Power system scheduling method, device and equipment based on frequency security constraint

Abstract

The invention discloses an electric power system scheduling method, device and equipment based on frequency security constraints, relates to the technical field of electric power operation and optimal scheduling, and can perform convex processing on originally nonlinear frequency lowest point constraints and significantly reduce the calculation complexity on the premise of ensuring high constraint precision. The method comprises the steps that an average system frequency model is established, a frequency transfer function of the average system frequency model is converted through time domain expression, the lowest frequency point of a power system is solved, and the average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters; on the premise that the set relaxation precision requirement is met, the lowest frequency point of the power system is relaxed into second-order cone constraint; setting a plurality of constraint conditions in combination with second-order cone constraint, and constructing a power system scheduling model by taking the lowest operation cost as an objective function; and solving the power system scheduling model to obtain a decision variable value for power system scheduling.

Description

Technical Field

[0001] This application relates to the field of power operation and optimal dispatching technology, and in particular to a power system dispatching method, apparatus and equipment based on frequency security constraints. Background Technology

[0002] With the gradual increase in the proportion of renewable energy, the issue of system frequency security has become increasingly prominent. Therefore, under the background of high-proportion renewable energy integration, the operation of the power system must gradually shift from prioritizing economy to giving equal importance to economy and security, with frequency security constraints being a key consideration as a core condition for optimized scheduling and operation.

[0003] In related technologies, there is considerable research on optimization scheduling algorithms that consider frequency security constraints. For example, considering source-load operation constraints, line power flow constraints, and security constraints, a multi-objective optimization method that considers frequency security constraints and is applicable to multi-regional power grids with uneven load distribution is obtained by relaxing frequency constraints and combining the model with a rolling optimization model. Another example is the addition of frequency security constraints to the scheduling algorithm to address the frequency security problem under power disturbances in high-proportion wind power systems. The complex Δf expression is approximated by a straight line with slope k from the initial point to the lowest frequency point, thus incorporating the frequency security constraint into the scheduling algorithm optimization model, resulting in a scheduling algorithm model that considers wind power uncertainty and frequency security constraints. Furthermore, to address the problem of the coupled and intertwined source-load fluctuation risks and frequency stability issues in high-proportion, low-inertia power grids, making it difficult to obtain safe and economical operation modes, an economic scheduling model with active power frequency security indicators is established, considering important frequency-related indicators in the power system. Although the above-mentioned optimization scheduling algorithm can introduce complex frequency dynamic constraints into the optimization model through linear approximation, greatly reducing the difficulty of solving the problem, this simplification cannot eliminate the non-convexity of the core nonlinearity of the frequency safety constraints, inevitably leading to a loss of accuracy and making the solution extremely difficult. Summary of the Invention

[0004] In view of this, this application provides a power system dispatching method, apparatus and equipment based on frequency security constraints. The main purpose is to solve the problem that the non-convexity of the core nonlinearity of frequency security constraints cannot be eliminated by the simplification of the optimization dispatching algorithm, which inevitably leads to the loss of accuracy and makes the solution extremely difficult.

[0005] According to the first aspect of this application, a power system dispatching method based on frequency security constraints is provided, comprising: An average system frequency model is established, and the lowest frequency point of the power system is obtained by transforming the frequency transfer function of the average system frequency model through time domain expression. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. Under the premise of meeting the set relaxation accuracy requirements, the lowest frequency point of the power system is relaxed to a second-order cone constraint. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error through a combination of sampling parameters. By combining multiple constraints including second-order cone constraints, and taking the minimum operating cost as the objective function, a power system dispatch model is constructed. Solve the power system dispatch model to obtain the values ​​of the decision variables used for power system dispatch.

[0006] Furthermore, the establishment of an average system frequency model, and the solution to obtain the minimum frequency point of the power system through the time-domain representation transformation of the frequency transfer function of the average system frequency model, includes: Within a pre-defined interconnected power grid area associated with power system dispatch, the frequency of the interconnected power grid is dynamically mapped to an equivalent single-machine system to obtain an average system frequency model. Based on the average system frequency model, the frequency transfer function of the equivalent single machine is transformed into a time-domain differential equation. By taking the derivative of the time-domain differential equation and substituting the extreme frequency moments into the frequency transfer function, the minimum frequency point of the power system can be calculated.

[0007] Furthermore, the step of dynamically mapping the frequency of the interconnected power grid within a pre-defined interconnected power grid region associated with power system dispatch to obtain an average system frequency model includes: Within a pre-defined interconnected power grid area associated with power system dispatch, the components participating in frequency response within the interconnected power grid area are aggregated into lumped equivalent parameters; By using a global frequency variable to ignore the frequency differences of each component within the region, the frequency dynamic process of the interconnected power grid region is mapped to an equivalent single-machine system, resulting in an average system frequency model.

[0008] Furthermore, before relaxing the lowest frequency point of the power system to a second-order cone constraint while meeting the set relaxation accuracy requirements, the method further includes: Based on the safety threshold of the power system under the minimum frequency value that meets the interference, a relaxation accuracy requirement is set for the minimum frequency point of the power system. Accordingly, relaxing the lowest frequency point of the power system to a second-order cone constraint, while meeting the set relaxation accuracy requirements, includes: Under the premise of meeting the set relaxation accuracy requirements, a second-order approximation function with the lowest frequency is constructed so that any variable in the second-order approximation function satisfies the approximation condition, which is that the error of the constructed second-order approximation function is controlled within the set engineering range under typical operation mode. The second-order approximation function that satisfies the approximation conditions is transformed into a standard approximation function that contains second-order cone characterization parameters; Solving the standard approximation function within the range of parameter variations yields the second-order cone constraint.

[0009] Furthermore, under the premise of satisfying the set relaxation accuracy requirements, constructing a second-order approximation function at the lowest frequency point, such that any variable in the second-order approximation function satisfies the approximation conditions, includes: Based on the nonlinear mechanism of power system frequency response, a combination of variables that significantly affects the lowest frequency point is selected to construct a complete quadratic polynomial. Under the premise of meeting the set relaxation accuracy requirements, the complete quadratic polynomial is approximated so that the approximated complete quadratic polynomial satisfies the approximation conditions, and the second-order approximation function of the lowest frequency point is obtained.

[0010] Furthermore, the step of solving the standard approximation function within the set parameter variation range to obtain the second-order cone constraint includes: Within the set parameter variation range, an effective parameter combination sample set that meets the relaxation accuracy requirements is constructed using the Latin hypercube sampling method. For each effective parameter combination sample, the lowest point of the true frequency obtained from time-domain simulation is used as the solution input, and the sum of squared fitting errors is used as the objective function. The optimal coefficient set of the standard approximation function is solved by the algorithm. Different core parameters are obtained by splitting the optimal coefficient set. The different core parameters are then used to derive the standard form of the second-order cone parameters from the quadratic constraints through mathematical operations. By introducing auxiliary variables, the derived second-order cone parameters are transformed into the standard norm form to obtain the second-order cone constraints.

[0011] Furthermore, combining multiple constraints including second-order cone constraints, including power balance constraints, spinning reserve constraints, start-up and shutdown time constraints, unit output constraints, ramp-up constraints, and frequency security constraints that take into account frequency security constraints; the objective function at least includes the operating costs of thermal power units, nuclear power units, and energy storage units, as well as the wind curtailment and solar curtailment costs of wind turbine units and photovoltaic units.

[0012] According to a second aspect of this application, a power system dispatching device based on frequency security constraints is provided, comprising: A unit is established to build an average system frequency model. The minimum frequency point of the power system is obtained by transforming the frequency transfer function of the average system frequency model through time-domain expression. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. The transformation unit is used to relax the lowest frequency point of the power system into a second-order cone constraint under the premise of meeting the set relaxation accuracy requirements. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error through a combination of sampling parameters. A construction unit is used to combine multiple constraints including second-order cone constraints to construct a power system dispatch model with the lowest operating cost as the objective function. The solution unit is used to solve the power system dispatch model and obtain the values ​​of the decision variables used for power system dispatch.

[0013] Furthermore, the establishment unit includes: The mapping module is used to dynamically map the frequency of the interconnected power grid into an equivalent single-machine system within a pre-defined interconnected power grid area associated with power system scheduling, thereby obtaining an average system frequency model. The conversion module is used to convert the frequency transfer function of the equivalent single machine into a time-domain differential equation based on the average system frequency model. The calculation module is used to substitute the frequency extremum obtained by differentiating the time-domain differential equation into the frequency transfer function to calculate the minimum frequency point of the power system.

[0014] Furthermore, the mapping module is specifically used for: Within a pre-defined interconnected power grid area associated with power system dispatch, the components participating in frequency response within the interconnected power grid area are aggregated into lumped equivalent parameters; By using a global frequency variable to ignore the frequency differences of each component within the region, the frequency dynamic process of the interconnected power grid region is mapped to an equivalent single-machine system, resulting in an average system frequency model.

[0015] Furthermore, the device also includes: The setting unit is used to set a relaxation accuracy requirement for the lowest frequency point of the power system based on a safety threshold for the lowest frequency value of the power system under the set disturbance condition, before relaxing the lowest frequency point of the power system to a second-order cone constraint under the premise of meeting the set relaxation accuracy requirement. Accordingly, the conversion unit includes: The construction module is used to construct a second-order approximation function at the lowest frequency point under the premise of meeting the set relaxation accuracy requirements, so that any variable in the second-order approximation function satisfies the approximation condition, which is that the error of the constructed second-order approximation function is controlled within the set engineering range under typical operation mode. The conversion module is used to convert the second-order approximation function that satisfies the approximation conditions into a standard approximation function that contains second-order cone characterization parameters. The solver module is used to solve the standard approximation function within the range of set parameter variations to obtain the second-order cone constraint.

[0016] Furthermore, the building module is specifically used for: Based on the nonlinear mechanism of power system frequency response, a combination of variables that significantly affects the lowest frequency point is selected to construct a complete quadratic polynomial. Under the premise of meeting the set relaxation accuracy requirements, the complete quadratic polynomial is approximated so that the approximated complete quadratic polynomial satisfies the approximation conditions, and the second-order approximation function of the lowest frequency point is obtained.

[0017] Furthermore, the solution module is specifically used for: Within the set parameter variation range, an effective parameter combination sample set that meets the relaxation accuracy requirements is constructed using the Latin hypercube sampling method. For each effective parameter combination sample, the lowest point of the true frequency obtained from time-domain simulation is used as the solution input, and the sum of squared fitting errors is used as the objective function. The optimal coefficient set of the standard approximation function is solved by the algorithm. Different core parameters are obtained by splitting the optimal coefficient set. The different core parameters are then used to derive the standard form of the second-order cone parameters from the quadratic constraints through mathematical operations. By introducing auxiliary variables, the derived second-order cone parameters are transformed into the standard norm form to obtain the second-order cone constraints.

[0018] Furthermore, combining multiple constraints including second-order cone constraints, including power balance constraints, spinning reserve constraints, start-up and shutdown time constraints, unit output constraints, ramp-up constraints, and frequency security constraints that take into account frequency security constraints; the objective function at least includes the operating costs of thermal power units, nuclear power units, and energy storage units, as well as the wind curtailment and solar curtailment costs of wind turbine units and photovoltaic units.

[0019] According to a third aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0020] According to a fourth aspect of this application, a readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0021] By utilizing the above technical solutions, this application provides a power system dispatching method, apparatus, and equipment based on frequency security constraints. Compared with the current approach of introducing complex dynamic frequency constraints into the optimization model through linear approximation to achieve power system dispatching, this application establishes an average system frequency model. The minimum frequency point of the power system is obtained by transforming the frequency transfer function of the average system frequency model through time-domain representation. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. Under the premise of meeting the set relaxation accuracy requirements, the minimum frequency point of the power system is relaxed to a second-order cone constraint. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error of a combination of sampled parameters. Multiple constraint conditions are set in conjunction with the second-order cone constraint, with the minimum operating cost as the objective function, to construct a power system dispatching model. The power system dispatching model is solved to obtain the values ​​of the decision variables used for power system dispatching. Based on the establishment of the average frequency model, the process applies a second-order cone approximation to the lowest frequency point of the power system, transforming the originally nonlinear lowest frequency point constraint into a set of solvable convex constraints. This significantly reduces computational complexity while ensuring high constraint accuracy, effectively guaranteeing that the system can meet frequency stability requirements during scheduling.

[0022] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating a power system dispatching method based on frequency security constraints in one embodiment of this application; Figure 2 yes Figure 1 A flowchart illustrating a specific implementation method of step 101; Figure 3 This is a flowchart illustrating a power system dispatching method based on frequency security constraints in another embodiment of this application; Figure 4 yes Figure 3 A flowchart illustrating a specific implementation method for step 302; Figure 5 yes Figure 3 A flowchart illustrating a specific implementation method for step 305; Figure 6 This is a flowchart illustrating a specific implementation of a power system dispatching method based on frequency security constraints in one embodiment of this application. Figure 7 This is a schematic diagram of the optimized scheduling results under different scenarios in one embodiment of this application; Figure 8 This is a simulation diagram illustrating how scheduling schemes under different scenarios are incorporated into the system frequency response model according to an embodiment of this application. Figure 9 This is a schematic diagram showing the results of the lowest frequency points corresponding to different scenarios in one embodiment of this application; Figure 10 This is a schematic diagram showing the results of different scenarios corresponding to the new energy reserve situation in one embodiment of this application; Figure 11 This is a schematic diagram of the structure of a power system dispatching device based on frequency security constraints in one embodiment of this application; Figure 12 This is a schematic diagram of the device structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0024] The invention will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are described merely to enable those skilled in the art to better understand and thus implement the invention, and are not intended to imply any limitation on the scope of the invention.

[0025] As used herein, the term "comprising" and its variations are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "at least partially based on". The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment". The term "another embodiment" is to be interpreted as "at least one other embodiment".

[0026] In related technologies, optimization scheduling algorithms can introduce complex frequency dynamic constraints into the optimization model through linear approximation methods, which greatly reduces the difficulty of solving the problem. However, this simplification cannot eliminate the non-convexity of the core nonlinearity of the frequency safety constraints, inevitably leading to a loss of accuracy and making the solution extremely difficult.

[0027] To address this problem, this embodiment provides a power system dispatching method based on frequency security constraints, such as... Figure 1 As shown, it includes the following steps: 101. Establish an average system frequency model, and use the frequency transfer function of the average system frequency model to transform it through time domain expression to obtain the minimum frequency point of the power system.

[0028] The average system frequency model is an equivalent single-machine system formed by aggregating parameters from multiple generators. Typically, a power system is a complex system with multiple generators operating in parallel, making it difficult to directly analyze the frequency variations of each unit. By establishing an average system frequency model, key parameters of all units, such as inertia and damping, can be integrated, and the multi-machine system can be equated to a virtual single machine, simplifying the analysis complexity.

[0029] In this embodiment, the frequency transfer function is used to describe the relationship between system input and output, mainly applied in frequency domain analysis. Here, the system input can be load changes, power generation fluctuations, etc., and the system output can be the system frequency change. The frequency transfer function can quantify the magnitude of an input disturbance in the frequency domain, but it cannot directly show the frequency change over time, such as the frequency values ​​1 second and 2 seconds after the disturbance occurs. Since the frequency transfer function only reflects the system's response characteristics under different frequency signals, it is a static mathematical relationship and does not show the dynamic trajectory of frequency over time. By converting the frequency transfer function to the time domain, the abstract frequency domain relationship can be transformed into an intuitive time history. Only the time domain expression can directly solve for the minimum frequency point, resulting in a frequency-time function, such as f(t), where t is time. This intuitively shows how the frequency decreases from the moment the disturbance occurs, the time it takes to reach the minimum point, the value of the minimum point, and how it subsequently recovers and stabilizes.

[0030] Understandably, power systems have a strict safety range for frequency, which typically includes the standard grid frequency of 50Hz, with permissible fluctuations usually within ±0.2Hz. During severe disturbances, the frequency drops rapidly. If it falls below the safety threshold at its lowest point (e.g., below 47Hz), it can lead to generator loss of synchronism, critical load outages, and even grid collapse. Therefore, the lowest frequency point is a crucial criterion for grid safety. If it's within the safety range, the system can stabilize on its own without additional control. If it falls below the safety threshold, control strategies need to be implemented in advance, such as accelerating the shedding of some loads or activating standby units to prevent the incident from escalating.

[0031] 102. Under the premise of meeting the set relaxation accuracy requirements, relax the lowest frequency point of the power system to a second-order cone constraint.

[0032] In this embodiment, the mathematical essence of the lowest frequency point is the minimum value of a time-domain function. The time-domain response of the average system frequency model is typically a second-order or higher-order dynamic equation, and its corresponding minimum value expression is nonlinear. Nonlinear expressions cannot be directly embedded into mainstream optimization algorithms, leading to difficulties in solving the optimization problem, or even preventing convergence, thus failing to meet the requirements of real-time power grid control. By relaxing the lowest frequency point of the power system into a second-order cone constraint, the nonlinear lowest frequency point can be transformed into a linear or convex constraint form, making the optimization problem efficient and solvable.

[0033] The relaxation accuracy requirements set here refer to the prerequisites that must be met during the conversion process. Essentially, they are boundary conditions to ensure the safe and stable operation of the power system. Common relaxation accuracy requirements include, but are not limited to: frequency safety constraints: such as the lowest frequency point not being lower than 47Hz, and the steady-state frequency needing to return to around 50Hz; unit operation constraints: the generator's power regulation rate not exceeding the maximum value, and the output power not exceeding the rated range; system dynamic constraints: the inertial time constant, damping coefficient, and other parameters of the equivalent single-machine model must conform to the actual system characteristics to ensure the model's effectiveness; and control action constraints: such as the load shedding amount not exceeding a certain proportion, and the standby unit start-up time not exceeding a threshold.

[0034] Specifically, the relaxation of the lowest frequency point to a second-order cone constraint can be achieved as follows: First, the dynamic characteristics of the average system frequency model can usually be described by a second-order linear differential equation. After its frequency domain transfer function is transformed into the time domain, the frequency response is a continuous function of time, such as a damped oscillation or a monotonic curve. In this case, the lowest frequency point is equivalent to the global minimum of the time domain function. Then, through mathematical derivation, such as the boundary conditions and extremum conditions of the differential equation, the requirement that the minimum value is greater than or equal to the safety threshold is transformed into a set of mathematical inequalities containing variables. When the mathematical form of this set of inequalities conforms to the definition of the second-order cone constraint, the relaxation is completed. Here, equivalence means that the transformed second-order cone constraint is completely equivalent to the original requirement that the lowest frequency point is greater than or equal to the safety threshold; satisfying the second-order cone constraint necessarily satisfies the safety requirement of the lowest frequency point.

[0035] 103. Combining multiple constraints including second-order cone constraints, construct a power system dispatch model with the minimum operating cost as the objective function.

[0036] In this embodiment, the objective function is the core direction that the scheduling model needs to optimize. Generally speaking, the lowest operating cost is the ultimate goal of the model. The types of costs it covers include, but are not limited to, the fuel cost of generators (such as coal consumption of thermal power plants and natural gas consumption of gas turbine units), the start-up and shutdown costs of units (preheating costs when starting up and maintenance costs when shutting down), and the cost of reserve capacity (the cost of generating capacity reserved to cope with sudden loads). In some scenarios, environmental protection costs (such as carbon emission control costs) will also be included.

[0037] Understandably, the objective function is a function of scheduling variables. For example, the minimum total operating cost equals the sum of fuel costs for all units plus the sum of start-up and shutdown costs plus the sum of reserve costs. Scheduling variables include the generating capacity, start-up / shutdown status, and reserve capacity of each unit. The optimization process of the corresponding model involves adjusting these scheduling variables to minimize the total operating cost while satisfying all constraints.

[0038] Setting multiple constraints is to ensure the feasibility of the dispatching scheme and the safety of the power grid. Among them, the second-order cone constraint is one of the core constraints. Through the mathematical form of the second-order cone constraint, the requirement that the minimum frequency point not fall below the safety threshold is solidified into the model, preventing frequency collapse due to disturbances. In practical applications, the second-order cone constraint can limit the range of values ​​for the dispatching frequency threshold, ensuring that the minimum system frequency point remains within the safe range regardless of any preset disturbances. Specifically, the second-order cone constraint, combined with other basic constraints, can guarantee the normal operation of the units and the system. These constraints serve as the basic feasibility boundary of the dispatching scheme, ensuring that the units and the system can physically achieve the dispatching results. Specifically, the second-order cone constraint, combined with other relaxation accuracy requirements, can expand the power grid safety boundary to supplement other constraints that ensure system stability.

[0039] In practical applications, power system dispatch models can be used for economic dispatching of power systems. On the one hand, they can be used in short-term dispatching, such as intraday 15-minute interval dispatching, to adjust unit output in real time, cope with load fluctuations, and balance cost and frequency security. On the other hand, they can be used in medium- and long-term dispatching, such as day-ahead dispatching, to formulate unit start-up and shutdown plans and power allocation schemes for the next day, reserve reserves in advance, and ensure the safe and economical operation of the power grid throughout the day.

[0040] 104. Solve the power system dispatch model to obtain the values ​​of the decision variables used for power system dispatch.

[0041] In this embodiment, decision variables are location variables that need to be determined through solving in the power system dispatch model, and are also the core control variables for subsequent dispatch operations. Common types include, but are not limited to: Unit output decision variables, i.e., the real-time generating power and start-up / shutdown status of each generator. For example, thermal power plant A has a real-time generating power of 300MW, thermal power plant B has a real-time generating power of 80MW, thermal power plant C is in the start-up state, and photovoltaic plant D is grid-connected. Reserve capacity decision variables, i.e., the reserve power that each unit needs to reserve to cope with sudden loads or unit failures. For example, nuclear power plant E reserves 50MW of reserve, and gas turbine plant F reserves 30MW of reserve. Load allocation decision variables: i.e., the allocation scheme of adjustable loads, including load shedding in some scenarios. For example, industrial load G is reduced by 20MW, and commercial load H is transferred to off-peak hours. Network-related decision variables: i.e., the upper limit of power allocation for transmission lines, the adjustment position of transformer taps, etc., which need to be considered in complex power grid dispatch.

[0042] Solving the power system dispatch model involves using optimization tools to find the optimal solution for the objective function within the constraint boundaries. This process includes the following steps: transforming the objective function and constraints of the dispatch model into a standard mathematical form recognizable by the optimization solver; then using a convex optimization solver tool to iteratively calculate and continuously adjust the values ​​of the decision variables while verifying whether all constraints are met; finding the variable combination that minimizes operating costs and satisfies all constraints; and finally, verifying the obtained variable values ​​in engineering to confirm whether they conform to the actual characteristics of the power grid equipment, thus avoiding a disconnect between theoretical and practical solutions.

[0043] In practical applications, the specific values ​​of the decision variables obtained from the solution can serve as direct instructions for dispatchers to execute operations. These direct instructions can be used for unit dispatching, such as instructing power plants to adjust their generating capacity and arranging unit start-ups and shutdowns. They can also be used for reserve configuration to determine the reserve capacity of each unit and ensure the system's ability to cope with disturbances. Furthermore, they can be used for load control, issuing control commands to adjustable loads and executing load decisions in extreme situations. Finally, they can be used for network adjustment to adjust the operating status of transmission equipment, ensuring that line power flow does not exceed limits and that voltage remains stable.

[0044] The power system dispatching method based on frequency security constraints provided in this application differs from current methods that introduce complex dynamic frequency constraints into optimization models using linear approximation. This application establishes an average system frequency model, transforms the frequency transfer function of the average system frequency model through time-domain representation, and solves for the lowest frequency point of the power system. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. Under the premise of meeting the set relaxation accuracy requirements, the lowest frequency point of the power system is relaxed to a second-order cone constraint. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error of a combination of sampled parameters. Multiple constraint conditions are set in conjunction with the second-order cone constraint, with the lowest operating cost as the objective function, to construct a power system dispatching model. Solving the power system dispatching model yields the values ​​of decision variables used for power system dispatching. Based on the establishment of the average frequency model, the process applies a second-order cone approximation to the lowest frequency point of the power system, transforming the originally nonlinear lowest frequency point constraint into a set of solvable convex constraints. This significantly reduces computational complexity while ensuring high constraint accuracy, effectively guaranteeing that the system can meet frequency stability requirements during scheduling.

[0045] In practical applications, the average system frequency model requires frequency domain modeling, time domain analysis, and extreme value solving to find the minimum frequency point. The final obtained minimum frequency point serves as a safety criterion for power grid operation. Specifically, for example... Figure 2 As shown, step 101 above includes the following steps: 201. Within the pre-defined interconnected power grid area associated with power system dispatch, the frequency of the interconnected power grid is dynamically mapped to an equivalent single-machine system to obtain an average system frequency model.

[0046] 202. Based on the average system frequency model, the frequency transfer function of the equivalent single machine is transformed into a time-domain differential equation.

[0047] 203. Substitute the frequency extremum obtained by differentiating the time-domain differential equation into the frequency transfer function to calculate the minimum frequency point of the power system.

[0048] Specifically, within a pre-defined interconnected power grid area associated with power system dispatch, components participating in frequency response within the interconnected grid area can be aggregated into lumped equivalent parameters. Using global frequency variables to ignore frequency differences among components within the area, the frequency dynamics of the interconnected grid area are mapped to an equivalent single-machine system, resulting in an average system frequency model. Here, the interconnected grid area is typically defined based on dispatch authority or physical grid connections to avoid including irrelevant components without boundaries. The aggregation objects for lumped equivalent parameters include generators, such as inertia time parameters, damping coefficients, and governor droop coefficients, as well as loads, such as load frequency regulation coefficients. The corresponding aggregation method can be weighted by rated power. In this embodiment, the average system frequency model is the Laplace transform of the system frequency deviation of an equivalent single-machine system formed by aggregating multi-machine parameters. It can be expressed by the following formula:

[0049] in, It is the undamped natural frequency. For the damping ratio, The reheat time constant is This is the adjustment coefficient. For active disturbance, is the inertial time constant.

[0050] The above damping ratio It can be expressed using the following formula:

[0051] in, For mechanical power gain factor, This represents the power ratio of the high-pressure cylinder.

[0052] When ζ < 1, it is called underdamped, and the frequency response exhibits oscillatory decay. The lowest frequency point appears at the first trough. Taking the inverse Laplace transform of the system's frequency deviation, we obtain the time-domain expression. It can be expressed by the following formula:

[0053]

[0054]

[0055]

[0056]

[0057] When the rate of change of frequency deviation is 0, the frequency drops to its lowest point. With inertial time constant Adjustment coefficient Time constant High-pressure cylinder work ratio Power gain coefficient Related, recorded as Here, the rate of change of frequency deviation is 0, which can be obtained by differentiating the time-domain expression, and can be expressed by the following formula:

[0058] Furthermore, such as Figure 3 As shown, prior to step 102, the above method further includes the following steps: 301. Based on the safety threshold of the power system under the condition of setting the minimum frequency value under the interference, a relaxation accuracy requirement is set for the minimum frequency point of the power system.

[0059] Accordingly, step 102 above includes the following steps: 302. Under the premise of meeting the set relaxation accuracy requirements, construct a second-order approximation function at the lowest frequency point, so that any variable in the second-order approximation function satisfies the approximation conditions.

[0060] 303. Transform the second-order approximation function that satisfies the approximation conditions into a standard approximation function containing second-order cone characterization parameters.

[0061] 304. Solve the standard approximation function within the range of parameter variation to obtain the second-order cone constraint.

[0062] In this embodiment, the relaxation accuracy requirement needs to ensure that the frequency does not exceed the safety margin under the most severe disturbance. Under a 5% load step disturbance, the specific lowest frequency point can be expressed by the following formula:

[0063] Correspondingly, in the process of transforming the lowest frequency point of the power system into a second-order cone constraint, it is necessary to find a second-order approximation function that satisfies the approximation conditions while meeting the set relaxation accuracy requirements. Here, the approximation condition is that the constructed second-order approximation function g, under typical operating conditions, has its error controlled within a set engineering range (e.g., 0.05Hz), ensuring the power dispatching model possesses conservatism and safety when used for dispatching. This can be considered to satisfy the frequency constraint. The specific second-order approximation function is expressed by the following formula:

[0064] Understandably, second-order approximation functions are prone to nonlinear approximation problems. To transform these nonlinear approximation problems into efficiently solvable second-order cone programming problems, it is necessary to convert the second-order approximation function that satisfies the approximation conditions into a standard approximation function that includes the second-order cone characterization parameters. The specific standard approximation function is shown in the following formula:

[0065] in, For a matrix, For a vector, It is a scalar, and is a constant term. The parameter vector contains the function at its lowest frequency. Variables.

[0066] Specifically, such as Figure 4 As shown, step 302 above includes the following steps: 401. Based on the nonlinear mechanism of power system frequency response, select the combination of variables that have a significant impact on the lowest frequency point to construct a complete quadratic polynomial.

[0067] 402. Under the premise of meeting the set relaxation accuracy requirements, the complete quadratic polynomial is approximated so that the complete quadratic polynomial after approximation satisfies the approximation conditions, and the second-order approximation function of the lowest frequency point is obtained.

[0068] Specifically, such as Figure 5 As shown, step 305 above includes the following steps: 501. Within the range of parameter variation, construct an effective parameter combination sample set that meets the relaxation accuracy requirements using Latin hypercube sampling.

[0069] 502. For each effective parameter combination sample, the lowest point of the true frequency obtained from the time-domain simulation is used as the solution input, the sum of squared fitting errors is used as the objective function, and the optimal coefficient set of the standard approximation function is solved by the algorithm.

[0070] 503. Different core parameters are obtained by splitting the optimal coefficient set. The different core parameters are then used to derive the standard form of the second-order cone parameters from the quadratic constraints through mathematical operations. By introducing auxiliary variables, the derived second-order cone parameters are transformed into the standard norm form to obtain the second-order cone constraints.

[0071] In this embodiment, considering the strong uniform coverage of the entire domain, in order to efficiently cover the entire domain and control the correlation of samples within the range of parameter changes, while accurately meeting the relaxation accuracy requirements, the Latin hypercube sampling method performs stratified sampling according to the parameter dimensions. The value range of each dimension is evenly divided into several intervals, and only one sample is drawn from each interval. This avoids the local clustering problem of random sampling and ensures that there are representative samples in each region of the parameter space.

[0072] It should be noted that, to ensure sufficient accuracy, the objective function should be determined as minimizing the fitting error, in order to solve for the optimal quadratic function coefficient matrix. ,vector and scalar The specific process is shown in the following formula:

[0073] in, That is The values ​​of the independent variables are consistent with those of the standard approximation function.

[0074] In practical application scenarios, multiple constraints are set in combination with second-order cone constraints, including power balance constraints, spinning reserve constraints, start-up and shutdown time constraints, unit output constraints, ramping constraints, and frequency safety constraints that take into account frequency security constraints; the objective function includes at least the operating costs of thermal power units, nuclear power units, and energy storage units, as well as the wind curtailment and solar curtailment costs of wind turbine units and photovoltaic units.

[0075] Specifically, based on the lowest operating cost The objective function can be expressed by the following formula:

[0076] in, For operating costs, These represent the costs of wind curtailment, solar curtailment, thermal power units, nuclear power units, and energy storage, respectively.

[0077] The costs of wind and solar power curtailment can be expressed by the following formula:

[0078]

[0079] The operating costs of nuclear power units and energy storage units can be expressed by the following formula:

[0080]

[0081] The above This refers to the cost coefficient for wind and solar power curtailment. This represents the total power of wind and solar power curtailed during the scheduling cycle. This represents the cost coefficient for nuclear power generation. Indicates the output of the nuclear power unit. Cost per unit of power This indicates the output of the energy storage unit.

[0082] The operating costs of the aforementioned thermal power units can be broken down into operating costs and start-up and shutdown costs, as expressed by the following formula:

[0083]

[0084] in, These represent the constant term, the linear term, and the quadratic term of the power generation cost function, respectively. Let i be the output of the thermal power unit. Indicates the unit It is in operation. Indicates the unit Shutdown Indicates the unit exist The cost of generating electricity at any given time Indicates the unit Startup costs.

[0085] The specific optimization scheduling problem must satisfy certain constraints during runtime, which can be expressed by the following formula: Constraint 1

[0086] Constraint 2

[0087] Constraint 3

[0088] Constraint 4

[0089] Constraint 5

[0090] Constraint 6

[0091] in, for Total load at any time These represent the output of wind turbines, photovoltaic power units, nuclear power units, and energy storage units, respectively. For system backup capacity, For the unit arrive The number of periods of continuous operation or continuous shutdown. and Representing the units Minimum running time and minimum downtime. and These represent the upper and lower limits of the required ramp rate, respectively.

[0092] It should be noted that the above constraints constitute all the constraints considered in the optimal scheduling model that takes into account frequency security constraints. Constraint 1 represents the power balance constraint, constraint 2 represents the spinning reserve constraint, constraint 3 represents the start-up and shutdown time constraint, constraint 4 represents the unit output constraint, constraint 5 represents the ramp constraint, and constraint 6 is the frequency security constraint. By solving the power system scheduling model, the optimal scheduling scheme of the power system that takes into account frequency security constraints can be obtained.

[0093] The power system dispatching method based on frequency security constraints provided in this invention employs a second-order cone programming representation method to convexize the originally nonlinear frequency minimum point constraint. Specifically, by applying a second-order cone approximation to the frequency dynamics equation, the non-convex nonlinear constraint is transformed into a set of solvable convex constraints. While ensuring high constraint accuracy, this significantly reduces computational complexity and avoids excessive iterations during the solution process. By slightly relaxing some nonlinear precision, the originally difficult-to-solve problem can be transformed into an efficient and solvable convex optimization problem, thereby quickly obtaining a high-quality feasible solution. The specific implementation process is described in [reference needed]. Figure 6 As shown, The power system dispatching method based on frequency security constraints provided in this invention not only theoretically guarantees the accuracy of frequency security constraints, but also balances computational efficiency and feasibility in dispatching. The algorithm is widely applicable to power systems with a high proportion of renewable energy, including scenarios with a high proportion of fluctuating power sources such as wind and solar power. It can compensate for insufficient ramp-up capability of thermal power units and dynamically ensure the system's frequency security margin.

[0094] In practical applications, three comparative scenarios were set up for different modeling methods of system frequency security constraints. Scenario 1 does not consider frequency security constraints, but to ensure sufficient system frequency response capability, the operating capacity of thermal power units is increased, reserving a hot reserve equivalent to 10% of the system load to meet frequency regulation requirements. Simultaneously, given that renewable energy needs to reserve in advance if it participates in frequency regulation, renewable energy operates at full power grid connection in this scenario and does not participate in frequency control. Scenario 2 uses the hyperplane method in existing technology to characterize system frequency security constraints and considers the frequency regulation response of renewable energy in the scheduling stage, thereby reserving corresponding frequency regulation reserves. Scenario 3 uses the frequency security constraint method based on the second-order cone proposed in this invention. This method can more precisely characterize the dynamic frequency characteristics of the system and simultaneously consider the frequency regulation reserves of renewable energy at the scheduling level. By calculating and comparing Scenario 1 to Scenario 3, their operating costs can be obtained respectively, as shown in Table 1 below, and the optimized scheduling results are also shown in [reference missing]. Figure 7 As shown, this demonstrates the improvement in accuracy and effectiveness of the method of the present invention compared to existing methods.

[0095] Table 1. Operating Costs for Different Scenarios

[0096] For a detailed simulation process of incorporating scheduling schemes under different scenarios into the system frequency response model, please refer to [link to simulation process]. Figure 8 As shown, compared to Scenario 1, Scenarios 2 and 3 introduce renewable energy participation in frequency control at the scheduling level, thereby improving the system frequency response capability, reducing the start-up capacity requirement, and significantly reducing operating costs. This demonstrates that renewable energy participation in frequency regulation can effectively alleviate the frequency regulation and reserve pressure of conventional units. Furthermore, the scheduling schemes under each scenario are substituted into the system frequency response (SFR) model for dynamic simulation. Under a typical disturbance condition of a 3% load surge, with 49.8Hz as the frequency safety boundary, the verification results show that the method of this invention can maintain frequency stability and ensure frequency regulation margin under sudden disturbances. The results for the corresponding minimum frequency points for different scenarios are shown in [reference needed]. Figure 9 For the results of the corresponding new energy reserve status under different scenarios, please refer to [link / reference]. Figure 10 .

[0097] Correspondingly, the results for the lowest frequency points corresponding to different scenarios can be found in [reference needed]. Figure 9In Scenario 1, to ensure the system's resistance to frequency disturbances, a large amount of thermal power unit hot reserve is required. While this meets the frequency regulation requirements, it significantly increases system operating costs, resulting in insufficient economic efficiency. In Scenario 2, due to the limited accuracy of existing hyperplane approximation methods, the system experiences a frequency exceeding the safety boundary at 14:00, leading to a significant deviation between the actual frequency regulation capability and the approximate result, posing a potential risk. In contrast, Scenario 3 employs the second-order cone frequency safety constraint method proposed in this invention. This not only ensures system frequency regulation performance and frequency safety while reserving renewable energy frequency regulation reserves in an optimal manner at the scheduling level, but also enables dynamic participation of renewable energy. The results for renewable energy reserve situations in different scenarios are detailed below. Figure 10 Therefore, this invention significantly improves the system's frequency support capability, minimizes the hot standby requirements of conventional units, achieves dual optimization of frequency security and economy, fully leverages the frequency regulation potential of new energy units, and demonstrates significant technical advantages and practical value far exceeding existing technologies, providing a reliable and innovative solution for the safe operation and economic dispatch of power systems.

[0098] Furthermore, as a specific implementation of the above method, embodiments of this application provide a power system dispatching device based on frequency security constraints, such as... Figure 11 As shown, the device includes: a setup unit 61, a transformation unit 62, a construction unit 63, and a solution unit 64.

[0099] Establishment unit 61 is used to establish an average system frequency model, and to obtain the minimum frequency point of the power system by transforming the frequency transfer function of the average system frequency model through time domain expression. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. The transformation unit 62 is used to relax the lowest frequency point of the power system into a second-order cone constraint under the premise of meeting the set relaxation accuracy requirements. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error through a combination of sampling parameters. Construction unit 63 is used to combine multiple constraints including second-order cone constraints to construct a power system dispatch model with the lowest operating cost as the objective function; Solver 64 is used to solve the power system dispatch model to obtain the values ​​of decision variables for power system dispatch.

[0100] The power system dispatching device based on frequency security constraints provided in this invention differs from current methods that introduce complex dynamic frequency constraints into optimization models using linear approximation. This application establishes an average system frequency model, transforms the frequency transfer function of the average system frequency model through time-domain representation, and solves for the lowest frequency point of the power system. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. Under the premise of meeting the set relaxation accuracy requirements, the lowest frequency point of the power system is relaxed to a second-order cone constraint. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error of a combination of sampled parameters. Multiple constraint conditions are set in conjunction with the second-order cone constraint, with the lowest operating cost as the objective function, to construct a power system dispatching model. Solving the power system dispatching model yields the values ​​of decision variables used for power system dispatching. Based on the establishment of the average frequency model, the process applies a second-order cone approximation to the lowest frequency point of the power system, transforming the originally nonlinear lowest frequency point constraint into a set of solvable convex constraints. This significantly reduces computational complexity while ensuring high constraint accuracy, effectively guaranteeing that the system can meet frequency stability requirements during scheduling.

[0101] In specific application scenarios, the establishment unit includes: The mapping module is used to dynamically map the frequency of the interconnected power grid into an equivalent single-machine system within a pre-defined interconnected power grid area associated with power system scheduling, thereby obtaining an average system frequency model. The conversion module is used to convert the frequency transfer function of the equivalent single machine into a time-domain differential equation based on the average system frequency model. The calculation module is used to substitute the frequency extremum obtained by differentiating the time-domain differential equation into the frequency transfer function to calculate the minimum frequency point of the power system.

[0102] In specific application scenarios, the mapping module is specifically used for: Within a pre-defined interconnected power grid area associated with power system dispatch, the components participating in frequency response within the interconnected power grid area are aggregated into lumped equivalent parameters; By using a global frequency variable to ignore the frequency differences of each component within the region, the frequency dynamic process of the interconnected power grid region is mapped to an equivalent single-machine system, resulting in an average system frequency model.

[0103] In specific application scenarios, the device further includes: The setting unit is used to set a relaxation accuracy requirement for the lowest frequency point of the power system based on a safety threshold for the lowest frequency value of the power system under the set disturbance condition, before relaxing the lowest frequency point of the power system to a second-order cone constraint under the premise of meeting the set relaxation accuracy requirement. Accordingly, the conversion unit includes: The construction module is used to construct a second-order approximation function at the lowest frequency point under the premise of meeting the set relaxation accuracy requirements, so that any variable in the second-order approximation function satisfies the approximation condition, which is that the error of the constructed second-order approximation function is controlled within the set engineering range under typical operation mode. The conversion module is used to convert the second-order approximation function that satisfies the approximation conditions into a standard approximation function that contains second-order cone characterization parameters. The solver module is used to solve the standard approximation function within the range of set parameter variations to obtain the second-order cone constraint.

[0104] In specific application scenarios, the building module is specifically used for: Based on the nonlinear mechanism of power system frequency response, a combination of variables that significantly affects the lowest frequency point is selected to construct a complete quadratic polynomial. Under the premise of meeting the set relaxation accuracy requirements, the complete quadratic polynomial is approximated so that the approximated complete quadratic polynomial satisfies the approximation conditions, and the second-order approximation function of the lowest frequency point is obtained.

[0105] In specific application scenarios, the solution module is specifically used for: Within the set parameter variation range, an effective parameter combination sample set that meets the relaxation accuracy requirements is constructed using the Latin hypercube sampling method. For each effective parameter combination sample, the lowest point of the true frequency obtained from time-domain simulation is used as the solution input, and the sum of squared fitting errors is used as the objective function. The optimal coefficient set of the standard approximation function is solved by the algorithm. Different core parameters are obtained by splitting the optimal coefficient set. The different core parameters are then used to derive the standard form of the second-order cone parameters from the quadratic constraints through mathematical operations. By introducing auxiliary variables, the derived second-order cone parameters are transformed into the standard norm form to obtain the second-order cone constraints.

[0106] In specific application scenarios, multiple constraints including second-order cone constraints are combined, including power balance constraints, spinning reserve constraints, start-up and shutdown time constraints, unit output constraints, ramp-up constraints, and frequency security constraints that take into account frequency security constraints; the objective function includes at least the operating costs of thermal power units, nuclear power units, and energy storage units, as well as the wind curtailment and solar curtailment costs of wind turbine units and photovoltaic units.

[0107] It should be noted that other corresponding descriptions of the functional units involved in the power system dispatching device based on frequency security constraints provided in this embodiment can be found in [reference needed]. Figures 1-5 The corresponding descriptions in [the document] will not be repeated here.

[0108] Based on the above, Figures 1-5 Accordingly, this application embodiment also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method. Figures 1-5 The power system dispatching method based on frequency security constraints is shown.

[0109] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.

[0110] Based on the above, Figures 1-5 The method shown, and Figure 11 To achieve the above objectives, this application also provides a physical device for power system dispatching based on frequency security constraints, as illustrated in the virtual device embodiment. Specifically, this physical device can be a computer, smartphone, tablet, smartwatch, server, or network device, etc. The physical device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to implement the above-described... Figures 1-5 The power system dispatching method based on frequency security constraints is shown.

[0111] Optionally, the physical device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0112] In an exemplary embodiment, see Figure 12 The aforementioned physical devices include a communication bus, a processor, a memory, and a communication interface. They may also include input / output interfaces and a display device. The various functional units can communicate with each other via the bus. The memory stores computer programs, and the processor executes the programs stored in the memory to perform the frequency security constraint-based power system dispatching method described in the above embodiments.

[0113] Those skilled in the art will understand that the physical equipment structure of power system dispatch based on frequency security constraints provided in this embodiment does not constitute a limitation on the physical equipment, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0114] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned power system dispatching entity based on frequency security constraints, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing entity.

[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented in hardware. By applying the technical solution of this application, compared with the existing methods, this application, based on the establishment of an average frequency model, performs a second-order cone approximation on the frequency minimum point of the power system, and convexizes the originally nonlinear frequency minimum point constraint, so that the non-convex nonlinear constraint is transformed into a set of solvable convex constraints. While ensuring high constraint accuracy, it can significantly reduce computational complexity and effectively ensure that the system can meet the frequency stability requirements during scheduling.

[0116] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0117] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A power system dispatching method based on frequency security constraints, characterized in that, include: An average system frequency model is established, and the lowest frequency point of the power system is obtained by transforming the frequency transfer function of the average system frequency model through time domain expression. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. Under the premise of meeting the set relaxation accuracy requirements, the lowest frequency point of the power system is relaxed to a second-order cone constraint. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error through a combination of sampling parameters. By combining multiple constraints including second-order cone constraints, and taking the minimum operating cost as the objective function, a power system dispatch model is constructed. Solve the power system dispatch model to obtain the values ​​of the decision variables used for power system dispatch.

2. The method according to claim 1, characterized in that, The establishment of an average system frequency model, and the solution to obtain the minimum frequency point of the power system through the time-domain transformation of the frequency transfer function of the average system frequency model, includes: Within a pre-defined interconnected power grid area associated with power system dispatch, the frequency of the interconnected power grid is dynamically mapped to an equivalent single-machine system to obtain an average system frequency model. Based on the average system frequency model, the frequency transfer function of the equivalent single machine is transformed into a time-domain differential equation. By taking the derivative of the time-domain differential equation and substituting the extreme frequency moments into the frequency transfer function, the minimum frequency point of the power system can be calculated.

3. The method according to claim 2, characterized in that, The step of dynamically mapping the frequency of the interconnected power grid within a pre-defined interconnected power grid region associated with power system dispatch to an equivalent single-machine system, thereby obtaining an average system frequency model, includes: Within a pre-defined interconnected power grid area associated with power system dispatch, the components participating in frequency response within the interconnected power grid area are aggregated into lumped equivalent parameters; By using a global frequency variable to ignore the frequency differences of each component within the region, the frequency dynamic process of the interconnected power grid region is mapped to an equivalent single-machine system, resulting in an average system frequency model.

4. The method according to claim 1, characterized in that, Before relaxing the lowest frequency point of the power system to a second-order cone constraint while meeting the set relaxation accuracy requirements, the method further includes: Based on the safety threshold of the power system under the minimum frequency value that meets the interference, a relaxation accuracy requirement is set for the minimum frequency point of the power system. Accordingly, relaxing the lowest frequency point of the power system to a second-order cone constraint, while meeting the set relaxation accuracy requirements, includes: Under the premise of meeting the set relaxation accuracy requirements, a second-order approximation function with the lowest frequency is constructed so that any variable in the second-order approximation function satisfies the approximation condition, which is that the error of the constructed second-order approximation function is controlled within the set engineering range under typical operation mode. The second-order approximation function that satisfies the approximation conditions is transformed into a standard approximation function that contains second-order cone characterization parameters; Solving the standard approximation function within the range of parameter variations yields the second-order cone constraint.

5. The method according to claim 4, characterized in that, Under the premise of meeting the set relaxation accuracy requirements, a second-order approximation function at the lowest frequency point is constructed, such that any variable in the second-order approximation function satisfies the approximation condition, including: Based on the nonlinear mechanism of power system frequency response, a combination of variables that significantly affects the lowest frequency point is selected to construct a complete quadratic polynomial. Under the premise of meeting the set relaxation accuracy requirements, the complete quadratic polynomial is approximated so that the approximated complete quadratic polynomial satisfies the approximation conditions, and the second-order approximation function of the lowest frequency point is obtained.

6. The method according to claim 4, characterized in that, The step of solving the standard approximation function within the set parameter variation range to obtain the second-order cone constraint includes: Within the set parameter variation range, an effective parameter combination sample set that meets the relaxation accuracy requirements is constructed using the Latin hypercube sampling method. For each effective parameter combination sample, the lowest point of the true frequency obtained from time-domain simulation is used as the solution input, and the sum of squared fitting errors is used as the objective function. The optimal coefficient set of the standard approximation function is solved by the algorithm. Different core parameters are obtained by splitting the optimal coefficient set. The different core parameters are then used to derive the standard form of the second-order cone parameters from the quadratic constraints through mathematical operations. By introducing auxiliary variables, the derived second-order cone parameters are transformed into the standard norm form to obtain the second-order cone constraints.

7. The method according to any one of claims 1-6, characterized in that, Combining the multiple constraints including second-order cone constraints, including power balance constraints, spinning reserve constraints, start-up and shutdown time constraints, unit output constraints, ramp-up constraints, and frequency security constraints that take into account frequency security constraints; the objective function includes at least the operating costs of thermal power units, nuclear power units, and energy storage units, as well as the wind curtailment and solar curtailment costs of wind turbine units and photovoltaic units.

8. A power system dispatching device based on frequency security constraints, characterized in that, include: A unit is established to build an average system frequency model. The minimum frequency point of the power system is obtained by transforming the frequency transfer function of the average system frequency model through time-domain expression. The average system frequency model is an equivalent single-machine system formed by aggregating multi-machine parameters. The transformation unit is used to relax the lowest frequency point of the power system into a second-order cone constraint under the premise of meeting the set relaxation accuracy requirements. The second-order cone constraint is constructed using second-order cone characterization parameters and obtained through mathematical transformation. The second-order cone characterization parameters are obtained by minimizing the fitting error through a combination of sampling parameters. The construction unit is used to combine multiple constraints, including second-order cone constraints, to construct a power system dispatch model with the minimum operating cost as the objective function. The solution unit is used to solve the power system dispatch model and obtain the values ​​of the decision variables used for power system dispatch.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power system dispatching method based on frequency security constraints as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power system dispatching method based on frequency security constraints as described in any one of claims 1 to 7.

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