Large base dc sending curve optimization planning method, system, device and medium

Abstract

The application discloses a large base direct current sending curve optimization planning method, system, equipment and medium, belongs to the power system planning and optimization operation technical field, obtains new energy time sequence output data, the scale and operation characteristic parameters of various power sources of sending terminal, and the direct current sending operation constraint parameter; the new energy time sequence output data is split to the single month dimension respectively according to month Clustering is handled, the direct current sending three step shape sequence is constructed based on clustering processing result, and the direct current sending three step shape sequence is executed smoothing correction, and the direct current sending three step shape sequence after smoothing correction is obtained; the direct current sending three step shape sequence after smoothing correction is used as a reference constraint, and a mathematical optimization model is established according to the scale and operation characteristic parameters of various power sources of sending terminal and the direct current sending operation constraint parameter; the economic optimization of large base sending terminal system is taken as the target to solve the mathematical optimization model, and the time direct current sending curve and large base sending terminal system operation index are obtained.

Description

Technical Field

[0001] This invention belongs to the field of power system planning and optimization operation technology, specifically involving the method, system, equipment and medium for optimizing the planning of DC transmission curves in large-scale power bases. Background Technology

[0002] With the in-depth development of large-scale wind and solar power bases in the "Shagolan Desert," the planning and design of the sending-end system, as the hub for energy collection and transmission, faces new technical challenges. Due to the randomness and volatility of new energy power generation, and the fact that the sending-end system usually includes multiple power sources such as wind, solar, thermal, and energy storage, how to reasonably formulate the DC transmission curve during the planning and design stage has become the key to determining the channel utilization rate and system economy.

[0003] However, traditional methods often rely on simplified simulations based on typical days or weeks, which makes it difficult to accurately reflect the temporal characteristics and multi-energy complementarity potential of new energy sources, resulting in a lack of scientific basis for the formulation of DC transmission curves. At the same time, the process of simulating the operating status of various power sources and calculating technical and economic indicators is complex and cumbersome, which restricts the refined comparison and selection of planning schemes. Summary of the Invention

[0004] The purpose of this invention is to solve the problems faced by the large-scale power transmission system in the desert and wasteland during the planning and design stage, such as the difficulty in reasonably formulating the DC power transmission curve and the cumbersome calculation process of related technical and economic indicators. The invention proposes a method, system, equipment and medium for optimizing the planning of DC power transmission curves in large-scale power transmission systems.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing and planning large-scale DC transmission curves, comprising the following steps: Acquire new energy power output data, scale and operating characteristic parameters of various power sources at the sending end, and DC transmission operation constraint parameters; The new energy time-series output data is split into monthly dimensions and clustered separately to obtain clustering results. Based on the clustering results, a three-step shape sequence of DC transmission is constructed, and a smoothing correction is performed on the three-step shape sequence of DC transmission to obtain the smoothed three-step shape sequence of DC transmission. Using the smoothed and corrected three-step shape sequence of DC transmission as the benchmark constraint, a mathematical optimization model is established based on the scale and operating characteristic parameters of various power sources at the transmitting end and the DC transmission operation constraint parameters. The mathematical optimization model was solved with the goal of optimizing the economy of the large-base sending-end system, and the hourly DC transmission curve and the operating indicators of the large-base sending-end system were obtained.

[0006] Furthermore, after splitting the new energy time-series output data into a single-month dimension by month, the k-means clustering algorithm is used to cluster the output samples at each time point within a single month, resulting in two clustering results: high output and low output.

[0007] Furthermore, based on the clustering results, a three-step shape sequence for DC power transmission is constructed, specifically as follows: The frequency proportion of the same hourly location belonging to two different clustering results within a single month is statistically analyzed. Each hour is labeled as a high step, low step, or undetermined step according to a preset probability threshold. The labels for a specified time period are adjusted in accordance with the preset time period operation requirements to construct a three-step shape sequence for DC transmission.

[0008] Furthermore, iterative smoothing correction is performed on the three-step shape sequence of DC power transmission, including: Correct isolated jumps in the three-step shape sequence of DC transmission where a single time period label differs from both adjacent time period labels.

[0009] Furthermore, the baseline constraints include DC power transmission value range constraints set for each type of step label in the three-step shape sequence of DC power transmission.

[0010] Furthermore, the mathematical optimization model aims to minimize the sum of the cost of abandoned electricity and the cost of purchased electricity in the large-scale base transmission system. The constraints include hourly power balance constraints, operational boundary constraints of various power sources at the transmission end, DC power boundary constraints, total DC power transmission constraints, and DC power constraints for specific time periods.

[0011] Furthermore, after solving the mathematical optimization model, the system outputs hourly DC power transmission curves, hourly operating status data of various power sources at the sending end, and operating indicators of the large-scale base station sending end system.

[0012] Secondly, the present invention provides a large-scale DC transmission curve optimization planning system, comprising: The parameter acquisition module is used to acquire new energy time-series output data, the scale and operating characteristic parameters of various power sources at the sending end, and DC transmission operation constraint parameters; The external transmission sequence construction module is used to split the new energy time-series output data into single-month dimensions by month and perform clustering processing to obtain the clustering results. Based on the clustering results, a DC external transmission three-step shape sequence is constructed, and a smoothing correction is performed on the DC external transmission three-step shape sequence to obtain the smoothed DC external transmission three-step shape sequence. The mathematical optimization model building module is used to establish a mathematical optimization model based on the smoothed and corrected three-step shape sequence of DC transmission as the benchmark constraint, and the scale and operating characteristic parameters of various power sources at the sending end and the DC transmission operation constraint parameters. The mathematical optimization model solving module is used to solve the mathematical optimization model with the goal of optimizing the economy of the large-base sending-end system, and obtain the hourly DC transmission curve and the operating indicators of the large-base sending-end system.

[0013] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for optimizing the planning of large-base DC transmission curves.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for optimizing the planning of large-scale DC transmission curves.

[0015] Compared with the prior art, the present invention has the following beneficial technical effects: This invention proposes an optimized planning method for large-scale DC transmission curves. Given the characteristics of new energy resources, power supply scale and operating parameters, and DC operation constraints, it first extracts a three-step shape sequence through intra-month clustering and smoothing rules. Then, it constructs an objective function based on economic optimization, superimposing constraints such as power balance, power supply boundaries, and upper and lower limits of DC transmission power, while also considering the requirements of the receiving-end grid, to establish and solve an optimization model. This invention can efficiently output hourly DC transmission curves for 8760 hours, operating status of various power sources, and technical and economic results, providing a reference for planning and design personnel and offering quantitative support for the planning, design, and scheme evaluation of large-scale DC transmission systems in desert and Gobi regions. This invention can quantitatively calculate the transmission curves and related technical indicators of new energy bases, and is characterized by convenient and rapid calculation. Attached Figure Description

[0016] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely schematic to aid in understanding the invention and do not specifically limit the shapes and proportions of the components. In the drawings: Figure 1 This is a flowchart of the large-scale DC transmission curve optimization planning method of the present invention.

[0017] Figure 2 This is a simplified structural diagram of the large-scale DC transmission curve optimization planning system of the present invention.

[0018] Figure 3 This is an electronic device diagram of the large-scale DC transmission curve optimization planning method of the present invention.

[0019] Figure 4 This is a flowchart illustrating the strategy for optimizing the DC transmission curve planning method for large-scale base stations in this embodiment of the invention.

[0020] Figure 5 This is a heat map showing the monthly curve shape in an embodiment of the present invention.

[0021] Figure 6 This is a monthly DC curve diagram in an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] Example 1 See Figure 1 The method for optimizing the planning of large-scale DC transmission curves includes the following steps: Acquire time-series output data of new energy sources, scale and operating characteristic parameters of various power sources at the sending end, and DC transmission operation constraints. Break down the time-series output data of new energy sources into monthly data and perform clustering processing on each month. Based on the clustering results, construct a three-step shape sequence for DC transmission and perform smoothing correction on the sequence to obtain a smoothed three-step shape sequence. Use this smoothed three-step shape sequence as the baseline constraint, and establish a mathematical optimization model based on the scale and operating characteristic parameters of various power sources at the sending end and the DC transmission operation constraints. Solve the mathematical optimization model with the goal of optimizing the economic efficiency of the large-base sending-end system, obtaining hourly DC transmission curves and operating indicators of the large-base sending-end system.

[0024] This invention addresses the lack of scientific basis and cumbersome calculation process for technical and economic indicators in the planning stage of large-scale power transmission systems in desert and wasteland areas. It adopts a two-step architecture of shape benchmark construction and numerical optimization solution for planning and design. By constructing a three-stage shape sequence for DC transmission based on monthly clustering and performing smoothing corrections, it can fully adapt to the temporal fluctuation characteristics of new energy output, forming a physically feasible curve shape benchmark and avoiding the shortcomings of traditional simplified simulations of typical days that cannot reflect the temporal characteristics throughout the year. Based on this, using the shape sequence as a benchmark constraint, and combining the operating characteristics of various power sources with DC operating constraints, an optimization model is established. Solving for system economy, it can efficiently output hourly DC transmission curves and multi-dimensional system operating indicators for the whole year. This method can improve the rationality and computational efficiency of DC transmission curve formulation, accurately explore the potential of multi-energy complementarity, help improve the utilization level of DC channels and the economic operation level of the system, and provide reliable quantitative support for the design and comparison of large-scale power transmission system planning schemes.

[0025] For details, see Figure 4 The overall process of the large-scale DC transmission curve optimization planning method is as follows: input data, shape recognition, model construction, optimization solution, and result output. Input includes reading new energy output, power supply scale, and DC curve requirements; secondly, monthly clustering is performed and smoothing and platform dwell rules are applied; next, a mathematical optimization model is constructed with economic optimization as the objective, considering constraints such as power balance, power supply operation, and DC operation requirements; finally, the solution yields the DC curve, hourly plans for the whole year, and related technical indicators. The process includes the following steps: 1) Collect and obtain the characteristic curves of the new energy 8760, power supply scale and operating characteristic parameters, and DC operation requirement constraint parameters; 2) Cluster the output of new energy sources on a monthly basis and construct a three-tiered shape sequence; 3) Establish a mathematical optimization model based on the shape sequence described above; 4) Solve for and output the hourly operation plan and indicator results with the goal of achieving optimal economic efficiency.

[0026] The specific steps are as follows: (1) S1 collects and obtains the characteristic curve of new energy 8760, power supply scale and operating characteristic parameters, and DC operation requirement constraint parameters; The installed capacity of new energy sources is 6 million kilowatts of wind power and 9 million kilowatts of photovoltaic power. The theoretical output of wind and photovoltaic power is collected over 8760 hours at per-unit values. The installed capacity of thermal power is 2.64 million kilowatts, with a minimum technical output of 20%. Energy storage is 1.5 million kilowatts, operating on a 4-hour schedule. The dispatch strategy does not cross days. The maximum / minimum DC transmission power is 8 million kilowatts / 800,000 kilowatts, and the planned utilization hours for DC are 4570 hours, of which the DC transmission power from 19:00 to 22:00 is no less than 3 million kilowatts.

[0027] (2) Cluster the output of new energy sources on a monthly basis and construct a three-stage shape sequence; Based on the characteristics and scale of new energy sources, the output of new energy sources is clustered into two categories on a monthly basis using the k-means method. Each time point is marked as either high output or low output. Substituting the above parameters, the calculation results are shown in the table below.

[0028] Table 1 Clustering Results of New Energy Output (Unit: 10,000 kW)

[0029] Based on the above data, the probabilities of high and low power output are calculated for each month from 1 to 24 hours. High output with a probability greater than 80% is marked as 1, representing high-step DC power transmission; low output is marked as 0, representing low-step DC power transmission; and output with a probability less than 80% is marked as undetermined (high / low step pending). Considering the sending-end demand, some time periods are marked as undetermined; in this embodiment, the period from 19:00 to 22:00 is marked as undetermined.

[0030] Taking January and February as examples, the calculation results are shown in the table below.

[0031] Table 2 Clustering Results of New Energy Output (Unit: 10,000 kW)

[0032] To eliminate oscillations, this invention introduces smoothing constraints on the three-step sequence, iteratively corrects local isolated jumps, and suppresses "0-1-0" or "1-0-1" type spikes, thereby avoiding non-physical frequent flips of the curve in adjacent hours. In this embodiment, 18:00 in January is corrected from 1 to 0.5.

[0033] Substituting the data above, we obtain the curve, as shown below. Figure 5 As shown.

[0034] (3) Establish a mathematical optimization model based on the shape sequence; The objective function of the established optimization model is: min × + ×

[0035] In the formula, in the formula, The cost per kilowatt-hour for the electricity moved. The cost per kilowatt-hour of electricity purchased from the main grid; This represents the total amount of electricity wasted by the system. In this embodiment, the total purchased electricity is... It costs 0.5 yuan / kWh. It costs 0.25 yuan / kWh.

[0036] The constraints to be considered are: (1) No. Momentary power balance constraints: × + + +

[0037] In the formula, , The first The theoretical per-unit output values ​​of wind power and photovoltaic power at any given time; , These refer to the installed capacity of wind power and solar power, respectively. , , The first The power of thermal power, energy storage charging, and energy storage discharging at all times. For the first Always purchase electricity from the main grid; For the first Power curtailment at any given time; For the first DC power is delivered at all times.

[0038] (2) Various power supply constraints: Energy storage constraints:

[0039] In the formula, For the first Constant state of energy storage and charge. The efficiency of energy storage charging and discharging is 0.85.

[0040] 0≤ ≤

[0041] In the formula, The maximum energy storage capacity is 150 kWh. 4 kilowatt-hours.

[0042] Thermal power constraints: ≤ ≤

[0043] In the formula, , These represent the maximum and minimum power output of thermal power plants. The maximum is 2.64 million kilowatts, and the minimum is 528,000 kilowatts.

[0044] (3) DC constraint:

[0045] In the formula, Total planned DC power consumption; The rated power for DC is 8 million kilowatts; The planned DC utilization hours are 4570 hours.

[0046] ≤ ≤

[0047] In the formula, The minimum DC power output is 800,000 kilowatts.

[0048] (4) Solve and output the hourly operation plan and index results with the goal of optimal economic efficiency.

[0049] Substituting the above parameters into the calculation, the DC curve can be obtained, as follows: Figure 6 As shown in the table below, the relevant technical specifications are also provided.

[0050] Table 3 Calculation results for the given power supply scheme

[0051] This invention employs a "shape recognition + numerical optimization" process to output a three-step shape for each month's 24 hours and implements oscillation elimination and rising platform control. Based on the fixed shape, a mathematical optimization model is established, aiming for optimal economic efficiency while considering constraints such as power balance, power supply operation, and DC operation requirements. The solution yields the DC curve, hourly scheme for the entire year, and related technical indicators. Using this invention, the DC transmission curve and related technical indicators of the sending-end renewable energy base can be conveniently obtained, providing a reference for planning and operation.

[0052] Example 2 See Figure 2 The large-scale DC transmission curve optimization planning system includes: The parameter acquisition module is used to acquire new energy time-series output data, the scale and operating characteristic parameters of various power sources at the sending end, and DC transmission operation constraint parameters; The external transmission sequence construction module is used to split the new energy time-series output data into single-month dimensions by month and perform clustering processing to obtain the clustering results. Based on the clustering results, a DC external transmission three-step shape sequence is constructed, and a smoothing correction is performed on the DC external transmission three-step shape sequence to obtain the smoothed DC external transmission three-step shape sequence. The mathematical optimization model building module is used to establish a mathematical optimization model based on the smoothed and corrected three-step shape sequence of DC transmission as the benchmark constraint, and the scale and operating characteristic parameters of various power sources at the sending end and the DC transmission operation constraint parameters. The mathematical optimization model solving module is used to solve the mathematical optimization model with the goal of optimizing the economy of the large-base sending-end system, and obtain the hourly DC transmission curve and the operating indicators of the large-base sending-end system.

[0053] Example 3 See Figure 3 An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for optimizing the planning of large-base DC transmission curves.

[0054] Example 4 A computer-readable storage medium storing a computer program that, when executed by a processor, implements a method for optimizing the planning of large-scale DC transmission curves.

[0055] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, read-only optical discs, optical storage, etc.) containing computer-usable program code.

[0056] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0057] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0058] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing the planning of DC transmission curves from large-scale power plants, characterized in that: Includes the following steps: Acquire new energy power output data, scale and operating characteristic parameters of various power sources at the sending end, and DC transmission operation constraint parameters; The new energy time-series output data is split into monthly dimensions and clustered separately to obtain clustering results. Based on the clustering results, a DC transmission three-step shape sequence is constructed, and the DC transmission three-step shape sequence is smoothed to obtain a smoothed DC transmission three-step shape sequence. Using the smoothed and corrected three-step shape sequence of DC transmission as the benchmark constraint, a mathematical optimization model is established based on the scale and operating characteristic parameters of various power sources at the transmitting end and the DC transmission operating constraint parameters. The mathematical optimization model is solved with the goal of optimizing the economy of the large-base sending-end system, and the hourly DC transmission curve and the operating indicators of the large-base sending-end system are obtained.

2. The method for optimizing and planning the DC transmission curve of a large-scale power base according to claim 1, characterized in that, After splitting the new energy time-series output data into a single-month dimension by month, the k-means clustering algorithm is used to cluster the output samples at each time point within a single month, resulting in two clustering results: high output and low output.

3. The method for optimizing and planning the DC transmission curve of a large-scale power base according to claim 2, characterized in that, The construction of the three-step shape sequence for DC power transmission based on the clustering results is specifically as follows: The frequency proportion of the same hourly location within a single month belonging to the two clustering results is statistically analyzed. Each hour is labeled as a high step, a low step, or an undetermined step according to a preset probability threshold. The labels for a specified time period are adjusted in combination with the preset time period operation requirements to construct the DC transmission three-step shape sequence.

4. The method for optimizing and planning the DC transmission curve of a large-scale power base according to claim 3, characterized in that, The iterative smoothing correction of the three-step shape sequence of DC power transmission includes: This corrects isolated jumps in the three-step shape sequence of DC transmission where a single time period label differs from both adjacent time period labels.

5. The method for optimizing and planning the DC transmission curve of a large-scale power base according to claim 1, characterized in that, The baseline constraints include DC power transmission value range constraints set for each type of step label in the DC power transmission three-step shape sequence.

6. The method for optimizing and planning the DC transmission curve of a large-scale power base according to claim 1, characterized in that, The mathematical optimization model aims to minimize the sum of the cost of abandoned electricity and the cost of purchased electricity in the large-scale power transmission system. The constraints include hourly power balance constraints, operational boundary constraints of various power sources at the transmission end, DC power transmission boundary constraints, total DC power transmission constraints, and DC power constraints for specific time periods.

7. The method for optimizing and planning the DC transmission curve of a large-scale power base according to claim 1, characterized in that, After the mathematical optimization model is solved, the hourly DC power transmission curve, the hourly operating status data of various power sources at the transmitting end, and the operating indicators of the large-base transmitting end system are output.

8. A large-scale DC transmission curve optimization planning system, characterized in that, include: The parameter acquisition module is used to acquire new energy time-series output data, the scale and operating characteristic parameters of various power sources at the sending end, and DC transmission operation constraint parameters; The external transmission sequence construction module is used to split the new energy time-series output data into single-month dimensions according to the month and perform clustering processing respectively to obtain the clustering processing results. Based on the clustering processing results, a DC external transmission three-step shape sequence is constructed, and the DC external transmission three-step shape sequence is smoothed and corrected to obtain the smoothed DC external transmission three-step shape sequence. The mathematical optimization model establishment module is used to establish a mathematical optimization model based on the smoothed and corrected three-step shape sequence of DC transmission as the benchmark constraint, and according to the scale and operating characteristic parameters of various power sources at the transmitting end and the DC transmission operating constraint parameters. The mathematical optimization model solving module is used to solve the mathematical optimization model with the goal of optimizing the economy of the large-base sending-end system, and obtain the hourly DC transmission curve and the operating indicators of the large-base sending-end system.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the large-base DC transmission curve optimization planning method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the large-base DC transmission curve optimization planning method as described in any one of claims 1-7.

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