A spot market simulation method considering grid inertia support

Abstract

The application discloses a kind of method for simulating spot market considering inertia support of power grid, and relates to the technical field of power system planning, solve the problem that it is difficult to accurately evaluate the system security of power grid under planning scheme.The inertia constraint of system and the inertia component of node electricity price are embedded in the SCUC / SCED simulation of spot market, so as to accurately quantify the support value of various facilities to frequency safety, so as to accurately and truly evaluate the system security of power grid under corresponding planning scheme in the background of the increase of wind and light penetration rate, improve the stability and security of power system, and better promote the development of clean energy.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a spot market simulation method that takes into account grid inertia support. Background Technology

[0002] When simulating the entire operation of the Beijing-Tianjin-Hebei power market, security-constrained unit commitment (SCUC) and security-constrained economic dispatch (SCED) are typically used to simulate the time-series operation in order to obtain nodal prices that satisfy both safety and economy. The security constraints in existing models mainly cover active power balance, line thermal stability limits, and cross-sectional capacity limits. System frequency security is often treated as an adequacy condition and is not explicitly modeled in the clearing model.

[0003] However, with the significant increase in wind and solar power penetration, the system's equivalent inertia continues to decline, posing challenges to frequency stability. The nodal price obtained through traditional simulations lacks the marginal cost component of inertia, making it difficult to accurately assess the system security of the power grid under the planned scheme when planning power grid facilities.

[0004] Therefore, a spot market simulation method that takes into account the inertia support of the power grid is needed. Summary of the Invention

[0005] To address the problem that existing technologies struggle to accurately assess the system security of power grids under planning schemes, this invention provides a spot market simulation method that considers grid inertia support, enabling accurate and realistic assessment of power grid system security under facility planning schemes. The specific technical solution is as follows: In a first aspect, embodiments of this application provide a spot market simulation method that takes into account grid inertia support, including: Obtain the minimum inertia requirement of the target power grid; wherein, the target power grid is the simulated power grid after the implementation of the simulation planning scheme; based on the minimum inertia requirement, construct the SCUC model and the SCED model; based on the SCUC model and the SCED model, simulate the whole network node electricity price sequence under different wind and solar power output prediction error scenarios; wherein, the electricity price sequence includes the node electricity price inertia component; based on the inertia component, output the simulation evaluation report.

[0006] Preferably, obtaining the minimum inertia requirement of the target power grid includes: obtaining the frequency change rate limit of the target power grid; and determining the minimum inertia requirement based on the maximum power disturbance of the target power grid in the corresponding time period and the frequency change rate limit.

[0007] Preferably, the construction of a Safety-Constrained Unit Combination (SCUC) model and a Safety-Constrained Economic Dispatch (SCED) model based on the minimum inertia requirement and the frequency change rate constraint includes: converting the minimum inertia requirement into a linearized time-varying inertia requirement constraint; wherein, the time-varying inertia requirement constraint is used to constrain the total system inertia to be no less than the minimum inertia requirement, the total system inertia being the sum of the inherent inertia contributions of all synchronous generators plus the sum of the virtual inertia contributions of all grid-connected energy storage, the virtual inertia contribution of grid-connected energy storage being determined by the virtual inertia gain coefficient of the energy storage unit, the current remaining power, the rated power of the energy storage converter, and the ratio of the rated energy capacity of the energy storage to the system baseline capacity; and constructing the SCUC model and the SCED model based on the time-varying inertia requirement constraint.

[0008] Preferably, the SCED model includes energy storage operation constraints, which include: the sum of the energy storage charging and discharging power and the primary frequency regulation reserve capacity does not exceed the rated power of the energy storage converter; and the state of charge of the energy storage minus the energy required for primary frequency regulation reserve has a margin greater than a preset lower limit.

[0009] Preferably, the calculation process of the node price inertia component includes: obtaining the Lagrange multiplier of the time-varying inertia demand constraint in the SCED model; calculating the marginal sensitivity coefficient of the injected power of each node in the target power grid to the total system inertia, the marginal sensitivity coefficient being obtained based on the linear relationship between the virtual inertia of energy storage and the state of charge and the network power flow rescheduling logic; multiplying the Lagrange multiplier by the marginal sensitivity coefficient to obtain the node price inertia component of the corresponding node.

[0010] Preferably, each scenario corresponds to a time period; the simulation of the whole network node electricity price sequence under different wind and solar power output prediction error scenarios based on the SCUC model and the SCED model includes: solving the time sequence recursively for each scenario, and iteratively solving to obtain the whole network node electricity price sequence under multiple scenarios.

[0011] Preferably, the simulation evaluation report output based on the inertia component includes: the spatiotemporal distribution of the inertia component, the improvement of the frequency safety margin, and the probability of exceeding the frequency change rate limit.

[0012] Secondly, embodiments of this application provide a spot market simulation system that takes into account grid inertia support, characterized in that the system includes: The acquisition module is used to obtain the minimum inertia requirement of the target power grid; wherein, the target power grid is the simulated power grid after the implementation of the simulation planning scheme; The building module is used to construct the Safety-Constrained Unit Combination (SCUC) model and the Safety-Constrained Economic Dispatch (SCED) model based on this minimum inertia requirement. The simulation module is used to simulate the whole network node electricity price sequence under different wind and solar power output prediction error scenarios based on the SCUC model and the SCED model; wherein, the electricity price sequence includes the node electricity price inertia component; The output module is used to output a simulation evaluation report based on the inertia component.

[0013] Thirdly, embodiments of this application provide a computing device, including: a memory for storing a program; and a processor for loading the program to execute the method as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the method described in the first aspect.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: by embedding the system inertia constraint and the nodal price inertia component into the SCUC / SCED simulation of the spot market, the supporting value of various facilities for frequency security can be accurately quantified. Thus, in the context of the increase in wind and solar penetration, the system security of the power grid under the corresponding planning scheme can be accurately and realistically evaluated, thereby improving the stability and security of the power system and better promoting the development of clean energy. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 A flowchart illustrating a spot market simulation method that takes into account grid inertia support, provided for an embodiment of this application; Figure 2 A system architecture diagram of a spot market simulation system that takes into account grid inertia support is provided for an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0018] 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.

[0019] It should be understood that, when used in this specification and the appended claims, 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.

[0020] 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.

[0021] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] To address the problem of accurately assessing the system security of the power grid under planning schemes in traditional technologies, this invention provides a spot market simulation method that takes into account the inertia support of the power grid, which can accurately and realistically assess the system security of the power grid under facility planning schemes.

[0023] Please see Figure 1 , Figure 1 This application provides a flowchart illustrating a spot market simulation method that takes into account grid inertia support, applied to a computing device; as shown below. Figure 1 As shown, the method includes: Step 101: Calculate the minimum inertia requirement of the target power grid using the calculation device.

[0024] The target power grid refers to the simulated power grid after the implementation of the simulation planning scheme. Specifically, the planning scheme can refer to the distribution and storage planning scheme of the target power grid.

[0025] In the current power system environment, the proportion of wind and solar installed capacity continues to rise. To promote absorption and smooth fluctuations, renewable energy power plants are mostly equipped with energy storage in proportion. In this way, energy storage can have network construction capabilities such as virtual inertia and primary frequency regulation through power electronic devices. Therefore, the embodiments of this application specifically take the inertia factor into account in the clearing simulation process. By comparing the inertia component in the clearing price under different energy storage allocation schemes, the contribution value to the system inertia is evaluated, thereby guiding the implementation of safer energy storage allocation schemes and improving the system security of the power grid.

[0026] The minimum inertia requirement refers to the minimum limit that the system inertia of the target power grid must exceed.

[0027] Specifically, the computing device can directly obtain the pre-set minimum inertia requirement from the corresponding database; or it can calculate it based on the system attribute values ​​of the target power grid.

[0028] Preferably, the computing device can obtain the frequency change rate limit of the target power grid; and determine the minimum inertia requirement based on the maximum power disturbance of the target power grid in the corresponding time period and the frequency change rate limit.

[0029] The minimum inertia requirement can be expressed as: ; in, The system inertia of the target power grid, To meet the minimum inertia requirement, For maximum power disturbance, The system's rated frequency, For the limit of the rate of change of frequency, This represents the system's baseline capacity.

[0030] Among them, the maximum power disturbance It can be a fixed value preset based on historical data, or a predicted value obtained from real-time prediction.

[0031] Specifically, in scenarios with high wind and solar penetration, the maximum power perturbation In addition to factors related to generator disconnection from the grid, it is also necessary to consider factors such as sudden power drops caused by abrupt changes in wind and solar resources. Therefore, the computing equipment can dynamically estimate the maximum potential power disturbance for each simulation period based on the network topology, generator start-up mode, and wind and solar power output forecasts. For example, considering the current output of renewable energy power plants and the potential risk of concentrated grid disconnection, and overlaying the maximum single conventional fault, the maximum power disturbance is determined. This means that the inertia safety constraint is no longer a fixed constant, but rather adapts to the wind and solar power output and the unit's operational status.

[0032] Step 102: Based on the minimum inertia requirement, the computing device constructs a security-constrained unit commitment (SCUC) model and a security-constrained economic dispatch (SCED) model.

[0033] The computing device can transform the minimum inertia requirement into a specific constraint, and build a clearing model based on this constraint to simulate the spot market.

[0034] Preferably, the computing device can convert the minimum inertia requirement into a linearized time-varying inertia requirement constraint; based on the time-varying inertia requirement constraint, the SCUC model and the SCED model are constructed.

[0035] The time-varying inertia requirement constraint is used to ensure that the total inertia of the system is not less than the minimum inertia requirement. The total inertia of the system is the sum of the inherent inertia contributions of all synchronous generators plus the sum of the virtual inertia contributions of all grid-connected energy storage. The virtual inertia contribution of grid-connected energy storage is determined by the virtual inertia gain coefficient of the energy storage unit, the current remaining power, the rated power of the energy storage converter, and the ratio of the rated energy capacity of the energy storage to the system reference capacity.

[0036] Specifically, for the j-th energy storage unit in the target power grid, the virtual inertia constant it can provide is... Related to real-time controllable dynamic capabilities, its physical basis lies in the converter's ability to rapidly absorb or release active power from the battery side. This capability is strictly limited by the battery's current state of charge and the converter's rated power, as expressed below: ; in, The inertia simulation gain coefficient of this energy storage unit is a constant preset based on the converter control response speed; Let be the remaining charge of the energy storage unit at time t; This refers to the rated capacity of the energy storage unit; This is the rated AC power of the energy storage converter; This represents the system's baseline capacity. The formula indicates that the virtual inertia of energy storage is proportional to its available power; when power is insufficient, its inertia support capacity decreases linearly.

[0037] Furthermore, the expression for the equivalent inertia of the entire system can be: ; Where G represents the set of all generators, and These represent the inertia constant and rated capacity of unit i, respectively; B represents the set of all energy storage systems that are built in conjunction with the unit and can provide grid-connected services.

[0038] The expression for the time-varying inertia requirement constraint is: .

[0039] This linearization process allows for the connection between the physical nature of the frequency security of the target power grid and the model required for market clearing.

[0040] Next, we can first explain the construction of the SCUC model.

[0041] The SCUC model's objective function, based on traditional generation costs, incorporates terms related to the call costs or opportunity losses of energy storage providing grid-connected services, aiming to minimize the total cost. The expression for this objective function includes: ; Where t is the time period index, i is the generator set index, and G is the set of all generators; Let be the active power output of unit i in time period t, which is a continuous decision variable; Let be the power generation cost function of unit i; , These are 0 / 1 integer variables representing the start-up and shutdown action indications of unit i in time period t, where 0 indicates no change in status and 1 indicates start-up or shutdown. , These represent the single start-up and shutdown costs for unit i, respectively. The cost per unit of energy loss or lifespan reduction during energy storage charging and discharging. To reserve the unit opportunity cost of frequency regulation backup, and Let be the charging and discharging power of energy storage j during time period t. These are the reserve capacities for upward and downward frequency regulation reserved by energy storage j during time period t.

[0042] By introducing these cost items, the importance of providing energy arbitrage versus providing frequency security support can be weighed during model simulations.

[0043] In addition to traditional constraints such as active power balance constraints, generator output and start-up / shutdown constraints, ramp rate constraints, and line transmission capacity constraints, the SCUC model also incorporates time-series recursive constraints for electricity, joint constraints for power and reserve, coordination constraints for electricity and reserve, and time-varying inertia demand constraints transformed from the above.

[0044] Specifically, the energy time-series recursive constraint means that the energy stored in time period t is equal to the energy stored in the previous time period plus the charging amount and minus the discharging amount; the power and reserve joint constraint means that the energy storage cannot be charged and discharged at the same time, and the reserved reserve capacity occupies the power capacity of the converter, so that the available charging and discharging power is reduced accordingly; the energy and reserve coordination constraint is used to prevent the energy storage from being quickly depleted or overcharged due to providing primary frequency regulation reserve, and adds energy-based reserve feasibility constraints.

[0045] Preferably, the SCUC model also includes a primary frequency modulation reserve capacity safety constraint to ensure that primary frequency modulation can suppress frequency drops sufficiently quickly and persistently after a disturbance. The expression for this primary frequency modulation reserve capacity safety constraint includes: ; ; The first row of equations represents the constraint on the backup demand for the next frequency modulation. , The upward / downward primary frequency regulation backup is provided for thermal power unit i respectively. , The upward / downward primary frequency regulation backups are provided for energy storage j, respectively. , These represent the primary frequency regulation reserve requirements for time period t, which can be determined by the maximum disturbance or a certain percentage of the load.

[0046] The completed SCUC model is a mixed-integer linear programming problem, which can be solved by calling mature commercial solvers.

[0047] Next, the constructed SCED model will be explained.

[0048] The SCED model essentially forms a convex programming problem, which is used to optimize the active power output of each online unit and solve for the marginal electricity price of each node under fixed unit start-up and shutdown conditions, with the goal of minimizing the system power generation cost during that period.

[0049] Specifically, the objective function of this SCED model, in addition to generation costs, also includes the call costs for energy storage to participate in the energy market and provide grid-connected services, allowing it to balance economic efficiency with frequency security. The expression for this objective function includes: ; Where B is the set of energy storage facilities that are configured and have the capability to form a network, and j is the index; These are the charging and discharging power of the energy storage, respectively, and are continuous decision variables. , Reserved capacity for upward / downward primary frequency regulation for energy storage j to be actually dispatched; Let be the approximate cost per unit energy loss of energy storage j. The opportunity cost function for providing primary frequency regulation reserve capacity for energy storage.

[0050] The introduction of these cost items allows the value of energy storage network services to be directly incorporated into the marginal pricing loop.

[0051] It should be noted that, , The estimated value is determined in the output of the SCUC model in step 1, but fine-tuning is allowed within a certain range in the SCED model to discover its true marginal value.

[0052] Preferably, the SCED model includes energy storage operation constraints, which include: the sum of the energy storage charging and discharging power and the primary frequency regulation reserve capacity does not exceed the rated power of the energy storage converter; and the state of charge of the energy storage minus the energy required for primary frequency regulation reserve has a margin greater than a preset lower limit.

[0053] It is understandable that the other constraints of this SCED model are similar to those of the SCUC model mentioned above, and will not be repeated here.

[0054] Step 103: The computing device simulates the whole network node electricity price sequence under different wind and solar power output prediction error scenarios based on the SCUC model and the SCED model.

[0055] In step 2, after constructing the SCUC model and SCED model, the computing device can obtain the first input data of the SCUC model and solve the work plan, total cost and cost components of each unit and energy storage of the target power grid through the SCUC model. Then, based on the second input data including the output results of the SCUC model, the marginal electricity price of each node is solved through the SCED model and decomposed into components of four dimensions: energy, network loss, congestion and inertia.

[0056] Specifically, the first input data includes power grid topology parameters, generator set parameters, new energy data, energy storage parameters, system load, and frequency safety standards.

[0057] For example, power grid topology parameters include line connection relationships, line reactance and thermal stability limits, node load distribution, and power transfer distribution factor matrix.

[0058] For example, generator set parameters include rated capacity, upper and lower output limits, segmented or secondary power generation cost coefficient, start-up cost, shutdown cost, minimum start-up and shutdown time, up / down ramp rate, inertia constant, and primary frequency regulation reserve capacity provision capability.

[0059] For example, new energy data includes the time-series predicted power output of wind farms and photovoltaic power plants, and their geographical node locations. Different prediction error scenarios for wind and solar power output can be obtained by superimposing different prediction errors on this predicted power output.

[0060] For example, the parameters for energy storage configuration include the rated power of the energy storage unit, the rated energy capacity, the charging and discharging efficiency, the virtual inertia gain coefficient of the grid, the minimum safe power, the charging and discharging loss cost coefficient, and the opportunity cost coefficient for providing primary frequency regulation backup.

[0061] For example, frequency security standards include frequency change rate limits and primary frequency regulation reserve requirements.

[0062] Specifically, the output of the SCUC model includes the start-up and shutdown status of all thermal power units in each time period, the active power output baseline plan of thermal power units in each time period, the full-time charging and discharging power plan and power change trajectory of each energy storage unit, the reserved upward and downward primary frequency regulation reserve capacity of energy storage units and thermal power units, as well as the total cost and each cost component.

[0063] Specifically, the second input data includes the output of the SCUC model, generator economic parameters, network and load parameters, energy storage parameters and status, and predicted output of new energy sources.

[0064] For example, the economic parameters of a generator set include the power generation cost function of the online unit, the upper and lower limits of output, and the ramp rate limit.

[0065] For example, network and load parameters include node load values, network loss sensitivity parameters, line transmission capacity, and transfer distribution factor.

[0066] Specifically, the output of the SCED model includes the final active power output of each online unit, the active power flow and congestion status of each line, the constrained Lagrange multiplier subset, and the calculated marginal electricity price of each node.

[0067] The Lagrange multiplier subset of this constraint includes the shadow price of the system active power balance constraint, which is the basis for calculating the energy component; the shadow price of the network loss balance constraint, used to calculate the network loss component; the shadow price of the transmission constraints of each line, used to calculate the congestion component; and the shadow price of the time-varying inertia demand constraint, used to calculate the inertia component.

[0068] Preferably, the calculation process of the node price inertia component includes: obtaining the Lagrange multiplier of the time-varying inertia demand constraint in the SCED model; calculating the marginal sensitivity coefficient of the injected power of each node in the target power grid to the total system inertia, the marginal sensitivity coefficient being obtained based on the linear relationship between the virtual inertia of energy storage and the state of charge and the network power flow rescheduling logic; multiplying the Lagrange multiplier by the marginal sensitivity coefficient to obtain the node price inertia component of the corresponding node.

[0069] The computing device can apply unit power perturbation to each node successively after the SCED model converges, and quickly solve the rescheduling problem to obtain the change in the total inertia of the system, that is, to obtain the marginal sensitivity coefficient of the injected power of the node to the total inertia of the system.

[0070] In order to simulate the uncertainty under high proportion of wind and solar radiation, the computing device can generate data that covers multiple meteorological models and maintains temporal correlation.

[0071] Specifically, the computing device can use Monte Carlo sampling or Latin hypercube sampling techniques to generate a large number of initial weather models from the probability distribution, and then use a reduction algorithm to select a few representative weather models. Each weather model corresponds to a full-time wind power and photovoltaic power output time series vector.

[0072] Then, the computing device can perform independent clearing simulations for each weather model to obtain the electricity price sequence for each weather model.

[0073] Preferably, in a single meteorological model, each scenario of wind and solar power output prediction error corresponds to a time period; the computing device can solve for each scenario in a time-series recursive manner, and iteratively solve to obtain the full network node electricity price sequence under multiple scenarios.

[0074] Step 104: The computing device outputs a simulation evaluation report based on the inertia component.

[0075] Preferably, the computing device can output the simulation evaluation report based on the spatiotemporal distribution of the inertia component, the improvement of the frequency safety margin, and the probability of exceeding the frequency change rate limit.

[0076] The computing device can perform weighted summation of the inertia components for each scenario and each time period based on the statistical probability of occurrence of various meteorological models, so as to obtain the expected inertia component of any node n in any time period t. This value reflects the average electricity price increase brought about by the scarcity of inertia, and can provide signals for transmission pricing or energy storage site selection.

[0077] Preferably, it can also statistically analyze the percentage and distribution of positive values ​​in the inertia component across the entire network, identify key nodes and periods of inertia stress, calculate the total inertia component payment for each node throughout the year or the entire simulation cycle, and assess the flow direction and total scale of inertia costs under different storage configurations.

[0078] The computing device can statistically analyze the proportion of scenarios and time periods where the minimum inertia constraint is a tight constraint, i.e., the shadow price of the instantaneous inertia demand constraint is greater than 0. If this proportion decreases, it indicates that the energy storage network has effectively reduced periods of inertia scarcity.

[0079] Among them, the computing device can be based on the total system inertia for each time period. Based on the estimated maximum power disturbance during that period, the system frequency change rate is calculated. and will With the limit of the rate of change of frequency Comparison. Statistical analysis of the entire scenario's time series. Greater than The probability of occurrence is determined. By comparing different energy storage configurations, the effectiveness of grid-based energy storage in reducing the risk of frequency exceeding limits is quantified.

[0080] Then, the computing device can summarize the above quantitative analysis and generate a simulation evaluation report.

[0081] In this embodiment, by embedding system inertia constraints and nodal price inertia components into the SCUC / SCED simulation of the spot market, the supporting value of various facilities for frequency security can be accurately quantified. Thus, in the context of increased wind and solar power penetration, the system security of the power grid under the corresponding planning scheme can be accurately and realistically evaluated, thereby improving the stability and security of the power system and better promoting the development of clean energy.

[0082] The method provided in the embodiments of this application has been described above. The system provided in the embodiments of this application will be described below.

[0083] Please see Figure 2 , Figure 2 A system architecture diagram for a spot market simulation considering grid inertia support is provided for embodiments of this application, as follows: Figure 2 As shown, the system 20 includes: The acquisition module 201 is used to acquire the minimum inertia requirement of the target power grid; wherein, the target power grid is the simulated power grid after the implementation of the simulation planning scheme; Module 202 is used to construct a Safety-Constrained Unit Combination (SCUC) model and a Safety-Constrained Economic Dispatch (SCED) model based on the minimum inertia requirement. The simulation module 203 is used to simulate the whole network node electricity price sequence under different wind and solar power output prediction error scenarios based on the SCUC model and the SCED model; wherein, the electricity price sequence includes the node electricity price inertia component; Output module 204 is used to output a simulation evaluation report based on the inertia component.

[0084] Preferably, the acquisition module 201 is specifically used to acquire the frequency change rate limit of the target power grid; and to determine the minimum inertia requirement based on the maximum power disturbance of the target power grid in the corresponding time period and the frequency change rate limit.

[0085] Preferably, the construction module 202 is specifically used to convert the minimum inertia requirement into a linearized time-varying inertia requirement constraint; wherein, the time-varying inertia requirement constraint is used to constrain the total system inertia to be no less than the minimum inertia requirement, the total system inertia being the sum of the inherent inertia contributions of all synchronous generators plus the sum of the virtual inertia contributions of all grid-connected energy storage, the virtual inertia contribution of grid-connected energy storage being determined by the virtual inertia gain coefficient of the energy storage unit, the current remaining power, the rated power of the energy storage converter, and the ratio of the rated energy capacity of the energy storage to the system baseline capacity; based on the time-varying inertia requirement constraint, the SCUC model and the SCED model are constructed.

[0086] Preferably, the SCED model includes energy storage operation constraints, which include: the sum of the energy storage charging and discharging power and the primary frequency regulation reserve capacity does not exceed the rated power of the energy storage converter; and the state of charge of the energy storage minus the energy required for primary frequency regulation reserve has a margin greater than a preset lower limit.

[0087] Preferably, the calculation process of the node price inertia component includes: obtaining the Lagrange multiplier of the time-varying inertia demand constraint in the SCED model; calculating the marginal sensitivity coefficient of the injected power of each node in the target power grid to the total system inertia, the marginal sensitivity coefficient being obtained based on the linear relationship between the virtual inertia of energy storage and the state of charge and the network power flow rescheduling logic; multiplying the Lagrange multiplier by the marginal sensitivity coefficient to obtain the node price inertia component of the corresponding node.

[0088] Preferably, each scenario corresponds to a time period; the simulation module 203 is specifically used to solve for each scenario by time-series recursion, and iteratively solve to obtain the full network node electricity price sequence under multiple scenarios.

[0089] Preferably, the output module 204 is specifically used to generate the output simulation evaluation report based on the spatiotemporal distribution of the inertia component, the improvement of the frequency safety margin, and the probability of exceeding the frequency change rate limit.

[0090] The spot market simulation provided in this application, which takes into account the inertia of the power grid, can be understood by referring to the relevant content in the foregoing method embodiment section, and will not be repeated here.

[0091] like Figure 3 As shown, Figure 3 This is a schematic diagram of a possible logical structure of a computing device provided in an embodiment of this application. The computing device 30 includes a processor 301, a communication interface 302, a memory 303, and a bus 304. The processor 301, the communication interface 302, and the memory 303 are interconnected via the bus 304. In an embodiment of this application, the processor 301 is used to control and manage the operation of the computing device 30. For example, the processor 301 is used to execute... Figure 1 The steps in the embodiments and / or other processes used in the techniques described herein. Communication interface 302 is used to support communication by computing device 30. Memory 303 is used to store program code and data of computing device 30.

[0092] The processor 301 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. The bus 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0093] In another embodiment of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the above-described... Figure 1 The method described in the embodiments.

[0094] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. 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 the invention.

[0095] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of 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 displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0097] 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.

[0098] 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.

[0099] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A spot market simulation method taking into account grid inertia support, characterized in that, The method includes: Obtain the minimum inertia requirement of the target power grid; wherein, the target power grid is the simulated power grid after the implementation of the simulation planning scheme; Based on the minimum inertia requirement, a safety-constrained unit combination (SCUC) model and a safety-constrained economic dispatch (SCED) model are constructed. Based on the SCUC model and the SCED model, the whole network node electricity price sequence under different wind and solar power output prediction error scenarios is simulated; wherein, the electricity price sequence includes the node electricity price inertia component; Based on the inertia components, a simulation evaluation report is output.

2. The method according to claim 1, characterized in that, The process of obtaining the minimum inertia requirement of the target power grid includes: Obtain the frequency change rate limit of the target power grid; The minimum inertia requirement is determined based on the maximum power disturbance of the target power grid during the corresponding time period and the frequency change rate limit.

3. The method according to claim 2, characterized in that, Based on the minimum inertia requirement and the frequency change rate constraint, the safety-constrained unit combination (SCUC) model and the safety-constrained economic dispatch (SCED) model are constructed, including: The minimum inertia requirement is converted into a linearized time-varying inertia requirement constraint. The time-varying inertia requirement constraint is used to ensure that the total system inertia is not less than the minimum inertia requirement. The total system inertia is the sum of the inherent inertia contributions of all synchronous generators plus the sum of the virtual inertia contributions of all grid-connected energy storage. The virtual inertia contribution of grid-connected energy storage is determined by the virtual inertia gain coefficient of the energy storage unit, the current remaining power, the rated power of the energy storage converter, and the ratio of the rated energy capacity of the energy storage to the system baseline capacity. Based on the time-varying inertia requirement constraint, the SCUC model and the SCED model are constructed.

4. The method according to claim 3, characterized in that, The SCED model includes energy storage operation constraints, which include: the sum of the energy storage charging and discharging power and the primary frequency regulation reserve capacity does not exceed the rated power of the energy storage converter; and the state of charge of the energy storage minus the energy required for primary frequency regulation reserve has a margin greater than a preset lower limit.

5. The method according to claim 3, characterized in that, The calculation process for the nodal electricity price inertia component includes: Obtain the Lagrange multipliers of the time-varying inertia requirement constraint in the SCED model; The marginal sensitivity coefficient of the injected power at each node in the target power grid to the total inertia of the system is calculated. The marginal sensitivity coefficient is obtained based on the linear relationship between the virtual inertia of energy storage and the state of charge, as well as the network power flow rescheduling logic. Multiplying the Lagrange multiplier by the marginal sensitivity coefficient yields the nodal price inertia component of the corresponding node.

6. The method according to any one of claims 1-5, characterized in that, Each scenario corresponds to a time period; the simulation of the entire network node electricity price sequence under different wind and solar power output prediction error scenarios based on the SCUC model and the SCED model includes: For each scenario, the solution is obtained by recursively solving in time sequence, and the full network node electricity price sequence under multiple scenarios is obtained by iterative solution.

7. The method according to claim 6, characterized in that, The simulation evaluation report output based on the inertia component includes: Based on the spatiotemporal distribution of the inertia components, the improvement of the frequency safety margin, and the probability of exceeding the frequency change rate limit, the simulation evaluation report is output.

8. A spot market simulation system taking into account grid inertia support, characterized in that, The system includes: The acquisition module is used to acquire the minimum inertia requirement of the target power grid; wherein, the target power grid is the simulated power grid after the implementation of the simulation planning scheme; The construction module is used to construct the Safety-Constrained Unit Combination (SCUC) model and the Safety-Constrained Economic Scheduling (SCED) model based on the minimum inertia requirement. The simulation module is used to simulate the whole network node electricity price sequence under different wind and solar power output prediction error scenarios based on the SCUC model and the SCED model; wherein, the electricity price sequence includes node electricity price inertia components; The output module is used to output a simulation evaluation report based on the inertia component.

9. A computing device, characterized in that, include: Memory, used to store programs; A processor for loading the program to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method of any one of claims 1-7.

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