Frequency security constraint-containing two-stage unit commitment modeling method and system

Abstract

The invention discloses a frequency security constraint-containing two-stage unit commitment modeling method and system, and belongs to the field of power system dispatching optimization. The method comprises the following steps: reading power grid static data, a scene support set and a frequency tangent plane parameter, and constructing a network connection matrix; organizing multi-source disturbance into a unified uncertainty vector, and generating a line commissioning state matrix; dividing fast and slow units according to the minimum starting time, and constructing a second-stage variable structure; a first-stage slow unit mixed integer linear programming model is established and solved; constructing a second-stage scenarized correction model based on a preorder result, and applying a multi-dimensional constraint; and outputting the two-stage model for joint solution. Through two-stage architecture, fast and slow unit layering, energy storage refined constraint, scenarized power flow control and frequency safety quantitative guarantee, the problems that a traditional model is poor in adaptability, insufficient in safety constraint and the like are solved, the economical efficiency, safety and engineering feasibility of a scheduling scheme are improved, and the method is suitable for optimized operation of a complex power grid.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatch optimization and security constraint modeling technology, specifically involving a two-stage unit combination modeling method and system with frequency security constraints. Background Technology

[0002] As the penetration rate of intermittent renewable energy sources such as wind and solar power continues to rise in the power system, the operating characteristics of the power system are undergoing profound changes. On the one hand, renewable energy units are typically connected to the grid through power electronic equipment, which lack the inertia and primary frequency regulation capabilities of traditional synchronous generators. This leads to a decrease in the overall equivalent inertia of the system, a deterioration in frequency dynamic characteristics, and a weakening of disturbance immunity. On the other hand, the strong randomness and volatility of renewable energy output pose a significant challenge to power balance, necessitating more flexible regulation resources.

[0003] In this context, traditional unit commitment (UC) and economic dispatch (ED) methods are no longer sufficient, and the main technical problems they face include: Frequency security constraints are poorly defined: Traditional methods typically employ a fixed-proportion spinning reserve constraint to implicitly address frequency risks. This only considers the amount of power deficit, completely ignoring key factors that determine the dynamic frequency process, such as system inertia and frequency regulation response speed. Therefore, scheduling schemes based on traditional models may be optimal in terms of economy and static security, but in the event of large-power disturbances, they cannot guarantee that the system frequency will not drop below the safety threshold, posing a significant risk of frequency instability.

[0004] The confusion surrounding the characteristics of regulating resources: In real-world systems, coal-fired and nuclear power units have slow start-up and shutdown times and high regulation costs, classifying them as "slow resources"; while gas turbines and energy storage units can start-up and shutdown quickly and regulate flexibly, classifying them as "fast resources." Traditional models optimize all units on the same timescale, either failing to fully utilize the flexibility of fast resources, resulting in economic losses, or imposing unrealistic rapid regulation requirements on slow resources, rendering the solution unfeasible.

[0005] Energy storage modeling is oversimplified: Energy storage systems have complex operating characteristics such as mutually exclusive charge and discharge states, continuous evolution of energy states, and the impact of charge and discharge efficiency. Many existing studies either simplify energy storage as a "negative load" or ignore its energy constraints, which may lead to energy storage scheduling plans given by the models that are not time-coordinated (such as requiring simultaneous charge and discharge) or energy-unsustainable (such as over-discharge), thus becoming disconnected from actual operation.

[0006] Poor adaptability to network topology changes: Existing studies on stochastic unit composition often focus on source and load uncertainties, frequently assuming a fixed network topology. However, network topology changes caused by line faults, planned maintenance, etc., are common in power grid operation. Stochastic scenarios that do not consider topology changes may generate scheduling schemes that lead to power flow congestion and voltage problems under fault conditions, rendering the schemes practically infeasible.

[0007] In summary, existing technologies lack a unified, precise, and computable framework for modeling unit combinations that can characterize the differences in fast and slow adjustment resources, energy storage operation details, network topology changes with the scenario, and frequency dynamic security constraints. Summary of the Invention

[0008] The technical problem to be solved by this invention is to provide a two-stage unit combination modeling method and system with frequency security constraints, which addresses the shortcomings of the prior art. This method is used to solve the technical problem that traditional unit combination / economic dispatch models cannot simultaneously take into account network feasibility and frequency security under conditions including wind power disturbances, scenario-based line topology changes, and the participation of fast-starting units and energy storage in rapid regulation.

[0009] The present invention adopts the following technical solution: A two-stage unit combination modeling method with frequency safety constraints includes the following steps: S1. Read the static data of the power grid, the scenario support set and nominal weights containing disturbance information, and the tangent plane parameters of the lowest frequency point, complete the internal numbering conversion and construct the network connection matrix; S2. Organize the line status, wind power status and load disturbance supported by the scenario into a unified uncertainty vector in sequence, and generate the commissioning status matrix of each line in each time period under each scenario based on this uncertainty vector. S3. Based on the minimum start-up time of the units, the set of units is divided into slow units and fast units; a second-stage variable structure is constructed, which includes the real-time output of the slow units, the on / off status and output of the fast units, the energy storage mode and energy status, network variables and frequency aggregation variables. S4. For the slow unit, establish a first-stage mixed integer linear programming model including start-stop state, start-stop variables, planned output and corresponding constraints, and solve for the start-stop state and planned output of the slow unit. S5. For each scenario, the second-stage correction model is constructed using the second-stage variable structure built in step S3 as the variable set, and the slow unit start-stop status and planned output obtained in step S4 as constraints or input parameters. S6. Output the first-stage model established in step S4 and the second-stage model for each scenario constructed in step S5, and use them to jointly solve for a scheduling scheme that satisfies frequency security and network feasibility.

[0010] Preferably, in step S1, the static power grid data is obtained by reading power flow calculation examples in MATPOWER format, including bus, branch, unit, and cost data; the network connection matrix includes unit injection matrix Cg, load matrix Cd, branch correlation matrix Cft, wind power injection matrix Cw, and energy storage injection matrix Cess; the minimum frequency tangent plane parameters include Af and bf, and when Af and bf are empty, the frequency tangent plane constraint is masked.

[0011] Preferably, in step S2, the length of the unified uncertainty vector is nu*T, where nu is the uncertainty dimension and T is the time domain length, and each scenario corresponds to a column of the uncertainty vector; the commissioning status matrix is ​​a Boolean matrix, generated by reading the status information of the corresponding line in the uncertainty vector.

[0012] Preferably, in step S3, the criteria for classifying the fast and slow units are as follows: units with a minimum start-up time minon ≥ 1 are slow units, and units with a minimum start-up time minon == 0 are fast units; the network variables in the second stage variable structure include power curtailment pd, phase angle theta, branch power flow pf, and wind power output pw, and the frequency aggregation variables include hs, rs, ds, and nadir, as well as the bus power balance slack pdp / pdn.

[0013] Preferably, in step S4, the constraints of the first-stage mixed-integer linear programming model include state transition constraints, minimum start-up and shutdown time constraints, ramp-up constraints, and output-state coupling constraints. The output-state coupling constraints satisfy: IG·Pmin≤PG≤IG·Pmax, where IG is the start-up and shutdown state of the slow unit, PG is the planned output of the slow unit, Pmin is the minimum output of the unit, and Pmax is the maximum output of the unit. The objective function of the model is composed of the start-up and shutdown cost of the slow unit and the linear cost of the planned output.

[0014] Preferably, in step S5, the constraints included in the correction model of the second stage include bus power balance constraints, scenario-based DC power flow constraints, fast generator start-stop-output coupling constraints, energy storage charge-discharge mutual exclusion and energy state constraints, frequency aggregation variables and Nadir tangent plane constraints.

[0015] Preferably, the generation logic of the scenario-based DC power flow constraint is as follows: based on the commissioning state matrix obtained in step S2, update the commissioning state of each line; if the line is online, force -pf(l) + Bf(l,:)*theta = Pfinj(l); if the line is offline, force pf(l) = 0 and decouple it from the phase angle theta, where Bf is the DC admittance matrix, Pfinj is the injected power vector, and pf is the branch power flow.

[0016] Preferably, the mutual exclusion of energy storage charging and discharging and the energy state constraint include: The mutual exclusion between charging and discharging is controlled by the binary variable iess, where the discharge power pdc ≤ iess * Pess_dc_ub and the charging power pch ≤ (1 - iess). Pess_ch_ub ,in Pess_dc_ub Maximum discharge power Pess_ch_ub Maximum charging power; Energy storage state of energy meets ess(t)-ess(t-1)-η_ch pch(t)+(1 / η_dc)*pdc(t)=0, and E_lb≤ess≤E_ub, where η_ch is the charging efficiency, η_dc is the discharging efficiency, ess is the stored energy, E_lb is the minimum energy, and E_ub is the maximum energy.

[0017] Preferably, the linear relationship of the frequency aggregation variables includes: the contribution of the slow unit is formed by combining the start / stop state IG obtained in step S4 with the capacity limit Pg_ub; the contribution of the fast unit is formed by aggregating the fast unit switching state igs with the output limit Pgs_ub; the wind power contribution is injected through the HW / DW components in the scenario support set; and the energy storage contribution is included in the constant term according to the equivalent scale SB_ess; the Nadir tangent plane constraint is embedded in the form of a linear inequality, satisfying nadir≥Af*[ ]+bf.

[0018] Secondly, embodiments of the present invention provide a two-stage unit combination modeling system with frequency safety constraints, comprising: The data module is used to read static power grid data, scenario support sets and nominal weights containing disturbance information, and tangent plane parameters at the lowest frequency point, complete internal numbering conversion, and construct the network connection matrix. The status module is used to organize the line status, wind power status and load disturbance of the scenario support set into a unified uncertainty vector in sequence, and based on this uncertainty vector, generate the commissioning status matrix of each line in each time period under each scenario. The partitioning module is used to divide the set of units into slow units and fast units based on the minimum start-up time of the units; and to construct a second-stage variable structure that includes the real-time output of the slow units, the on / off status and output of the fast units, the energy storage mode and energy status, network variables and frequency aggregation variables. The first-stage module is used to establish a first-stage mixed integer linear programming model for the slow unit, including start-stop states, start-stop variables, planned output and corresponding constraints, and solve for the start-stop states and planned output of the slow unit. The second-stage module is used to construct a second-stage correction model for each scenario, using the second-stage variable structure as the variable set and the start-stop status and planned output of the slow generator as constraints or input parameters. The output module is used to output the first-stage model and the second-stage model for each scenario, and to jointly solve them to obtain a scheduling scheme that satisfies frequency security and network feasibility.

[0019] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described two-stage unit combination modeling method with frequency security constraints.

[0020] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described two-stage unit combination modeling method with frequency security constraints.

[0021] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described two-stage unit combination modeling method with frequency security constraints.

[0022] In a sixth aspect, embodiments of the present invention provide an electronic device including a computer program, wherein when the computer program is executed by the electronic device, it implements the steps of the above-described two-stage unit combination modeling method with frequency security constraints.

[0023] Compared with the prior art, the present invention has at least the following beneficial effects: A two-stage unit combination modeling method with frequency security constraints is proposed. This method constructs a complete two-stage unit combination modeling process, including data input and initialization, construction of scenario-based uncertainties, division of fast and slow units and design of variable structures, first-stage slow unit combination modeling, and second-stage scenario-based correction modeling and model output. This method establishes a core framework of "slow unit commitment and fast unit correction," and integrates scenario-based line states and frequency security constraints into the second stage. By placing slow units in the first stage for traditional MILP combination optimization, the economic and feasibility foundation for long-term operation is ensured. Secondly, the introduction of scenario-based correction in the second stage allows fast units and energy storage to respond in real-time and flexibly under different scenarios such as wind power disturbances and line faults, which greatly improves the adaptability of the scheduling scheme to uncertainties. Finally, embedding frequency security constraints and scenario-dependent network power flow constraints into the second stage ensures that the final generated scheduling scheme is not only economical but also simultaneously guarantees system frequency stability and network connectivity under fault conditions, solving the problem that traditional models struggle to address both simultaneously.

[0024] Furthermore, specifying MATPOWER, a widely used open-source data format in power system analysis, as the input standard greatly enhances the compatibility of this method with existing research tools and actual power grid data, and reduces data preprocessing costs. The explicitly listed five types of network connection matrices precisely define the injection mapping relationships between various physical components and network nodes. This forms the mathematical foundation for constructing accurate power balance and flow constraints, ensuring the consistency between the model and the physical power grid. The design allowing null values ​​for the frequency tangent plane parameters Af and bf provides flexible configuration options. When an accurate frequency dynamic model is not needed or not yet available, this constraint can be masked, causing the model to degenerate into a more traditional safety-constrained unit combination model; when the relevant parameters are available, advanced frequency safety constraints can be seamlessly enabled, allowing the method to adapt to research or application needs at different stages.

[0025] Furthermore, random factors of varying origins and nature, such as line status, wind power fluctuations, and load disturbances, are concatenated into a high-dimensional vector in a fixed order, creating conditions for the matrix representation of all subsequent constraints. This organization ensures that differences in different scenarios are only reflected in the numerical values ​​of this unified vector, while the coefficient matrix structure of the model remains unchanged. This greatly simplifies the complexity of model construction, eliminating the need to manually write different constraints for hundreds or thousands of scenarios; the program can automatically generate them in batches. It also provides a clear interface for applying large-scale optimization algorithms, and changes in the right-hand side are easily handled. The generated Boolean-type commissioning state matrix serves as a bridge connecting uncertainty and network topology changes, directly driving the dynamic generation of subsequent scenario-based power flow constraints.

[0026] Furthermore, using the minimum start-up time—an inherent physical parameter of the generating unit—as the sole criterion for classifying speed, the rules are clear and objective, avoiding subjective assumptions and ensuring that the classification results match the actual adjustment capabilities of the units. Slower units enter the first stage (MILP), reflecting their commitment to long-term operating costs; faster units only participate in the second stage correction, accurately reflecting their rapid start-up and shutdown, and flexible adjustment characteristics. This hierarchical decision-making logic is closer to the actual timescale of power system operation. Simultaneously, the variable set included in the second stage is clearly listed, specifically indicating the inclusion of bus power balance slack (pdp / pdn), which comprehensively defines all degrees of freedom required for decision-making in the correction stage. The introduction of slack variables is crucial; it ensures that the model has feasible solutions even under extremely adverse scenarios, and the high penalty term in the objective function indicates the degree of infeasibility, improving the model's robustness and practicality.

[0027] Furthermore, state transitions and minimum start-up / shutdown time constraints accurately simulate the operating procedures of slow-speed resources such as thermal power units, avoiding unrealistic frequent start-ups and shutdowns. The ramp-up constraint limits the rate of change of unit output between adjacent time periods. The core output-state coupling constraint is a classic technique in mixed-integer linear programming modeling, linking discrete start-up / shutdown states with continuous output variables through linear inequalities, allowing optimization problems involving integer variables to be effectively handled by standard solvers. The objective function focuses on the start-up / shutdown costs and planned output costs of slow-speed units, ensuring that while pursuing economic efficiency, the first-stage model's output start-up / shutdown state IG and planned output PG become an economically reasonable and physically feasible benchmark plan, reserving necessary adjustment space for the second stage to address uncertainties.

[0028] Furthermore, bus power balance constraints ensure supply and demand balance, scenario-based DC power flow constraints adapt to line dynamic states, fast generator coupling constraints regulate fast generator operation, energy storage charging and discharging mutual exclusion and energy state constraints ensure energy storage safety and efficiency, and frequency aggregation variables and Nadir tangent plane constraints directly address the core of frequency security. This comprehensive multi-constraint coverage solves the scheduling risks caused by the lack of constraints in traditional models. These various constraints work together to form a complete constraint chain encompassing power balance, network security, equipment specifications, and frequency assurance. This not only addresses the uncertainties brought by new energy access but also ensures the operational safety of all aspects of the system. Based on the variable structure and data foundation design of the preceding steps, this constraint system is logically coherent and highly targeted, avoiding constraint redundancy or conflicts, improving the model's solution efficiency and the feasibility of the solution, and enabling the second-stage real-time correction to accurately respond to various disturbances.

[0029] Furthermore, the state of each line in each scenario and time period is determined in real time using the Boolean matrix z_line(l,t,s). For online lines, standard linear DC power flow equations are used to constrain them and ensure the rationality of power transmission; for offline lines, their transmission power is forced to zero, and they are decoupled from the node phase angle equations, which is equivalent to temporarily removing the line from the current network model. This mechanism allows the same optimization model to automatically adapt to many scenarios such as N-1 faults, planned maintenance, and network reconfiguration, without the need to manually rebuild the model for each possible topology change. This not only significantly reduces the modeling workload but also fundamentally ensures that the scheduling scheme meets network connectivity and security constraints in all scenarios, enhancing engineering practicality.

[0030] Furthermore, the binary variable `iess` controls the mutual exclusion of charging and discharging, clearly defining the operational boundaries of charging and discharging, avoiding equipment damage caused by simultaneous charging and discharging, and ensuring the safety of energy storage operation. The maximum charging and discharging power limit closely matches the actual equipment performance, making the scheduling scheme feasible for engineering implementation. Energy state constraints, through dynamic equations, accurately characterize the energy change process of energy storage, and combined with the upper and lower limits of E_lb and E_ub, avoid overcharging and over-discharging problems, extending the service life of energy storage equipment. It considers both the instantaneous operating state of energy storage and the long-term energy balance, enabling energy storage to effectively play the role of peak shaving and valley filling and emergency response, improving the system's flexibility in dealing with uncertainties, and solving the shortcomings of traditional energy storage scheduling models that emphasize output but neglect safety.

[0031] Furthermore, the frequency aggregation variable integrates the contributions of various resources, including slow-speed generators, fast-speed generators, wind power, and energy storage, clarifying the supporting role of each entity in frequency stability. This gives frequency security constraints clear physical meaning and quantitative basis, avoiding the insufficient protection or resource waste caused by traditional models relying solely on single reserve capacity. The contribution of slow-speed generators is based on the first-stage start-stop state, while the contribution of fast-speed generators is related to the second-stage switching state, ensuring deep integration of frequency constraints with the two-stage model and improving the rationality and operability of the constraints. The Nadir tangent plane constraint is embedded in the form of a linear inequality, ensuring that the minimum frequency point does not exceed the limit while avoiding the solution difficulties caused by complex nonlinear constraints, achieving a balance between security and computational efficiency. This transforms frequency security from a qualitative requirement into a quantitative constraint, significantly improving the frequency stability of the scheduling scheme and adapting to the operating conditions of decreased system inertia under high penetration of new energy sources.

[0032] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0033] In summary, the method of this invention constructs a two-stage model through hierarchical scheduling of fast and slow components, scenario-based uncertainty handling, refined energy storage constraints, and quantitative frequency security assurance. This achieves coordinated optimization of scheduling economy, security, and feasibility, solves the problems of disconnected constraints and poor adaptability in traditional models, and is computationally efficient and has strong engineering applicability.

[0034] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 3 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0036] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0039] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0040] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0041] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0042] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0043] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0044] This invention provides a two-stage unit combination modeling method with frequency safety constraints. The overall framework is "two-stage (commitment-correction)". The slow unit combination is placed in the first stage, and the start-up and shutdown of fast units and the charging and discharging of energy storage are used as scenario-based correction variables in the second stage. In the second stage, scenario-based line state + DC power flow constraints and the linear tangent plane constraints of the lowest frequency point (Nadir) are automatically generated, forming a unified optimization model that can be directly processed by the MILP solver.

[0045] Please see Figure 1 This invention discloses a two-stage unit combination modeling method with frequency security constraints. The first stage completes the start-up, shutdown, and planning of slow units; the second stage utilizes fast units and energy storage for real-time error correction under specific scenarios, while simultaneously applying minimum frequency point constraints and scenario-based DC power flow constraints, thereby obtaining a scheduling scheme that satisfies both frequency security and network feasibility. The specific steps are as follows: S1. Data Input and System Object Initialization The system first reads three types of input data and performs unified preprocessing: S101. Power Grid Static Data Reading and Internal Numbering Conversion Read power flow examples (e.g., in MATPOWER format) to obtain data such as bus, branch, unit, and cost, and convert external numbers to internal numbers to obtain basic information such as baseMVA, number of buses nb, number of branches nl, and number of units ng. Then construct the network connectivity matrix: Unit injection matrix Cg: maps unit output to the bus; Load matrix Cd: maps "shearable load / abandoned load variables" to load buses; Branch correlation matrix Cft: associates branch power flow with bus phase angle; Wind power injection matrix Cw: maps wind power output to the wind power access bus; Energy storage injection matrix Cess: Maps the net energy storage injection to the energy storage access bus.

[0046] S102, Scene Support Set and Nominal Weight Reading Read `support_reduced` and `weight` from the scenario file. `support_reduced` describes the disturbance information (line status, wind power availability, load disturbance, etc.) for each scenario and time period. `weight` is the nominal scenario weight. Construct scenario support vectors `support` arranged in the order of "line-wind power-load-wind power inertia / damping equivalent term". Generate the scenario-time branch online state matrix `z_line(l, t, s)` for subsequent scenario-based power flow constraints.

[0047] S103, Reading parameters of the tangent plane at the lowest frequency point Read Af and bf (empty is allowed). When Af / bf is empty, it is equivalent to masking the frequency tangent plane constraint; when Af / bf exists, the second stage will embed the Nadir constraint in the form of a linear inequality.

[0048] S2. Construction of Scenario-based Uncertainties and Generation of Line States Before the system enters the two-stage optimization, the scene data needs to be organized into a unified high-dimensional vector support(:, s), and the line commissioning status z_line(l, t, s) needs to be generated accordingly: S201, Organization of the Unified Uncertainty Vector Support Line status, wind power status, load disturbance, etc., are concatenated into a vector of length nu*T in a fixed order, and a column of support(:, s) is generated for each scenario s. This "unified vectorization" ensures that all subsequent constraints can be written in standard form:

[0049] That is, the same matrix structure is used to achieve scene differences through the right-hand items of different scenarios.

[0050] S202, Generation of scenario-based line commissioning status z_line For each scene s and each time period t, the system reads the status of the l-th line from support((t-1)*nu+LINE+l, s) and obtains a Boolean matrix:

[0051] This z_line is subsequently used to automatically generate DC power flow constraints for both "online and offline modes" of the line, enabling scenario-based modeling of topology changes.

[0052] S3. Design of fast and slow unit division and two-stage variable structure To achieve a two-stage structure of "slow units making commitments and fast units making corrections," the system divides the unit set according to the minimum unit start-up time and embeds the fast units and energy storage into the second-stage variable vector: S301. Construction of the fast / slow unit division and selection matrix The system reads the unit's minimum startup time (minon): Units with minon>=1 are identified as slow units and enter the first stage to participate in unit combination. Units with minon==0 are classified as fast units and do not enter the first stage of combination. They are only started, stopped, and have their output adjusted according to the scenario in the second stage.

[0053] Then, selection matrices Cgg (slow machine group) and Cgs (fast machine group) are constructed, and the upper and lower bounds of the output of the fast machine group, Pgs_lb / Pgs_ub, are obtained from them.

[0054] S302, Second-stage variable structure embedding The system concatenates the second-stage variables in the following order to form variable blocks of length ny for each time period, and replicates them across the entire time domain T: Real-time output pg(nggG) of slow unit; The fast generator switch igs(ngs) (binary) and the fast generator output pgs(ngs); Abandoned power supply pd(nd), phase angle theta(nb), branch power flow pf(nl), wind power output pw(nw); Energy storage mode iess(ness) (binary), stored energy ess(ness), charging pch(ness), discharging pdc(ness); Aggregate frequency variables hs / rs / ds and nadir; Bus power balance relaxation pdp / pdn; This embedded variable structure is the key to how this method can naturally incorporate fast generator units and energy storage into the two-stage model while maintaining consistency in matrix dimensions.

[0055] S4, First-stage slow unit combination model modeling The first phase establishes the unit combination MILP only for the slow units, obtaining the committed status and planned output across time periods. The unit set is divided into slow units (g_slow) and fast units (g_fast) according to the minimum start-up time. In the first phase, the slow units decide on the start-up / shutdown status (IG), start-up ALPHA, shutdown BETA, and planned output (PG); constraints such as minimum start-up / shutdown, ramp-up, and upper and lower bounds of output are established and the first phase MILP is formed.

[0056] Its core includes: 1) Variable definition For each time period, establish the startup ALPHA, shutdown BETA, status IG (0 / 1), and planned output PG (continuous) for the slow unit.

[0057] 2) Constraint Set State transition constraints (start / stop and state consistency) Minimum start / stop time constraints Uphill / downhill constraints Output and state coupling: IG·Pmin≤PG≤IG·Pmax 3) Objective function Composed of start-up and shutdown costs and planned output linear costs The key output of the first stage is IG(t) of the slow machine group, which will serve as an important input for the frequency aggregation constraint and feasible region in the second stage.

[0058] S5, Second-stage scenario-based real-time correction modeling The second phase constructs a correction problem for each scenario s, which includes two types of constraints: network feasibility (power balance + DC power flow) and frequency security (aggregate variables + Nadir tangent plane).

[0059] For each scenario s and each time period t, establish a second-stage continuous / integer variable: Real-time output pg(t, s) of slow unit (following the first stage of start-up and shutdown) Fast-start unit startup and output igs(t,s), pgs(t,s) (fast-start resources) Energy storage modes and energy: iess(t,s) (charge-discharge mutual exclusion mode), pch / pdc / ess(SoC) (power and state of energy). Network variables: theta / pf, wind power pw, load shedding pd, node relaxation pdp / pdn Furthermore, a net energy storage injection term Cess*(pdc-pch) is uniformly added to the node power balance to form a feasible real-time balance in the scenario.

[0060] S501, Bus Power Balance Constraint The system constructs the bus power balance equation for each time period and writes the injection terms into Eeq in a matrix manner, including: Slow-speed injection: Cg*Cgg'*pg Fast injection: Cg*Cgs'*pgs Wind power injection: Cw*pw Net energy storage injection: Cess*(pdc-pch) Abandoned contribution: Cd*pd Branch flow: -Cft'*pf PDP / PDN is added as a relaxation method to ensure that the solution is still available in extreme scenarios and that the penalty function can reflect the cost of default.

[0061] S502, Automatic Generation of Scenario-Based DC Power Flow Constraints For each scenario s and each time period t, the system updates the branch commissioning status based on z_line(:, t, s), and calls the DC admittance generation process to obtain Bf and Pfinj. Then, constraints are generated line-by-line. If the line is online: Force -pf(l) + Bf(l,:) * theta = Pfinj(l) If the line is offline: force pf(l) = 0 and decouple it from theta. This allows the same model to automatically adapt to different topology scenarios, avoiding the need for manual reconstruction of network constraints for each scenario.

[0062] S503, Fast Train Start-Stop - Output Coupling Constraint To ensure that the switching on and off of the high-speed generator unit is consistent with its output, two sets of linear inequalities are incorporated into the system: pgs≥igs*Pgs_lb pgs≤igs*Pgs_ub When igs=0, the output of the fast generator is reduced to 0; when igs=1, the output is between the upper and lower limits.

[0063] S504, Energy Storage Charging and Discharging Mutual Exclusion and Energy State Constraints 1) Charge-discharge mutual exclusion and power limit The charge / discharge mode is controlled by the binary variable iess: Discharge: pdc ≤ iess * Pess_dc_ub Charging: pch≤(1-iess)*Pess_ch_ub This ensures that charging and discharging do not occur simultaneously within the same time period, and also meets the power limit.

[0064] 2) SoC Evolution Constraints Initial time: ess(:,1)=E0 Subsequent time steps: ess(t) - ess(t-1) - η_ch*pch(t) + (1 / η_dc)*pdc(t) = 0 And apply E_lb≤ess≤E_ub.

[0065] S505, Frequency Aggregation Variables and Nadir Tangent Plane Constraints To avoid introducing high-order dynamic models while still reflecting frequency security, this method introduces aggregated variables hs / rs / ds in the second stage and aggregates the contributions of slow-speed generating units, fast-speed generating units, wind power, and energy storage into a linear relationship: 1) Slow machine group contribution: determined by the Phase 1 commitment state IG The system combines the IG(t) of the slow unit with the capacity limit Pg_ub to obtain the linear contribution terms to the aggregate inertia / droop / damping, and writes them into the coefficient matrix Geq_temp of x in the second stage.

[0066] 2) Fast machine group contribution: determined by the second phase IGS The fast unit is aggregated into the relationship between hs / rs / ds using igs(t,s) and Pgs_ub, which reflects the modeling idea that the fast unit can be used as a high-frequency support resource.

[0067] 3) Wind power contribution: injected by scenario uncertainties The code uses the HW / DW components in the support section to input the equivalent inertia and damping of wind power as scene parameters.

[0068] 4) Energy storage contribution: Incorporated into the aggregate variables at an equivalent scale. The current implementation incorporates the available energy storage capacity into the constant term of hs / ds by SB_ess.

[0069] The system then applies the Nadir linear tangent plane to each time period:

[0070] Achieve computable constraint embedding of the lowest frequency point.

[0071] S6. Scene Model Output and Saving For each scenario s, the system organizes the two-stage model into a standard matrix form and outputs it: W_s: Two-stage variable coefficient matrix T_s: First-stage variable coefficient matrix hs_s: Right-hand item of the scene It also saves model_first_stage and model_second_stage for subsequent centralized or decomposition algorithm solutions, and facilitates consistency verification with MATPOWER power flow results.

[0072] In another embodiment of the present invention, a two-stage unit combination modeling system with frequency safety constraints is provided. This system can be used to implement the above-mentioned two-stage unit combination modeling method with frequency safety constraints. Specifically, the two-stage unit combination modeling system with frequency safety constraints includes a data module, a status module, a partitioning module, a first-stage module, a second-stage module, and an output module.

[0073] The data module is used to read static power grid data, scenario support sets and nominal weights containing disturbance information, and tangent plane parameters at the lowest frequency point, complete internal numbering conversion, and construct the network connection matrix. The status module is used to organize the line status, wind power status and load disturbance of the scenario support set into a unified uncertainty vector in sequence, and based on this uncertainty vector, generate the commissioning status matrix of each line in each time period under each scenario. The partitioning module is used to divide the set of units into slow units and fast units based on the minimum start-up time of the units; and to construct a second-stage variable structure that includes the real-time output of the slow units, the on / off status and output of the fast units, the energy storage mode and energy status, network variables and frequency aggregation variables. The first-stage module is used to establish a first-stage mixed integer linear programming model for the slow unit, including start-stop states, start-stop variables, planned output and corresponding constraints, and solve for the start-stop states and planned output of the slow unit. The second-stage module is used to construct a second-stage correction model for each scenario, using the second-stage variable structure as the variable set and the start-stop status and planned output of the slow generator as constraints or input parameters. The output module is used to output the first-stage model and the second-stage model for each scenario, and to jointly solve them to obtain a scheduling scheme that satisfies frequency security and network feasibility.

[0074] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function. The processor described in this embodiment can be used in the operation of a two-stage unit combination modeling method with frequency security constraints, including: The system reads static grid data, scenario support sets and nominal weights containing disturbance information, and tangent plane parameters at the lowest frequency point. Internal numbering conversion is performed to construct a network connection matrix. Line states, wind power states, and load disturbances from the scenario support set are organized sequentially into a unified uncertainty vector. Based on this uncertainty vector, an operational status matrix for each line in each time period under each scenario is generated. The unit set is divided into slow-running and fast-running units according to the minimum start-up time. A system is constructed that includes the real-time output of the slow-running units, the on / off status and output of the fast-running units, energy storage modes and energy states, and network variables. The second-stage variable structure of frequency aggregation variables is established; for the slow unit, a first-stage mixed integer linear programming model including start / stop status, start / stop variables, planned output and corresponding constraints is established, and the start / stop status and planned output of the slow unit are solved; for each scenario, the second-stage correction model is constructed using the constructed second-stage variable structure as the variable set and the obtained start / stop status and planned output of the slow unit as constraints or input parameters; the established first-stage model and the constructed second-stage models under each scenario are output for joint solution to obtain a scheduling scheme that satisfies frequency security and network feasibility.

[0075] Please see Figure 2The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the two-stage unit combination modeling method with frequency security constraints in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the two-stage unit combination modeling system with frequency security constraints in this embodiment. To avoid repetition, these details are not elaborated here.

[0076] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 2 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0077] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0078] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0079] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0080] Please see Figure 3 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0081] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0082] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0083] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0084] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0085] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0086] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0087] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0088] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0089] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the two-stage unit combination modeling method with frequency security constraints in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: The system reads static grid data, scenario support sets and nominal weights containing disturbance information, and tangent plane parameters at the lowest frequency point. Internal numbering conversion is performed to construct a network connection matrix. Line states, wind power states, and load disturbances from the scenario support set are organized sequentially into a unified uncertainty vector. Based on this uncertainty vector, an operational status matrix for each line in each time period under each scenario is generated. The unit set is divided into slow-running and fast-running units according to the minimum start-up time. A system is constructed that includes the real-time output of the slow-running units, the on / off status and output of the fast-running units, energy storage modes and energy states, and network variables. The second-stage variable structure of frequency aggregation variables is established; for the slow unit, a first-stage mixed integer linear programming model including start / stop status, start / stop variables, planned output and corresponding constraints is established, and the start / stop status and planned output of the slow unit are solved; for each scenario, the second-stage correction model is constructed using the constructed second-stage variable structure as the variable set and the obtained start / stop status and planned output of the slow unit as constraints or input parameters; the established first-stage model and the constructed second-stage models under each scenario are output for joint solution to obtain a scheduling scheme that satisfies frequency security and network feasibility.

[0090] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0091] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0092] Simulation Experiment Design To verify the effectiveness of the method of this invention, simulation tests were conducted based on an improved IEEE 39-bus system. The system includes 32 conventional turbine generators (8 of which have a minimum uptime of 0 and are designated as fast turbine generators), a wind farm (with a penetration rate of approximately 25%), and an energy storage power station. One hundred scenarios were set up, covering wind power output fluctuations and N-1 line faults.

[0093] Comparison of options: Option A (Traditional UC): A deterministic unit combination model that does not have frequency constraints, does not consider the distinction between fast and slow units, and simplifies the energy storage model.

[0094] Scheme B (the present invention): adopts the proposed two-stage randomized unit combination modeling method with frequency security constraints.

[0095] Simulation results: Frequency safety: Under simulated high-power deficit disturbances, Solution A experiences a system frequency minimum point below the safety threshold of 49.0Hz in over 30% of scenarios, posing a risk of instability. Solution B, on the other hand, maintains the frequency minimum point above 49.2Hz in all scenarios, ensuring 100% frequency safety.

[0096] Economic efficiency: The total expected operating cost of Option B (including start-up and shutdown, fuel, wind curtailment, and load shedding penalties) is about 4.2% higher than that of Option A. This incremental cost can be regarded as an "insurance premium" paid to obtain higher frequency security and scenario robustness.

[0097] Network Feasibility: In 15 scenarios involving critical path failures, the scheduling result of Solution A will lead to power flow exceeding the limit, making the solution infeasible. Solution B, due to its embedded scenario-based DC power flow constraints, can automatically satisfy network constraints in all 100 scenarios, achieving 100% feasibility.

[0098] Solution performance: Under the same computing environment, Solution A (deterministic model) takes approximately 120 seconds to solve. Solution B (two-stage stochastic model) is solved using a decomposition algorithm, taking approximately 580 seconds in total. Although the time is increased, it is still within an acceptable range for day-ahead scheduling applications, and it results in a significant improvement in global security.

[0099] Data analysis results: Simulation data show that although the method of the present invention (Scheme B) slightly increases the economic cost, it can completely eliminate the risks of frequency security and network feasibility of the traditional scheme (Scheme A). With an acceptable computational cost, it achieves an essential improvement in the scheduling scheme from economically optimal but high-risk to economically suboptimal but globally secure, and is particularly suitable for modern power systems with a high proportion of renewable energy access.

[0100] In summary, this invention presents a two-stage unit combination modeling method and system with frequency security constraints, unifying traditionally separate considerations of economic dispatch, network power flow security, and system frequency dynamic security within a single optimization framework. The generated dispatch scheme is no longer optimal under a single objective, but rather a collaborative optimal solution under multiple objective constraints, fundamentally solving the problem of traditional models neglecting certain aspects and significantly improving the comprehensive safety level of power system operation schemes. By introducing scenario-based modeling and a two-stage decision structure, this invention endows the dispatch scheme with strong robustness and adaptability. The first stage establishes an economical basic plan for slow resources; the second stage allows fast resources to be flexibly adjusted based on real-time scenarios such as wind power fluctuations and line faults. This design enables the scheme to effectively cope with the strong uncertainties brought about by a high proportion of new energy sources, ensuring reliable system operation under various anticipated scenarios. A refined model including charge-discharge mutual exclusion, SoC dynamics, and energy limiting is established for the energy storage system, and computable constraints based on aggregate variables and linear tangent planes are adopted for frequency security. These modeling details closely align with actual physical equipment and operational requirements, avoiding the problem of theoretical feasibility but practical infeasibility caused by oversimplification, and greatly enhancing the engineering practicality and implementability of the scheduling scheme. Although the model integrates integer variables, complex constraints, and multiple scenarios, through ingenious mathematical transformations and clear two-stage decomposition, a standardized model that can be processed by commercial MILP solvers is ultimately formed, ensuring the feasibility of applying this advanced method to solve real-world large-scale power grids.

[0101] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0102] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0103] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0104] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0105] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0106] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0107] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0108] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] 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.

[0110] 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.

[0111] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A two-stage unit combination modeling method with frequency safety constraints, characterized in that, Includes the following steps: S1. Read the static data of the power grid, the scenario support set and nominal weights containing disturbance information, and the tangent plane parameters of the lowest frequency point, complete the internal numbering conversion and construct the network connection matrix; S2. Organize the line status, wind power status and load disturbance supported by the scenario into a unified uncertainty vector in sequence, and generate the commissioning status matrix of each line in each time period under each scenario based on this uncertainty vector. S3. Based on the minimum start-up time of the units, the set of units is divided into slow units and fast units; a second-stage variable structure is constructed, which includes the real-time output of the slow units, the on / off status and output of the fast units, the energy storage mode and energy status, network variables and frequency aggregation variables. S4. For the slow unit, establish a first-stage mixed integer linear programming model including start-stop state, start-stop variables, planned output and corresponding constraints, and solve for the start-stop state and planned output of the slow unit. S5. For each scenario, the second-stage correction model is constructed using the second-stage variable structure built in step S3 as the variable set, and the slow unit start-stop status and planned output obtained in step S4 as constraints or input parameters. S6. Output the first-stage model established in step S4 and the second-stage model for each scenario constructed in step S5, and use them to jointly solve for a scheduling scheme that satisfies frequency security and network feasibility.

2. The two-stage unit combination modeling method with frequency safety constraints according to claim 1, characterized in that, In step S1, the static power grid data is obtained by reading the MATPOWER format power flow calculation example, including bus, branch, unit and cost data; the network connection matrix includes unit injection matrix Cg, load matrix Cd, branch correlation matrix Cft, wind power injection matrix Cw and energy storage injection matrix Cess; the minimum frequency point tangent plane parameters include Af and bf, and when Af and bf are empty, the frequency tangent plane constraint is masked.

3. The two-stage unit combination modeling method with frequency safety constraints according to claim 1, characterized in that, In step S2, the length of the unified uncertainty vector is nu*T, where nu is the uncertainty dimension and T is the time domain length, and each scenario corresponds to a column of the uncertainty vector; the commissioning status matrix is ​​a Boolean matrix, which is generated by reading the status information of the corresponding line in the uncertainty vector.

4. The two-stage unit combination modeling method with frequency safety constraints according to claim 1, characterized in that, In step S3, the criteria for classifying the fast and slow units are as follows: units with a minimum start-up time minon≥1 are slow units, and units with a minimum start-up time minon==0 are fast units; the network variables in the second stage variable structure include power curtailment pd, phase angle theta, branch power flow pf, and wind power output pw, and the frequency aggregation variables include hs, rs, ds, and nadir, as well as the bus power balance slack pdp / pdn.

5. The two-stage unit combination modeling method with frequency safety constraints according to claim 1, characterized in that, In step S4, the constraints of the first-stage mixed-integer linear programming model include state transition constraints, minimum start-up and shutdown time constraints, ramp-up constraints, and output-state coupling constraints. The output-state coupling constraints satisfy: IG·Pmin≤PG≤IG·Pmax, where IG is the start-up and shutdown state of the slow unit, PG is the planned output of the slow unit, Pmin is the minimum output of the unit, and Pmax is the maximum output of the unit. The objective function of the model is composed of the start-up and shutdown cost of the slow unit and the linear cost of the planned output.

6. The two-stage unit combination modeling method with frequency safety constraints according to claim 1, characterized in that, In step S5, the constraints included in the correction model of the second stage include bus power balance constraints, scenario-based DC power flow constraints, fast generator start-stop-output coupling constraints, energy storage charge-discharge mutual exclusion and energy state constraints, frequency aggregation variables and Nadir tangent plane constraints.

7. The two-stage unit combination modeling method with frequency safety constraints according to claim 6, characterized in that, The generation logic of the scenario-based DC power flow constraint is as follows: based on the commissioning state matrix obtained in step S2, update the commissioning state of each line; if the line is online, force -pf(l)+Bf(l,:)*theta=Pfinj(l); if the line is offline, force pf(l)=0 and decouple it from the phase angle theta, where Bf is the DC admittance matrix, Pfinj is the injected power vector, and pf is the branch power flow.

8. The two-stage unit combination modeling method with frequency safety constraints according to claim 6, characterized in that, The energy storage charge / discharge mutual exclusion and energy state constraints include: The mutual exclusion between charging and discharging is controlled by the binary variable iess, where the discharge power pdc ≤ iess * Pess_dc_ub and the charging power pch ≤ (1 - iess). Pess_ch_ub ,in Pess_dc_ub Maximum discharge power Pess_ch_ub Maximum charging power; Energy storage state of energy meets ess(t)-ess(t-1)-η_ch pch(t)+(1 / η_dc)*pdc(t)=0, and E_lb≤ess≤E_ub, where η_ch is the charging efficiency, η_dc is the discharging efficiency, ess is the stored energy, E_lb is the minimum energy, and E_ub is the maximum energy.

9. The two-stage unit combination modeling method with frequency safety constraints according to claim 1, characterized in that, The linear relationship of the frequency aggregation variables includes: the contribution of slow units is formed by combining the start / stop state IG obtained in step S4 with the capacity limit Pg_ub; the contribution of fast units is formed by aggregating the fast unit switching state igs with the output limit Pgs_ub; the wind power contribution is injected through the HW / DW components in the scenario support set; and the energy storage contribution is included in the constant term according to the equivalent scale SB_ess; the Nadir tangent plane constraint is embedded in the form of a linear inequality, satisfying nadir≥Af*[ ]+bf.

10. A two-stage unit combination modeling system with frequency safety constraints, characterized in that, include: The data module is used to read static power grid data, scenario support sets and nominal weights containing disturbance information, and tangent plane parameters at the lowest frequency point, complete internal numbering conversion, and construct the network connection matrix. The status module is used to organize the line status, wind power status and load disturbance of the scenario support set into a unified uncertain vector in sequence, and based on this uncertain vector, generate the commissioning status matrix of each line in each time period under each scenario. The partitioning module is used to divide the set of units into slow units and fast units based on the minimum start-up time of the units; and to construct a second-stage variable structure that includes the real-time output of the slow units, the on / off status and output of the fast units, the energy storage mode and energy status, network variables and frequency aggregation variables. The first-stage module is used to establish a first-stage mixed integer linear programming model for the slow unit, including start-stop states, start-stop variables, planned output and corresponding constraints, and solve for the start-stop states and planned output of the slow unit. The second-stage module is used to construct a second-stage correction model for each scenario, using the second-stage variable structure as the variable set and the start-stop status and planned output of the slow generator as constraints or input parameters. The output module is used to output the first-stage model and the second-stage model for each scenario, which are used to jointly solve for a scheduling scheme that satisfies frequency security and network feasibility.

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