A multi-terminal flexible direct current (MT-DC) system capacity planning device

Abstract

This invention discloses a capacity planning device for a multi-terminal flexible DC collection and transmission system, belonging to the technical field of flexible DC transmission planning in power systems. The device includes modules for data input, mode selection, operation analysis, model building, optimization solution, and result evaluation. The data input module collects, processes, and fuses multi-source data to obtain a standardized dataset. The mode selection module receives the planning mode. The operation analysis module determines the transient overload risk safety boundary based on the standardized dataset. The model building module integrates the standardized dataset, the planning mode, and the transient overload risk safety boundary to construct a capacity optimization planning model. The optimization solution module uses a dual-path solution to obtain the capacity configuration scheme. The result evaluation module visualizes the capacity configuration scheme, performs risk assessment and comparison, and outputs a structured capacity planning evaluation report. This invention achieves refined capacity planning for multi-terminal flexible DC collection and transmission systems, improving the rationality and robustness of capacity planning schemes.

Description

Technical Field

[0001] This invention relates to the field of flexible DC transmission planning technology for power systems, specifically to a capacity planning device for a multi-terminal flexible DC collection and transmission system. Background Technology

[0002] With the deepening of my country's energy structure transformation, new energy sources, represented by wind power and photovoltaics, have achieved rapid and large-scale development. By 2030, my country plans to construct new energy power generation bases with a total installed capacity of approximately 455 million kilowatts. These large-scale new energy bases are characterized by concentrated resources and large-scale development. However, due to geographical limitations, they are generally located in remote areas with weak support from surrounding conventional power sources and relatively weak power grid structures. This places higher technical demands on the grid support capacity of the transmission system and the ability to collect and transmit new energy power.

[0003] Flexible DC transmission technology, based on fully controllable power electronic devices, possesses the ability to achieve self-commutation without relying on the AC grid and to actively construct and maintain AC grid voltage and frequency, making it particularly suitable for scenarios involving the aggregation and transmission of isolated renewable energy sources. Through multi-terminal flexible DC systems, multiple dispersed renewable energy islands can be aggregated and transmitted centrally through a single channel, effectively solving the problem of long-distance, large-capacity renewable energy consumption. As a key node for power aggregation and flow in multi-terminal flexible DC aggregation and transmission systems, the converter station's capacity configuration directly affects the economic efficiency of system investment, operational reliability, and renewable energy utilization rate. Insufficient capacity configuration can easily lead to converter station overload blocking or insufficient system regulation capacity, causing power outages or curtailment of wind and solar power; excessive capacity configuration will result in wasted investment and reduced project economics.

[0004] Currently, traditional methods for converter station capacity planning in multi-terminal flexible DC systems are mostly based on deterministic analysis or simple empirical estimation, failing to fully consider the randomness and volatility of renewable energy output, as well as the dynamic power interaction and coordinated control characteristics among converter stations within a multi-terminal system. On the one hand, existing planning techniques often simply link converter station capacity to renewable energy installations, or only aim to meet maximum transmission demand, lacking quantitative assessment and constraints on the system's transient steady-state operation risks. On the other hand, existing planning techniques also fail to construct differentiated optimization models based on different planning orientations and lack efficient and robust solution and verification mechanisms, making it difficult to achieve an optimal balance between economy and safety in planning schemes, and unable to meet the needs of refined planning for flexible DC systems with a high proportion of renewable energy integration.

[0005] In summary, existing capacity planning devices and optimization methods for multi-terminal flexible DC systems have significant shortcomings in handling the uncertainties of new energy sources, analyzing the capacity of multi-terminal centralized transmission, and interpreting the various operating modes of converter stations and the regulation characteristics of the DC system. Therefore, there is an urgent need for a capacity planning device that can comprehensively consider the uncertainties of new energy output, system operating characteristics, and multi-objective optimization requirements, enabling the scientific and optimized allocation of converter station capacity in multi-terminal flexible DC transmission systems to support the safe, economical, and efficient transmission of new energy from my country's large-scale new energy bases. Summary of the Invention

[0006] The purpose of this invention is to provide a capacity planning device for a multi-terminal flexible DC collection and transmission system, so as to solve the problems mentioned in the background art.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] A capacity planning device for a multi-terminal flexible DC collection and transmission system includes a data input module, a mode selection module, an operation analysis module, a model building module, an optimization solution module, and a result evaluation module.

[0009] The data input module is used to collect multi-source data from the multi-terminal flexible DC collection and transmission system, and to process and fuse the multi-source data to obtain a standardized dataset.

[0010] The mode selection module is used to receive the planning mode from the upper-level system;

[0011] The operation analysis module is used to determine the transient overload risk safety boundary of the multi-terminal flexible DC collection and transmission system based on the standardized dataset.

[0012] The model building module is used to integrate standardized datasets, planning patterns and transient overload risk safety boundaries to construct a capacity optimization planning model for a multi-terminal flexible DC collection and transmission system.

[0013] The optimization solution module is used to perform dual-path solution on the capacity optimization planning model of the multi-terminal flexible DC collection and transmission system to obtain the capacity configuration scheme.

[0014] The result evaluation module is used to visualize the capacity configuration scheme and output a structured capacity planning evaluation report after comparing and assessing the risks of the capacity configuration scheme.

[0015] Preferably, the multi-terminal flexible DC collection and transmission system is adapted to the power transmission scenario of large-scale new energy bases. It connects to and collects the power generation output of several new energy islands, including wind power and photovoltaic power, through several converter stations. The multi-terminal flexible DC collection and transmission system is equipped with only a single transmission channel to transmit the power generation output of each new energy island over a long distance through a single transmission channel. The new energy island is a new energy power generation area.

[0016] Preferably, the multi-source data includes historical power output data of new energy power plants, DC network topology information, and system static parameters; the data processing and fusion includes preprocessing the historical power output data of new energy power plants, rotating door data compression and uncertainty quantification, topology structure identification and converter station function classification of DC network topology information based on graph theory algorithm, and multi-dimensional verification of system static parameters;

[0017] The preprocessing involves the data input module communicating with the SCADA system via OPC UA or IEC 104 protocol to collect historical power output data of new energy power plants with a time resolution of no less than 15 minutes and a time span covering at least one full year. The data is then processed using the Laida criterion based on a sliding window to remove gross error data points, and missing data points are filled using time series linear interpolation or spline interpolation. Finally, normalization is performed to convert the actual power values ​​into per-unit values ​​based on rated capacity. The revolving door data compression uses a revolving door compression algorithm, adaptively adjusting the deviation tolerance threshold to compress the amount of preprocessed historical power output data of new energy power plants to 30%-50% of the original data while retaining key inflection points including but not limited to the ramp-up start point and peak point. Uncertainty quantification involves applying kernel density estimation and Gaussian mixture models to the revolving door data-compressed historical power output data of new energy power plants to generate a set of typical scenarios for power plant output, and using quantile regression to calculate the power fluctuation envelope at different confidence levels, thereby forming the historical power output time series data of new energy power plants.

[0018] The topology identification is as follows: Converter stations in the DC network topology information are abstracted as nodes, and DC lines are abstracted as edges. A circuit network for a multi-terminal flexible DC collection and transmission system is constructed using switch status information or network connection description files. A depth-first search algorithm is used to traverse the circuit network and identify all electrical connections, thereby obtaining the DC network topology. The converter station functional classification is based on preset rules: nodes that only connect to new energy power plants and have unidirectional power feed into the DC network are marked as islanded converter stations; nodes that connect to active AC systems and have bidirectional power regulation capabilities are marked as regulating converter stations, thereby obtaining the converter station classification results.

[0019] The multi-dimensional verification is as follows: The data input module receives the static parameter configuration file of the multi-terminal flexible DC aggregation and transmission system imported based on IEC 61850SCL format or CIM / E format. First, it performs syntax and semantic verification on the static parameter configuration file, and then performs CRC verification and reasonableness range judgment on all static parameters in the static parameter configuration file. If abnormal static parameters are detected, the retransmission mechanism is triggered. The verified static parameters are stored in a structured form to form the system static parameters.

[0020] Preferably, the standardized dataset is: the DC network topology, converter station classification results, historical power output time series data of new energy power plants, and verified system static parameters are aligned and matched based on high-precision timestamps and then encapsulated into JSON or HDF5 format to form a standardized dataset containing metadata header, time series data body, and topology description appendix. The standardized dataset includes DC network topology, converter station classification results, historical power output time series data of new energy power plants, and verified system static parameters.

[0021] Preferably, the planning mode includes a first planning mode and a second planning mode. Both planning modes are based on the transmission capacity requirements of the multi-terminal flexible DC collection and transmission system determined by the system static parameters after centralized verification of standardized data. Based on this, the first planning model is the total planned capacity of the converter station. This equals the transmission capacity requirement of a multi-terminal flexible DC collection and transmission system, i.e. This approach aims to achieve precise matching of equipment capacity in multi-terminal flexible DC collection and transmission systems, maximizing the economic efficiency of project investment. It is suitable for planning scenarios with high requirements for investment cost control and where fluctuations in new energy output can be precisely controlled through scheduling. The second planning mode is the total planned capacity of the converter station. The power transmission capacity requirement is no less than that of a multi-terminal flexible DC collection and transmission system. ,Right now To adapt to the randomness and fluctuation of power output from new energy power plants, as well as the changes in operating conditions and equipment maintenance during the operation of multi-terminal flexible DC collection and transmission systems, this system ensures the safety and robustness of operation under all operating conditions. It is suitable for planning scenarios with large fluctuations in new energy power output, weak grid support capabilities, and high requirements for power supply reliability. Both planning modes can be selected by the upper-level system according to actual engineering needs and then distributed to the mode selection module.

[0022] Preferably, the method for determining the transient overload risk safety boundary based on the standardized dataset is as follows:

[0023] S1. Based on the converter station classification results in the standardized dataset, determine the operation control mode of each converter station in the multi-terminal flexible DC collection and transmission system. The operation control mode is any one of VF control mode, DC voltage mode, and VP droop mode. For isolated converter stations, the VF (Voltage-Frequency) control mode, also known as grid-type control, is forcibly configured. Because the wind power, photovoltaic, and other new energy power plants connected to isolated converter stations lack traditional synchronous generator support and cannot independently establish stable AC voltage and frequency, the converter station simulates the physical characteristics of a synchronous generator. It establishes and maintains the voltage amplitude and frequency of the connected AC bus through an internal control loop, providing a stable grid connection point for the new energy power generation units. Furthermore, the new energy power plants are equipped with a VP droop mechanism to respond to natural resource fluctuations. For regulating converter stations, a constant DC voltage mode or VP droop mode is configured. Specifically, when there is only one regulating converter station, the constant DC voltage mode is configured, which is responsible for determining and maintaining the reference voltage of the multi-terminal flexible DC collection and transmission system. When there are multiple regulating converter stations, a VP droop mode is configured to achieve coordinated power distribution among the regulating converter stations. The control law for the VP droop mode is described as follows:

[0024] ;

[0025] in, The actual transmission power of converter station i. The power setpoint for converter station i, The droop coefficient is... This is the measured value of DC voltage;

[0026] The VP droop mode allows power to be automatically distributed among regulating converter stations according to the droop coefficient ratio, enhancing the robustness of the multi-terminal flexible DC collection and transmission system. This is especially important when power imbalances occur in the multi-terminal flexible DC collection and transmission system. At this time, the DC voltage will deviate from the reference value. For regulating converter stations operating in VP droop mode, the power change will be significant. Determined by drooping characteristics: ,in, The power change of the i-th regulating converter station operating in VP droop mode is caused by unbalanced power and DC voltage deviation from the reference value in the multi-terminal flexible DC collection and transmission system.

[0027] The AC grid of the regulating converter station is equipped with flexible resources to support the converter station in completing its power regulation tasks. If the total capacity of the flexible regulation resources on the AC side corresponding to all regulating converter stations cannot cover the total power imbalance demand caused by the fluctuation of new energy sources, that is, when regulation capacity mismatch occurs, the multi-terminal flexible DC collection and transmission system will not be able to maintain stable operation. This type of system-level operational risk can be quantified as follows: ;

[0028] in, This is the sum of the flexible regulation resource capacity of the AC power grid corresponding to all regulating converter stations i. To adjust the probability of capability mismatch;

[0029] S2. Based on the operation control mode of the converter station and combined with the historical output time series data of new energy power plants in the standardized dataset, a droop coefficient is introduced to characterize the response mechanism of Pf droop of the new energy power plant. The correlation calculation relationship between the power change of the new energy power plant, the power change of the islanded converter station and the frequency deviation is established, and a transient power allocation model is constructed. The transient power allocation model is used to accurately quantify the unbalanced power caused by the power generation output fluctuation of the new energy island, the dynamic transmission process between the new energy power plant and the islanded converter station, and the overall dynamic allocation relationship of the unbalanced power between the islanded converter station and the regulating converter station, and among the regulating converter stations.

[0030] S3. Based on the calculation results of the transient power allocation model, and combined with the historical output time-series data of new energy power plants in the standardized dataset, the transient overload probability of the islanded converter station and the regulation capacity mismatch probability of the regulating converter station are solved respectively. The transient overload probability is transformed into a planning constraint of the minimum necessary capacity and reserve capacity ratio of the islanded converter station, and the regulation capacity mismatch probability is transformed into a planning constraint of the total capacity of the AC flexible regulation resources and the overall capacity demand of the regulating converter station. The preset safety thresholds of the transient overload probability and the regulation capacity mismatch probability are used as the transient overload risk safety boundaries of the islanded converter station and the regulating converter station, respectively. Together, they constitute the transient overload risk safety boundary of the multi-terminal flexible DC collection and transmission system, which is used to achieve the optimal balance between economic benefits and operational safety in the planning of the multi-terminal flexible DC collection and transmission system.

[0031] Preferably, the capacity optimization planning model is a nonlinear programming model. The model building module first constructs a general basic constraint system based on a standardized dataset, including AC-side flexible resource output characteristic constraints, DC power flow constraints, and converter station operation constraints. Among them, the AC-side flexible resource output characteristic constraints are used to define the upper and lower limits of charging and discharging power, the upper and lower limits of energy storage state, and the continuity relationship of energy storage state changes between different time periods for energy storage systems configured in multi-terminal flexible DC collection and transmission systems. The DC power flow constraints are used to characterize the physical coupling relationship between the power injected into the DC network by all converter stations and the voltage of each node in the DC network topology based on the electrical connection relationship of the DC network topology, while ensuring that the power flow of each branch does not exceed the limit and achieving the minimum transmission loss of the multi-terminal flexible DC collection and transmission system. The converter station operation constraints are used to specify the allowable capacity configuration range of each converter station, the power and voltage control logic corresponding to its specific operating mode, and embed the transient overload risk safety boundary quantified by the operation analysis module to ensure that the capacity configuration scheme meets the dynamic operation safety requirements of the multi-terminal flexible DC collection and transmission system.

[0032] Based on the construction of a general basic constraint system, the model building module determines the optimization objective according to the planning model of the mode selection module, and constructs corresponding capacity optimization planning models for different planning modes.

[0033] For the first planning mode, the model building module constructs a capacity optimization planning model with the goal of maximizing the total collected electricity throughout the entire time period. Under the constraint that the total planned capacity of the converter stations is equal to the transmission capacity demand of the multi-terminal flexible DC collection and transmission system, it optimizes the capacity share and operation strategy of each converter station to minimize the renewable energy curtailment rate. For the second planning mode, the model building module constructs a capacity optimization planning model with the goal of minimizing the comprehensive cost throughout the entire life cycle. Its objective function integrates the equivalent cost of capacity investment of the converter stations and the cost of renewable energy curtailment, and quantifies the system operation risk into economic costs for weighted processing. Under the constraint that the total planned capacity of the converter stations is not less than the transmission capacity demand of the multi-terminal flexible DC collection and transmission system, it seeks the optimal solution for balancing investment costs, renewable energy collection benefits and risk costs, and achieves the best balance between cost and benefit throughout the entire life cycle.

[0034] The capacity optimization planning models corresponding to each planning mode are all well-defined nonlinear programming problems, including decision variables, objective functions that match the planning mode, and a set of conditions consisting of multiple constraints, which can directly provide a computable mathematical model for the optimization solution module.

[0035] Preferably, the dual-path solution includes heuristic algorithm solution and commercial solver solution. Both solve the capacity optimization planning model simultaneously or stepwise and output corresponding capacity configuration schemes respectively, providing scheme support for subsequent risk assessment and comparison. The heuristic algorithm solution uses a genetic algorithm to directly solve the capacity optimization planning model. The commercial solver solution approximates the nonlinear core constraint DC power flow equation in the capacity optimization planning model by performing piecewise linearization of the system. By introducing auxiliary discrete variables and linear constraints, the original capacity optimization planning model is transformed into a mixed integer linear programming model. Then, the embedded commercial solver is called to solve the mixed integer linear programming model. The commercial solver is either Gurobi or CPLEX.

[0036] Preferably, the result evaluation module provides a dynamic loading and display of the capacity configuration scheme under the planning mode given by the mode selection module, and visualizes the capacity planning results of each converter station in the multi-terminal flexible DC collection and transmission system; the risk assessment comparison compares the results of the two capacity configuration schemes obtained by dual-path solution, verifies transient overload risk, and verifies regulation capacity mismatch risk. Among them, the result difference comparison quantitatively compares and analyzes the converter station capacity configuration value, total system investment cost, and new energy curtailment rate of the two capacity configuration schemes. The transient overload risk verification verifies whether the transient overload probability of each islanded converter station under the two capacity configuration schemes is lower than the preset safety threshold based on the transient overload risk safety boundary determined by the operation analysis module. The regulation capacity mismatch risk verification verifies whether the regulation capacity mismatch probability of the regulating converter station under the two preset safety thresholds meets the preset safety threshold. For the capacity configuration scheme that does not meet the transient overload risk safety boundary, the risk point is marked and optimization adjustment prompts are given.

[0037] The results evaluation module provides a comprehensive rating and optimal suggestion for the solution based on the results of visualization and risk assessment, combined with the scenario adaptability of the planning mode. The final output is a structured capacity planning evaluation report that includes a description of the planning mode, detailed parameters of the dual-path capacity configuration scheme, indicator difference analysis, risk verification results, optimal scheme suggestions, and capacity configuration implementation details.

[0038] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0039] 1. This invention breaks through the limitations of traditional planning methods based on deterministic analysis or empirical estimation. By quantifying the uncertainty of historical power output data of new energy power plants and combining it with the operation and control characteristics of converter stations, a transient power allocation model is constructed. This enables an accurate characterization of the randomness, volatility, and dynamic power interaction between converter stations of new energy power output, and quantitatively determines the safety boundary of transient overload risk, filling the gap in the existing technology for quantitative assessment of system transient steady-state operation risk.

[0040] 2. This invention designs two differentiated planning modes for different engineering planning orientations. It combines a nonlinear programming model to construct a general basic constraint system and matches the corresponding optimization objectives. This solves the problem that traditional planning technology does not establish differentiated models according to planning orientation and is difficult to balance economy and safety. It can be adapted to planning scenarios that prioritize investment cost control or power supply reliability.

[0041] 3. This invention adopts a dual-path solution strategy that combines heuristic algorithms with commercial solvers, and is equipped with a multi-dimensional result evaluation and risk verification mechanism. This not only ensures the efficiency and accuracy of the capacity optimization planning model solution, but also enables comprehensive risk verification and comparative analysis of the configuration scheme, thereby improving the robustness and feasibility of the planning scheme.

[0042] 4. This invention constructs a full-process capacity planning system from multi-source data standardization processing to structured evaluation report output, realizing refined capacity planning for multi-terminal flexible DC collection and transmission systems. It effectively solves the problems of investment waste or high system operation risks caused by unreasonable converter station capacity configuration in traditional planning, and adapts to the planning needs of flexible DC transmission systems with high proportion of new energy access. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0044] Figure 1 This is a schematic diagram of the power allocation model between the converter station and the renewable energy island under VF operation mode.

[0045] Figure 2 This is a schematic diagram of a multi-terminal flexible DC collection and transmission system.

[0046] Figure 3 This is a schematic diagram of the device module architecture of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Examples, such as Figure 3 The aforementioned capacity planning device for a multi-terminal flexible DC collection and transmission system includes a data input module, a mode selection module, an operation analysis module, a model building module, an optimization solution module, and a result evaluation module, which work together to complete the capacity optimization planning of the converter station of the multi-terminal flexible DC collection and transmission system.

[0049] The data input module is used to collect multi-source data from the multi-terminal flexible DC collection and transmission system, and to process and fuse the multi-source data to obtain a standardized dataset.

[0050] The mode selection module is used to receive the planning mode from the upper-level system;

[0051] The analysis module is used to determine the safety boundary of transient overload risk for multi-terminal flexible DC collection and transmission systems based on standardized datasets.

[0052] The model building module is used to integrate standardized datasets, planning patterns, and transient overload risk safety boundaries to construct a capacity optimization planning model for a multi-terminal flexible DC collection and transmission system.

[0053] The optimization solution module is used to solve the capacity optimization planning model of the multi-terminal flexible DC collection and transmission system through a dual-path solution to obtain the capacity configuration scheme.

[0054] The results evaluation module is used to visualize capacity configuration schemes and output a structured capacity planning evaluation report after comparing and assessing the risks of the capacity configuration schemes.

[0055] Furthermore, the following example, using a multi-terminal flexible DC collection and transmission system of a large-scale new energy base in Northwest my country, illustrates the specific application process of the capacity planning device for the multi-terminal flexible DC collection and transmission system described in this invention:

[0056] The planned power transmission capacity of this new energy base is 4500MW, and it will use a multi-terminal flexible DC system to collect and transmit power. The structure of the multi-terminal flexible DC system is as follows: Figure 2As shown, there are a total of 4 new energy power stations, of which 3 are new energy islands without conventional power support (named New Energy Base A, New Energy Base B, and New Energy Base C), and 1 is a new energy power station equipped with regulation resources (named New Energy Base D). The new energy output of this new energy base has significant fluctuations: the annual average output fluctuation rate of New Energy Base A is 25%, that of New Energy Base B is 30%, that of New Energy Base C is 28%, and that of New Energy Base D is 25%.

[0057] The data input module of this invention collects historical power output data, system static parameters, and DC network topology information of various new energy bases with a time resolution of 15 minutes. The historical power output data of the new energy bases covers a full year. A preprocessing procedure is performed on the collected new energy power output data: a sliding window-based Laida criterion is used to remove gross error points; spline interpolation is used to fill in short-term missing data; and finally, normalization is performed to convert the actual power values ​​into per-unit values ​​based on rated capacity. Subsequently, an improved rotating door compression algorithm is used to compress the original data volume to 35% of the original data volume while retaining key inflection points such as ramp-up starting points and peak points. Simultaneously, kernel density estimation and Gaussian mixture models are applied to generate a set of 50 typical new energy power output scenarios, and quantile regression is used to calculate the power output fluctuation envelope at different confidence levels, completing the uncertainty quantification of the new energy power output data. A circuit network for a multi-terminal flexible DC collection and transmission system is constructed based on the DC network topology information, and a depth-first search algorithm is used to automatically traverse the network. The circuit network identifies all electrical connections and classifies converter stations according to preset rules. Converter stations connected to new energy bases A, B, and C are marked as islanded converter stations, while converter stations connected to new energy base D are marked as regulating converter stations. For system static parameters, the system receives static parameter configuration files imported in IEC61850SCL format and performs syntax and semantic checks, CRC checks, and reasonableness range judgments in sequence. If no abnormal data is triggered, the qualified static parameters are stored in a structured form. The data input module uses high-precision timestamps as a reference to align and match the DC network topology, converter station classification results, historical output time-series data of new energy power plants, and the verified system static parameters, and encapsulates them into a standardized dataset in JSON format, providing reliable input for subsequent capacity planning.

[0058] The operation analysis module of this invention configures VF control mode for islanded converter stations and constant DC voltage mode for regulating converter stations based on the converter station classification results in the standardized dataset. Furthermore, it constructs a transient power allocation model by combining historical output time-series data of new energy power plants in the standardized dataset. Taking a certain new energy islanded converter station as an example, its rated capacity... For a capacity of 1500MW, the operation analysis module simulates the operating behavior of the renewable energy station under typical fluctuation scenarios based on the historical output fluctuation envelope provided by the data input module, such as... Figure 1 As shown, the new energy island converter station is in VF control mode. The new energy station is equipped with a Pf droop mechanism. The operation analysis module introduces the droop coefficient to characterize the response mechanism. (1) With equation (2) The change in the switching power of the converter station was derived. Fluctuations in power output from new energy sources The relationship is Set the droop coefficient for new energy power stations Island frequency droop coefficient Let the parameter This represents the ratio of the change in the converter station's switching power to the total fluctuating power, used for calculation. Assume the typical output of the new energy source at time t. The capacity is 1450 MW, and its volatility follows a normal distribution. ,make Based on the cumulative probability value of the standard normal distribution, calculate the overcurrent risk of the converter station at time t. Based on the historical output time-series data of the new energy station (total step length T=96), the typical output values ​​and output fluctuation boundaries for the entire time period were statistically analyzed, and the transient overload probability was calculated. The preset safety threshold of the transient overload probability is used as the transient overload risk safety boundary of the islanded converter station. At the same time, the regulation capacity mismatch probability of the regulating converter station is solved and its safety boundary is determined. The two together constitute the transient overload risk safety boundary of the multi-terminal flexible DC collection and transmission system, which is then transformed into planning constraints and input into the model building module.

[0059] Based on the planning objectives of the new energy base, the mode selection module of this invention sets two planning modes with the 4500MW transmission capacity demand centrally determined by standardized data as the benchmark. The mode selection module receives the constraints and optimization objectives of one planning mode from the upper-level application and outputs them to the model building module to construct the corresponding capacity optimization planning model. The first planning mode requires the total planned capacity to be strictly equal to the transmission capacity demand, while the second planning mode allows the total planned capacity to be no less than the transmission capacity demand.

[0060] The model building module of this invention is based on a standardized dataset. First, it constructs a general basic constraint system that includes AC side flexible resource output characteristic constraints, DC power flow constraints, and converter station operation constraints. The converter station operation constraints embed the transient overload risk safety boundary determined by the operation analysis module. Then, combined with the planning mode output by the mode selection module, a corresponding capacity optimization planning model is constructed. For the first planning mode, an optimization model is constructed with the goal of maximizing the total collected power throughout the time period. Under the constraint that the total capacity is equal to 4500MW, the capacity share and operation strategy of each converter station are optimized. For the second planning mode, a multi-objective optimization model is constructed with the goal of minimizing the comprehensive cost throughout the entire life cycle. Under the constraint that the total capacity is not less than 4500MW, the optimal balance between investment cost, new energy collection revenue, and risk cost is sought.

[0061] The optimization solution module of this invention adopts a dual-path solution strategy combining heuristic algorithms and commercial solvers. The heuristic algorithm uses a genetic algorithm for global search, with a population size of 200, a maximum number of iterations of 500, a crossover probability of 0.8, and a mutation probability of 0.1, to directly solve the capacity optimization planning model. The commercial solver first approximates the nonlinear core constraint DC power flow equation in the capacity optimization planning model by piecewise linearization, introduces auxiliary discrete variables and linear constraints to transform it into a mixed-integer linear programming model, and then calls the Gurobi commercial solver for high-precision solution, obtaining two capacity configuration schemes.

[0062] The result evaluation module of this invention, based on the planning mode of the mode selection module, visualizes and compares the capacity configuration schemes obtained from the dual-path solution, providing a comprehensive risk assessment. It intuitively presents core information such as the planned capacity value, capacity allocation ratio, and total system investment cost of each converter station through the power grid topology interface and data reports. The module compares the results of the two capacity configuration schemes, verifies transient overload risk, and verifies the risk of regulation capacity mismatch. The results show that both capacity configuration schemes passed the risk verification, and the difference in total collected electricity is less than 0.2%. The total annual collected electricity for the first capacity configuration scheme is 385,000 MWh, and for the second capacity configuration scheme it is 392,000 MWh. Based on this, a comprehensive rating and optimization recommendation are given for the two capacity configuration schemes, and a structured capacity planning evaluation report is finally output. The structured capacity planning evaluation report includes a description of the planning mode, detailed parameters of the dual-path configuration scheme, indicator difference analysis, risk verification results, and implementation details, providing a reliable engineering decision-making basis for the capacity planning of the multi-terminal flexible DC system in this new energy base.

[0063] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A capacity planning device for a multi-terminal flexible DC collection and transmission system, characterized in that, include: The data input module is used to collect multi-source data from the multi-terminal flexible DC collection and transmission system, and to process and fuse the multi-source data to obtain a standardized dataset. The mode selection module is used to receive the planning mode from the upper-level system; The analysis module is used to determine the safety boundary of transient overload risk for multi-terminal flexible DC collection and transmission systems based on standardized datasets. The model building module is used to integrate standardized datasets, planning patterns, and transient overload risk safety boundaries to construct a capacity optimization planning model for a multi-terminal flexible DC collection and transmission system. The optimization solution module is used to solve the capacity optimization planning model of the multi-terminal flexible DC collection and transmission system through a dual-path solution to obtain the capacity configuration scheme. The results evaluation module is used to visualize capacity configuration schemes and output a structured capacity planning evaluation report after comparing and assessing the risks of the capacity configuration schemes.

2. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 1, characterized in that, The multi-terminal flexible DC collection and transmission system collects the power output of several new energy islands through converter stations, and then transmits the collected power output of several new energy islands through a single transmission channel.

3. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 2, characterized in that, The multi-source data includes historical power output data of new energy power plants, DC network topology information, and system static parameters; the data processing and fusion includes preprocessing the historical power output data of new energy power plants, rotating door data compression and uncertainty quantification, topology structure identification and converter station function classification based on graph theory algorithm for DC network topology information, and multi-dimensional verification of system static parameters; the converter station function classification is divided into islanded converter stations and regulating converter stations.

4. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 3, characterized in that, The planning modes include a first planning mode and a second planning mode. In the first planning mode, the total planned capacity of the converter station is equal to the transmission capacity requirement of the multi-terminal flexible DC collection and transmission system. In the second planning mode, the total planned capacity of the converter station is not less than the transmission capacity requirement of the multi-terminal flexible DC collection and transmission system.

5. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 4, characterized in that, The method for determining the safety boundary of transient overload risk based on a standardized dataset is as follows: S1. Based on the converter station classification results in the standardized dataset, determine the operation control mode of each converter station in the multi-terminal flexible DC collection and transmission system; S2. Based on the operation control mode of the converter station and combined with the historical output time series data of new energy power stations in the standardized dataset, a transient power allocation model is constructed. The transient power allocation model is used to quantify the dynamic distribution relationship between the unbalanced power caused by the power generation output fluctuation of the new energy island and the regulating converter station. S3. Based on the calculation results of the transient power allocation model, the transient overload probability of the islanded converter station and the regulation capacity mismatch probability of the regulating converter station are solved respectively. The preset safety thresholds of the transient overload probability and the regulation capacity mismatch probability are used as the transient overload risk safety boundaries of the islanded converter station and the regulating converter station, respectively, which are the transient overload risk safety boundaries of the multi-terminal flexible DC collection and transmission system.

6. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 5, characterized in that, The operation control mode is any one of VF control mode, DC voltage mode and VP droop mode. Specifically, VF control mode is configured for islanded converter stations, and constant DC voltage mode or VP droop mode is configured for regulating converter stations. When there is a single regulating converter station, constant DC voltage mode is configured, and when there are multiple regulating converter stations, VP droop mode is configured.

7. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 6, characterized in that, The capacity optimization planning model is a nonlinear programming model. The model building module first constructs a general basic constraint system based on a standardized dataset, which includes AC side flexible resource output characteristic constraints, DC power flow constraints, and converter station operation constraints. Then, combined with the planning mode and transient overload risk safety boundary, it constructs corresponding capacity optimization planning models for different planning modes.

8. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 7, characterized in that, The dual-path solution includes heuristic algorithm solution and commercial solver solution. The heuristic algorithm solution uses a genetic algorithm to directly solve the capacity optimization planning model. The commercial solver solution uses either Gurobi or CPLEX.

9. The capacity planning device for a multi-terminal flexible DC collection and transmission system according to claim 8, characterized in that, The result evaluation module is based on the planning mode received by the mode selection module and visualizes the capacity planning results of each converter station in the multi-terminal flexible DC collection and transmission system; the risk assessment comparison is to compare the results of the two capacity configuration schemes obtained by the dual-path solution, verify the transient overload risk and verify the regulation capacity mismatch risk.

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