Inertia-standby joint transaction and dynamic clearing settlement method and system
By using a joint trading and dynamic clearing settlement method for inertia and standby services, the problem of redundant resource compensation caused by separate modeling of inertia and standby services is solved, thereby optimizing frequency security and improving market efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-03-31
AI Technical Summary
In the current market, separate modeling of inertia and reserve leads to redundant resource compensation, increases operating costs, and frequency security constraints cannot be accurately quantified, resulting in low market efficiency.
By combining inertia with backup services, and decoupling overlapping values through a quantified inertia-backup compensation coefficient matrix, a dynamic joint clearing model is constructed. Frequency security constraints are introduced, and multi-type variable collaborative solution is adopted for phased settlement.
It achieves synergistic optimization of inertia and reserve resources, reduces operating costs, ensures frequency security, incentivizes flexible resources to participate in the market, and adapts to the future development of the power system.
Smart Images
Figure CN121767028A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of high-proportion renewable energy grid connection technology, specifically to an inertia-reserve joint trading and dynamic clearing, settlement method and system. Background Technology
[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.
[0003] With the large-scale grid connection of new energy sources such as wind power and photovoltaics, and the integration of a large number of renewable energy sources (IBRs) based on power electronic converters, the rotational inertia level of the power system has decreased significantly, leading to an increase in the system frequency change rate (RoCoF) and a decrease in the frequency minimum point (Nadir). Traditional synchronous generator sets are gradually being phased out of the market, and the frequency security of the power grid increasingly relies on ancillary services, mainly system inertia and backup services.
[0004] However, existing market reserve and inertia separation modeling methods still have the following limitations: (1) When the existing market separates reserve and inertia modeling, reserve and inertia are treated as independent constraints or features. This kind of mechanism may result in the same physical resource (such as a synchronous generator or an energy storage system with VSG control) being able to provide both inertia and active frequency response reserve. Such resources may be paid for both inertia and reserve at the same time, resulting in double compensation, increasing operating costs and reducing market efficiency.
[0005] (2) In addition, frequency security constraints often adopt non-precise forms such as non-second-order cones, which cannot effectively link the synergistic effect of inertia and multiple types of backup, resulting in a disconnect between market demand and physical constraints.
[0006] Therefore, a new market mechanism and clearing and settlement system is needed that can characterize the complementary relationship between inertia and reserve under a unified framework, adapt to the dynamic capabilities of multiple resources, and accurately quantify frequency security constraints. Summary of the Invention
[0007] To address the aforementioned issues, this disclosure proposes an inertia-reserve joint trading and dynamic clearing settlement method and system. This method combines the inertia and reserve services required by the power system, establishes a dynamic response feasible domain for the joint resource service capability, introduces a quantifiable inertia-reserve compensation coefficient matrix to correct the service coupling value, constructs a dynamic joint clearing model, achieves synergistic optimization of the two key resources of inertia and reserve, and effectively incentivizes flexible resources such as energy storage to participate in the market through precise value accounting, adapting to the future development needs of the power system.
[0008] According to some embodiments, the present disclosure adopts the following technical solutions: A method for joint trading and dynamic clearing settlement of inertia-reserve assets includes: Define a joint auxiliary service product for resource inertia service and backup service, quantify the synergistic effect of inertia and multiple types of backup, and construct frequency security constraints; Using the real-time frequency dynamic characteristics of the power system as input, and integrating the rotational inertia and damping adaptive control logic of the virtual synchronous machine, the dynamic capability boundary of the synchronous generator (SG) and battery energy storage (BESS) is characterized, and the feasible domain of resource dynamic response is constructed. Using the feasible domain of dynamic resource response, frequency security, and conventional power constraints as constraints, a dynamic clearing model is constructed with the goal of minimizing scheduling costs. A multi-type variable collaborative solution is adopted, and the solution results are used in the clearing and settlement process. During clearing and settlement, a quantified inertia-reserve mutual compensation coefficient matrix is introduced to decouple the overlapping value of inertia service and reserve service. The settlement is divided into two stages: day-ahead and real-time. The benchmark payment calculation and dynamic adjustment are performed in the two stages respectively to obtain the settlement result, and finally the clearing and settlement are completed.
[0009] According to some embodiments, the present disclosure adopts the following technical solutions: An inertia-reserve joint trading and dynamic clearing settlement system includes: The constraint construction module is used to define the joint auxiliary service products of resource inertia service and backup service, quantify the synergistic effect of inertia and multiple types of backup, and construct frequency security constraints. Taking the real-time frequency dynamic characteristics of the power system as input, it integrates the rotational inertia and damping adaptive control logic of the virtual synchronous machine to characterize the dynamic capability boundary of the synchronous generator (SG) and battery energy storage (BESS) and construct the feasible domain of resource dynamic response. The clearing and settlement module is used to construct a dynamic clearing model with the goal of minimizing scheduling costs, taking the feasible domain of dynamic resource response, frequency security, and conventional power constraints as constraints. It employs multi-type variable collaborative solution, and the solution results are used in the clearing and settlement process. During clearing and settlement, a quantified inertia-reserve mutual compensation coefficient matrix is introduced to decouple the overlapping value of inertia service and reserve service. The settlement is divided into two stages: day-ahead and real-time. The benchmark payment calculation and dynamic adjustment are performed in the two stages respectively to obtain the settlement result, and finally complete the clearing and settlement.
[0010] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product includes a computer program that, when executed by a processor, implements the aforementioned inertia-backup joint trading and dynamic clearing settlement method.
[0011] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the aforementioned inertia-backup joint trading and dynamic clearing settlement method.
[0012] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the inertia-standby joint trading and dynamic clearing settlement method.
[0013] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosure presents a method for joint inertia-reserve trading and dynamic clearing settlement, which combines the inertia and reserve services required by the power system. Mathematically defining these services as a unified vector, this vector is bundled into a novel two-dimensional joint trading product. This product directly targets core frequency security indicators (RoCoF, Nadir), ensuring consistency between market demand and physical constraints. This disclosure utilizes the dynamic response feasible region to accurately characterize resource capabilities and leverages the inertia-reserve compensation coefficient matrix to decouple service value during clearing and settlement, achieving synergistic optimization of the two key resources: inertia and reserve. This mechanism ensures grid frequency security in a more economical way and effectively incentivizes flexible resources such as energy storage to participate in the market through precise value accounting, adapting to the future development needs of the power system.
[0014] This disclosure presents an inertia-reserve joint trading and dynamic clearing settlement method, which constructs a dynamic joint clearing model with the goal of minimizing scheduling costs and including key frequency security constraints such as the rate of change of frequency (RoCoF) and the lowest frequency point (Nadir), to ensure the frequency stability of the system under a high proportion of renewable energy penetration.
[0015] This disclosure presents an inertia-reserve joint trading and dynamic clearing settlement method, which establishes a dynamic response feasible region that can accurately describe the joint service capabilities of synchronous generator sets (SG) and battery energy storage systems (BESS) resources; introduces a quantifiable "inertia-reserve compensation coefficient matrix K" to correct the coupling value of services in clearing and settlement, avoiding redundant calculations; through the inertia-reserve compensation coefficient matrix K and the dynamic response feasible region, it avoids repeated compensation for the same resource, reduces operating costs, and dynamically adapts to the grid status to improve the efficiency of market resource allocation. Attached Figure Description
[0016] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.
[0017] Figure 1 This is a schematic diagram of the process for constructing a dynamic response feasible domain for resources according to an embodiment of this disclosure; Figure 2 This is a schematic diagram of the process for determining the inertial-backup compensation coefficient matrix according to an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the clearing and settlement process of the dynamic joint clearing model according to an embodiment of this disclosure. Detailed Implementation
[0018] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.
[0019] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.
[0020] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0021] Example 1 One embodiment of this disclosure provides a method for inertia-reserve joint trading and dynamic clearing settlement, the method steps including: Step 1: Define the resource inertia service and backup service joint auxiliary service product, quantify the synergistic effect of inertia and multiple types of backup, and construct frequency security constraints; Step 2: Using the real-time frequency dynamic characteristics of the power system as input, integrate the rotational inertia and damping adaptive control logic of the virtual synchronous machine to characterize the dynamic capability boundary of the synchronous generator (SG) and battery energy storage (BESS), and construct the feasible domain for dynamic resource response. Step 3: Using the feasible domain of dynamic resource response, frequency security, and conventional power constraints as constraints, a dynamic clearing model is constructed with the goal of minimizing scheduling costs. A multi-type variable collaborative solution is adopted, and the solution results are used in the clearing and settlement process. During clearing and settlement, a quantified inertia-reserve mutual compensation coefficient matrix is introduced to decouple the overlapping value of inertia service and reserve service. The settlement is divided into two stages: day-ahead and real-time. The benchmark payment calculation and dynamic adjustment are performed in the two stages respectively to obtain the settlement result, and finally the clearing and settlement are completed.
[0022] As one embodiment, this disclosure presents a joint trading and dynamic clearing settlement method for inertia and reserve, which treats reserve and rotating inertia as two-dimensional service products for joint clearing. A quantified inertia-reserve mutual compensation coefficient matrix K is introduced to calculate duplicate value during clearing and settlement. This achieves synergistic optimization of the two key resources, inertia and reserve. This mechanism ensures grid frequency security in a more economical way and effectively incentivizes flexible resources such as energy storage to participate in the market through accurate value accounting, adapting to the future development needs of the power system. The specific implementation process is as follows: Step 1: Define a joint auxiliary service product for resource inertia service and backup service, quantify the synergistic effect of inertia and multiple types of backup, and construct frequency security constraints; Specifically, the inertia service and backup service, which are crucial for power system frequency security, are combined and defined as a "two-dimensional joint ancillary service product." The inertia service measures the ability to suppress the rate of frequency change, while the backup service measures the ability to restore the lowest frequency point. The product is mathematically represented as a vector (M, R), where M represents the inertia value provided by resources, and R represents the backup capacity provided by resources. This product directly targets the two core indicators of frequency security: the system frequency change rate and the lowest frequency point, ensuring that market demand aligns with physical constraints. (1) The rate of change of system frequency (RoCoF) is an important indicator for measuring the rate at which the system frequency decreases after a fault, and it needs to be controlled within a safe range. To this end, this disclosure proposes the following constraints to link the total inertia of the system with the frequency change limit: (1) (2) in, The total inertia of the system. This represents a typical N-1 fault power deficit (MW). The limit for the rate of change of frequency (Hz / s) (Rated frequency); This represents the maximum possible failure margin. This reduces the shortfall in the unit's partial loading. M i It refers to the first iThe inertia value provided by a resource (such as a synchronous generator or an energy storage unit) is the basic unit that constitutes the total inertia of the system.
[0023] (2) The minimum frequency point (Nadir) constraint adopts a second-order cone constraint form to accurately quantify the synergistic effect of inertia and multiple types of backup: (3) in, This represents the total system reserve capacity. This is the minimum frequency limit. For the first i Backup response time. R i It refers to the first i The spare capacity provided by each resource; n This represents the total number of resources involved in providing ancillary services.
[0024] Step 2: Using the real-time frequency dynamic characteristics of the power system as input, integrate the rotational inertia and damping adaptive control logic of the virtual synchronous machine to characterize the dynamic capability boundary of the synchronous generator SG and the battery energy storage BESS, and construct the feasible domain of resource dynamic response. Specifically, this disclosure upgrades the traditional capacity feasible region to a dynamic feasible region, using real-time frequency dynamic characteristics (rotor angular velocity, angular velocity offset) as the core input, and integrates the rotational inertia and damping adaptive control logic of the virtual synchronous machine (VSG). This not only accurately characterizes the dynamic capability boundaries of the synchronous generator SG and the battery energy storage BESS, but also constructs a dynamic combination of inertia, reserve, and damping that adapts to frequency changes.
[0025] As one embodiment, this disclosure uses real-time rotor angular velocity and angular velocity offset as core inputs, constructs a basic dynamic characteristic model of the synchronous generator set (SG) and a VSG adaptive control mechanism of the battery energy storage system (BESS) according to resource type, and characterizes the dynamic capability boundaries of the synchronous generator set (SG) and battery energy storage system (BESS), representing them uniformly as a general mathematical representation, and finally outputs the constructed dynamic response feasible region. Specifically, this includes: Step 21: Dynamic response feasible region of synchronous generator set (SG); The dynamic response capability of a synchronous generator set (SG) originates from the inertia of the physical rotor and the damping characteristics of the speed control system. Its core limitation is that the physical inertia is relatively fixed, but the damping coefficient will change dynamically with the rotor angular velocity, and the standby response rate is constrained by the time constant of the speed governor.
[0026] Step 211: Construct the basic model of the dynamic characteristics of SG; Specifically, the rotor motion and standby response of SG satisfy: (4) (5) in, The moment of inertia of the SG rotor (kg) m²); Rated capacity for SG; The rated angular velocity, ; , These are mechanical torque and electromagnetic torque (N) m); SG dynamic damping coefficient (N) m s / rad), with change; The actual response power of the SG backup is subject to the governor time constant. constraint. P sg,op This refers to the current real-time active power output of a synchronous generator set (SG). M sg This refers to the inertia provided by the synchronous generator set; R sg This refers to the reserve capacity provided by synchronous generator sets.
[0027] Step 212: Mathematical expression of the feasible region of the dynamic response of SG; The feasible region of SG's dynamic response needs to first define the inertia M in the two-dimensional variables through the "inertia-start-stop correlation". sg The boundary is then combined with dynamic damping constraints and dynamic backup constraints. Since the physical inertia of SG is relatively fixed, the feasible region of SG's dynamic response can be simplified to a two-dimensional set of dynamic constraints, the core contents of which include: (1) Inertia-Start-Stop Correlation Constraints: Synchronous generators cannot provide inertia services when they are stopped. Therefore, inertia services are closely related to the operating status of the unit. The specific constraints are as follows: (6) in, This indicates the unit's start-up and shutdown status. ( =1 indicates that the program is running. =0 indicates that the service is suspended). Let SG be the inertial constant. This is the rated power of SG.
[0028] (2) Dynamic damping constraint: The damping coefficient must match the current angular velocity offset to avoid frequency oscillation. (7) in, , These are the upper and lower limits of the SG damping coefficient; This is the damping adjustment coefficient. The larger the value, the more drastic the frequency fluctuation. The higher the lower limit, the stronger the ability to suppress oscillations.
[0029] (3) Dynamic reserve constraints: Reserve capacity must meet the current output and response rate limits. (8) in, This represents the maximum output of SG (MW). The maximum instantaneous response power of the SG is reserved. Increase and decrease; To meet the system's response time requirements for backup, ensuring that backup can play a timely role after a failure.
[0030] Meanwhile, the availability of backup capacity is also constrained by the start / stop status: (9) Step 22: Dynamic response feasible region of the battery energy storage system (BESS); Energy storage uses VSG to control virtual inertia response and adjusts control parameters in real time based on rotor angular velocity and angular velocity offset. This maximizes the utilization of standby capacity while suppressing the system frequency change rate RoCoF and optimizing the lowest frequency point Nadir.
[0031] Step 221: BESS's VSG adaptive control mechanism; BESS's VSG control is based on classical rotor motion equations, combined with a three-step logic of "deviation triggering - parameter adjustment - capacity adaptation." It uses a Multi-Axe Blade (MAB) algorithm to learn from a large amount of frequency data and distinguish critical values for different response scenarios, ultimately providing scenario discrimination boundaries for the piecewise adaptive control of the VSG. The core of VSG control is to simulate the rotor motion characteristics of a synchronous generator; its dynamic equation is: (10) in, For the virtual rotational inertia of BESS, Corresponding virtual inertia constant; , These are virtual mechanical torque and electromagnetic torque, respectively. This is the BESS reference power; Angular velocity droop coefficient; This is a virtual damping coefficient, which directly affects the ability to suppress frequency oscillations. P e,bess This refers to the virtual electromagnetic power output by BESS.
[0032] By performing small-signal linearization on equation (10), the equivalent inertial time constant of the system can be obtained. With peak response coefficient : (11) in, For the virtual rotational inertia of BESS, This is the virtual damping coefficient. Angular velocity droop coefficient.
[0033] Furthermore, the piecewise adaptive control strategy is based on angular frequency deviation. The threshold segmentation design divides the control scenarios into three categories: (1) =0.01 rad / s corresponds to a frequency deviation , correspond .when In the case of normal frequency fluctuations, the control objective is to "save inertia reserves and prioritize backups", with the virtual inertia and virtual damping coefficient being taken as minimum values. (2) When At that time, in the scenario of moderate frequency fluctuation, the control objective was to "balance the suppression of frequency change rate with reserve supply". , exist , Based on adjusting the coefficients , Make adjustments, and follow It increases linearly with increasing size; M bess,min This represents the minimum value of the BESS virtual inertia. D bess,min This is the minimum value of the BESS virtual damping coefficient.
[0034] (3) When In the case of a scenario with drastic frequency fluctuations, the control objective is to "prioritize the suppression of RoCoF". , Take the maximum value.
[0035] The virtual inertia upper limit is constrained by the current energy state and the system frequency fluctuation amplitude provided by BESS, which is jointly determined by the real-time state of charge, maximum frequency deviation, and inertia response time of the energy storage system. (12) in, Rated energy for BESS (MWh); Reserve a state of charge for inertia; For discharge efficiency; This represents the maximum frequency deviation. This is the inertial response time.
[0036] Step 222: Mathematical expression of the feasible region of BESS dynamic response; BESS dynamic response feasible region yes The core constraints of a convex set in three-dimensional space include: (1) Dynamic constraints on virtual inertia: (13) in, = 0, meaning no inertia is reserved. It is determined by equation (12). K M It is the inertia adjustment factor, virtual inertia. M bess It will increase linearly with frequency deviation from the minimum value. K M This is the slope of this linear growth.
[0037] (2) Dynamic constraints on reserve capacity: BESS provides reserved power for backup, deducting the virtual inertia load. And the backup response rate is affected constraint: (14) in, Rated power for BESS; The backup response time constant for BESS; The standby response rate is (MW / s). The larger the value, the lower the upper limit of the speed, thus avoiding frequency overshoot caused by a sudden increase in backup power. P bessinertia Reserve power for inertial response; R bess Backup capacity provided for BESS; R bess,max The maximum amount of spare capacity provided for BESS; D bess,max This represents the maximum virtual damping coefficient.
[0038] (3) Damping dynamic constraints: (15) in, , Ensure damping ratio ; K D It is the damping adjustment coefficient.
[0039] (4) Dynamic energy constraints: (16) in, , These represent the charging and discharging power of the energy storage at time t, respectively. For energy storage systems in Energy state at any given moment For the scheduling period, For charging efficiency. F lim This refers to the rate of change limit of frequency, which determines the boundary of the instantaneous energy release rate required to provide maximum inertia support; SOC t This refers to the current state of charge. SOC min This refers to the minimum permissible state of charge.
[0040] Step 23: Unified representation of the dynamic feasible region; The dynamic feasible region of resources is uniformly represented by a set of piecewise convex polygon inequalities. For the th k For resource classes, the general form of their dynamic feasible domain is: (17) in, It is a dynamic coefficient matrix, and the matrix elements are updated in real time with the rotor angular velocity and angular velocity offset; It is a boundary vector, determined by the resource rating parameters and real-time operating status.
[0041] Step 3: Using the feasible region of dynamic resource response, frequency security, and conventional power constraints as constraints, a dynamic clearing model is constructed with the goal of minimizing scheduling costs. A multi-variable collaborative solution is employed, and the results are used in the clearing and settlement process. During clearing and settlement, a quantified inertia-reserve mutual compensation coefficient matrix is introduced to decouple the overlapping value of inertia services and reserve services. The settlement is divided into two stages: day-ahead and real-time. Base payment calculations and dynamic adjustments are performed in both stages to obtain the settlement results, ultimately completing the clearing and settlement process. Specific details include: Step 31: Dimensions and definition of inertia-spare mutual compensation coefficient; First, the dimensions and function of the inertia-reserve mutual compensation coefficient matrix introduced by clearing settlement are defined. Due to the synergistic effect of inertia and reserve in physical effects, simply adding the inertia price and reserve price may double-count the frequency support value they jointly bring. Therefore, the inertia-reserve compensation coefficient matrix K disclosed in this paper is used to decouple the overlapping value of the two services.
[0042] Furthermore, the inertial-backup compensation coefficient matrix is as follows: The matrix, where 5 represents the quantity of resources, and 5 represents the quantity of value coupling compensation factors. Furthermore, the resource dimension includes traditional synchronous generator units. Wind power Photovoltaics BESS (Battery Energy Storage) and Virtual Power Plant (VPP) Value coupling compensation factor Including response speed compensation factor Inertia density compensation factor Backup availability compensation factor Fault adaptation compensation factor and multi-service coupling compensation factor The response speed compensation factor quantifies service latency, such as millisecond-level response of BESS and second-level response of gas turbine units; the inertia density compensation factor quantifies the inertia provided per unit capacity; the standby availability compensation factor quantifies the standby call success rate, such as the standby redundancy of VPP aggregated resources; the fault adaptation compensation factor quantifies the service effectiveness under N-1 faults, such as the RoCoF suppression capability of photovoltaic VSM under high penetration; and the multi-service coupling compensation factor quantifies the value conflict when resources simultaneously provide inertia and standby.
[0043] Furthermore, the matrix elements in the inertial-reserve compensation coefficient matrix This represents the value correction coefficient for the i-th type of resource under the j-th compensation factor, and its value range is... ;when ,resource The The quality of this service has reached the system's benchmark level; ,resource The The quality of this service is better than the benchmark; ,resource The The quality of this service is below the benchmark.
[0044] In summary, the mathematical expression for matrix K is: (18) Step 32: Method for determining the elements of matrix K; This disclosure employs a "static benchmark + dynamic adjustment" mechanism to determine the compensation matrix K. Specific details include: (1) Static Baseline: Determined by the physical characteristics of the resources, calibrated through historical data and simulation. Response Speed: Resources with "fast" response are rewarded, with gas turbine units having a slower response as the baseline. Energy storage with millisecond-level response (BESS) can obtain a higher coefficient due to its speed advantage. Inertia Density: Resources with "good" performance are rewarded, with the stable physical inertia of synchronous generators as the baseline. Virtual inertia generated by the algorithm is given a corresponding coefficient value based on the effectiveness and stability of its control. System Adaptability: Resources with "stable" performance are rewarded. Resources with stable performance under weak grid conditions can be used as the baseline value, while virtual power plants (VPPs) that can aggregate multiple resources and flexibly respond to faults should obtain a premium coefficient due to their higher flexibility.
[0045] (2) Dynamic Adjustment: Based on the real-time operating status of the power grid, the benchmark value is dynamically adjusted. When the penetration rate of renewable energy is too high, the system becomes more vulnerable, and the demand for active support capabilities increases. The compensation coefficients of rapid and active support resources such as wind power and energy storage should be dynamically increased to incentivize them to play a role at critical moments. When the total inertia of the system is below the safety line, inertial resources become extremely valuable, and their value is highlighted due to their scarcity. The compensation coefficients of all inertial resources should be increased, especially those of traditional units that can provide high-quality physical inertia, to ensure the most basic frequency security of the power grid. Step 33: Using the feasible region of dynamic resource response, frequency security, and conventional power constraints as constraints, construct a dynamic clearing model with the goal of minimizing scheduling costs; This disclosure constructs a dynamic joint clearing model with the goal of minimizing scheduling costs, while incorporating key frequency security constraints such as the dynamic response feasible region, the rate of change of frequency (RoCoF), and the lowest frequency point (Nadir), to ensure the frequency stability of the system under a high proportion of renewable energy penetration.
[0046] Step 331: Construct the objective function; With the objective of minimizing the total system cost within a spot market dispatch cycle (15 minutes / period, totaling 96 periods), encompassing generation costs, energy storage operation costs, and inertial-standby service costs, and introducing a compensation coefficient matrix K, the dynamic clearing model expression is as follows: (19) Where T is the set of spot scheduling cycles, and G and B are the SG set and BESS set, respectively; for Electrical output during time period t Its unit cost of electricity (yuan / MWh); express During the start / stop state of time period t For start-up and shutdown costs; for physical inertia For BESS's virtual inertia, , Its unit cost; , They are respectively and BESS during the time period t Backup capacity, , Its unit cost; , They are respectively The charging and discharging power of BESS during time period t, , The costs are the charging and discharging costs, respectively. This is the inertia-reserve compensation coefficient matrix. These are the matrix calibration coefficients.
[0047] Step 332: Core constraints; (1) Resource dynamic response feasible domain constraints, including: inertia-start-stop correlation constraints, dynamic damping constraints and dynamic reserve constraints, as well as virtual inertia dynamic constraints, reserve capacity dynamic constraints, damping dynamic constraints and energy dynamic constraints.
[0048] (2) Frequency safety constraints include the system frequency change rate RoCoF constraint, as shown in equations (1) and (2) and the lowest frequency point Nadir constraint.
[0049] Lowest frequency point Nadir constraint: (20) in, (Nadir limit 49.5 Hz) For frequency response time, This refers to the SG response time.
[0050] (3) Conventional constraints of the power system; 1) Power balance constraints Power balance is a fundamental requirement for the operation of a power system, ensuring real-time matching between power generation and load. This disclosure considers the uncertainty of wind power output and establishes the following power balance constraints: (twenty one) in, To contribute to wind power, System load (MW); (Network loss). The total output of all synchronous generator units during time period t; Net discharge power (discharge minus charge) for all energy storage systems. To contribute to wind power forecasting; This represents the total load demand of the system during time period t. This represents the power loss (set to 2% of the load).
[0051] 2) Generator output constraints To ensure the safe operation of the generator set, its output must be limited to the permissible range. This disclosure sets the following constraints on the output of the generator set: (twenty two) in, For the first i A generator in t Minimum output for a given time period; For the first i A generator in t Actual dispatch output during the time period; For the first i A generator in t Maximum output during a given time period.
[0052] 3) Gradient constraint The rate of change of generator output is physically limited, and the ramp rate constraint ensures that the output variation of the unit in adjacent time periods does not exceed its technical upper limit. This disclosure adopts the following ramp rate constraint: (twenty three) in, This refers to the increase in unit output during adjacent time periods; For the unit i Maximum uphill climbing rate; This represents the reduction in unit output during adjacent time periods; For the unit i The maximum downhill climbing rate.
[0053] Step 34: Solving the dynamic clearing model BESS Dynamic Feasibility Domain Piecewise transformation, using the "Big M method" to transform piecewise constraints into mixed integer linear constraints (MILP): defining scene identifier variables. (Only one is 1, the rest are 0), corresponding to "normal / moderate / severe" frequency fluctuations respectively. The model contains discrete and continuous variables, requiring collaborative solution of multiple types of variables. Gurobi 10.0 is used to solve the MILP problem, combining a parallel strategy of continuous and discrete SCUC, and extracting inertia and backup shadow prices for settlement.
[0054] Step 35: Clearing and Settlement Process This disclosure introduces the inertia-reserve mutual compensation coefficient matrix K into the settlement system, correcting resources while providing the coupling value of inertia M and reserve R, avoiding duplicate compensation. A static benchmark of K is stored in a graph database as an attribute of resource nodes; real-time adjustments are calculated using off-chain fast services and written into smart contract parameters or recorded as on-chain transactions / state updates. The spot scheduling cycle employs a two-stage architecture: day-ahead + real-time. In the day-ahead stage, the static benchmark corrects payments during clearing and settlement to obtain the benchmark payment; in the real-time stage, dynamic adjustments are made based on real-time security boundaries and scarcity through dynamic factors to correct real-time deviations and service.
[0055] Let the resource index be i The time period is t=15min. Step 351: Settle the benchmark revenue; Defines the base revenue of resources in day-ahead and real-time markets, without considering compensation adjustments. Day-ahead base revenue: (twenty four) in, Based on the current day's benchmark income; This refers to the electricity that was cleared out recently; The day-ahead marginal electricity price (RMB / MWh); The inertia service capacity is (as of today) in MW·s / Hz; This is the day-ahead marginal price (shadow price); Reserve capacity (as of the day) allocated to resources; This is the day-ahead reserve marginal price (shadow price).
[0056] Based on real-time deviation income: (25) in, This refers to the electricity that was cleared out recently; To provide real-time, actual output; , These are the day-ahead and real-time marginal electricity prices (RMB / MWh), respectively. To allocate resources i Inertia service capacity (MW·s / Hz); To allocate resources i The spare capacity; , These are the day-ahead and real-time marginal prices of inertia (RMB / (MW·s / Hz)); , These are the day-ahead and real-time reserve marginal prices, respectively. For real-time deviation revenue; This refers to the electrical energy deviation. The deviation serves the inertia. , These are real-time inertia and day-ahead inertia, respectively. This is the deviation in reserve capacity. , These are real-time standby capacity and day-ahead standby capacity, respectively.
[0057] Step 352: Inertia-Spare Mutual Compensation Coefficient Matrix Correction Term; In the compensation coefficient matrix K Take a set of weight vectors (Determined by system requirements), then resources are... t Conversion factor for time period: (26) in, It is a resource i The static compensation coefficient vector contains the impact of different factors on resource value.
[0058] Then it is used for coupling term reduction: (27) in, , This is a normalized dimensional constant.
[0059] Step 353: Settlement Calculation Formula: (28) in, For resources i exist t The final total net income for the period; This is the benchmark income calculated by the market at the current time; Revenue is adjusted for deviations calculated in real-time market data; This is a term for the coupling value conversion.
[0060] As one embodiment, the settlement calculation method disclosed herein follows a two-stage architecture of "day-to-day + real-time" in actual operation: First, in the day-ahead phase, the system runs the day-ahead clearing model to establish a baseline, determine the unit's electrical energy, inertia, and reserve bid amount, and calculate the day-ahead baseline revenue. At the same time, it uses static compensation coefficients calibrated based on resource physical characteristics to conduct a preliminary assessment and correction of the service's coupling value.
[0061] Then, in the real-time phase, the system calculates dynamic adjustment factors based on the real-time grid operation status, such as the penetration rate of new energy sources and frequency safety margin. It calculates real-time deviation revenue by obtaining the real-time actual output and the deviation of each service through clearing. If the system is detected to be in a high-risk state such as insufficient total inertia, the compensation coefficient will be dynamically increased to increase the incentive for scarce resources.
[0062] Finally, in the settlement and value decoupling stage, the system uses a formula to calculate key coupling conversion items to separate the overlapping value of inertia and reserves, and subtracts the conversion item after summing the day-ahead and real-time revenues to obtain the final payment amount.
[0063] Example 2 One embodiment of this disclosure provides an inertia-backup joint trading and dynamic clearing settlement system, including: The constraint construction module is used to define the joint auxiliary service products of resource inertia service and backup service, quantify the synergistic effect of inertia and multiple types of backup, and construct frequency security constraints. Taking the real-time frequency dynamic characteristics of the power system as input, it integrates the rotational inertia and damping adaptive control logic of the virtual synchronous machine to characterize the dynamic capability boundary of the synchronous generator (SG) and battery energy storage (BESS) and construct the feasible domain of resource dynamic response. The clearing and settlement module is used to construct a dynamic clearing model with the goal of minimizing scheduling costs, taking the feasible domain of dynamic resource response, frequency security, and conventional power constraints as constraints. It employs multi-type variable collaborative solution, and the solution results are used in the clearing and settlement process. During clearing and settlement, a quantified inertia-reserve mutual compensation coefficient matrix is introduced to decouple the overlapping value of inertia service and reserve service. The settlement is divided into two stages: day-ahead and real-time. The benchmark payment calculation and dynamic adjustment are performed in the two stages respectively to obtain the settlement result, and finally complete the clearing and settlement.
[0064] Example 3 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned inertia-backup joint trading and dynamic clearing settlement method.
[0065] Example 4 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When these computer instructions are executed by a processor, they implement the aforementioned inertia-backup joint trading and dynamic clearing settlement method.
[0066] Example 5 One embodiment of this disclosure provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the inertia-standby joint trading and dynamic clearing settlement method.
[0067] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0068] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0069] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.
Claims
1. A method for joint inertia-reserve trading and dynamic clearing settlement, characterized in that, include: Define a joint auxiliary service product for resource inertia service and backup service, quantify the synergistic effect of inertia and multiple types of backup, and construct frequency security constraints; Using the real-time frequency dynamic characteristics of the power system as input, and integrating the rotational inertia and damping adaptive control logic of the virtual synchronous machine, the dynamic capability boundary of the synchronous generator (SG) and battery energy storage (BESS) is characterized, and the feasible domain of resource dynamic response is constructed. Using the feasible domain of dynamic resource response, frequency security, and conventional power constraints as constraints, a dynamic clearing model is constructed with the goal of minimizing scheduling costs. A multi-type variable collaborative solution is adopted, and the solution results are used in the clearing and settlement process. During clearing and settlement, a quantified inertia-reserve mutual compensation coefficient matrix is introduced to decouple the overlapping value of inertia service and reserve service. The settlement is divided into two stages: day-ahead and real-time. The benchmark payment calculation and dynamic adjustment are performed in the two stages respectively to obtain the settlement result, and finally the clearing and settlement are completed.
2. The inertia-reserve joint trading and dynamic clearing settlement method as described in claim 1, characterized in that, The definition of the resource inertia service and backup service joint auxiliary service product quantifies the synergistic effect of inertia and multiple types of backup, and constructs frequency security constraints, including: The inertial service and backup service that power system frequency security depends on are combined and defined as a two-dimensional joint ancillary service product. Inertia service measures the ability to suppress the rate of frequency change, while backup service measures the ability to recover the lowest frequency point. The two-dimensional joint auxiliary service product is quantified, and the mathematical expression of the quantification is a vector (M, R), where M represents the inertia value provided by the resource and R represents the reserve capacity provided by the resource. The two core indicators for benchmarking frequency safety constraints for two-dimensional joint auxiliary service products are the system frequency change rate and the lowest frequency point.
3. The inertia-reserve joint trading and dynamic clearing settlement method as described in claim 1, characterized in that, Using the real-time frequency dynamic characteristics of the power system as input, a feasible region for dynamic resource response is constructed, including: Using real-time rotor angular velocity and angular velocity offset as core inputs, and integrating the rotational inertia and damping adaptive control logic of the virtual synchronous machine, the dynamic characteristic basic model of the synchronous generator set SG and the VSG adaptive control mechanism of the battery energy storage system BESS are constructed according to the resource type. It accurately characterizes the dynamic capability boundaries of the synchronous generator set (SG) and the battery energy storage system (BESS), as well as the dynamic combination of inertia, reserve, and damping that adapts to frequency changes.
4. The inertia-reserve joint trading and dynamic clearing settlement method as described in claim 3, characterized in that, The feasible region of dynamic response of synchronous generator set SG is first defined by inertia-start-stop correlation to clarify the boundary of inertia in two-dimensional variables. Then, combined with dynamic damping constraints and dynamic reserve constraints, since the physical inertia of synchronous generator set SG is relatively fixed, the feasible region of dynamic response of synchronous generator set SG is simplified to a set of two-dimensional dynamic constraints, including inertia-start-stop correlation constraints, dynamic damping constraints and dynamic reserve constraints.
5. The inertia-reserve joint trading and dynamic clearing settlement method as described in claim 3, characterized in that, The Battery Energy Storage System (BESS) controls the virtual inertia response through VSG. Based on the rotor angular velocity and angular velocity offset, it adjusts the control parameters in real time. While suppressing the system frequency change rate and optimizing the lowest frequency point, it maximizes the utilization rate of the reserve capacity. The feasible region of the dynamic response of the BESS is a convex set in three-dimensional space. The core constraints include the virtual inertia dynamic constraint, the reserve capacity dynamic constraint, the damping dynamic constraint, and the energy dynamic constraint.
6. The inertia-reserve joint trading and dynamic clearing settlement method as described in claim 1, characterized in that, The introduced quantized inertia-backup compensation coefficient matrix decouples the overlapping value of inertia service and backup service. The inertia-backup compensation coefficient matrix is as follows: The matrix, 5 represents the number of resources, and 5 represents the number of value coupling compensation factors. The resource dimension includes traditional synchronous generators, wind power, photovoltaics, battery energy storage (BESS), and virtual power plants. The value coupling compensation factors include response speed compensation factor, inertia density compensation factor, standby availability compensation factor, fault adaptability compensation factor, and multi-service coupling compensation factor. Matrix elements... Indicates the first i Class resources in the j Value correction coefficient under each compensation factor; The inertia-backup mutual compensation coefficient matrix is determined by combining a static benchmark with a dynamic adjustment mechanism.
7. An inertia-reserve joint trading and dynamic clearing settlement system, characterized in that, include: The constraint construction module is used to define the joint auxiliary service product of resource inertia service and backup service, quantify the synergistic effect of inertia and multiple types of backup, and construct frequency safety constraints. Using the real-time frequency dynamic characteristics of the power system as input, and integrating the rotational inertia and damping adaptive control logic of the virtual synchronous machine, the dynamic capability boundary of the synchronous generator (SG) and battery energy storage (BESS) is characterized, and the feasible domain of resource dynamic response is constructed. The clearing and settlement module is used to construct a dynamic clearing model with the goal of minimizing scheduling costs, taking the feasible domain of dynamic resource response, frequency security, and conventional power constraints as constraints. It employs multi-type variable collaborative solution, and the solution results are used in the clearing and settlement process. During clearing and settlement, a quantified inertia-reserve mutual compensation coefficient matrix is introduced to decouple the overlapping value of inertia service and reserve service. The settlement is divided into two stages: day-ahead and real-time. The benchmark payment calculation and dynamic adjustment are performed in the two stages respectively to obtain the settlement result, and finally complete the clearing and settlement.
8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the inertia-backup joint trading and dynamic clearing settlement method according to any one of claims 1-6.
9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement an inertia-backup joint trading and dynamic clearing settlement method as described in any one of claims 1-6.
10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to perform an inertia-backup joint trading and dynamic clearing settlement method as described in any one of claims 1-6.