Sewage plant safety capacity scheduling method and device for virtual power plant peak regulation

Abstract

The invention provides a virtual power plant peak regulation-oriented sewage plant safety capacity scheduling method and device. The method comprises the following steps of: carrying out load-process dynamic coupling modeling; after the peak regulation task is received, calculating the maximum power regulation quantity which can be safely executed in the response time window Twin under the current working condition for each load object; in the day-ahead plan scheduling, the power adjustment starting time of the load object is advanced to be before the response time window Twin, and the declarable capacity is planned according to the maximum technical adjustment power; in intra-day scheduling, all Pdisgt are aggregated; and forming a safe schedulable resource pool by the load objects of 0, carrying out load object combination optimization and generating a scheduling instruction. The method has the advantages that under the given peak regulation task of the power grid, the safety capacity delta Pmax of each load object in the sewage plant under the current working condition is calculated and determined in real time, safety dispatching is achieved based on the safety capacity delta Pmax, and the problems of uncredibility of dispatching instructions and process safety risks are solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power system dispatching, and particularly relates to a sewage plant safe capacity dispatching method and device for virtual power plant peak shaving. BACKGROUND

[0002] Currently, industrial adjustable load has become an important flexible resource of virtual power plant. In the sewage treatment industry, the existing technology mainly focuses on the static assessment of adjustable potential. For example, by analyzing the historical power consumption data of the whole plant or a process section and the equipment nameplate parameters, a theoretical and time-periodic power regulation range is calculated by using a fixed efficiency formula or a proportional coefficient.

[0003] However, such static assessment method has a fundamental defect: it equates the complex sewage treatment process with significant biochemical lag characteristics to a pure electrical load that can respond instantaneously. The existing method completely ignores the dynamic time lag and safe coupling relationship between the power regulation action of key electrical equipment (such as a blower and a lifting pump) and the core water quality process parameters (such as dissolved oxygen and sludge concentration) to reach a new stable state. This will lead to two major problems when actually participating in power grid peak shaving, especially in the daily or real-time power market with strict requirements on response time: the dispatching result is not reliable, and the dispatching instruction based on static potential may fail to be executed due to the inability of the process to stabilize within the specified time window, or be forced to terminate midway to protect water quality, resulting in the failure of the power grid side regulation target; the process safety risk is uncontrollable, and the dispatching system cannot predict whether the water quality will exceed the standard during the response period after calling a certain device, which will put the passive response to production safety at high risk. SUMMARY

[0004] Therefore, the present application aims to overcome the above-mentioned problems in the prior art and provides a sewage plant safe capacity dispatching method and device for virtual power plant peak shaving.

[0005] To achieve the above-mentioned purposes, the technical solution of the present application is as follows: The present application provides a sewage plant safe capacity dispatching method for virtual power plant peak shaving, comprising the following steps: S1. Load-process dynamic coupling modeling, for each load object in the sewage plant, a digital model including an electrical operation layer, a process safety layer and a dynamic coupling layer is constructed, wherein the load object is an independently controllable electrical equipment; the electrical operation layer includes the rated power, the current power P_cur, the safe operation power interval [P_min, P_max] and the power regulation inertia dead zone ΔP_db of the load object; the process safety layer includes the safety threshold of the core process parameters associated with the load object; the dynamic coupling layer is used to establish the mapping relationship from the power regulation amount ΔP of the load object to the dynamic prediction trajectory Y(t) of the core process parameters in the future period of time; S2. Online solution of safety capacity, when receiving the peak shaving task instruction containing the response time window T_win, for each load object, based on the digital model, combining the safe operation power interval constraint of the electrical operation layer and the core process parameter safety threshold constraint of the process safety layer, the maximum power regulation amount that can be safely executed within the response time window T_win under the current working condition is calculated, denoted as safety capacity ΔP_max, and the real-time schedulable net capacity P_dis is obtained, and P_dis=|ΔP_max|-ΔP_db; S3. Multi-time scale scheduling, in the day-ahead scheduling, the power regulation start time of the load object is advanced to before the response time window T_win, to ensure that the process is in a stable and safe state within the response time window T_win of the power grid evaluation, and the reportable capacity is planned according to the maximum technical regulation power ΔP_dis_max, wherein the maximum technical regulation power ΔP_dis_max is the difference between the current power P_cur of the load object and the boundary of the safe operation power interval [P_min, P_max]; the reportable capacity is the power regulation amount that the sewage plant promises to safely provide within the response time window T_win when participating in the peak shaving of the virtual power plant; in the day-ahead scheduling, all load objects with P_dis>0 are aggregated to form a safe schedulable resource pool, and load object combination optimization is carried out based on the safe schedulable resource pool to generate a scheduling instruction; S4. Safety monitoring and intervention, in the process of executing the peak shaving task instruction by the load object, the actual value of the core process parameter is collected in real time, and is compared with the dynamic prediction trajectory Y(t) of the dynamic coupling layer, if the deviation between the actual value and the dynamic prediction trajectory Y(t) exceeds the preset safety margin and there is an out-of-limit trend, a preset active safety intervention instruction is automatically triggered.

[0006] Further, the dynamic coupling layer in step S1 is established by a historical data driven method, a mechanism analysis method or a method combining historical data driven and mechanism analysis, and the current power P_cur and the power regulation amount ΔP provided by the electrical operation layer are taken as inputs, and the safety threshold provided by the process safety layer is taken as a benchmark for evaluating the safety of the dynamic prediction trajectory Y(t). Among them, the historical data-driven method is based on system identification or machine learning algorithms, using historical data for model training. Historical data includes electrical operation data, process operation data, operating environment data, and dispatch response data. The electrical operation data includes historical power curves, start-stop records, frequency regulation records, power factor, and current and voltage waveform data of the load objects; the process operation data includes dissolved oxygen, water level, sludge concentration, pH value, oxidation-reduction potential, influent and effluent chemical oxygen demand, and ammonia nitrogen content; the operating environment data includes influent flow rate, influent water quality, water temperature, air temperature, and air pressure; and the dispatch response data includes the dispatch instructions in historical peak-shaving tasks, the actual response power curve, and the core process parameter changes data for the corresponding time period. Mechanistic analysis methods are based on the physical, chemical, and biological principles of wastewater treatment processes, forming a framework of model equations. Specifically, mechanistic analysis methods include: deriving the equation for the rate of change of dissolved oxygen during aeration based on the gas-liquid mass transfer two-film theory; and / or describing the relationship between substrate consumption and microbial growth based on the Mono equation; and / or establishing dynamic balance equations for water quantity, water quality, and sludge quantity based on the principles of mass conservation and energy conservation. The approach that combines historical data-driven and mechanistic analysis uses mechanistic analysis to determine the basic structure of the dynamic coupling layer model and historical data-driven methods to identify or calibrate key parameters in the model online, including the time constant and gain coefficient.

[0007] Furthermore, the dynamic coupling layer specifically adopts a state-space model or a time-varying model with linear parameters.

[0008] Furthermore, in step S2, the safety capacity ΔP_max is calculated by solving a constrained optimization problem, where the objective of the optimization problem is to maximize |ΔP|. If there is no feasible solution, then ΔP_max = 0. The constraints include: Power regulation constraint: P_min≤P_cur+ΔP≤P_max; Dynamic safety constraints are achieved by predicting the dynamic trajectory Y(t) within the response time window T_win through the dynamic coupling layer, which satisfies the safety threshold defined by the process safety layer throughout the entire process.

[0009] Furthermore, in step S2, the calculation of the safe capacity ΔP_max is achieved by querying the pre-generated safe capacity boundary curve database. The safe capacity boundary curve database stores the safe capacity ΔP_max corresponding to different response time windows T_win under different typical operating conditions. The typical operating conditions are determined by at least the following key variables, including influent flow rate, influent water quality concentration, water temperature, and mixed liquor sludge concentration.

[0010] Furthermore, in step S2, the calculation of the safe capacity ΔP_max is achieved through iterative search verification. The trial value of the power regulation amount ΔP is adjusted by using the binary search method or the golden section method, and the dynamic coupling model is called to verify the safety feasibility until the maximum |ΔP| that satisfies the constraint conditions is found as the value of the safe capacity ΔP_max.

[0011] Furthermore, in step S3, within the safe and schedulable resource pool, at least one of the following factors is comprehensively considered: combination simplicity and reliability, dynamic response characteristics, process safety margin, adjustment economy, and equipment status and lifespan balance; and the optimal load object combination scheme is solved by a mixed integer linear programming algorithm.

[0012] Furthermore, in the solution process of the mixed-integer linear programming algorithm, the decision variables include the actual adjustment power values ​​of each candidate load object in the safe and schedulable resource pool. The objective function is to minimize the weighted comprehensive cost, and the weighted comprehensive cost is constituted by weighted summation of at least one of the following factors: adjustment economic cost, number of activated load objects, load object lifetime loss, dynamic response performance index, and process safety margin index. Finally, the optimal load object combination scheme that minimizes the weighted comprehensive cost is obtained. Among them, the actual regulation power of each load object does not exceed the corresponding real-time dispatchable net capacity P_dis, and the sum of the actual regulation power of all load objects meets the total regulation required by the power grid peak shaving task instruction.

[0013] A second aspect of the present invention provides a wastewater treatment plant safety capacity scheduling device for peak shaving in virtual power plants, comprising: The load-process dynamic coupling modeling module is used to construct a digital model for each load object in the wastewater treatment plant, including an electrical operation layer, a process safety layer, and a dynamic coupling layer. The online safety capacity calculation module is used to calculate the maximum power regulation that can be safely executed within the response time window T_win under the current operating conditions for each load object after receiving a peak shaving task instruction containing a response time window T_win. This is based on a digital model and combines the safe operating power range constraints of the electrical operation layer with the safety threshold constraints of the core process parameters of the process safety layer. The maximum power regulation is denoted as the safety capacity ΔP_max, and the real-time schedulable net capacity P_dis is obtained. The multi-timescale scheduling module is used to advance the power regulation start time of load objects to before the response time window T_win in day-ahead planning scheduling, ensuring that the process is in a stable and safe state within the response time window T_win of the power grid assessment, and to plan the applicable capacity according to the maximum technical adjustment power ΔP_dis_max; and to aggregate all load objects with P_dis>0 to form a safe and schedulable resource pool in intraday scheduling, and to perform load object combination optimization and generate scheduling instructions based on the safe and schedulable resource pool. The safety monitoring and intervention module is used to collect the actual values ​​of core process parameters in real time during the execution of peak shaving task instructions by the load object, and compare them with the dynamic prediction trajectory Y(t) of the dynamic coupling layer. If the deviation between the actual value and the dynamic prediction trajectory Y(t) exceeds the preset safety margin and there is a tendency to exceed the limit, the preset active safety intervention instruction will be automatically triggered.

[0014] Compared with the prior art, the present invention has the following advantages: The wastewater treatment plant safety capacity scheduling method for virtual power plant peak shaving described in this invention, under a given peak shaving task from the power grid, solves for safety capacity online, preventing process overrun risks before command generation, transforming passive response into proactive prevention. Furthermore, the output safety capacity is a reliable capacity rigorously endorsed by a dynamic model, greatly enhancing the confidence of the power grid dispatching system. It employs a lightweight model and efficient solution strategy to achieve second-level safety capacity decision-making. Simultaneously, by distinguishing between day-ahead time substitution and real-time safety resource pool modes, it enables wastewater treatment plants to maximize their participation in the full range of electricity market services under safe conditions, obtaining better returns. The method has strong universality and can be extended to high-energy-consuming process industries with similar process dynamics, such as chemical and metallurgical industries, providing a core technical path for highly reliable participation in new power systems in the industrial sector. Attached Figure Description

[0015] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Fig. 1 This is a flowchart of the wastewater treatment plant safety capacity scheduling method for peak shaving of virtual power plants as described in Embodiment 1 of the present invention; Fig. 2 This is a flowchart illustrating the wastewater treatment plant safety capacity scheduling method for peak shaving of virtual power plants as described in Embodiment 1 of the present invention. Fig. 3 This is a schematic diagram of the digital model structure described in Embodiment 1 of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0017] In the description of this invention, it should be understood that these descriptions are merely exemplary and not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0018] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0019] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0020] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Example 1 like Figs. 1 to 3 As shown, the wastewater treatment plant safety capacity scheduling method for peak shaving of virtual power plants includes the following steps: S1. Load-process dynamic coupling modeling, such as Fig. 3As shown, for each load object within the wastewater treatment plant, a digital model is constructed, comprising an electrical operation layer, a process safety layer, and a dynamic coupling layer. The load object is an independently controllable electrical device (such as blower #1 and influent pump #2). The electrical operation layer includes the load object's rated power, current power P_cur, safe operating power range [P_min, P_max], and power regulation inertia dead zone ΔP_db. The process safety layer includes the safety thresholds of core process parameters associated with the load object, such as the lower safe limit of dissolved oxygen concentration DO_lim for the blower's aeration tank and the upper safe limit of the forebay water level H_max for the lift pump. The dynamic coupling layer is used to establish... The mapping relationship between the power regulation amount ΔP of the load object and the dynamic prediction trajectory Y(t) of the core process parameters over a future period; the power regulation inertia dead zone ΔP_db is the minimum power change amplitude that the load equipment cannot generate an effective response due to its own physical characteristics or control logic limitations when receiving a power regulation command; the safe operating power range [P_min, P_max] is determined based on the nameplate parameters of the load object, the manufacturer's performance curve, field test data, historical safe operation statistics and process design. This range defines the power limit that the load object can reach physically and technically, and constrains the maximum technical regulation power ΔP_dis_max; S2. Online calculation of safe capacity: Upon receiving a peak shaving task instruction containing a response time window T_win, for each load object, based on the digital model constructed in step S1, and combining the safe operating power range constraints of the electrical operation layer and the core process parameter safety threshold constraints of the process safety layer, calculate the maximum power regulation that can be safely executed within the response time window T_win under the current operating conditions, denoted as the safe capacity ΔP_max, and obtain the real-time schedulable net capacity P_dis, where P_dis=|ΔP_max|-ΔP_db. If ΔP_max=0, then P_dis=0. S3. Multi-timescale scheduling: In day-ahead scheduling, the power regulation start time of load objects is advanced to before the response time window T_win to ensure that the process is in a stable and safe state within the response time window T_win of the power grid assessment. The applicable capacity is planned according to the maximum technical regulation power ΔP_dis_max, where the maximum technical regulation power ΔP_dis_max is the difference between the current power P_cur of the load object and the boundary of the safe operating power interval [P_min, P_max], and does not involve process safety constraints; the applicable capacity is the amount of power regulation that the wastewater treatment plant declares to the virtual power plant before participating in power grid peak shaving, which it promises to provide safely within the response time window T_win; In intraday scheduling, all load objects with P_dis>0 in step S2 are aggregated to form a safe and schedulable resource pool. Based on the safe and schedulable resource pool, load object combination optimization is performed and scheduling instructions are generated. S4. Safety monitoring and intervention: During the execution of peak shaving task instructions by the load object, the actual values ​​of core process parameters are collected in real time and compared with the dynamic prediction trajectory Y(t) of the dynamic coupling layer. If the deviation between the actual value and the dynamic prediction trajectory Y(t) exceeds the preset safety margin and there is a tendency to exceed the limit, the preset active safety intervention instructions are automatically triggered, such as pausing adjustment, power backoff or switching to the backup plan, and alarms are issued to the operators, with process safety as the highest priority.

[0023] In step S1, the dynamic coupling layer is established using a historical data-driven method, a mechanism analysis method, or a combination of historical data-driven and mechanism analysis methods. The current power P_cur and power adjustment amount ΔP provided by the electrical operation layer are used as inputs, and the safety threshold provided by the process safety layer is used as the benchmark for evaluating the safety of the dynamic prediction trajectory Y(t). The historical data-driven approach is based on system identification or machine learning algorithms, using historical data for model training. Historical data includes electrical operation data, process operation data, operating environment data, and dispatch response data. Electrical operation data includes historical power curves, start-stop records, frequency regulation records, power factor, and current / voltage waveform data of the load, used to identify the static relationship between power and speed of the load equipment, as well as the dynamic response characteristics of start-stop and speed regulation. Process operation data includes dissolved oxygen (DO), water level (H), sludge concentration (MLSS), pH value, oxidation-reduction potential (ORP), and influent / effluent chemistry. Oxygen demand (COD) and ammonia nitrogen content are the direct training targets for establishing the dynamic mapping model, used to fit the model parameters; environmental data, including influent flow rate, influent water quality (COD, BOD5, NH3-N, TN, TP), water temperature, air temperature, and air pressure, serve as disturbance variables or time-varying parameters for the model, explaining why the same power regulation amount ΔP under different influent conditions produces different dynamic prediction trajectories Y(t); scheduling response data, including scheduling instructions from historical peak-shaving tasks, actual response power curves, and core process parameter changes during corresponding periods, are used for model correction to improve the model's adaptability and robustness. Mechanistic analysis methods are based on the physical, chemical, and biological principles of wastewater treatment processes, forming a framework of model equations. These principles include mass conservation, mass transfer kinetics, and microbial reaction kinetics. Specifically, mechanistic analysis methods include: deriving the equation for the rate of change of dissolved oxygen during aeration based on the gas-liquid mass transfer two-film theory; and / or describing the relationship between substrate consumption and microbial growth based on the Monod equation; and / or establishing dynamic balance equations for water quantity, water quality, and sludge quantity based on the principles of mass and energy conservation. The approach combining historical data-driven and mechanistic analysis utilizes mechanistic analysis to determine the basic structure of the dynamic coupling layer model, and uses historical data-driven methods to identify or calibrate key parameters in the model online, including the time constant and gain coefficient. In this embodiment, system identification refers to the process of automatically deriving a lightweight predictive model (such as a state-space model) that can describe the dynamic causal relationship between power regulation and core process parameter changes during the historical operation of the wastewater treatment plant.

[0024] The dynamic coupling layer specifically adopts a low-order state-space model or a linear parameter time-varying (LPV) model.

[0025] In this embodiment, the following three strategies can be used to calculate the safety capacity ΔP_max.

[0026] Strategy A calculates the safety capacity ΔP_max by solving a constrained optimization problem. The objective of the optimization problem is to maximize |ΔP|, denoted as the safety capacity ΔP_max. If there is no feasible solution, then ΔP_max = 0. The constraints include: Power regulation constraint: P_min≤P_cur+ΔP≤P_max; Dynamic safety constraints are achieved by ensuring that the dynamically predicted trajectory Y(t) within the response time window T_win, predicted by the dynamic coupling layer, satisfies the safety threshold defined by the process safety layer throughout its entire duration. In this embodiment, the maximum |ΔP| is solved using gradient descent, interior point method, or sequential quadratic programming.

[0027] Strategy B calculates the safe capacity ΔP_max by querying a pre-generated safe capacity boundary curve database. This database stores the safe capacity ΔP_max corresponding to different response time windows T_win under different typical operating conditions. The typical operating conditions are determined by at least the following key variables: influent flow rate, influent water quality concentration, water temperature, and mixed liquor sludge concentration (MLSS). In this embodiment, the corresponding boundary curve is matched based on the current real-time operating condition, and the safe capacity ΔP_max under a given response time window T_win is read instantaneously.

[0028] Strategy C calculates the safe capacity ΔP_max through iterative search and verification. It employs a binary search method or the golden section method to adjust the trial values ​​of the power regulation ΔP, and calls a dynamic coupling model to verify safety feasibility until the maximum |ΔP| satisfying the constraints is found as the value of the safe capacity ΔP_max. In this embodiment, the binary search method or the golden section method is used to quickly find the safe and unsafe boundary, and this boundary value is used as an approximate solution for ΔP_max.

[0029] In step S3, within the safe and schedulable resource pool, at least one of the following factors is comprehensively considered: combination simplicity and reliability, dynamic response characteristics, process safety margin, adjustment economy, and equipment status and lifespan balance; and the optimal load object combination scheme is solved by a mixed integer linear programming algorithm. Specifically, the following considerations are considered: **Simplicity and Reliability:** Under the premise of meeting the total regulation and safety requirements, prioritize combinations with fewer load objects to reduce the complexity of collaborative control and communication risks, and improve the reliability of instruction execution. **Dynamic Response Characteristics:** For instructions with short response time windows (T_win) or requiring precise tracking, prioritize load objects with fast dynamic response speeds (small time constant, small hysteresis) and high regulation accuracy (small dead zone). **Process Safety Margin:** Under the premise of meeting safety constraints, prioritize combinations that can maintain core process parameters at a higher safety level or provide a larger buffer for subsequent disturbances, enhancing system robustness. **Regulation Economy:** Define an economic cost coefficient per unit regulation for each load object (e.g., comprehensive equipment losses, energy efficiency changes, additional reagent consumption, etc.), prioritizing load objects with lower costs. **Equipment Status and Lifespan Balance:** Considering the cumulative regulation mileage, fatigue, or health status of load objects, prioritize equipment in better condition and with fewer recent regulation needs to achieve long-term balanced utilization of adjustable load resources across the plant.

[0030] In the mixed-integer linear programming algorithm solution process, the decision variables include the actual regulation power values ​​of each candidate load object in the safe and schedulable resource pool. The objective function is to minimize the weighted comprehensive cost, which is constituted by weighted summation of at least one of the following factors: regulation economic cost, number of activated load objects, load object lifetime loss, dynamic response performance index, and process safety margin index. Finally, the optimal load object combination scheme that minimizes the weighted comprehensive cost is obtained. Among them, the actual regulation power of each load object does not exceed the corresponding real-time schedulable net capacity P_dis, and the sum of the actual regulation power of all load objects meets the total regulation amount required by the power grid peak shaving task command. The regulation economic cost is calculated by summing the product of the unit regulation power cost coefficient of each load object and the actual regulation power. The load object lifetime loss is calculated based on the cumulative regulation mileage or health status index of each load object.

[0031] In step S3, when planning the declared capacity, for a certain declaration period, i.e., the response time window T_win, the start time for pre-adjustment of the load object is located before the response time window T_win. This ensures that from the application of the maximum technical adjustment power of ΔP_dis_max to the start of the response time window T_win, the core process parameters have dynamically transitioned and stabilized within the safe threshold. Within the response time window T_win, only the power needs to be kept constant, so its declared capacity can be directly calculated based on the maximum technical adjustment power. Specifically, the ΔP_dis_max of all load objects in the entire plant that can participate in the response through time substitution within the same response time window T_win is aggregated to obtain the total declared capacity of the entire plant for that period. The above planning is executed for all 96 periods of the next day with a granularity of 15 minutes, generating the plant-wide daytime peak-shaving capacity declaration curve.

[0032] The following two specific examples illustrate the above method.

[0033] Example 1 During the intraday real-time safety dispatch, a wastewater treatment plant received an instruction at 14:00 to reduce its total load by 200kW within 30 minutes (T_win=30min).

[0034] The safe capacity of blower G1 is calculated as follows: (1) P_cur=400kW, [P_min,P_max]=[200,500]kW, and the safe lower limit of dissolved oxygen concentration DO_lim=1.6mg / L for the aeration tank corresponding to the blower. The system adopts strategy A to calculate the maximum ΔP, so that when the power changes by ΔP, the dissolved oxygen predicted by the digital model corresponding to blower G1 is ≥1.6mg / L throughout the 30 minutes; (2) The optimization solver based on the interior point method is called to solve the problem, and the safe capacity of blower G1 ΔP_max_G1=-88kW is directly output (negative value indicates reduction, positive value indicates increase). After deducting the dead zone of 2kW, the real-time schedulable net capacity of blower G1 P_dis_G1=86kW is obtained.

[0035] Calculating the safe capacity of variable frequency pump P1: The current operating condition matches well with the pre-stored curve library. The system adopts strategy B and directly queries to obtain the safe capacity of variable frequency pump P1 ΔP_max_P1=-70kW. After deducting the power regulation inertia dead zone of 1kW, the real-time dispatchable net capacity of variable frequency pump P1 P_dis_P1=69kW.

[0036] Aggregated safe and schedulable resource pool: In addition to blower G1 and variable frequency pump P1, two dewatering machine motors M1 and M2 have been verified, contributing 15kW and 35kW respectively. The selected value {G1(86kW)+P1(69kW)+M1(15kW)+M2(35kW)}=205kW>the target amount of 200kW, meeting the requirements.

[0037] Final scheduling: The system combines the safe and schedulable resources (including blower G1, variable frequency pump P1, dewatering machine motors M1 and M2, etc.) to select the safe solution with a total reduction of 205kW, and the system automatically issues the combined load reduction command.

[0038] Process monitoring: At 14:16, it was found that the pressure in the pipeline associated with the variable frequency pump P1 had risen slightly and was close to the alarm threshold. The system automatically reduced its adjustment range to 60kW and activated the backup safety resource M3 (9kW) to compensate for the difference and maintain the overall response capability.

[0039] Example 2 In the pre-planned scheduling, a wastewater treatment plant submitted its peak-shaving capacity for the period 10:00-10:30 the following day. The system uses time resource substitution for blower G1, planning to start adjustment at 09:40 and complete load reduction in 10 minutes. Verification and calculation: Because the adjustment process is scheduled before the execution window, within the assessment response time window T_win of 10:00-10:30, the process of blower G1 has stabilized. For example, if the [P_min,P_max]=[200,500]kW of blower G1, and the current power P_cur is 450kW, then its maximum technical adjustment power ΔP_dis_max=250kW. After deducting the 15kW power adjustment inertia dead zone, the credible declared capacity P_dis=250kW-ΔP_db=235kW is obtained. This value is included in the plant's total declared capacity for this period. Declaration: The capacities of all equipment in the plant are summarized to form the pre-planned declaration.

[0040] Example 2 A wastewater treatment plant safety capacity dispatching device for peak shaving in virtual power plants includes: The load-process dynamic coupling modeling module is used to construct a digital model for each load object in the wastewater treatment plant, including an electrical operation layer, a process safety layer, and a dynamic coupling layer. The load object is an independently controllable electrical device. The electrical operation layer includes the load object's rated power, current power P_cur, safe operating power range [P_min, P_max], and power regulation inertia dead zone ΔP_db. The process safety layer includes the safety thresholds of the core process parameters associated with the load object. The dynamic coupling layer is used to establish a mapping relationship from the load object's power regulation ΔP to the dynamic prediction trajectory Y(t) of the core process parameters over a future period. The online safety capacity calculation module is used to calculate the maximum power regulation that can be safely executed within the response time window T_win under the current operating conditions for each load object after receiving a peak shaving task instruction containing a response time window T_win. This is based on a digital model and combines the safe operating power range constraints of the electrical operation layer with the safety threshold constraints of the core process parameters of the process safety layer. The maximum power regulation is denoted as the safety capacity ΔP_max. The real-time schedulable net capacity P_dis is also obtained, where P_dis = |ΔP_max| - ΔP_db. If ΔP_max = 0, then P_dis = 0. The multi-timescale scheduling module is used to advance the power regulation start time of load objects to before the response time window T_win in day-ahead planning scheduling, ensuring that the process is in a stable and safe state within the response time window T_win of the power grid assessment, and to plan the claimable capacity based on the maximum technical regulation power ΔP_dis_max. Here, the maximum technical regulation power ΔP_dis_max is the difference between the current power P_cur of the load object and the boundary of the safe operating power interval [P_min, P_max], which does not involve process safety constraints; the claimable capacity is the amount of power regulation that the wastewater treatment plant declares to the virtual power plant before participating in power grid peak shaving, promising to provide safely within the response time window T_win; and in intraday scheduling, it is used to aggregate all load objects with P_dis>0 in step S2 to form a safe and schedulable resource pool, and to perform load object combination optimization and generate scheduling instructions based on the safe and schedulable resource pool. The safety monitoring and intervention module is used to collect the actual values ​​of core process parameters in real time during the execution of peak shaving task instructions by the load object, and compare them with the dynamic prediction trajectory Y(t) of the dynamic coupling layer. If the deviation between the actual value and the dynamic prediction trajectory Y(t) exceeds the preset safety margin and there is a tendency to exceed the limit, the preset active safety intervention instruction will be automatically triggered.

[0041] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dispatching the safety capacity of a sewage plant for peak shaving of a virtual power plant, characterized in that: The method comprises the following steps: S1. Load-process dynamic coupling modeling, for each load object in the sewage plant, a digital model including an electrical operation layer, a process safety layer and a dynamic coupling layer is constructed, wherein the load object is an independently controllable electrical equipment; the electrical operation layer includes the rated power, the current power P_cur, the safe operation power interval [P_min, P_max] and the power regulation inertia dead zone ΔP_db of the load object; the process safety layer includes the safety threshold of the core process parameter associated with the load object; the dynamic coupling layer is used to establish the mapping relationship from the power regulation amount ΔP of the load object to the dynamic prediction trajectory Y(t) of the core process parameter in the future period of time; S2. Online solution of safety capacity, when receiving a peak shaving task instruction containing a response time window T_win, for each load object, based on the digital model, combining the safe operation power interval constraint of the electrical operation layer and the core process parameter safety threshold constraint of the process safety layer, the maximum power regulation amount that can be safely executed within the response time window T_win under the current working condition is calculated, recorded as safety capacity ΔP_max, and the real-time schedulable net capacity P_dis is obtained, and P_dis=|ΔP_max|-ΔP_db; S3. Multi-time scale scheduling, in the day-ahead scheduling, the power regulation start time of the load object is advanced to before the response time window T_win to ensure that the process is in a stable and safe state within the response time window T_win of the power grid evaluation, and the maximum technical regulation power ΔP_dis_max is used to plan the reportable capacity, wherein the maximum technical regulation power ΔP_dis_max is the difference between the current power P_cur of the load object and the boundary of the safe operation power interval [P_min, P_max]; the reportable capacity is the power regulation amount that the sewage plant promises to safely provide within the response time window T_win when participating in the peak shaving of the virtual power plant; in the daily scheduling, all load objects with P_dis>0 are aggregated to form a safe schedulable resource pool, and load object combination optimization is performed based on the safe schedulable resource pool to generate a scheduling instruction; S4. Safety monitoring and intervention, in the process of executing the peak shaving task instruction by the load object, the actual value of the core process parameter is collected in real time, and is compared with the dynamic prediction trajectory Y(t) of the dynamic coupling layer, if the deviation between the actual value and the dynamic prediction trajectory Y(t) exceeds the preset safety margin and there is an out-of-limit trend, a preset active safety intervention instruction is automatically triggered.

2. The method of claim 1, wherein the method is used for peak shaving of a virtual power plant. In step S1, the dynamic coupling layer is established by a historical data driven method, a mechanism analysis method or a combination of historical data driven and mechanism analysis method, and takes the current power P_cur and ΔP provided by the electrical operation layer as input, and takes the safety threshold provided by the process safety layer as the basis for evaluating the safety of the dynamic prediction trajectory Y(t); The historical data driven method is based on system identification or machine learning algorithm, and historical data are used for model training, and the historical data include electrical operation data, process operation data, working condition environment data and dispatching response data; and the electrical operation data include historical power curve, start-stop record, frequency regulation record, power factor and current-voltage waveform data of the load object; the process operation data include dissolved oxygen, water level, sludge concentration, pH value, oxidation-reduction potential, chemical oxygen demand and ammonia nitrogen content of inlet and outlet water; the working condition environment data include inlet water flow, inlet water quality, water temperature, air temperature and air pressure; the dispatching response data include dispatching instruction content in historical peak shaving tasks, actual response power curve and core process parameter change data in the corresponding period. The mechanism analysis method is a model equation framework constructed based on physical, chemical and biological principles of the wastewater treatment process, and the mechanism analysis method specifically includes: deriving a dissolved oxygen change rate equation in air blowing aeration according to the gas-liquid mass transfer double membrane theory; and / or describing the relationship between substrate consumption and microbial growth according to the Monod equation; and / or establishing dynamic balance equations of water quantity, water quality and sludge quantity according to the mass conservation and energy conservation principles. The method combining historical data driven method and mechanism analysis method is to determine the basic structure of the dynamic coupling layer model by the mechanism analysis method, and to identify or calibrate the key parameters in the model online by the historical data driven method, wherein the key parameters include time constant and gain coefficient.

3. The method of claim 2, wherein the method is used for peak shaving of a virtual power plant. The dynamic coupling layer specifically adopts a state space model or a linear parameter time-varying model.

4. The method of claim 1, wherein, In step S2, the calculation of the safety capacity ΔP_max is realized by solving an optimization problem with constraints, and the objective of the optimization problem is to maximize |ΔP|, and if there is no feasible solution, ΔP_max=0, and the constraints include: Power regulation constraint, P_min≤P_cur+ΔP≤P_max; Dynamic safety constraint, the dynamic prediction trajectory Y(t) predicted by the dynamic coupling layer within the response time window T_win satisfies the safety threshold defined by the process safety layer throughout.

5. The method of claim 1, wherein the method is used for peak shaving of a virtual power plant. In step S2, the calculation of the safety capacity ΔP_max is realized by querying the pre-generated safety capacity boundary curve database, and the safety capacity boundary curve database stores the safety capacity ΔP_max corresponding to different response time windows T_win under different typical working conditions, wherein the typical working conditions are determined by at least the following key variables, including inlet water flow, inlet water quality concentration, water temperature and mixed sludge concentration.

6. The method of claim 1, wherein the method is used for peak shaving of a virtual power plant. In step S2, the calculation of the safety capacity ΔP_max is realized by iterative search verification, and the binary search method or the golden section method is used to adjust the trial value of the power regulation amount ΔP, and the dynamic coupling model is called for safety feasibility verification, until the maximum |ΔP| satisfying the constraint condition is found as the value of the safety capacity ΔP_max.

7. The method of claim 1, wherein, In step S3, at least one of the following factors is considered in the safety dispatchable resource pool: combination simplicity and reliability, dynamic response characteristics, process safety margin, regulation economy, equipment state and life balance; and the optimal load object combination scheme is solved by a mixed integer linear programming algorithm.

8. The method of claim 7, wherein, In the solving process of the mixed integer linear programming algorithm, the decision variables include actual regulation power values of each candidate load object in a safe schedulable resource pool, the objective function is to minimize the weighted comprehensive cost, and the weighted comprehensive cost is composed by weighted summation of at least one of the following factors: regulation economic cost, number of enabled load objects, load object life consumption, dynamic response performance index and process safety margin index; and finally an optimal load object combination scheme with the lowest weighted comprehensive cost is obtained; wherein the actual regulation power of each load object does not exceed the corresponding real-time schedulable net capacity P_dis, and the sum of the actual regulation powers of all load objects meets the total regulation amount required by the grid peak shaving task instruction.

9. The sewage plant safety capacity scheduling device for virtual power plant peak shaving, configured to implement the sewage plant safety capacity scheduling method for virtual power plant peak shaving according to any one of claims 1 to 8, characterized in that, Comprise: a load-process dynamic coupling modeling module for constructing a digital model including an electrical operation layer, a process safety layer and a dynamic coupling layer for each load object in the sewage plant; a safe capacity online solving module for calculating, based on the digital model, the maximum power regulation amount that can be safely executed within the response time window T_win under the current working condition for each load object, denoted as safe capacity ΔP_max, and obtaining the real-time schedulable net capacity P_dis, in combination with the safe operation power interval constraint of the electrical operation layer and the core process parameter safety threshold constraint of the process safety layer, after receiving the peak shaving task instruction containing the response time window T_win; a multi-time scale scheduling module for advancing the power regulation start time of the load object to before the response time window T_win in day-ahead scheduling to ensure that the process is in a stable and safe state within the response time window T_win of the grid evaluation, and planning the reportable capacity according to the maximum technical regulation power ΔP_dis_max; and for aggregating all load objects with P_dis>0 to form a safe schedulable resource pool in intraday scheduling, performing load object combination optimization based on the safe schedulable resource pool and generating scheduling instructions; a safety monitoring and intervention module for real-time acquisition of actual values of core process parameters during execution of the load object peak shaving task instruction, and comparison with the dynamic prediction trajectory Y(t) of the dynamic coupling layer, and if the deviation between the actual value and the dynamic prediction trajectory Y(t) exceeds the preset safety margin and there is an out-of-limit trend, the preset active safety intervention instruction is automatically triggered.

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