A method for low-carbon peak regulation and dispatch of wind-thermal-storage coordination considering revenue acceptability and responsibility weight allocation

Abstract

The application discloses a wind-fire-storage collaborative low-carbon peak regulation and dispatching method considering benefit acceptability and responsibility weight allocation, and belongs to the technical field of multi-source collaborative optimal dispatching. The method comprises the following steps: taking a regional power system composed of a wind power plant, a pumped storage power station, a conventional thermal power unit and a coupled thermal power unit as a research object; a top-layer net load boundary shaping model is established; the top-layer net load boundary shaping model takes the minimum net load fluctuation and the maximum pumped storage calling benefit as targets, and sets a storage capacity constraint, an output and operation condition constraint and a start-stop times constraint; on the basis of constructing a lower-layer key equipment model, a pumped storage dispatching sequence corresponding to a top-layer optimal solution and a net load boundary formed by the pumped storage dispatching sequence are taken as known inputs, a lower-layer wind-fire collaborative peak regulation and optimization model is established and solved; the lower-layer wind-fire collaborative peak regulation and optimization model takes the minimum lower-layer system operation cost as a target, introduces a benefit acceptability constraint and a responsibility weight allocation mechanism on the basis of satisfying a system power balance constraint, a system reserve capacity constraint, a wind power abandoned wind cost and an output constraint. The application improves the multi-subject benefit coordination relationship while improving the system economy.

Description

Technical Field

[0001] This invention relates to a low-carbon peak-shaving scheduling method that considers the acceptability of benefits and the apportionment of responsibility weights, and belongs to the field of multi-source collaborative optimization scheduling technology. Background Technology

[0002] Against the backdrop of the construction of a new power system, the grid connection of high-proportion renewable energy sources such as wind power and photovoltaics has transformed from a policy-driven trend into an irreversible structural trend. However, the large-scale integration of wind and solar power also poses new safety requirements for system power balance, frequency stability, and regulation margin: wind power output is driven by meteorological conditions and is inherently random, volatile, and intermittent, making it difficult to accurately predict its short-term power output; in typical wind power bases, peak wind power generation periods often coincide with off-peak load periods. This mismatch between output and load timing will cause the system's net load curve to dip, widen the daily peak-to-valley difference, and sharply increase short-term ramp-up demand, thus concentrating peak-shaving pressure on off-peak periods, inducing wind curtailment risks and tightening operational safety constraints.

[0003] Under the aforementioned uncertainties, conventional thermal power units remain an indispensable regulatory resource for the system due to their controllable output and dispatchable inertia. By modifying their flexibility to reduce minimum stable output or even implementing deep peak shaving, thermal power can broaden the system's capacity to absorb wind power output during off-peak hours. However, excessively concentrating deep peak shaving tasks on the thermal power side will directly harm its operational economy: long-term operation of units deviating from the optimal coal consumption range leads to poor combustion stability at low loads, accelerated component lifespan wear caused by thermal stress cycles, and reduced enterprise profits; if the scheduling objective is only the single goal of "lowest system cost" or "minimum wind curtailment," it is easy to transfer too much peak shaving responsibility to the thermal power side, affecting its enthusiasm for continuous participation in peak shaving.

[0004] To address the aforementioned issues, relying solely on wind-fired dual-emission systems is insufficient. While carbon capture systems can significantly reduce the carbon emission intensity of coal-fired units, their absorbent regeneration requires additional thermal / electrical energy, altering the unit's net output and peak-shaving boundaries. Molten salt thermal energy storage, with its heat shifting capability, can extend the equivalent regulation depth of thermal power units through off-peak heat storage and peak heat release, providing a more flexible "electricity-heat-carbon" coupled regulation method for the system. However, for low-carbon coupled thermal power units that simultaneously integrate molten salt thermal energy storage, carbon capture turbine extraction heating, and carbon capture load, existing scheduling methods often fail to adequately characterize the heat storage / release paths between subsystems, extraction-carbon capture energy consumption coupling, grid-connected net output boundaries, and carbon emission flows, resulting in a gap between low-carbon and peak-shaving objectives at the coordination level. On the other hand, although pumped storage has the advantages of large capacity, fast start-up and shutdown, and strong bidirectional regulation capabilities, and can undertake the tasks of net load smoothing and peak shaving, it is limited by installed capacity, reservoir capacity constraints and operating revenue mechanisms. Relying solely on pumped storage cannot fundamentally eliminate the pressure of peak shaving during off-peak periods, nor can it replace the inertial support and backup function of thermal power.

[0005] In view of this, the present invention is hereby proposed. Summary of the Invention

[0006] This invention provides a low-carbon peak-shaving scheduling method that considers the acceptability of benefits and the apportionment of responsibility weights. It constructs a multi-source collaborative scheduling framework of wind power, pumped storage, conventional thermal power units, and low-carbon coupled thermal power units. At the same time, it considers the reasonable matching relationship between peak-shaving responsibility and compensation benefits, thereby improving the wind power absorption level while taking into account the system operation safety, the economic sustainability of thermal power units, and the goal of low-carbon transformation.

[0007] The technical solution of this invention is:

[0008] According to a first aspect of the present invention, a method for coordinated low-carbon peak-shaving scheduling of wind, thermal, and energy storage considering revenue acceptability and responsibility weighting is provided, comprising:

[0009] The study focuses on a regional power system consisting of wind farms, pumped storage power stations, conventional thermal power units, and coupled thermal power units; the coupled thermal power units consist of molten salt thermal storage (MSTS), carbon capture turbine (CCT), and carbon capture system (CCS).

[0010] Establish an upper-level net load boundary shaping model; the upper-level net load boundary shaping model aims to minimize net load fluctuation and maximize pumped storage call benefits, and sets reservoir capacity constraints, output and operating condition constraints, and start-stop frequency constraints;

[0011] Based on the construction of the lower-level key equipment model, the pumped storage scheduling sequence corresponding to the upper-level optimal solution and the resulting net load boundary are used as known inputs to establish and solve the lower-level wind-thermal coordinated peak shaving optimization model. The lower-level wind-thermal coordinated peak shaving optimization model aims to minimize the operating cost of the lower-level system. On the basis of satisfying the system power balance constraints, system reserve capacity constraints, wind power curtailment cost and output constraints, it introduces the revenue acceptability constraints and the responsibility weight sharing mechanism.

[0012] Furthermore, the solution strategy is as follows: NSGA-III is used to solve the multi-objective optimization problem of the upper-level net load boundary shaping model to obtain a relatively uniform set of non-dominated candidate solutions; then, the entropy-weighted TOPSIS method is used to objectively and comprehensively evaluate the non-dominated solutions, thereby determining the unique upper-level preferred solution as a known input to be passed to the lower-level wind-fire coordinated peak shaving optimization model; the nonlinear terms in the wind-fire coordinated peak shaving optimization model are linearized, and the wind-fire coordinated peak shaving optimization model is transformed into a mixed integer linear programming problem for solution.

[0013] Furthermore, the objective function of the upper-level net load boundary shaping model is constructed as follows:

[0014] (1) Net load fluctuation Construct the first upper-level objective function with the minimum objective as the goal:

[0015] ;

[0016] ;

[0017] In the formula: t represents the scheduling period; The scheduling period; Let be the net load value at time t; This represents the average net load over a scheduling cycle. Let be the day-ahead predicted load power of the regional power system at time t; Let t be the predicted power of the wind farm. This refers to the number of pumped storage units; and These represent the power generation and pumping power of the pumped-storage unit at time t, respectively.

[0018] (2) Revenue from pumped storage unit dispatch Construct a second upper-level objective function with the goal of maximizing:

[0019] ;

[0020] ;

[0021] In the formula: Let S be the start-up and shutdown loss cost of the pumped storage unit at time t. Let be the time-of-use electricity price of the power grid at time t; The scheduling time interval; and These are the two variables representing the pumped storage unit s at time t, which is in power generation and pumping operation respectively. and These are the losses incurred during a single start-up and shutdown of the pumped-storage unit, respectively.

[0022] Furthermore, the lower-level key equipment model includes a conventional thermal power unit peak-shaving model, a coupled thermal power unit phased deep peak-shaving cost model, a coupled thermal power unit thermodynamic coupling model, and constraints; among which, the constraints include: extraction steam ratio constraints, MSTS operation constraints, CCS system constraints, and extraction steam thermal power balance constraints.

[0023] Furthermore, the lower-level wind-fire coordinated peak-shaving optimization model uses the total system operating cost Construct the lower-level objective function with the minimum objective as the goal:

[0024] ;

[0025] In the formula, and These refer to the number of conventional thermal power units and coupled thermal power units, respectively. This refers to the operating cost of conventional thermal power units; To couple the operating costs of thermal power units; To account for the cost of CO2 sequestration generated by the carbon capture system in thermal power units; To reduce the operating and maintenance costs of molten salt thermal energy storage in thermal power units; and These are the costs of wind curtailment penalties and carbon trading costs, respectively.

[0026] Furthermore, the aforementioned responsibility weighting mechanism is specifically as follows:

[0027] Introducing a trough to lower the coefficient Peak and valley influence coefficient :

[0028] ;

[0029] In the formula, This represents the minimum daytime load value of the system load; This represents the minimum daily net load. For the installed capacity of new energy sources; This represents the peak-to-valley difference in daytime load. This represents the peak-to-valley difference in daytime net load.

[0030] trough pulls down coefficient Peak and valley influence coefficient Construct a peak-shaving responsibility sharing coefficient :

[0031] ;

[0032] Based on the peak-shaving responsibility sharing coefficient, the peak-shaving compensation costs borne by conventional thermal power units and wind power units are determined respectively.

[0033] Furthermore, the revenue acceptability constraints include: constructing revenue acceptability constraints for conventional thermal power units and coupled thermal power units participating in deep peak shaving based on the peak shaving compensation costs borne by conventional thermal power units; and constructing wind power revenue acceptability constraints based on the peak shaving compensation costs borne by wind power.

[0034] According to a second aspect of the present invention, a wind-thermal-storage coordinated low-carbon peak-shaving scheduling system considering the acceptability of benefits and the apportionment of responsibility weights is provided, comprising a module of the wind-thermal-storage coordinated low-carbon peak-shaving scheduling method considering the acceptability of benefits and the apportionment of responsibility weights as described above.

[0035] According to a third aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0036] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium comprising a stored program, wherein, when the program is executed, the device on which the computer-readable storage medium is located executes the steps of any one of the methods described above.

[0037] The beneficial effects of this invention are:

[0038] 1) After pumped storage participates in the shaping of the net load boundary, it can optimize the pumping, power generation and start-up and shutdown sequence, smooth the system net load curve, alleviate the pressure of off-peak peak shaving under high proportion of wind power access, and provide a more reasonable scheduling boundary for subsequent coordinated peak shaving of thermal power units and coupled thermal power units.

[0039] 2) MSTS-CCT-CCS coupled thermal power units can coordinate the operation of molten salt thermal storage, extraction steam path switching and carbon capture system, reduce the unit's net grid-connected output and expand the wind power consumption space during off-peak hours, release thermal storage and support system output during peak hours, and at the same time reduce the system's carbon emission level and deep peak shaving operation cost.

[0040] 3) After the participation of the revenue acceptability constraint and responsibility weight sharing mechanism in the formulation of dispatch results, it can avoid excessive compression of thermal power revenue during joint peak shaving, and reasonably share peak shaving compensation costs according to the impact of wind power on net load trough and peak-valley difference, so that peak shaving responsibility, compensation revenue and main body revenue are matched, and the enthusiasm and sustainability of multiple entities to participate in coordinated peak shaving are improved. Attached Figure Description

[0041] Figure 1 This is the overall framework of the model of this invention;

[0042] Figure 2 This is a flowchart of the model solution process of this invention;

[0043] Figure 3 This is a sensitivity diagram of responsibility apportionment intensity in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0045] Example 1: As Figures 1-3 As shown, according to a first aspect of the present invention, a wind-thermal-storage coordinated low-carbon peak-shaving scheduling method considering the acceptability of benefits and the apportionment of responsibility weights is provided, comprising:

[0046] Step 1: The research object is a regional power system consisting of wind farms, pumped storage power stations, conventional thermal power units, and coupled thermal power units. Among them, the coupled thermal power units are MSTS-CCT-CCS coupled thermal power units consisting of molten salt thermal storage (MSTS), carbon-capture turbine (CCT), and carbon capture system (CCS).

[0047] Step 2: Establish the upper-level net load boundary shaping model; The upper-level net load boundary shaping model aims to minimize net load fluctuation and maximize pumped storage call benefits, and sets reservoir capacity constraints, output and operating condition constraints, and start-stop frequency constraints.

[0048] Step 3: Based on the construction of the lower-level key equipment model, the pumped storage scheduling sequence corresponding to the upper-level optimal solution and the resulting net load boundary are used as known inputs to establish and solve the lower-level wind-thermal coordinated peak shaving optimization model. The lower-level wind-thermal coordinated peak shaving optimization model aims to minimize the operating cost of the lower-level system. On the basis of satisfying the system power balance constraints, system reserve capacity constraints, wind power curtailment cost and output constraints, it introduces the revenue acceptability constraints and the responsibility weight sharing mechanism.

[0049] The solution strategy is as follows: NSGA-III is used to solve the multi-objective optimization problem of the upper-level net load boundary shaping model to obtain a relatively uniform set of non-dominated candidate solutions; then, the entropy-weighted TOPSIS method is used to objectively and comprehensively evaluate the non-dominated solutions, thereby determining the unique upper-level preferred solution as a known input to be passed to the lower-level wind-fire coordinated peak shaving optimization model; the nonlinear terms in the wind-fire coordinated peak shaving optimization model are linearized, and the wind-fire coordinated peak shaving optimization model is transformed into a mixed integer linear programming problem for solution.

[0050] In this invention, pumped storage hydropower in the regional power system undertakes system-level peak shaving and valley filling, as well as time-period energy transfer tasks. Wind power, as a fluctuating clean power source, provides priority for power consumption. Conventional thermal power units provide basic output support. Coupled thermal power units are used to reconstruct the coupling relationship between the power generation steam path, thermal storage path, and carbon capture and heating path through molten salt thermal storage system, carbon capture turbine, and carbon capture system, so as to form two types of operating conditions: off-peak thermal storage and peak thermal release. Compared with the modification method of configuring thermal storage or carbon capture devices alone, the coupled thermal power units of this paper are simultaneously affected by power output constraints, heat time-shifting capability, and carbon capture and heating path.

[0051] The present invention will be further described below with reference to specific embodiments:

[0052] I. Upper Net Load Boundary Shaping Model

[0053] The upper net load boundary shaping model includes:

[0054] (1) Net load fluctuation Construct the first upper-level objective function with the minimum objective as the goal:

[0055] ;

[0056] ;

[0057] In the formula: t represents the scheduling period, and each scheduling period is one hour apart; The scheduling cycle is 24 hours. Let be the net load value at time t; This represents the average net load over a scheduling cycle. Let be the day-ahead predicted load power of the regional power system at time t; Let t be the predicted power of the wind farm. This refers to the number of pumped storage units; and The power generation and pumping power of the pumped-storage unit at time t are respectively (abbreviated as in this invention). , ).

[0058] (2) Revenue from pumped storage unit dispatch Construct a second upper-level objective function with the goal of maximizing:

[0059] ;

[0060] ;

[0061] In the formula: Let S be the start-up and shutdown loss cost of the pumped storage unit at time t. Let be the time-of-use electricity price of the power grid at time t; The scheduling interval is 1 hour. and Let be the 0-1 variables representing the pumped-storage unit s at time t in power generation and pumping modes, respectively; when the unit is in power generation mode... =1, otherwise 0; when the unit is in pumping mode =1, otherwise 0; and These are the losses incurred during a single start-up and shutdown of the pumped-storage unit, respectively.

[0062] The constraints include three categories: storage capacity constraints, output and operating condition constraints, and start / stop frequency constraints, as detailed below:

[0063] 1) Storage capacity constraints

[0064] ;

[0065] ;

[0066] In the formula: Let t be the capacity of the upper reservoir of the pumped storage power station; , The water-to-electricity conversion factor under pumping and power generation conditions; and These represent the minimum and maximum capacities of the upper reservoir, respectively.

[0067] 2) Output and operating condition constraints

[0068] ;

[0069] In the formula: , For 0-1 variables, This indicates the operating condition of the pumped storage unit; , , , These represent the upper and lower limits of the power output of pumped storage unit S under both power generation and pumping conditions.

[0070] 3) Start-stop count constraint

[0071] ;

[0072] In the formula: This represents the maximum number of start-ups and shutdowns for a single pumped storage unit.

[0073] II. Lower-level key equipment model

[0074] The lower-level key equipment model includes a conventional thermal power unit peak shaving model, a coupled thermal power unit phased deep peak shaving cost model, a coupled thermal power unit thermodynamic coupling model, and constraints; among which, the constraints include: extraction steam ratio constraints, MSTS operation constraints, CCS system constraints, and extraction steam thermal power balance constraints.

[0075] 1. Peak-shaving model for conventional thermal power units:

[0076] Conventional thermal power units undertake basic power output and routine peak-shaving tasks, and their operating status directly determines the feasible region of the underlying model. Therefore, it is necessary to establish a conventional unit model that includes operating costs and basic operating boundaries.

[0077] Operating costs of conventional thermal power units It consists of coal consumption costs and start-up and shutdown costs:

[0078] ;

[0079] ;

[0080] In the formula: For conventional thermal power units Coal consumption cost at time t; Indicates conventional thermal power units Start-up and shutdown costs at time t; , , For conventional thermal power units The corresponding consumption cost characteristic coefficient; For conventional thermal power units at time t Start and stop status variables; , For conventional thermal power units The cost of a single start-up and shutdown; For conventional thermal power units at time t of effort.

[0081] When conventional thermal power units participate in joint peak shaving, they must meet constraints such as upper and lower limits of output, ramping capability, and minimum start-up and shutdown time.

[0082] ;

[0083] In the formula: For conventional thermal power units at time t contribution; , They are conventional units Maximum and minimum technical output; For conventional thermal power units Maximum climbing rate; , For conventional units Minimum continuous operation and downtime; For conventional thermal power units The start / stop status variable is a 0-1 variable. , They are conventional thermal power units The continuous running time and continuous downtime up to time t-1.

[0084] 2. Cost model for deep peak shaving in coupled thermal power components at different stages

[0085] To characterize the operating costs of MSTS-CCT-CCS coupled thermal power units at different peak-shaving depths, this paper divides the peak-shaving process of such units into three stages: conventional peak-shaving, peak-shaving without oil injection, and peak-shaving with oil injection, and establishes corresponding segmented cost models.

[0086] ;

[0087] In the formula: The operating cost of MSTS-CCT-CCS coupled thermal power units; Let t be the coal consumption cost of MSTS-CCT-CCS coupled thermal power unit i at time t; Let t be the start-up and shutdown cost of MSTS-CCT-CCS coupled thermal power unit i at time t; The additional loss cost of MSTS-CCT-CCS coupled thermal power unit i after entering the deep peak shaving stage at time t; The fuel injection cost incurred when MSTS-CCT-CCS coupled thermal power unit i enters the fuel injection depth peak shaving stage at time t; For the maximum technical output of MSTS-CCT-CCS coupled thermal power unit i; Minimum technical output for MSTS-CCT-CCS coupled thermal power unit i; The minimum output for peak shaving without oil injection of MSTS-CCT-CCS coupled thermal power unit i; The minimum output for peak shaving of MSTS-CCT-CCS coupled thermal power unit i; The output of thermal power unit i is coupled to MSTS-CCT-CCS.

[0088] The coal consumption cost of MSTS-CCT-CCS coupled thermal power units is expressed as follows:

[0089] ;

[0090] In the formula: , , For MSTS-CCT-CCS coupled thermal power units Coal consumption cost characteristic coefficient.

[0091] The loss cost of the MSTS-CCT-CCS coupled thermal power unit is expressed as follows:

[0092] When the peak-shaving demand of an MSTS-CCT-CCS coupled thermal power unit is lower than the minimum technical output, the unit enters the deep peak-shaving stage without oil injection. At this time, rotor components experience fatigue and creep losses due to changes in thermal stress. This paper uses the Manson-Coffin method for calculation.

[0093] ;

[0094] In the formula: The rotor cracking cycle of the MSTS-CCT-CCS coupled thermal power unit is calculated as a cubic function of the output of the MSTS-CCT-CCS coupled thermal power unit. The purchase cost of MSTS-CCT-CCS coupled thermal power unit i; This is the loss coefficient.

[0095] The unit's fuel injection cost is expressed as follows:

[0096] When deep peak shaving without oil injection still cannot meet the system's peak shaving needs, the unit needs to further reduce its output and enter the deep peak shaving phase with oil injection. To maintain the normal operation of the boiler and water circulation system, this article includes the corresponding oil injection cost in the peak shaving expense.

[0097] ;

[0098] In the formula: The amount of oil injected during the peak shaving stage of the MSTS-CCT-CCS coupled thermal power unit i at time t; This refers to the oil price for the current season.

[0099] MSTS-CCT-CCS coupled thermal power units only generate a measurable deep peak-shaving space and receive compensation when the actual operating output is lower than the conventional minimum technical output. To reflect the difference in peak-shaving depth, this paper adopts a segmented compensation method.

[0100] ;

[0101] Compensation coefficient:

[0102]

[0103] In the formula: The compensation benefits obtained by MSTS-CCT-CCS coupled thermal power unit i at time t when participating in different deep peak shaving stages; , , These are the unit power compensation coefficients for coupled thermal power units during the conventional peak shaving phase, the non-oil deep peak shaving phase, and the oil deep peak shaving phase, respectively, and a tiered pricing method is adopted; , , These represent the effective peak-shaving space formed by MSTS-CCT-CCS coupled thermal power unit i at time t during the conventional peak-shaving, non-oil deep peak-shaving, and oil deep peak-shaving stages; This is the scheduling time interval.

[0104] 3. Coupled Thermal Power Unit Thermodynamic Coupled Model

[0105] Based on the aforementioned deep peak-shaving model of MSTS-CCT-CCS coupled thermal power units, this paper further describes the thermodynamic coupling relationship between steam extraction, thermal storage, and carbon capture processes, and establishes a thermodynamic coupling model for coupled thermal power units. This model is used to characterize the impact of the coordinated operation of MSTS, CCT, and CCS subsystems on the unit's power output, thermal storage status, and carbon capture energy consumption.

[0106] 1) Steam extraction loss model

[0107] Under thermal storage conditions, the unit stores reheat steam in the MSTS, and its total extraction steam power... and power loss due to steam extraction for:

[0108] ;

[0109] ;

[0110] In the formula: The extraction ratio of reheat steam; This represents the total flow rate of reheat steam. This refers to the enthalpy of the reheat section inlet steam. This refers to the enthalpy of the condensate at the reboiler outlet. , These are 0-1 state variables representing the coupled thermal power unit's heat storage and heat release conditions, used to characterize the unit's operating state during the heat storage and heat release processes, and satisfying the following conditions: ; Reheat steam extraction heat power under energy storage conditions; This is the conversion factor for power loss caused by reheat steam extraction.

[0111] Under energy release conditions, the unit switches to extracting steam from the exhaust side of the intermediate pressure cylinder, and its total extraction steam power... and extraction steam power loss for:

[0112] ;

[0113] ;

[0114] In the formula: The proportion of steam extracted from the outlet of the intermediate pressure cylinder; This refers to the total exhaust steam flow rate at the outlet of the intermediate pressure cylinder. This is the enthalpy of the steam at the outlet of the intermediate-pressure cylinder; The heat power of the exhaust steam extraction from the intermediate pressure cylinder under energy release conditions; This is the conversion factor for the power loss caused by the exhaust and extraction of steam from the intermediate pressure cylinder.

[0115] 2) Molten Salt Thermal Storage Model

[0116] Molten salt thermal storage systems (MSTS) perform thermal storage tasks during off-peak hours and release heat and generate net output gain during peak hours. Therefore, it is necessary to establish separate models for charging power, heat release gain, and operation and maintenance costs.

[0117] MSTS thermal power :

[0118] ;

[0119] In the formula: This is the enthalpy of the steam after it has passed through the molten salt heat exchanger; This represents the thermal storage efficiency of the MSTS under energy storage conditions.

[0120] MSTS heat generation gain :

[0121] ;

[0122] In the formula: The heat release power of the molten salt thermal storage system (MSTS) at time t; is the exothermic thermoelectric conversion coefficient of MSTS.

[0123] Molten salt thermal energy storage operation and maintenance costs :

[0124] ;

[0125] In the formula: This refers to the unit cost of a molten salt thermal energy storage system. Let i be the heat charging power of the molten salt thermal storage at time t; Let i be the heat release power of the molten salt thermal storage at time t.

[0126] 3) Modeling of the heat and power output of carbon capture turbines:

[0127] ;

[0128] ;

[0129] In the formula: Let be the heat consumption power of turbine i at time t under energy storage conditions; The enthalpy of the steam at the outlet of the carbon-capturing turbine; Let be the power generation of turbine i under energy storage conditions at time t; The thermoelectric conversion efficiency of the carbon capture steam turbine under energy storage conditions; Let be the heat consumption power of turbine i under the energy release condition at time t; This is the enthalpy of the reboiler inlet steam. Let be the power generation of turbine i under the energy release condition at time t; The thermoelectric conversion efficiency of the carbon capture turbine under energy release conditions.

[0130] 4) Carbon capture system modeling:

[0131] ;

[0132] ;

[0133] ;

[0134] In the formula: For the free carbon emission allowance of coupled thermal power unit i at time t; , is the carbon emission quota coefficient per unit power generation of coupled thermal power unit i; This represents the actual total CO2 production of coupled thermal power unit i at time t. The actual carbon emission coefficient of coupled thermal power unit i; Let be the carbon capture amount of the coupled thermal power unit i at time t in the CCS. This represents the carbon capture rate.

[0135] Total heat loss of CCS system under energy storage and energy release conditions , :

[0136] ;

[0137] In the formula: This is the enthalpy value of the condensate at the reboiler outlet.

[0138] Total CCS heat consumption of coupled thermal power unit i at time t :

[0139] ;

[0140] Actual carbon capture heat consumption of CCS reboiler :

[0141] ;

[0142] In the formula: The unit heat consumption for carbon capture in a carbon capture reboiler; This refers to the fixed heat consumption of the reboiler.

[0143] Net power consumption of carbon capture system :

[0144] ;

[0145] In the formula: Let be the carbon capture amount of the coupled thermal power unit i at time t in the CCS. The power consumption of a CCS unit for capturing CO2; and These represent the power generation of the carbon capture turbine under energy storage and energy release conditions, respectively. Since the CCT and CCS systems have an energy coupling relationship, the power generated by the CCT can offset part of the power demand of the carbon capture process, so it is deducted when calculating the net power consumption of the CCS system.

[0146] Carbon trading costs :

[0147] ;

[0148] In the formula: The unit price for carbon trading; This represents the actual total CO2 production of coupled thermal power unit i at time t. Let be the carbon capture amount of the coupled thermal power unit i at time t in the CCS. The free carbon emission allowance for coupled thermal power unit i at time t; This represents the number of coupled thermal power units.

[0149] Cost of carbon dioxide storage :

[0150] ;

[0151] In the formula: This represents the unit cost of CO2 storage.

[0152] 5) Net output power and equivalent deep adjustment space of the coupled unit

[0153] Total net output power of coupled thermal power units at time t for:

[0154] ;

[0155] In the formula: The electrical power loss of the extracted steam from the coupled unit is due to the reheat steam extraction loss. and intermediate pressure cylinder exhaust steam extraction loss Together they form; This represents the net power consumption of the CCS. This is for the gain of heat release and power generation of MSTS.

[0156] Considering that the ancillary service market typically measures peak-shaving services based on the actual output power at the grid connection point, this paper uses the difference between the net output of the coupled unit and the theoretical minimum technical output to characterize its equivalent downward peak-shaving capacity:

[0157] ;

[0158] 6) Coupling unit operating constraints

[0159] In addition to the mathematical relationships at the equipment level, coupled thermal power units also need to meet operating conditions such as gross output boundary, ramp-up capability, and start-stop status.

[0160] ;

[0161] In the formula: Let t be the gross output of coupled thermal power unit i at time t; , These are the minimum and minimum technical outputs of the coupled thermal power units, respectively. This is the minimum stable operating output of the coupled thermal power unit i during the deep peak shaving phase without oil injection, and is used to represent the boundary output between the conventional peak shaving phase and the deep peak shaving phase without oil injection. This is the minimum stable operating output of the coupled thermal power unit i during the deep peak shaving stage, used to represent the deep peak shaving operating boundary after further load reduction; The maximum ramp rate of unit i; , The minimum continuous operation and downtime of the coupled thermal power unit; , , To couple the start-stop states of thermal power unit i under conventional peak shaving, peak shaving without oil injection, and peak shaving with oil injection (0-1 variables), , which are 0-1 variables representing the start-stop state of coupled thermal power unit i. , These represent the continuous operating time and continuous downtime of coupled thermal power unit i up to time t-1, respectively.

[0162] 4. Thermo-coupling operation constraints

[0163] To ensure that the molten salt thermal storage, steam extraction, and carbon capture processes operate within the feasible domain, it is necessary to further establish the boundary and energy constraints of the coupled unit's thermal system.

[0164] Steam extraction ratio constraint:

[0165] ;

[0166] In the formula: , These represent the maximum extraction ratios of reheat steam and intermediate-pressure cylinder exhaust, respectively.

[0167] MSTS operational constraints:

[0168] ;

[0169] ;

[0170] ;

[0171] ;

[0172] In the formula: , These represent the minimum and maximum values ​​of the steam enthalpy at the outlet side of the MSTS heat exchanger, respectively. The charge / discharge ramp rate of MSTS; , These are the maximum charging and discharging power of the MSTS, respectively. , These represent the lower and upper limits of the thermal storage capacity of the MSTS. To improve the thermal storage efficiency of MSTS under energy storage conditions, The heat release efficiency of the MSTS under energy storage conditions.

[0173] CCS system constraints:

[0174] ;

[0175] ;

[0176] In the formula: , These are the maximum and minimum heat dissipation power of the CCS, respectively. , These represent the maximum and minimum carbon capture rates of CCS, respectively.

[0177] Extraction steam heat power balance constraints:

[0178] ;

[0179] III. Lower-level wind-fire coordinated peak-shaving optimization model

[0180] The lower-level wind and fire coordinated peak shaving optimization model uses the total system operating cost. Minimum objective function to construct the lower-level objective function:

[0181] ;

[0182] In the formula, and These are the numbers of conventional thermal power units and MSTS-CCT-CCS coupled thermal power units, respectively. This refers to the operating costs of conventional thermal power units, including coal consumption costs and start-up and shutdown costs. The operating cost of MSTS-CCT-CCS coupled thermal power units includes coal consumption cost, start-up and shutdown cost, deep peak shaving loss cost, and oil injection cost. Cost of CO2 sequestration generated by carbon capture systems; The operating and maintenance costs of molten salt thermal energy storage; and These are the costs of wind curtailment penalties and carbon trading costs, respectively.

[0183] The constraints include:

[0184] Based on the net load regulation boundary obtained from the upper-level optimization, the lower-level wind-thermal coordinated peak-shaving optimization model first satisfies the system power balance constraint relationship, which is expressed as follows:

[0185] ;

[0186] In the formula, Let t be the actual grid-connected power output of the wind farm.

[0187] Considering the uncertainties caused by wind power fluctuations and load forecasting errors, the lower-level wind-thermal coordinated peak-shaving optimization model further satisfies the system reserve capacity constraint:

[0188] ;

[0189] In the formula: , These are the reserve factors considering the uncertainties of wind power and load, respectively, which are taken as 0.1 and 0.05 here; and The numbers of conventional thermal power units and MSTS-CCT-CCS coupled thermal power units are respectively: .

[0190] Wind curtailment costs and output constraints:

[0191] Wind power abandonment penalty costs :

[0192] ;

[0193] In the formula: This represents the wind curtailment penalty coefficient.

[0194] The actual grid-connected power of wind power must not exceed the predicted wind power output:

[0195] ;

[0196] In the formula: This represents the predicted wind power output at time t; Let t be the actual grid-connected power output of the wind farm.

[0197] Revenue acceptability constraints and responsibility weighting mechanisms:

[0198] 1) Deep Peak Shaving Responsibility Weight Sharing Mechanism

[0199] To characterize the disparities in responsibility for the impact of renewable energy fluctuations on system peak-shaving pressure and to improve the rationality of cost allocation for deep peak-shaving compensation, a low-valley pull-down coefficient is introduced. Peak and valley influence coefficient :

[0200] ;

[0201] In the formula, This represents the minimum daytime load value of the system load; This represents the minimum daily net load. For the installed capacity of new energy sources; This represents the peak-to-valley difference in daytime load. This represents the peak-to-valley difference in net load during the day.

[0202] Based on the above two indicators, a peak-shaving responsibility sharing coefficient is constructed. :

[0203] ;

[0204] In the formula: , These are the coefficients for the trough pull-down coefficient and the peak-trough influence coefficient, respectively.

[0205] Accordingly, MSTS-CCT-CCS coupled thermal power units provide deep peak-shaving services by reducing operating output and receive corresponding compensation benefits based on different peak-shaving stages. Considering that the high proportion of wind power integration leads to increased fluctuations in system net load, this paper further allocates peak-shaving compensation costs among the responsible entities based on the differences in responsibility between conventional thermal power units and wind farms for system peak-shaving needs.

[0206] Peak-shaving compensation costs borne by conventional thermal power units:

[0207] ;

[0208] In the formula, For conventional thermal power units at time t of effort.

[0209] Peak-shaving compensation costs borne by wind power:

[0210] ;

[0211] In the formula, The actual grid-connected power of wind farm k at time t; This refers to the number of conventional thermal power units; The number of wind farms; The compensation benefits obtained by MSTS-CCT-CCS coupled thermal power unit i at time t when participating in different depth peak shaving stages.

[0212] 2) Constraints on the acceptability of returns for participating entities

[0213] To further ensure the economic rationality of each participating entity assuming peak-shaving responsibility, this paper establishes a revenue acceptability constraint. The daily net revenue of each entity under the no-deep peak-shaving benchmark scenario is used as the comparison benchmark, and the acceptability of the revenue comparison of each entity after participating in joint peak-shaving is incorporated into the model.

[0214] The acceptable constraint for the overall benefit of conventional thermal power units and MSTS-CCT-CCS coupled thermal power units participating in deep peak shaving is as follows:

[0215] ;

[0216] In the formula: The compensation benefits obtained by MSTS-CCT-CCS coupled thermal power unit i at time t when participating in different deep peak shaving stages; The deep peak shaving cost of MSTS-CCT-CCS coupled thermal power unit i at time t is composed of deep peak shaving loss cost and oil injection cost. The benchmark on-grid electricity price for thermal power; The scheduling period; The deep peak-shaving compensation cost borne by conventional thermal power unit i at time t; , These represent the operational benefits of thermal power units when they do not participate in deep peak shaving and when they do participate in deep peak shaving, respectively. The incremental revenue from participating in deep peak shaving is used to construct the acceptable revenue constraints for thermal power units.

[0217] The acceptableness constraint for the revenue of wind farms participating in deep peak shaving is:

[0218] ;

[0219] In the formula: For wind power grid connection price; Let N be the actual grid-connected power of wind farm k at time t. wind The number of wind farms; The scheduling period; The scheduling time interval; To supplement the cost of peak shaving for wind farm group k at time t; Let $t$ be the cost of wind curtailment penalty for wind farm $k$ at time $t$. The total revenue after wind power participates in deep peak shaving. This represents the benchmark return for wind power when it does not participate in deep peak shaving. The incremental revenue from wind power participation before and after deep peak shaving is used to construct the acceptability constraints of wind power revenue.

[0220] The aforementioned revenue acceptability constraint is used to ensure that the revenue of each responsible party participating in deep peak shaving services is not lower than the baseline operating condition. The resulting revenue increment constraint... and It is further embedded in the lower-level wind and fire coordinated peak shaving optimization model to limit the range of changes in the benefits of each participating entity after participating in deep peak shaving, thereby ensuring that the peak shaving responsibility allocation results meet the requirements of economic acceptability.

[0221] IV. Solution Strategies

[0222] The two-layer collaborative scheduling model established in this invention involves upper-layer multi-objective pumped storage optimization, lower-layer unit start-up and shutdown state transitions, cross-time period energy balance, and MSTS-CCT-CCS coupled unit thermal-electrical-carbon process constraints, resulting in high solution complexity. Therefore, considering the hierarchical decision structure and the differences in variable roles within the model, this invention constructs a hierarchical solution mechanism.

[0223] 1. Solving and Determining the Optimal Solution of the Upper Net Load Boundary Plasticity Model

[0224] The upper-level net load boundary shaping model simultaneously considers two objectives: minimizing net load volatility and maximizing pumped storage utilization benefits. Since there may be a trade-off between the net load smoothing effect and the operational benefits of pumped storage, this invention employs NSGA-III to solve the upper-level multi-objective optimization problem to obtain a relatively uniform set of non-dominated candidate solutions. Subsequently, the entropy-weighted TOPSIS method is used to objectively and comprehensively evaluate the non-dominated solutions, thereby determining the unique upper-level optimal solution. The pumped storage pumping / generation power sequence obtained from the upper-level optimization and the resulting net load regulation boundary are then transferred to the lower-level wind-thermal coordinated peak-shaving model.

[0225] 2. Accurate solution of the lower-level wind-fire coordinated peak-shaving model

[0226] The lower-level wind-thermal coordinated peak-shaving model involves multiple integer and continuous variables, including unit start-up and shutdown states, output upper and lower limits, ramping constraints, MSTS heat charging and releasing processes, CCS operating constraints, and deep peak-shaving section selection. To improve model solution efficiency, this invention performs equivalent linearization on nonlinear terms: thermal power coal consumption costs and deep peak-shaving loss costs are represented using a piecewise linearization method; the product terms of state variables and continuous power variables are linearized using the Big-M method; the MSTS heat charging / releasing mutually exclusive states are achieved through 0-1 variable constraints; and the coupling relationship between CCS capture rate and capture amount is transformed into a linear expression under given carbon emission coefficients. After the above processing, the lower-level wind-thermal coordinated peak-shaving model can be transformed into a mixed integer linear programming model, which can be solved using solvers such as Gurobi. The overall solution process is as follows: Figure 2 As shown.

[0227] A multi-scenario comparative verification system was adopted based on typical daily scheduling data. Comparative scenarios were set up under different combinations of peak-shaving depth methods, revenue acceptability constraints, and compensation sharing mechanisms. Net load shaping effect, system operating cost, wind curtailment volume, wind curtailment rate, net CO2 emissions, peak-shaving compensation sharing results, and net revenue of each entity were used as evaluation indicators. Sensitivity analysis of the responsibility sharing enhancement coefficient was also conducted to verify the comprehensive effectiveness of the proposed wind-thermal-storage coordinated low-carbon peak-shaving scheduling method. Specific settings are as follows:

[0228] For example:

[0229] To analyze the effects of different mechanisms, five comparative scenarios were set up. Each scenario used the same load, wind power, electricity price, carbon price, compensation price, and unit boundary conditions, only changing the deep peak shaving method, compensation sharing mechanism, and revenue acceptability constraints.

[0230] Scenario 1: Without considering deep peak shaving, a conventional proportional compensation and allocation method is adopted, and no acceptability constraints are set;

[0231] Scenario 2: Considering conventional thermal power units participating in deep peak shaving, a conventional proportional compensation sharing method is adopted, and no revenue acceptability constraints are set;

[0232] Scenario 3: Considering conventional thermal power units participating in deep peak shaving, adopting a conventional proportional compensation sharing method, and setting revenue acceptability constraints;

[0233] Scenario 4: Consider MSTS-CCT-CCS coupled units participating in deep peak shaving, adopt the conventional proportional compensation sharing method, and set revenue acceptability constraints;

[0234] Scenario 5: Consider MSTS-CCT-CCS coupled units participating in deep peak shaving, adopt a responsibility weight compensation sharing method, and set revenue acceptability constraints.

[0235] As described above, simulation analysis was performed on the five scenarios, and the optimized scheduling results are shown in Table 1.

[0236] Table 1 shows the optimization scheduling results for each scenario in the previous stage.

[0237]

[0238] As shown in Table 1, Scenario 5 exhibits superior overall performance after adopting the complete method of this invention. Compared to the baseline Scenario 1, the total system operating cost decreased from RMB 12.1046 million to RMB 11.5252 million, a reduction of 4.79%; the wind curtailment rate decreased from 8.50% to 3.61%; and net CO2 emissions decreased from 45,600 tons to 44,000 tons, indicating that the proposed method can effectively improve wind power absorption and reduce carbon emissions. Simultaneously, net revenue from thermal power increased from RMB 7.5225 million to RMB 7.6558 million, and net revenue from wind power increased from RMB 6.963 million to RMB 7.4551 million, demonstrating that this invention improves the coordination of benefits among multiple stakeholders while enhancing system economics.

[0239] Comparing the various scenarios, in Scenario 2, the wind curtailment rate decreased significantly after introducing deep peak shaving with conventional thermal power, but the net revenue from thermal power dropped to 7.3325 million yuan, indicating that relying solely on deep peak shaving of thermal power weakens its economic incentive. In Scenario 3, after adding a revenue acceptability constraint, the net revenue from thermal power recovered to 7.5238 million yuan, verifying the effect of the revenue acceptability constraint. In Scenario 4, after introducing the MSTS-CCT-CCS coupled unit, the total operating cost decreased to 11.5253 million yuan, the wind curtailment rate decreased to 3.61%, and the net CO2 emissions decreased to 44,000 tons, demonstrating the low-carbon peak shaving effect of the coupled unit. Compared to Scenario 4, the operating cost, wind curtailment rate, and CO2 emissions in Scenario 5 remained basically unchanged, but the net revenue from thermal power increased from 7.5241 million yuan to 7.6558 million yuan. Figure 3 It can be seen that after the intensity of responsibility sharing increases, the compensation sharing ratio of wind power increases, while the sharing pressure of thermal power decreases. This indicates that the responsibility weight sharing mechanism can optimize the allocation of compensation costs and enhance the initiative of thermal power to continuously participate in low-carbon peak shaving.

[0240] According to a second aspect of the present invention, a wind-thermal-storage coordinated low-carbon peak-shaving scheduling system considering revenue acceptability and responsibility weight allocation is provided, comprising modules of the wind-thermal-storage coordinated low-carbon peak-shaving scheduling method considering revenue acceptability and responsibility weight allocation as described above. Specifically, it includes: a first module for establishing an upper-level net load boundary shaping model; the upper-level net load boundary shaping model aims to minimize net load volatility and maximize pumped storage call revenue, setting reservoir capacity constraints, output and operating condition constraints, and start-stop frequency constraints; a second module for establishing and solving a lower-level wind-thermal coordinated peak-shaving optimization model based on a lower-level key equipment model, using the pumped storage scheduling sequence corresponding to the upper-level optimal solution and the resulting net load boundary as known input; the lower-level wind-thermal coordinated peak-shaving optimization model aims to minimize the lower-level system operating cost, and introduces revenue acceptability constraints and a responsibility weight allocation mechanism while satisfying system power balance constraints, system reserve capacity constraints, wind power curtailment cost and output constraints. For parts of the modules not described in detail above, please refer to the relevant descriptions in this embodiment.

[0241] According to a third aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0242] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the steps of any one of the methods described above.

[0243] The specific embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A low-carbon peak-shaving scheduling method that considers the acceptability of benefits and the apportionment of responsibility weights, characterized in that, include: The study focuses on a regional power system consisting of wind farms, pumped storage power stations, conventional thermal power units, and coupled thermal power units; the coupled thermal power units consist of molten salt thermal storage (MSTS), carbon capture turbine (CCT), and carbon capture system (CCS). Establish an upper-level net load boundary shaping model; the upper-level net load boundary shaping model aims to minimize net load fluctuation and maximize pumped storage call benefits, and sets reservoir capacity constraints, output and operating condition constraints, and start-stop frequency constraints; Based on the construction of the lower-level key equipment model, the pumped storage scheduling sequence corresponding to the upper-level optimal solution and the resulting net load boundary are used as known inputs to establish and solve the lower-level wind-thermal coordinated peak shaving optimization model. The lower-level wind-thermal coordinated peak shaving optimization model aims to minimize the operating cost of the lower-level system. On the basis of satisfying the system power balance constraints, system reserve capacity constraints, wind power curtailment cost and output constraints, it introduces the revenue acceptability constraints and the responsibility weight sharing mechanism.

2. The wind-thermal-storage coordinated low-carbon peak-shaving dispatch method considering revenue acceptability and responsibility weighting as described in claim 1, characterized in that, The solution strategy is as follows: NSGA-III is used to solve the multi-objective optimization problem of the upper-level net load boundary shaping model to obtain a relatively uniform set of non-dominated candidate solutions; then, the entropy-weighted TOPSIS method is used to objectively and comprehensively evaluate the non-dominated solutions, thereby determining the unique upper-level preferred solution as a known input to be passed to the lower-level wind-fire coordinated peak shaving optimization model; the nonlinear terms in the wind-fire coordinated peak shaving optimization model are linearized, and the wind-fire coordinated peak shaving optimization model is transformed into a mixed integer linear programming problem for solution.

3. The wind-thermal-storage coordinated low-carbon peak-shaving dispatch method considering revenue acceptability and responsibility weighting as described in claim 1, characterized in that, The objective function of the upper-level net load boundary shaping model is constructed as follows: (1) Net load fluctuation Construct the first upper-level objective function with the minimum objective as the goal: ； ； In the formula: t represents the scheduling period; The scheduling period; Let be the net load value at time t; This represents the average net load over a scheduling cycle. Let be the day-ahead predicted load power of the regional power system at time t; Let t be the predicted power of the wind farm. This refers to the number of pumped storage units; and These represent the power generation and pumping power of the pumped-storage unit at time t, respectively. (2) Revenue from pumped storage unit dispatch Construct a second upper-level objective function with the goal of maximizing: ； ； In the formula: Let S be the start-up and shutdown loss cost of the pumped storage unit at time t. Let be the time-of-use electricity price of the power grid at time t; The scheduling time interval; and These are the two variables representing the pumped storage unit s at time t, which is in power generation and pumping operation respectively. and These are the losses incurred during a single start-up and shutdown of the pumped-storage unit, respectively.

4. The wind-thermal-storage coordinated low-carbon peak-shaving dispatch method considering revenue acceptability and responsibility weighting as described in claim 1, characterized in that, The lower-level key equipment model includes a conventional thermal power unit peak shaving model, a coupled thermal power unit phased deep peak shaving cost model, a coupled thermal power unit thermodynamic coupling model, and constraints; among which, the constraints include: extraction steam ratio constraints, MSTS operation constraints, CCS system constraints, and extraction steam thermal power balance constraints.

5. The wind-thermal-storage coordinated low-carbon peak-shaving dispatch method considering revenue acceptability and responsibility weighting as described in claim 1, characterized in that, The lower-level wind and fire coordinated peak shaving optimization model uses the total system operating cost. Minimum objective function to construct the lower-level objective function: ； In the formula, and These refer to the number of conventional thermal power units and coupled thermal power units, respectively. This refers to the operating cost of conventional thermal power units; To couple the operating costs of thermal power units; To account for the cost of CO2 sequestration generated by the carbon capture system in thermal power units; To reduce the operating and maintenance costs of molten salt thermal energy storage in thermal power units; and These are the costs of wind curtailment penalties and carbon trading costs, respectively.

6. The wind-thermal-storage coordinated low-carbon peak-shaving dispatch method considering revenue acceptability and responsibility weighting as described in claim 1, characterized in that, The aforementioned responsibility weighting mechanism is as follows: Introducing a trough to lower the coefficient Peak-valley influence coefficient : ； In the formula, This represents the minimum daytime load value of the system load; This represents the minimum daily net load. For the installed capacity of new energy sources; This represents the peak-to-valley difference in daytime load. This represents the peak-to-valley difference in daytime net load. trough pulls down coefficient Peak-valley influence coefficient Construct a peak-shaving responsibility sharing coefficient : ； Based on the peak-shaving responsibility sharing coefficient, the peak-shaving compensation costs borne by conventional thermal power units and wind power units are determined respectively.

7. The wind-thermal-storage coordinated low-carbon peak-shaving dispatch method considering revenue acceptability and responsibility weighting according to claim 6, characterized in that, The revenue acceptability constraints include: constructing revenue acceptability constraints for conventional thermal power units and coupled thermal power units participating in deep peak shaving based on the peak shaving compensation costs borne by conventional thermal power units; and constructing wind power revenue acceptability constraints based on the peak shaving compensation costs borne by wind power.

8. A wind-thermal-storage coordinated low-carbon peak-shaving dispatch system considering the acceptability of benefits and the apportionment of responsibility weights, characterized in that, The module includes any one of the wind, thermal, and energy storage coordinated low-carbon peak-shaving scheduling methods that consider the acceptability of benefits and the apportionment of responsibility weights as described in any one of claims 1-7.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

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

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