A power transmission network planning method and system considering multiple operation modes of new energy

Abstract

The application belongs to the technical field of power transmission network planning, and provides a power transmission network planning method and system considering multiple operation modes of new energy, comprising: obtaining operation data of different operation modes of new energy; based on the obtained operation data, constructing a two-stage power transmission network expansion planning model considering adaptive switching of active and passive operation strategies of wind power; solving the constructed power transmission network expansion planning model to obtain joint optimization results of new energy operation modes and network expansion, and completing power transmission network planning considering multiple operation modes of new energy.

Description

Technical Field

[0001] This invention belongs to the field of power transmission network planning technology, specifically relating to a power transmission network planning method and system that considers multiple operating modes of new energy sources. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] The penetration rate of new energy sources, represented by wind power and photovoltaics, in the power system is continuously increasing, gradually becoming an important support for the low-carbon transformation of the power structure. The uncertainty of new energy output not only affects system operational safety but also alters transmission capacity configuration and grid investment decisions. Therefore, conducting research on transmission network planning oriented towards high-proportion new energy integration is of great significance.

[0004] In power grid planning with a high proportion of renewable energy, how to characterize and address the uncertainty of renewable energy output is a core issue affecting the safety and economy of the planning scheme. The optimal operating strategy of the system changes with the system's operating state, and it is difficult to adapt to the safety and economic requirements under different operating conditions by using a single renewable energy operating strategy. Existing passive operating strategies mostly rely on preset disturbance boundaries and do not adequately characterize the synergistic optimization relationship between the acceptable disturbance range and the system's regulation capacity. Although active operating strategies can provide flexible regulation capabilities, existing research still lacks a sufficient description of the probability of fulfilling wind power reserve supply and its reliability impact. If the differences in the effects of different operating strategies and the possibility of switching with operating states are ignored during the planning stage, it is easy to cause biases in the assessment of renewable energy absorption potential, reserve demand, and transmission capacity configuration.

[0005] An adaptive operation strategy switching mechanism is introduced to flexibly select the operation mode according to the system status, so that new energy can maintain low-cost power generation in low-disturbance scenarios to improve the system's economy, and actively reduce load to provide backup resources in high-disturbance scenarios to enhance system security, thereby fully releasing the dual value of new energy in power supply and cost reduction and efficiency improvement. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a power grid planning method and system that considers multiple operating modes of new energy sources. It takes into account the adaptive switching between active and passive wind power operation strategies. By introducing strategy switching variables, the model achieves joint optimization of wind power operation modes and grid expansion schemes. This reduces planning costs while ensuring system reliability and improves the trade-off between wind power consumption, reserve configuration, and network investment.

[0007] According to some embodiments, the first aspect of the present invention provides a power grid planning method that considers multiple operating modes of new energy sources, employing the following technical solution: A power grid planning method that considers multiple operating modes of new energy sources includes: Obtain operational data for different operating modes of new energy sources; Based on the acquired operational data, a two-stage transmission network expansion planning model is constructed that considers the adaptive switching between active and passive wind power operation strategies. Solve the constructed transmission network expansion planning model to obtain the joint optimization results of new energy operation mode and network expansion, and complete the transmission network planning that considers multiple operation modes of new energy.

[0008] As a further technical limitation, the constructed transmission network expansion planning model takes minimizing the total cost as the objective function, which includes investment cost, operation and scheduling cost, and reliability cost; the constraints of the objective function include at least the power balance constraints under the predicted value and disturbance scenario, the unit output constraints, and the wind power operation strategy constraints.

[0009] As a further technical limitation, in the process of solving the constructed power grid expansion planning model, the integral terms and logical judgments in the power grid expansion planning model are linearized, transforming the power grid expansion planning model into a two-stage robust mixed integer linear programming problem.

[0010] Furthermore, the column and constraint generation method is used to iteratively solve the main subproblems of the obtained two-stage robust mixed integer linear programming problem. The main problem determines the network expansion and operation strategy, and the subproblems search for the most unfavorable disturbance scenario. In each round of the main problem, the feasible solution of the previous round is used for hot start.

[0011] As a further technical limitation, the operational data of different operating modes of new energy sources obtained shall include at least the expected load shedding and expected wind curtailment values ​​of wind farms, the output and active power flow of wind turbine units, and the output value of thermal power units.

[0012] As a further technical limitation, during the adaptive switching process between active and passive wind power operation strategies, the optimal operation mode is automatically determined based on the operating status of new energy sources. By jointly optimizing the grid expansion scheme and wind power operation mode selection through strategy switching variables, the joint optimization results of new energy operation mode and grid expansion are obtained.

[0013] According to some embodiments, a second aspect of the present invention provides a power grid planning system that considers multiple operating modes of new energy sources, employing the following technical solution: A power transmission network planning system that considers multiple operating modes of new energy sources includes: The acquisition module is configured to acquire operational data for different operating modes of new energy sources; The building module is configured to construct a two-stage transmission network expansion planning model that considers the adaptive switching between active and passive wind power operation strategies based on the acquired operational data. The planning module is configured to solve the constructed transmission network expansion planning model, obtain the joint optimization results of new energy operation mode and network expansion, and complete the transmission network planning that considers multiple operation modes of new energy.

[0014] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution: A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in a power grid planning method considering multiple operating modes of new energy sources as described in the first aspect of the present invention.

[0015] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in a power grid planning method that considers multiple operating modes of new energy sources as described in the first aspect of the present invention.

[0016] According to some embodiments, the fifth aspect of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code performs the steps of a power grid planning method considering multiple operating modes of new energy sources as described in the first aspect of the present invention.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention fully considers the adaptive switching between active and passive wind power operation strategies, achieving endogenous selection of wind power operation modes through strategy switching variables: the passive operation strategy characterizes the system's tolerance to wind power uncertainties by optimizing the acceptable disturbance range, while the active operation strategy reserves adjustment margins by reducing wind power load and characterizes the reserve fulfillment probability based on the cumulative distribution function of wind power output. For nonlinear terms and strategy switching constraints, linearization processing, column and constraint generation algorithms, and heuristic hot-start strategies are employed for solution. This invention reduces total cost while ensuring system reliability, adaptively selecting operation strategies according to different time periods and the operating states of different wind farms. When reserve pressure is high, active load reduction provides reserve; when the system's adjustment capacity is sufficient, the wind power acceptance level is increased. Attached Figure Description

[0018] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0019] Figure 1 This is a flowchart of a power grid planning method considering multiple operating modes of new energy sources, as described in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the IEEE-RTS 24-node system in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the operation strategy determination results of Model 4 in Embodiment 1 of the present invention under different wind turbine units and different time periods; Figure 4 This is a structural block diagram of a power grid planning system that considers multiple operating modes of new energy sources, as shown in Embodiment 2 of the present invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0024] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] Example 1 Embodiment 1 of this invention introduces a power transmission network planning method that considers multiple operating modes of new energy sources.

[0027] like Figure 1 The proposed power grid planning method considering multiple operating modes of new energy sources includes: Obtain operational data for different operating modes of new energy sources; Based on the acquired operational data, a two-stage transmission network expansion planning model is constructed that considers the adaptive switching between active and passive wind power operation strategies. Solve the constructed transmission network expansion planning model to obtain the joint optimization results of new energy operation mode and network expansion, and complete the transmission network planning that considers multiple operation modes of new energy.

[0028] In the process of power grid planning, this embodiment adopts a power grid expansion planning model that considers the adaptive switching between active and passive operation strategies of wind power. For the passive operation strategy, this embodiment uses the boundary of the acceptable disturbance range as a decision variable and optimizes it in conjunction with the system regulation capability to achieve the endogenous determination of the disturbance range that wind power can withstand. For the active operation strategy, this embodiment establishes a wind power load reduction reserve supply model and characterizes its supply reliability based on the reserve fulfillment probability.

[0029] In this embodiment, the objective function of the transmission network expansion planning model considering multiple wind power operation strategies is to minimize the total cost, including investment cost, operation and scheduling cost, and reliability cost. The constraints include operational constraints such as power balance constraints under predicted values ​​and disturbance scenarios, unit output constraints, and new constraints on wind power operation strategies, which are used to describe active and passive strategies and their switching constraints.

[0030] The objective function is to minimize the total cost of the system under all possible perturbations. This includes the first-phase investment cost, backup cost, reliability cost, and the second-phase operating cost, specifically expressed as: ; in, , , and These represent investment cost, standby cost, reliability cost, and unit operating cost, respectively. ; ; ; in, NL , NT , NG , NW These represent the sets of power transmission corridors, time periods, thermal power units, and wind farms, respectively. l , t , g , w These are indices for power transmission corridors, time periods, thermal power units, and wind farms, respectively. d Indicates the number of planning days; This represents the product of the investment cost per unit of line capacity and the line length. , They represent power transmission corridors. l The lower and upper limits of the number of loops allowed for the construction of the line; For power transmission corridor l Upper k The construction decision variable for each candidate line is 1 if construction is underway, and 0 otherwise. and These represent the unit standby supply costs for thermal power units and wind farms, respectively. , They represent thermal power units g exist t The system provides real-time adjustments to reserve capacity, both upward and downward. , They represent wind farms w exist t The system provides real-time adjustments to reserve capacity, both upward and downward. , They represent wind farms w exist t The expected load shedding and expected wind curtailment at any given moment; , , For thermal power units g Operating cost coefficient; Indicates thermal power units under disturbance conditions g exist t Efforts are made at all times.

[0031] The constraints of the transmission network expansion planning model considering multiple wind power operation strategies include constraints under predicted values ​​and constraints under disturbance values; specifically: The constraints under the predicted values ​​include: (1) Nodal power balance constraint, that is, the unit can meet the power balance under the load forecast output, specifically: ;in, , and These represent the correlation matrices between thermal power units, wind power units, transmission corridors, and nodes, respectively. , and ... Represents a node i exist t The load forecast value at any given time.

[0032] (2) DC power flow constraints, specifically: The sum of the power flow of all lines in transmission corridor l is The power flow of each line cannot exceed the transmission capacity of a single line. ; Calculation method for DC power flow of transmission lines The phase angle at the nodes cannot exceed ±60°, i.e. The restrictions on the maximum and minimum number of power transmission lines to be built on power transmission corridors, namely... ;in, Indicates power transmission corridor l exist t There is always a trend that brings success; Indicates power transmission corridor l Upper k The line is in t The ever-present trend of merit; Indicates power transmission corridor l Maximum transmission capacity of a single-circuit line; For power transmission corridor l Upper k The construction variable for each candidate route is set to 1 if the route is constructed, and 0 otherwise. , They represent power transmission corridors. l The minimum and maximum allowed number of circuits to be constructed; Indicates power transmission corridor l The susceptance of a single-circuit line; Indicates power transmission corridor l With nodes i The correlation matrix between them; Represents a node i exist t The voltage phase angle at a given moment.

[0033] (3) Thermal power output constraint, i.e. ; .

[0034] , They represent thermal power units g Minimum and maximum output; Indicates thermal power unit g exist t Efforts made at all times; , They represent thermal power units g exist t The ability to adjust reserve levels both upwards and downwards at any time; , They represent thermal power units g Uphill and downhill climbing abilities; Indicates the time interval between adjacent scheduling periods. (4) Thermal power reserve constraints, i.e. ; .

[0035] (5) Reserve supply and demand constraints, i.e. ; ;in, , Both refer to the backup power supply capacity of thermal power units; , Both refer to the backup power supply capacity of wind turbine units; , This indicates the reserve requirement for wind turbine units; , Both represent the reserve requirement of the load.

[0036] The constraints under the disturbance values ​​include: (1) Node power balance constraints, i.e. ; (2) DC power flow constraint, i.e. ; ; ; ;in, (3) Thermal power output constraints, ; .

[0037] It should be noted that the constraints under the perturbation values ​​are exactly the same as the constraints under the predicted values. The only difference is that "~" is added to some of the variables to indicate that such constraints are affected by uncertain parameters.

[0038] In this embodiment, there are two typical approaches to wind power operation strategies: a passive operation strategy that controls the endpoints of the disturbance range and an active operation strategy that controls the output of wind turbine units. The constraints of the wind power adaptive switching strategy are constructed by combining the above two operation strategies.

[0039] (1) Passive operation strategy constraints Under a passive operation strategy, disturbances generated by wind power need to be addressed by backup power supplied by other conventional units within the system, reflecting the role of wind turbines as backup consumers. The relevant constraints are: Under the passive operation strategy, the wind power dispatch output is the predicted output. ; The boundary of the worst-case scenario that the system needs to handle is , ; The reserve requirement for wind power is , ; The reserve supply of wind power is ; ;in, Indicates wind farm w exist t Predicting output at any given moment; , These respectively represent the system in t At all times, wind farms w The upper and lower boundaries of the acceptable range for power output disturbances; , They represent wind farms w exist t The upper and lower limits of the potential range of disturbances at any given time; Indicates wind farm w exist t The runtime strategy selection variable is a binary variable. , They represent wind farms w exist t The upward and downward adjustment of reserve requirements caused by the moment; Indicates wind farm w exist t A reserve that can be adjusted downwards at any time.

[0040] When the disturbance output exceeds the controllable range, a probability-weighted sum is performed on all disturbance scenarios exceeding the controllable boundary. The excess magnitude is multiplied by the corresponding probability density function and then integrated. This transforms the tail risk into the expected power shortage cost and the expected wind curtailment volume. , ;in, This represents the actual random power output of wind farm w at time t; The probability density function representing the random output of wind power; This indicates the expected load shedding amount when the actual wind power output is lower than the acceptable lower boundary or dispatch boundary. This refers to the expected amount of wind curtailment that occurs when the actual output of wind power exceeds the upper limit of the acceptable limit or the dispatch limit. This is a control variable for the wind power operation strategy. It is 0 when a passive operation strategy is adopted and 1 when an active operation strategy is enabled.

[0041] (2) Active operation strategy constraints Under the proactive operation strategy, wind power dispatch values ​​are suppressed below the predicted values, allowing wind power to provide backup to the system, demonstrating its role as a backup supplier. The adaptive switching strategy combines these two strategies, making wind power a backup producer-consumer, with the following constraints: The output status of wind power, under the active operation strategy, the wind power output scheduling value is the decision variable. The reserve requirement for wind power is , The reserve supply of wind power is , When the disturbance output exceeds the controllable range, the impact of the disturbance on the system is measured by calculating the expected loss. , ;in, This indicates a wind farm under an active operation strategy. w exist t The power output is scheduled at all times; Indicates wind farm w exist t A reserve that can be adjusted downwards at any time. and These represent whether the actual wind power output is greater than or less than the integral variable. u The probability of; u It is the integral variable.

[0042] (3) Strategy switching constraints The model utilizes variables The variable t determines whether the wind farm adopts an active or passive operating strategy under different operating conditions. The switching of operating strategies is limited by equipment performance, requiring the addition of operating strategy switching constraints to achieve reasonable control of the wind turbine units. The relevant constraints are: Through binary variables This reflects the switching of operating strategies; a value of 1 indicates a switch, and a value of 0 indicates the absence of a switch. After a switch in operating strategy occurs, the wind farm must maintain that mode. A period of time, namely Wind farm The limit on the number of times a runtime strategy can be switched, i.e. .

[0043] It should be noted that the nonlinearity of the model in this embodiment mainly comes from two types of structures: the integral form of the continuous function and the logical judgment of the operation strategy switching. These are handled by SOS2 piecewise interpolation and Indicator Constraints, respectively.

[0044] Because the active running strategy constraints contain a nonlinear component—the integral term—traditionally, multiple piecewise linearization methods are used to handle this, but this significantly increases the model size. This embodiment uses a linearization scheme based on SOS2 (Special Ordered Set of Type 2) interpolation to reduce model complexity. The integral term in this embodiment can be uniformly represented as: ;in, It is a continuous, single-variable, nonlinear function.

[0045] To maintain a linear structure, this embodiment... Select several discrete points within the range of values. Using SOS2 variables Construct piecewise interpolation, i.e. ; ; Among them, the SOS2 technical requirement only allows two adjacent... Non-zero values ​​enable a linear approximation. Since the functions involved are continuous and monotonic, a high degree of accuracy can be achieved by using an appropriate number of segments, while avoiding the introduction of a large number of binary variables.

[0046] This embodiment uses wind power's reserve supply as an example. Taking this as an example, analysis yields the following linear expression: ; ; Among them, parameters yes At a specific segmentation point The calculation results of the increased reserve capacity; s This represents the piecewise linearized node index. S Indicates the total number of nodes in the segment; Indicates wind farm w exist t The time corresponding to the first s The interpolation weights of each segment node are SOS2 variables, which satisfy that the sum of all weights is 1, and that at most two adjacent segment nodes have positive weights at any given time. Indicates the first s Wind power output boundary values ​​at each segment node; This represents the pre-calculated wind power reserve capacity function value at this segment node; and These are obtained through linear interpolation of adjacent segment nodes. In summary, other integral terms are processed similarly to eliminate nonlinearity in the model.

[0047] Because passive and active execution strategy constraints require the use of binary variables Switching between two types of operating strategies, with active and passive strategies corresponding to different constraint sets, constitutes a typical condition constraint structure; the above structure can be simplified as follows: ; ;in, and These are the constraint sets for passive and active operation strategies, respectively. Indicates wind farm w exist t The set of overall operational strategy constraints corresponding to each moment; and These represent the passive execution strategy constraint set and the active execution strategy constraint set, respectively. For wind farm w exist t The runtime strategy selection variable at any given time is a binary variable; when At that time, the wind farm adopts a passive operation strategy; when At that time, the wind farm adopts an active operation strategy; Indicates wind farm w exist t The vector consists of variables related to the operational strategy at any given time, mainly including wind power output, wind power acceptable boundary, wind power reserve demand, wind power reserve supply, and expected losses. , These represent the coefficient matrix and the right-hand side vector of the passive running strategy constraints, respectively. , These represent the coefficient matrix and right-hand side vector of the active running strategy constraints, respectively.

[0048] Using the big-M method, the above expression can be transformed into ;in, M It is a sufficiently large positive number.

[0049] This embodiment uses the solver's native Indicator Constraints technique to implement the above logical relationship. Compared to the traditional Big-M method, this embodiment does not require manually selecting large constants, which can reduce the problems of excessive relaxation and numerical instability, and improve the solution efficiency.

[0050] Besides integral expressions and logical relationships, the model in this embodiment also includes several nonlinear expressions, which can be linearized using mature techniques. After linearization, the model is transformed into a two-stage robust mixed-integer linear programming problem. This embodiment uses the Column and Constraint Generation (CCG) algorithm for iterative solution of the main and subproblems: the main problem determines the network expansion and operation strategy, and the subproblems search for the most unfavorable perturbation scenario. To improve computational efficiency, a hot start is performed in each round of the main problem using the feasible solution from the previous round. Although the uncertainty set structure in this model changes dynamically with the main variables, existing research has shown that for two-stage robust optimization problems with box-shaped perturbation sets, continuous deformation of the perturbation boundary within the feasible region does not compromise the convergence and optimality of the algorithm.

[0051] This embodiment sets up two cases—a standard test system and a real urban power grid—to verify the proposed model. The standard test case, built on the IEEE-RTS 24-node system, is mainly used to analyze the impact of different wind power operation strategies and their adaptive switching mechanisms on the transmission network expansion planning results. The real urban power grid case is used to further verify the applicability of the proposed method under actual grid conditions.

[0052] For example Figure 2 The IEEE-RTS 24-node system shown in this embodiment expands the original parameters to simulate high-proportion renewable energy integration and planned load growth. The system peak load is increased to approximately 3700MW, about 1.3 times the original peak load; three conventional thermal power units are added, with capacities of 400MW, 500MW, and 500MW respectively, bringing the total installed capacity of conventional units after expansion to approximately 4500MW; the system is configured with five wind farms, connected to nodes 5, 11, 12, 17, and 22 respectively, with a total installed wind power capacity of approximately 2800MW, accounting for 38% of the total installed capacity of the expanded system.

[0053] The uncertainty range for wind power output is set to ±10% of the predicted value, and disturbance samples are drawn from a truncated Gaussian distribution within the range of [90%, 110%]. The total number of lines in each existing transmission corridor is extended to a maximum of 4, and the parameters and construction costs of the new lines are set with reference to the corresponding existing lines. The model is built on the GAMS 48.1 platform and solved using CPLEX. The computing environment is a computer with an Intel i5 2.50GHz processor and 20GB of memory. In the CCG iteration, the gaps for the subproblems and the main problem are set to 0.1% and 0.5%, respectively.

[0054] To compare the impact of different wind power operation strategies on transmission network expansion decisions, this embodiment sets up four types of planning models for comparative analysis: Model 1: A planning model that only considers the full absorption of uncertainties in new energy sources; Model 2: Planning model considering only passive operation strategies; Model 3: A planning model that only considers proactive operation strategies; Model 4: A planning model that simultaneously considers the adaptive switching between active and passive operation strategies.

[0055] The total computation time for Model 4, the IEEE-RTS 24-node example, is approximately 40 minutes.

[0056] The comparison results of the planning schemes are shown in Table 1. All four models involve multiple expansions along the nl7 corridor, indicating that this corridor is the main power flow bottleneck of the system. The expansion strategies for the nl1, nl10, and nl11 transmission corridors show differences. Although Model 3 and Model 4 have the same number of lines, Model 4 has the lowest total expansion length and investment cost among the four schemes because the nl1 line is shorter (approximately 3 km) while the nl10 line is longer (approximately 16 km). This indicates that under the same system security level, the adaptive strategy significantly improves network utilization efficiency by actively reducing load in high-disturbance scenarios and fully absorbing load in low-disturbance scenarios.

[0057] Table 1 Comparison Results of Planning Schemes

[0058] The total system cost and composition of each model are shown in Table 2. Taking Model 1 as the baseline (100%), the total costs of Models 2, 3, and 4 are reduced to 95.5%, 95.3%, and 94.3%, respectively.

[0059] Table 2 Cost and Composition

[0060] Model 1, by treating wind power forecasts as rigid output, leads to significantly higher transmission investment and reserve costs to ensure the system can fully absorb all wind power output. Model 2, by limiting the boundaries of wind power disturbance intervals, reduces redundancy caused by unreasonable reserve configuration, thus lowering both investment and reserve costs. Model 3, by proactively compressing wind power output to release reserve capacity in high-risk scenarios, further reduces reserve demand, but at the cost of increased wind curtailment costs. Furthermore, it increases investment costs to ensure effective utilization of wind power reserves. However, this strategy effectively reduces operating costs, with the total cost only slightly lower than the passive operation strategy. Model 4 automatically selects a strategy under different operating scenarios. It proactively releases reserves in scenarios with high wind power output and efficiently absorbs wind power in scenarios with low output, achieving complementarity between the two mechanisms: reducing redundant line investment and suppressing reserve demand and wind curtailment losses. Ultimately, the total system cost is reduced to 94.3% of Model 1, making it the optimal solution among the four options.

[0061] Figure 3The results of Model 4's operational strategy determination under different wind turbine units and time periods are presented. Gray dots represent passive strategies, while red dots represent active strategies. Wind farms exhibit a clear mixed strategy distribution throughout the scheduling cycle: most wind farms employ passive strategies for the majority of the time, switching to active strategies only in certain periods. From a temporal distribution perspective, strategy switching is mainly concentrated during periods of high load and tight reserve, such as t8-t11 and t13-t17. During these periods, multiple wind farms simultaneously choose active strategies, indicating that the model tends to release reserve capacity by actively reducing load when system uncertainty increases. In low-risk periods (such as t1-t7 and t18-t24), the vast majority of units choose passive strategies to ensure full wind power consumption and reduce curtailment losses. From the perspective of wind farms, there are differences in strategy preferences among different wind farms: res1-res3 wind farms all choose active strategies in eight time periods; res4 and res5 mainly maintain passive strategies, switching only at a few high-risk points, exhibiting a certain "division of labor" characteristic. This indicates that the adaptive strategy does not evenly distribute the task of providing backup power from wind power, but rather automatically allocates the operating strategy according to the output characteristics of the wind farm and the system operating status, thus achieving a balance between risk mitigation and economic efficiency.

[0062] To evaluate the impact of the operational strategy lock-in time (Tlock) on the system's economy, this embodiment, while keeping other parameters constant, increases the lock-in time sequentially from 1 hour to 5 hours, comparing the changing trend of the total system cost. The results are shown in Table 3. Increasing the lock-in time significantly suppresses the switching frequency of the operational strategy, forcing the system to maintain a suboptimal mode for most of the time, leading to a decrease in reserve allocation and power flow regulation capabilities, thereby increasing the total system cost. The operating cost in the cost item remains basically unchanged, but the investment cost and other first-stage costs change significantly. When the lock-in time is small (≤2 hours), the model can flexibly switch strategies, effectively balancing reliability and economy. However, when the lock-in time increases to 5 hours, the total system cost and first-stage cost approach the levels of Model 2 and Model 3, essentially offsetting the strategy switching advantage of Model 4.

[0063] Table 3 Trend of Total Cost

[0064] To verify the applicability of the proposed model in a real-world system, this embodiment selects a city's power transmission network as an engineering example; it includes 155 nodes of 220kV and above, 186 transmission corridors, 225 transmission lines, and 43 thermal power units. The total installed capacity of conventional power sources in the system is approximately 16,000MW, the maximum load is approximately 15,000MW, and the installed capacity of new energy sources is approximately 5,000MW. In the planning scenario, the load level is set at 1.5 times the original maximum load, approximately 22,500MW; the installed capacity of new energy sources is expanded to 3 times the original scale, approximately 15,000MW, to simulate the system operation and planning requirements under conditions of high proportion of new energy access. Considering the sensitivity of actual power grid data, this embodiment simplifies some node numbers, line parameters, and cost parameters, but retains the main characteristics of the system's grid structure.

[0065] The transmission channel expansion schemes obtained from the four types of models show significant differences, as detailed in Table 4.

[0066] Table 4 Power Transmission Channel Expansion Plan

[0067] Table 4 shows that from Model 1 to Model 4, the number of transmission corridors, lines, and total line length all decrease in a stepwise manner. Model 1 has the largest planning margin; the passive operation strategy leads to some contraction, while the active operation strategy further reduces the scale by leveraging load reduction capabilities. Model 4 achieves the minimum values ​​for all three indicators, indicating that this embodiment, by adjusting the combination of expanded corridors, has to some extent achieved a reconfiguration of power flow distribution. This further demonstrates that the adaptive switching strategy can fully utilize the adjustment space at the operational level, significantly compressing the redundancy of transmission assets reserved for extreme conditions, and achieving an optimal balance between investment and safety while ensuring safety and power consumption.

[0068] Table 5 presents a comparison of investment costs, reserve costs, reliability costs, and first-stage costs under different models. Model 1, to ensure full absorption of renewable energy, relies on high investment and high reserves to reduce reliability costs. Model 2, through precise allocation of reserves, significantly reduces reliability costs, and investment and reserve costs also decrease simultaneously. Model 3, by leveraging wind power as a reserve, significantly reduces reserve costs, but wind curtailment costs increase, resulting in a total cost close to that of Model 2. Model 4, through adaptive switching between active and passive strategies, achieves the greatest possible investment compression among all models. Although reserve costs rise slightly compared to Model 3, wind curtailment costs are well controlled, and no load shedding occurs. Ultimately, its first-stage total cost is reduced to 82.5% of Model 1, showing advantages over both Model 2 and Model 3. The adaptive switching model proposed in this embodiment reduces reliance on reserve configuration and large-scale transmission capacity expansion by selecting active or passive wind power operation modes under different operating conditions, thereby improving the relationship between investment costs, reserve costs, and renewable energy absorption while ensuring controllable system risks.

[0069] Table 5 Comparison results of different models

[0070] Therefore, the adaptive switching model in this embodiment allows wind power to choose passive acceptance or active load shedding for reserve under different operating conditions. When the system's regulation capacity is sufficient, the model tends to expand the acceptable disturbance range to improve wind power absorption. When local transmission channels are limited or reserve pressure is high, the model can release reserve capacity through active load shedding, reducing the rigid demand for expanding some transmission channels. Therefore, the expansion scheme no longer simply revolves around the most unfavorable wind power disturbance for conservative configuration, but reflects the synergistic optimization characteristics between operation strategy and grid planning.

[0071] This embodiment proposes a transmission network expansion planning model that considers adaptive switching between active and passive wind power operation strategies. The model achieves endogenous selection of wind power operation modes through strategy switching variables: the passive operation strategy characterizes the system's tolerance to wind power uncertainties by optimizing the acceptable disturbance range, while the active operation strategy reserves adjustment margins by reducing wind power load and characterizes the reserve fulfillment probability based on the cumulative distribution function of wind power output. To address the nonlinear terms and strategy switching constraints in the model, this embodiment employs linearization processing, column and constraint generation algorithms, and a heuristic hot-start strategy for solution.

[0072] This embodiment employs an adaptive switching mechanism between active and passive strategies, automatically determining the optimal operating mode based on the operational status to fully unleash the flexible adjustment value of wind power. It coordinates the relationship between wind power consumption, reserve configuration, and investment costs by jointly optimizing the grid expansion scheme and wind power operation mode selection through strategy switching variables. After linearization, the model is solved using the column-and-constraint generation (CCG) algorithm, and the effectiveness of the proposed method is verified using the IEEE RTS-24 system.

[0073] Compared with traditional planning models, this embodiment reduces the total cost while ensuring system reliability. The model can adaptively select operating strategies based on different time periods and the operating status of different wind farms. When the reserve pressure is high, it provides reserves through active load shedding, and when the system's adjustment capacity is sufficient, it increases the wind power acceptance level. Sensitivity analysis results further show that the proposed method maintains good economy and adaptability even with changes in strategy switching lock-in time.

[0074] Example 2 Embodiment 2 of the present invention introduces a power transmission network planning system that takes into account multiple operating modes of new energy sources.

[0075] like Figure 4 The transmission network planning system shown includes multiple operating modes of new energy sources: The acquisition module is configured to acquire operational data for different operating modes of new energy sources; The building module is configured to construct a two-stage transmission network expansion planning model that considers the adaptive switching between active and passive wind power operation strategies based on the acquired operational data. The planning module is configured to solve the constructed transmission network expansion planning model, obtain the joint optimization results of new energy operation mode and network expansion, and complete the transmission network planning that considers multiple operation modes of new energy.

[0076] The detailed steps are the same as those provided in Example 1, which is a power grid planning method that considers multiple operating modes of new energy sources, and will not be repeated here.

[0077] Example 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0078] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in a power grid planning method considering multiple operating modes of new energy sources as described in Embodiment 1 of the present invention.

[0079] The detailed steps are the same as those provided in Example 1, which is a power grid planning method that considers multiple operating modes of new energy sources, and will not be repeated here.

[0080] Example 4 Embodiment 4 of the present invention provides an electronic device.

[0081] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in a power grid planning method that considers multiple operating modes of new energy sources as described in Embodiment 1 of the present invention.

[0082] The detailed steps are the same as those provided in Example 1, which is a power grid planning method that considers multiple operating modes of new energy sources, and will not be repeated here.

[0083] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0084] A computer program product includes software code, wherein the program in the software code executes the steps of a power grid planning method considering multiple operating modes of new energy sources as described in Embodiment 1 of the present invention.

[0085] The detailed steps are the same as those provided in Example 1, which is a power grid planning method that considers multiple operating modes of new energy sources, and will not be repeated here.

[0086] 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 implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0091] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0092] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A power transmission network planning method considering multiple operating modes of new energy sources, characterized in that, include: Obtain operational data for different operating modes of new energy sources; Based on the acquired operational data, a two-stage transmission network expansion planning model is constructed that considers the adaptive switching between active and passive wind power operation strategies. For the passive operation strategy, the acceptable disturbance range boundary is used as a decision variable and co-optimized with the system's regulation capability to ensure the determination of the disturbance range that wind power can withstand. When the disturbance output exceeds the controllable range, all disturbance scenarios exceeding the controllable boundary are summed with probability weights. The excess magnitude is multiplied by the corresponding probability density function and then integrated to transform the tail risk into the expected power shortage cost and the expected wind curtailment volume, i.e.: ;in, This represents the actual random power output of wind farm w at time t; , These respectively represent the system in t At all times, wind farms w The upper and lower boundaries of the acceptable range for power output disturbances; , They represent wind farms w exist t The upper and lower limits of the potential range of disturbances at any given time; The probability density function representing the random output of wind power; This indicates the expected load shedding amount when the actual wind power output is lower than the acceptable lower boundary or dispatch boundary. This refers to the expected amount of wind curtailment that occurs when the actual output of wind power exceeds the upper limit of the acceptable limit or the dispatch limit. These are the control variables for the operation strategy of wind power. Under the active operation strategy, the wind power dispatch value is reduced to below the predicted value to supply the system with reserves; when the disturbance output exceeds the controllable range, the impact of the disturbance on the system is measured by calculating the expected loss. Using variables The constraints for determining whether a wind farm adopts an active or passive operation strategy under different operating conditions are: through binary variables. This indicator reflects the switching of operating strategies; a value of 1 indicates a switch, while a value of 0 indicates the absence of a switch. After a switch occurs, the wind farm maintains that operating mode. Time period; wind farm There is a limit to the number of times the running strategy can be switched; Solve the constructed transmission network expansion planning model to obtain the joint optimization results of new energy operation mode and network expansion, and complete the transmission network planning that considers multiple operation modes of new energy.

2. The power grid planning method considering multiple operating modes of new energy sources as described in claim 1, characterized in that, The constructed transmission network expansion planning model takes minimizing the total cost as the objective function, which includes investment cost, operation and scheduling cost, and reliability cost. The constraints of the objective function include at least the power balance constraints under the predicted value and disturbance scenarios, the unit output constraints, and the wind power operation strategy constraints.

3. The power grid planning method considering multiple operating modes of new energy sources as described in claim 1, characterized in that, In solving the constructed power transmission network expansion planning model, the integral terms and logical judgments in the power transmission network expansion planning model are linearized, transforming the power transmission network expansion planning model into a two-stage robust mixed integer linear programming problem.

4. The power grid planning method considering multiple operating modes of new energy sources as described in claim 3, characterized in that, The column and constraint generation method is used to iteratively solve the main subproblems of the obtained two-stage robust mixed integer linear programming problem. The main problem determines the network expansion and operation strategy, and the subproblems search for the most unfavorable disturbance scenario. In each round of the main problem, the feasible solution of the previous round is used for hot start.

5. The power grid planning method considering multiple operating modes of new energy sources as described in claim 1, characterized in that, The operational data obtained for different operating modes of new energy sources include at least the expected load shedding and expected wind curtailment values ​​of wind farms, the output and active power flow of wind turbine units, and the output value of thermal power units.

6. The power grid planning method considering multiple operating modes of new energy sources as described in claim 1, characterized in that, During the adaptive switching between active and passive wind power operation strategies, the optimal operation mode is automatically determined based on the operating status of new energy sources. By jointly optimizing the grid expansion scheme and wind power operation mode selection through strategy switching variables, the joint optimization results of new energy operation mode and grid expansion are obtained.

7. A power transmission network planning system considering multiple operating modes of new energy sources, employing a power transmission network planning method considering multiple operating modes of new energy sources as described in any one of claims 1-6, characterized in that, include: The acquisition module is configured to acquire operational data for different operating modes of new energy sources; The building module is configured to construct a two-stage transmission network expansion planning model that considers the adaptive switching between active and passive wind power operation strategies based on the acquired operational data. The planning module is configured to solve the constructed transmission network expansion planning model, obtain the joint optimization results of new energy operation mode and network expansion, and complete the transmission network planning that considers multiple operation modes of new energy.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of a power grid planning method that considers multiple operating modes of new energy sources as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of a power grid planning method that considers multiple operating modes of new energy sources as described in any one of claims 1-6.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of a power grid planning method that considers multiple operating modes of new energy sources as described in any one of claims 1-6.

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