Power resource day-ahead optimization scheduling method considering frequency security constraint and related device

Abstract

The invention discloses an electric power resource day-ahead optimization scheduling method considering frequency security constraints, which is characterized in that a scheduling model considers frequency adjustment of new energy on the basis of a conventional unit, and introduces the frequency security constraints into constraints of the scheduling model, which specifically comprise a system minimum inertia demand constraint and a system minimum primary frequency modulation demand constraint; according to the scheduling method provided by the invention, planning modes of thermal power generating units, new energy units, battery energy storage output, frequency modulation standby and the like are reasonably arranged, and a frequency modulation standby scheme of the system is finely and dynamically arranged, so that the frequency supporting capacity of the system suffered from expected accident N-1 disturbance and source load fluctuation disturbance is effectively improved; resource waste caused by standby redundancy is avoided, and stable frequency and economic operation of a power system are guaranteed.

Description

Technical Field

[0001] This invention pertains to power dispatching technology, and particularly relates to a day-ahead optimization dispatching method and related apparatus for power resources that takes into account frequency security constraints. Background Technology

[0002] As renewable energy sources account for an increasingly larger share of the power system, while bringing clean and low-carbon benefits, they also pose significant challenges to system frequency stability. Because the operation of renewable energy units is greatly affected by weather conditions, their output exhibits strong fluctuations and randomness, significantly increasing system power disturbances and leading to decreased system frequency stability. Malicious incidents caused by frequency instability have occurred both domestically and internationally, revealing not only the vulnerability of highly penetrated renewable energy power systems but also highlighting the urgency of optimizing the power grid's frequency security defense system.

[0003] In frequency security and stability analysis, the system's frequency response capability depends on the generator configuration. Traditional power systems dominated by synchronous machines possess sufficient rotational inertia and frequency regulation capabilities. Furthermore, the system has fewer uncertainties, resulting in limited disturbance power, making frequency security and stability issues less prominent. Therefore, considering frequency security constraints is not a necessary condition for optimal system scheduling. However, with the continuous increase in the penetration rate of new energy sources in power systems, system power exhibits frequent fluctuations, while inertia support levels and frequency regulation resource margins have significantly decreased. This makes incorporating frequency security constraints into the scope of optimal power system scheduling increasingly important.

[0004] Currently, most primary frequency regulation reserve plans only consider N-1 emergency reserve or the percentage of maximum load. Reserve schemes based on this principle suffer from redundancy or insufficiency, easily leading to excessive costs. On the other hand, existing scheduling methods express frequency security constraints using simplified time-domain expressions nested within the scheduling model, resulting in nonlinear elements that hinder solution and lead to low accuracy. Furthermore, existing scheduling methods only formulate reserve plans for conventional synchronous generators for primary frequency regulation, failing to consider how to schedule future renewable energy sources when they participate in frequency regulation. Summary of the Invention

[0005] Based on this, the present invention aims to propose a day-ahead optimization scheduling method and related device for power resources that takes into account frequency security constraints. In the day-ahead scheduling of power systems involving wind, solar, thermal, and energy storage, frequency security constraints are incorporated to coordinate the frequency regulation capabilities of thermal power units, new energy sources, and energy storage, and to rationally arrange the processing and frequency regulation reserves of each unit.

[0006] In a first aspect, the present invention provides a day-ahead optimization scheduling method for power resources considering frequency security constraints, comprising:

[0007] A day-ahead optimization scheduling model for power resources is established with the goal of minimizing system operating costs, where system operating costs include primary frequency regulation costs and power resources include new energy sources.

[0008] The constraints for solving the day-ahead optimization scheduling model of power resources are constructed. The constraints include at least frequency security constraints, which include the minimum inertia requirement constraint and the minimum primary frequency regulation requirement constraint of the system.

[0009] The day-ahead optimal scheduling model for power resources is solved based on the constraints, and the scheduling results are obtained.

[0010] Furthermore, the day-ahead optimal scheduling model for power resources is expressed as follows:

[0011] ,

[0012] Where T represents the scheduling period, Indicates the number of thermal power units. This represents the fuel cost of the i-th thermal power unit. , , Let represent the coal consumption coefficients of the i-th thermal power unit, respectively. This represents the output power of the i-th thermal power unit during time period t. Let represent the start-up cost function of a thermal power unit at time t. This represents the start-up cost of the i-th thermal power unit. Indicates the unit's operating status. Indicates the cost of wind curtailment. Indicates the amount of wind curtailment. This indicates the amount of wind power reserved for future use. This represents the unit cost of wind curtailment. Indicates the cost of curtailment. Indicates the amount of light discarded. This indicates the amount of surplus light energy. This represents the unit cost of curtailment. This represents the standby cost for a single frequency regulation operation. Indicates the amount of reserve. Indicate the reserve quantity. This represents the reserve cost coefficient.

[0013] Furthermore, establishing the system's minimum inertia requirement constraint includes:

[0014] An equivalent frequency response model of the power system is established to simulate the frequency dynamic response of the power system;

[0015] Determine the maximum rate of frequency change and the maximum frequency deviation of the power system;

[0016] The inertia of the first system and the inertia of the second system are calculated using the equivalent frequency response model based on the maximum rate of change of frequency and the maximum frequency deviation, respectively.

[0017] The maximum value between the first system inertia and the second system inertia is denoted as the boundary value of the minimum inertia requirement constraint of the system.

[0018] Furthermore, the system's minimum inertia requirement constraint is expressed as:

[0019] ,

[0020] in, Let represent the minimum inertia that sustains the system's operation at time t. Let represent the system inertia at time t. This indicates the total number of conventional generating units. This indicates the rated power of a conventional unit. , , These represent the output power of the wind turbine, photovoltaic power station, and energy storage device at time t, respectively. , , , These represent the inertial time constants of conventional generating units, wind turbines, photovoltaic power plants, and energy storage devices, respectively. This indicates the operating status of a conventional generating unit. Indicates that the program is running. This indicates that the machine is out of service.

[0021] Furthermore, the constraints for establishing the minimum primary frequency regulation requirement of the system include:

[0022] An equivalent frequency response model of the power system is established to simulate the frequency dynamic response of the power system;

[0023] Determine the quasi-steady-state frequency deviation of the system;

[0024] The equivalent frequency response model is used to calculate the primary frequency modulation backup based on the quasi-steady-state frequency deviation;

[0025] The primary frequency regulation reserve is denoted as the boundary value of the minimum primary frequency regulation requirement constraint of the system.

[0026] Furthermore, the minimum primary frequency regulation requirement constraint of the system is expressed as:

[0027] ,

[0028] in, These represent the number of conventional generating units, new energy generating units, and energy storage power stations, respectively. This indicates the frequency regulation standby of the i-th conventional unit at time t. This indicates the frequency regulation standby of the j-th renewable energy unit at time t. This indicates the frequency regulation standby of the l-th energy storage power station at time t.

[0029] Furthermore, the constraints to be solved also include power balance constraints, DC power flow constraints, and unit constraints.

[0030] Secondly, the present invention provides a day-ahead power resource optimization scheduling device considering frequency security constraints, comprising:

[0031] The scheduling model establishment module is used to establish a day-ahead optimal scheduling model for power resources with the goal of minimizing system operating costs, where system operating costs include primary frequency regulation costs.

[0032] The constraint construction module is used to construct the solution constraints for the day-ahead optimal scheduling model of power resources. The solution constraints include at least frequency security constraints, which include the minimum inertia requirement constraint and the minimum primary frequency regulation requirement constraint of the system.

[0033] The scheduling model solving module is used to solve the day-ahead optimal scheduling model of power resources based on the solution constraints, and obtain the scheduling results.

[0034] Thirdly, the present invention provides an electronic device including a memory storing computer-executable instructions and a processor, wherein when the computer-executable instructions are executed by the processor, the device performs the steps of the day-ahead power resource optimization scheduling method considering frequency security constraints provided in the first aspect.

[0035] Fourthly, the present invention provides a readable storage medium storing a computer-executable program that, when executed, can implement the various steps of the day-ahead power resource optimization scheduling method considering frequency security constraints provided in the first aspect.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This invention proposes a day-ahead optimization scheduling method for power resources considering frequency security constraints. The scheduling model, based on conventional generating units, incorporates frequency regulation from renewable energy sources and introduces frequency security constraints into the model. These constraints specifically include minimum system inertia requirement constraints and minimum primary frequency regulation requirement constraints. A further embodiment assesses the minimum system inertia and minimum primary frequency regulation requirement based on frequency security evaluation indicators, using the assessment results as the frequency security boundary. Frequency security constraints are then established based on this boundary, avoiding the problems of difficulty and low accuracy caused by nonlinear elements. The proposed scheduling method rationally arranges the planning methods for thermal power units, renewable energy units, battery energy storage output, and frequency regulation reserves. It provides refined and dynamic arrangements for the system's frequency regulation reserve scheme, effectively improving the system's frequency support capability against anticipated N-1 disturbances and source-load fluctuation disturbances, avoiding resource waste caused by reserve redundancy, and ensuring the frequency stability and economical operation of the power system. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0039] Figure 1 A flowchart illustrating the implementation of a day-ahead optimization scheduling method for power resources considering frequency security constraints, provided in an embodiment of the present invention;

[0040] Figure 2 A structural diagram of an equivalent frequency response model for a power system provided in an embodiment of the present invention;

[0041] Figure 3 A schematic diagram of a day-ahead power resource optimization scheduling device considering frequency security constraints is provided in an embodiment of the present invention.

[0042] Figure 4 This is an electronic device architecture diagram provided for an embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] See Figure 1One embodiment of the present invention proposes a day-ahead optimization scheduling method for power resources considering frequency security constraints, comprising the following steps:

[0045] Step S110. Establish a day-ahead optimization scheduling model for power resources with the goal of minimizing system operating costs, where system operating costs include primary frequency regulation costs and power resources include new energy sources.

[0046] The day-ahead optimization scheduling model established in this step needs to ensure that the system can operate stably when facing large disturbances and meet the economic requirements of system operation. Therefore, the objective function of the scheduling model is set to minimize the system operating cost, including the cost of primary frequency regulation, and taking into account the output of renewable energy sources such as wind power and photovoltaic power.

[0047] Primary frequency regulation costs include additional operating costs incurred due to adjusting power generation (increasing or decreasing power output), typically taking into account fuel costs, start-up costs, and energy consumption for frequency regulation of the electric generator set.

[0048] Specifically, the day-ahead power resource optimization scheduling model established in this embodiment of the invention is represented as follows:

[0049]

[0050] Where T represents the scheduling period, Indicates the number of thermal power units. This represents the fuel cost of the i-th thermal power unit. , , Let represent the coal consumption coefficients of the i-th thermal power unit; This represents the output power of the i-th thermal power unit during time period t; Let represent the start-up cost function of a thermal power unit at time t, where This represents the start-up cost of the i-th thermal power unit. Indicates the unit's operating status. Indicates that the program is running. Indicates that the machine is stopped; Indicates the cost of wind curtailment. Indicates the amount of wind curtailment. This indicates the amount of wind power reserved for future use. This represents the unit cost of wind curtailment; Indicates the cost of curtailment. Indicates the amount of light discarded. This indicates the amount of surplus light energy. This represents the unit cost of curtailment; This represents the standby cost for a single frequency regulation operation. Indicates the amount of reserve. Indicate the reserve quantity. This represents the reserve cost coefficient.

[0051] Step S120. Construct the solution constraints for the day-ahead optimization scheduling model of power resources. The solution constraints include at least frequency security constraints, which include the minimum system inertia requirement constraint and the minimum system primary frequency regulation requirement constraint.

[0052] This step incorporates frequency security constraints into the constraints of the scheduling model. In power system scheduling, in addition to meeting basic power balance constraints (i.e., matching generation power with load power), frequency security of the power system must also be considered, especially to cope with frequency fluctuations caused by sudden load increases, generator failures, etc. Therefore, the constraints must not only cover basic physical limitations of resources (such as minimum / maximum generator output, charging and discharging capacity of energy storage devices, etc.), but also introduce frequency security constraints to ensure that the system can quickly restore frequency stability at different times.

[0053] Specifically, the frequency safety constraints constructed in this step include the minimum inertia requirement constraint and the minimum primary frequency regulation requirement constraint, which together ensure that the system can respond quickly and recover to a safe range when the frequency fluctuates.

[0054] The minimum inertia requirement constraint means that to ensure sufficient system immunity, the system's inertia at any given time must exceed the minimum inertia requirement. Inertia refers to the rotational mass of synchronous generators (such as thermal and hydroelectric generators) in the system. The greater the inertia, the smoother the system's response to frequency fluctuations. The inertia constraint ensures the system has sufficient "buffering" capacity to mitigate frequency fluctuations when facing faults or load changes. The system's inertia reflects the overall power system's resistance to frequency changes and is usually related to the size, number, and operating mode of the generators in the system. Systems with larger inertia respond more slowly to load changes and faults but can maintain frequency stability for longer periods, avoiding excessive instantaneous frequency changes. When introducing the minimum inertia requirement constraint, the scheduling model must ensure that the total inertia of the system at any given time meets the minimum inertia value required by the system. Otherwise, frequency changes may be too rapid (e.g., excessively high RoCoF), leading to the system's inability to recover in time or even frequency runaway.

[0055] Furthermore, the system's minimum inertia requirement constraint is expressed as:

[0056]

[0057] in, Let represent the minimum inertia that sustains the system's operation at time t. Let represent the system inertia at time t. This indicates the total number of conventional generating units. This indicates the rated power of a conventional unit. , , These represent the output power of the wind turbine, photovoltaic power station, and energy storage device at time t, respectively. , , , These represent the inertial time constants of conventional generating units, wind turbines, photovoltaic power plants, and energy storage devices, respectively. This indicates the operating status of a conventional generating unit. Indicates that the program is running. This indicates that the machine is out of service.

[0058] The minimum primary frequency regulation requirement constraint of a system refers to the requirement that the primary frequency regulation reserve of each resource in each time period of the system must exceed the minimum primary frequency regulation requirement of the system in each time period. Primary frequency regulation mainly depends on the regulation capabilities of each generator set and energy storage system. When the system frequency deviates, the generator sets will adjust according to the frequency change (usually frequency deviation). The power output of the generator set is adjusted accordingly. For example, when the frequency drops, the generator set will increase its power output, and when the frequency rises, it will decrease its power output. After introducing the minimum primary frequency regulation demand constraint, the scheduling model must ensure that the frequency regulation reserve resources in the system (such as energy storage, wind power, and thermal power units) can provide sufficient frequency regulation capability at any time. Otherwise, even if the system has a certain inertia, frequency fluctuations cannot be effectively corrected in a short period of time, and the system may experience frequency overshoot or instability.

[0059] Furthermore, the minimum primary frequency regulation requirement constraint of the system is expressed as:

[0060]

[0061] in, These represent the number of conventional generating units, new energy generating units, and energy storage power stations, respectively. This indicates the frequency regulation standby of the i-th conventional unit at time t. This indicates the frequency regulation standby of the j-th renewable energy unit at time t. This indicates the frequency regulation standby of the l-th energy storage power station at time t.

[0062] In a further embodiment, the boundary values ​​of the minimum inertia requirement constraint and the minimum primary frequency regulation requirement constraint of the system are, i.e. and This can be determined through frequency security assessment methods.

[0063] Specifically, an equivalent frequency response model of a power system including wind, solar, thermal, and energy storage units is first established to assess inertia and frequency requirements.

[0064] The following frequency security assessment indicators are established:

[0065] (1) Maximum rate of change of frequency ( )

[0066] The system frequency change rate RoCoF is a key indicator of frequency stability. On the one hand, a large RoCoF value can cause the synchronous machine to slip, resulting in damage to its internal structure. On the other hand, inverter-type power sources such as wind power and photovoltaics are sensitive to the RoCoF value. Exceeding its protection value will cause it to disconnect from the grid, further increasing the unbalanced power of the system.

[0067] Inertia has a direct impact on frequency fluctuations. When a system experiences load fluctuations or a generator failure, a larger inertia results in a smoother frequency change and a longer frequency recovery time, but it can maintain stability for a longer period. Systems with small inertia are more prone to drastic frequency changes. Therefore, systems with large inertia have a smaller RoCoF, resulting in slower frequency changes and facilitating response regulation; systems with small inertia have a larger RoCoF, leading to more rapid frequency changes and increased regulation difficulty.

[0068] According to the frequency response characteristics, the system frequency is unchanged at the instant the disturbance occurs; its frequency change depends on the system inertia and the magnitude of the disturbance power. At this moment, the system frequency change rate is at its maximum, and the system inertia requirement is at its minimum. The maximum frequency change rate index of the system is:

[0069]

[0070] (2) Maximum frequency deviation )index

[0071] Large frequency deviations can trigger under-frequency load shedding (UFLS) devices, leading to power outages. Therefore, under disturbances, the maximum frequency deviation of the system should be greater than the limit set by UFLS. The expression is as follows:

[0072]

[0073] (3) Quasi-steady-state frequency deviation )

[0074] When the system frequency drops below the primary frequency regulation dead zone, the primary frequency regulation reserve capacity provides active power support to compensate for the power shortfall. After all the primary frequency regulation capacity is activated, the system frequency will reach a stable value again. The difference between this value and the reference frequency is the steady-state frequency deviation. Therefore, the magnitude of the steady-state frequency deviation is an indicator of whether the primary frequency regulation reserve capacity is sufficient; a suitable steady-state frequency deviation can alleviate the pressure on secondary frequency regulation. When the system reaches the steady-state frequency value, the rate of frequency change tends to 0. According to the rotation equation, we have:

[0075]

[0076] In the formula, This represents the power of the disturbance experienced by the system. This indicates the system's primary frequency regulation reserve power. This represents the frequency regulation constant. It is evident that the quasi-steady-state frequency is related to the primary frequency regulation capacity and damping characteristics, but not directly to the system inertia level. Based on daily operation experience in southern my country, the primary frequency regulation reserve capacity is set at approximately 6% of the local peak load.

[0077] Finally, based on the above indicators, the minimum inertia and primary frequency regulation reserve requirements are assessed as follows:

[0078] The system inertia needs to simultaneously satisfy the RoCoF constraint and the maximum frequency deviation constraint; therefore, the minimum system inertia is the maximum of these two constraints. The minimum primary frequency regulation requirement of the system is based on the quasi-steady-state deviation. calculate.

[0079] For example, Figure 2 This illustrates one scenario for constructing an equivalent frequency response model of a system, based on which a preset disturbance power is given. Simulations were performed to determine the three frequency security assessment indicators mentioned above. Specifically, the inertia of the first system was calculated based on RoCoF and the maximum frequency deviation. Second system inertia Calculate the primary frequency modulation reserve based on the quasi-steady-state frequency deviation. The minimum inertia requirement of the system is taken as and The maximum of the two, This is denoted as the minimum primary frequency regulation requirement of the system.

[0080] Furthermore, the constraints for solving the scheduling model also include power balance constraints, DC power flow constraints, and unit constraints.

[0081] Power balance constraints refer to the requirement that a system must maintain power balance at all times to ensure stability, specifically manifested as the balance of output power from the source loads.

[0082]

[0083] In the formula, These represent the number of conventional generating units, wind turbine units, and photovoltaic power stations, respectively. This represents the output of synchronous unit i at time t; Indicates the start-up and shutdown status of the synchronous generator unit at time t; This represents the photovoltaic output at time t; This represents the wind power output at time t; This represents the charging and discharging power of the stored energy at time t; This represents the power of the load at time t.

[0084] DC power flow constraints refer to ensuring that the power flow of each transmission line is within a reasonable range during the optimal scheduling process. Its specific expression is as follows:

[0085]

[0086] In the formula, For the tidal current of branch l; Inject power vectors into each node; Let be the power transfer factor matrix of branch l; Let be the diagonal admittance matrix of branch l; This is the network correlation matrix; The branch admittance matrix is ​​used to neglect conductance; The power flow transmission limit of branch l.

[0087] The constraints constructed in the embodiments of the present invention include constraints on thermal power units, constraints on new energy units, and constraints on energy storage units.

[0088] The constraints for thermal power units include upper and lower output limits and ramping constraints. The upper and lower output limits take into account the capacity requirements for primary frequency regulation adjustments. To better reflect actual operating conditions, dynamic start-up and shutdown are not considered in the day-ahead operating schedule; the start-up and shutdown times are constants on the operating day. The upper and lower output limits and ramping constraints are expressed as follows:

[0089]

[0090]

[0091] In the formula, , They represent thermal power units The upper and lower limits of output; , These represent the upper and lower frequency regulation requirements of thermal power units, respectively. and Let represent the upslope rate and downslope rate of the power output of the i-th generator unit, respectively. Indicates the unit scheduling duration.

[0092] Constraints on renewable energy generating units include maximum output constraints and primary frequency regulation down-regulation constraints. The maximum output constraint requires that the output of a renewable energy generating unit at any given time should not exceed its maximum predicted power. The primary frequency regulation down-regulation constraint requires that the renewable energy generating unit have the capability to reduce its output, specifically expressed as follows:

[0093]

[0094] In the formula, This indicates the predicted maximum power output of the photovoltaic system. This represents the predicted maximum power output value of wind power. This indicates the need for down-regulation in photovoltaic systems. This indicates the down-regulation demand for wind power.

[0095] Energy storage unit constraints include upper and lower limits of energy storage power, energy storage capacity, and state of charge / discharge constraints, which are expressed as follows:

[0096]

[0097]

[0098]

[0099] In the formula, and These represent the maximum discharge and charging power of the energy storage, respectively; considering the energy storage's ability to provide up and down frequency regulation, This indicates the need for frequency regulation in energy storage. This indicates the need for frequency regulation in energy storage. This indicates the discharge state; it is 1 when energy is stored and discharged, and 0 when not discharging. This indicates the charging status; 1 represents energy storage charging, and 0 represents non-charging. and These represent the maximum and minimum energy storage capacities, respectively. This represents the remaining energy storage capacity at time t; This indicates the cycle efficiency of energy storage.

[0100] Step S130. Solve the day-ahead optimization scheduling model for power resources based on the constraints to obtain the scheduling results.

[0101] The scheduling model established in this embodiment of the invention satisfies the standard form of solving mixed-integer linear programming. The solver can be called to solve the linear programming problem and output the scheduling plan of each power generation resource in each time period, including the output power and start-stop status of each unit, the charging and discharging plan of the energy storage system, and the power generation of new energy resources.

[0102] The above-disclosed embodiments describe in detail a day-ahead optimization scheduling method for power resources based on frequency security constraints. The above-disclosed method can be implemented using various types of equipment. Therefore, the present invention also discloses a scheduling device corresponding to the above method. Specific embodiments are given below for detailed description.

[0103] like Figure 3 As shown, one embodiment of the present invention provides a day-ahead optimization scheduling device for power resources considering frequency security constraints, comprising:

[0104] The scheduling model establishment module 302 is used to establish a day-ahead optimal scheduling model for power resources with the goal of minimizing system operating costs, where system operating costs include primary frequency regulation costs.

[0105] The constraint construction module 304 is used to construct the solution constraints of the day-ahead optimization scheduling model of power resources. The solution constraints include at least frequency security constraints, which include the minimum inertia requirement constraint and the minimum primary frequency regulation requirement constraint of the system.

[0106] The scheduling model solving module 306 is used to solve the day-ahead optimal scheduling model of power resources based on the solution constraints, and obtain the scheduling results.

[0107] The device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0108] The methods and related apparatuses mentioned in the above embodiments are described with reference to the method flowcharts and / or structural diagrams provided in the embodiments of this application. Specifically, each block of the method flowchart and / or structural diagram, as well as combinations of blocks in the flowchart and / or block diagram, 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 device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The 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 operate 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 A schematic diagram of one or more processes and / or structures. Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 A process or multiple processes and / or structures illustrate the steps of the functions specified in one or more boxes.

[0109] The following embodiments illustrate the application of this method to a computer device. It is understood that the computer device can be any device with computing and processing capabilities, including but not limited to servers or personal laptops. In one embodiment, the computer device can be an application server, which can be a server used to run the application under test.

[0110] See Figure 4 This document illustrates a hardware block diagram of an electronic device intended to represent various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0111] like Figure 4 As shown, the electronic device includes: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;

[0112] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0113] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0114] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0115] The memory stores a program, which the processor can call. The program is used to implement the various processing steps of the aforementioned power resource day-ahead optimization scheduling scheme that takes into account frequency security constraints.

[0116] This invention also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements various processing flows of the day-ahead power resource optimization scheduling scheme considering frequency security constraints provided in any possible implementation of the above embodiments and / or in combination with the embodiments.

[0117] The invention has been described in particular detail above with respect to possible scenarios, and those skilled in the art will recognize that the invention can be practiced through other embodiments. Specific naming of components, capitalization of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or important, and the mechanisms or features of implementing the invention may have different names, forms, or procedures. The system can be implemented through a combination of hardware and software (as described), entirely through hardware elements, or entirely through software elements. The specific division of functions among the various system components described herein is merely exemplary and not mandatory; rather, the functions performed by a single system component can be performed by multiple components, or the functions performed by multiple components can be performed by a single component.

[0118] Those skilled in the art should understand that the various steps of the disclosed methods can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using device-executable program code, which can then be stored in a storage device for execution by the computing device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, the embodiments disclosed in this invention are not limited to any specific hardware and software combination.

[0119] The programs (also referred to as programs, software, software applications, or code) executable by these computing devices include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0120] Certain aspects of this invention include the process steps and instructions described herein in algorithmic form. It should be noted that the process steps and instructions of this invention can be implemented in software, firmware, and / or hardware, and when implemented in software, they can be downloaded, stored on various operating systems and operated from said platforms.

[0121] Those skilled in the art will understand that the structures shown in the figures are merely block diagrams of some structures related to the present application and do not constitute a limitation on the terminal device to which the present application is applied. Specific terminal devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0122] In the description of this specification, the use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "possible design," etc., refers to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0123] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0124] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A day-ahead optimization scheduling method for power resources considering frequency security constraints, characterized in that, include: A day-ahead optimization scheduling model for power resources is established with the goal of minimizing system operating costs, where system operating costs include primary frequency regulation costs and power resources include new energy sources. The solution constraints of the day-ahead optimization scheduling model of the power resources are constructed. The solution constraints include at least frequency security constraints, which include the minimum system inertia requirement constraint and the minimum system primary frequency regulation requirement constraint. The day-ahead optimal scheduling model for power resources is solved based on the aforementioned constraints to obtain the scheduling results.

2. The method according to claim 1, characterized in that, The day-ahead optimal scheduling model for power resources is expressed as follows: ， Where T represents the scheduling period, Indicates the number of thermal power units. This represents the fuel cost of the i-th thermal power unit. , , Let represent the coal consumption coefficients of the i-th thermal power unit, respectively. This represents the output power of the i-th thermal power unit during time period t. Let represent the start-up cost function of a thermal power unit at time t. This represents the start-up cost of the i-th thermal power unit. Indicates the unit's operating status. Indicates the cost of wind curtailment. Indicates the amount of wind curtailment. This indicates the amount of wind power reserved for future use. This represents the unit cost of wind curtailment. Indicates the cost of curtailment. Indicates the amount of light discarded. This indicates the amount of surplus light energy. This represents the unit cost of curtailment. This represents the standby cost for a single frequency regulation operation. Indicates the amount of reserve. Indicate the reserve quantity. This represents the reserve cost coefficient.

3. The method according to claim 1, characterized in that, The minimum inertia requirement constraint for the system includes: An equivalent frequency response model of the power system is established to simulate the frequency dynamic response of the power system; Determine the maximum rate of frequency change and the maximum frequency deviation of the power system; The inertia of the first system and the inertia of the second system are calculated using the equivalent frequency response model based on the maximum rate of change of frequency and the maximum frequency deviation, respectively. The maximum value between the first system inertia and the second system inertia is denoted as the boundary value of the minimum inertia requirement constraint of the system.

4. The method according to claim 1 or 3, characterized in that, The minimum inertia requirement constraint of the system is expressed as follows: ， in, Let represent the minimum inertia that sustains the system's operation at time t. Let represent the system inertia at time t. This indicates the total number of conventional generating units. This indicates the rated power of a conventional unit. , , These represent the output power of the wind turbine, photovoltaic power station, and energy storage device at time t, respectively. , , , These represent the inertial time constants of conventional generating units, wind turbines, photovoltaic power plants, and energy storage devices, respectively. This indicates the operating status of a conventional generating unit. Indicates that the program is running. This indicates that the machine is out of service.

5. The method according to claim 1, characterized in that, The minimum primary frequency regulation requirement constraint for the system includes: An equivalent frequency response model of the power system is established to simulate the frequency dynamic response of the power system; Determine the quasi-steady-state frequency deviation of the system; The equivalent frequency response model is used to calculate the primary frequency modulation backup based on the quasi-steady-state frequency deviation; The primary frequency regulation reserve is denoted as the boundary value of the minimum primary frequency regulation requirement constraint of the system.

6. The method according to claim 1 or 5, characterized in that, The minimum primary frequency regulation requirement constraint of the system is expressed as follows: ， in, These represent the number of conventional generating units, new energy generating units, and energy storage power stations, respectively. This indicates the frequency regulation standby of the i-th conventional unit at time t. This indicates the frequency regulation standby of the j-th renewable energy unit at time t. This indicates the frequency regulation standby of the l-th energy storage power station at time t.

7. The method according to claim 1, characterized in that, The constraints to be solved also include power balance constraints, DC power flow constraints, and unit constraints.

8. A power resource day-ahead optimization scheduling device considering frequency security constraints, characterized in that, include: The scheduling model establishment module is used to establish a day-ahead optimal scheduling model for power resources with the goal of minimizing system operating costs, where system operating costs include primary frequency regulation costs. The constraint construction module is used to construct the solution constraints for the day-ahead optimal scheduling model of power resources. The solution constraints include at least frequency security constraints, which include the minimum inertia requirement constraint and the minimum primary frequency regulation requirement constraint of the system. The scheduling model solving module is used to solve the day-ahead optimal scheduling model of power resources based on the solution constraints, and obtain the scheduling results.

9. An electronic device, characterized in that, The device includes a memory storing computer-executable instructions and a processor, which, when executed by the processor, causes the device to perform the day-ahead power resource optimization scheduling method based on considering frequency security constraints as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, It stores a computer-executable program that, when executed, implements the day-ahead optimization scheduling method for power resources based on considering frequency security constraints as described in any one of claims 1 to 7.

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