbing constraints for thermal power units under construction: Upper and lower limits of output constraints for renewable energy units under construction: wherein is the actual output of the renewable energy unit to be built, is the actual output of the renewable energy unit to be built, is the capacity factor of the renewable energy unit to be built in the time period .​ Upper and lower limits of power flow constraints for the planned connecting line: in For the planned connection line Current power, , For the planned connection line The lower and upper limits of transmission capacity; Renewable energy quota constraints: wherein, is the load in the time period power demand; is the proportion of total renewable energy consumption capacity to total load capacity; Considering the cluster's rated capacity after the construction of the planned thermal power units: Simplify node power balancing in a network: in, , , , , thermal power units Thermal power units to be built Renewable energy units Renewable energy units under construction ,load The node is located on the simplified network node. The power allocation coefficient; System backup constraints: wherein, is the reserve rate for the load; is the reserve rate for the renewable energy. 6.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer program product comprising computer programs or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method as described in any one of claims 1 to 5.

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