Carbon capture power plant coordinated peak shaving cost allocation optimization method based on multi-dimensional quantification and kernel method

Abstract

The application provides a carbon capture power plant coordinated peak shaving cost allocation optimization method based on multi-dimensional quantification and kernel method. The method comprises the following steps: respectively quantifying the peak shaving cost of wind, light and storage by using scene substitution method; setting peak shaving initiative constraint conditions for wind, light and fire; constructing a carbon capture thermal power plant peak shaving coupling model; constructing a peak shaving cost optimization model; allocating the peak shaving cost by using the kernel method; setting peak shaving cost allocation constraint conditions by driving the kernel method; allocating the peak shaving cost by using a carbon-electricity coordinated peak shaving double-layer optimization algorithm; wherein the carbon-electricity coordinated peak shaving double-layer optimization algorithm constructs a cost allocation model through the upper layer, and constructs a thermal power, energy storage and new energy output strategy through the lower layer. The application realizes accurate cost quantification, achieves the purpose of fair cost distribution, and produces beneficial technical effects of low-carbon economy.

Description

Technical Field

[0001] This invention relates to the technical field of power system peak shaving, and more specifically, to an optimization method for cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantification and the nucleolus method. Background Technology

[0002] Existing power system peak-shaving cost models are mainly based on the marginal cost theory of traditional thermal power units, failing to fully consider the dynamic carbon cost and nonlinear coupling characteristics when carbon capture, utilization, and storage (CCUS) units participate in peak shaving. Song Duoyang, Xue Tianliang, Li Yipu, et al., proposed a peak-shaving cost allocation method based on Shapley values ​​in their work on virtual power plant cooperative game scheduling and revenue distribution strategies considering wind and solar uncertainties, but this method did not address the issue of unfair cost allocation caused by carbon capture energy consumption fluctuations in CCUS units. Ba Jinyu, He Chuan, Nan Lu, et al., in their work on low-carbon economic dispatch of hydrogen-containing park integrated energy systems based on green certificate-tiered carbon trading linkage and Bruker opportunity constraints, used fixed carbon price parameters to calculate peak-shaving costs, ignoring the impact of carbon market price fluctuations on the flexible operation mode of carbon capture units. Liang Chao, in his work on wind power system-wide cost modeling and its application in multiple development modes, proposed a peak-shaving cost quantification framework based on scenario substitution, but this method did not introduce constraints on the active participation of peak-shaving entities, leading to an imbalance in cost allocation between traditional units and CCUS units.

[0003] The aforementioned prior art has the following drawbacks:

[0004] (1) Insufficient cost quantification: The spatiotemporal multidimensional dynamic coupling model of peak-shaving resources has not been realized, and the nonlinear relationship between carbon capture energy consumption and peak-shaving depth of CCUS units cannot be accurately quantified.

[0005] (2) Lack of fairness: Traditional allocation methods (such as Shapley value) do not take into account the alliance reorganization mechanism, which leads to the underestimation of the marginal contribution of carbon capture units;

[0006] (3) Economic constraints: Static carbon price models cannot adapt to dynamic changes in the carbon market, resulting in peak shaving costs and carbon emissions not reaching the optimal level. Summary of the Invention

[0007] The present invention aims to solve the problems of unfair distribution of peak-shaving costs and insufficient modeling of dynamic characteristics of carbon capture units in the prior art.

[0008] Therefore, the purpose of this invention is to propose an optimization method for cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantification and nucleolus method.

[0009] To achieve the above objectives, the present invention provides a method for optimizing the cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantification and the nucleolus method. This optimization method includes: Step S1: quantifying the peak shaving costs of wind, solar, and energy storage using a scenario substitution method; Step S2: setting proactive peak shaving constraints for wind, solar, and thermal power plants; Step S3: constructing a peak shaving coupling model for carbon capture thermal power plants; Step S3 specifically includes:

[0010] Step S3.1: Calculate the carbon capture efficiency to obtain the net available peak-shaving output of the deep-tunneling thermal power unit; the expression corresponding to the net available peak-shaving output of deep-tunneling thermal power unit i is:

[0011]

[0012] In the formula, To optimize the net available peak-shaving output of thermal power units; To determine the maximum available peak-shaving output of thermal power units; d i d is the carbon capture energy consumption coefficient. i η represents the percentage of electricity generated per unit mass of CO2 captured; CCUS For carbon capture efficiency; Benchmark carbon emission intensity;

[0013] Step S3.2: Set the carbon capture system to operate in FC mode and FlexC mode, and introduce a binary decision variable to characterize the switching between FC mode and FlexC mode, and establish a penalty coefficient for FlexC mode to accurately calculate the cost of different carbon capture operation modes; wherein, in FC mode, the carbon capture rate η CCUS The peak-shaving depth of the unit is kept constant at a certain design value, but is limited by the minimum technical output. In FlexC mode, the capture rate is dynamically adjusted to release unit output during peak demand periods, but this incurs additional carbon emission costs.

[0014] Step S3.3: Based on steps S3.1 and S3.2, construct a joint optimization objective function for carbon-electricity costs to obtain the total cost of carbon capture and peak shaving for deep-tuning thermal power units; the total cost of carbon capture and peak shaving for deep-tuning thermal power units. The corresponding expression is:

[0015]

[0016] In the formula, For carbon price; γ represents the real-time carbon emission intensity of unit i at time t; γ is the FlexC mode switching penalty coefficient. The additional carbon emissions resulting from the reduced capture rate; Represents a binary decision variable. Indicates FC mode, Indicates FlexC mode;

[0017] Step S4: Construct a peak-shaving cost optimization model; Step S4 specifically includes: Step S4.1: Minimize the total peak-shaving cost of the system as the objective function C all Objective function C all This is the peak-shaving cost optimization model; the objective function C all The corresponding expression is:

[0018]

[0019] In the formula, Unit output cost for wind and solar power; C represents the output of wind and solar power at time t; a The cost of charging a unit of energy storage; C b Cost per unit of energy storage for discharge;

[0020] Step S5: Allocate peak-shaving costs using the nucleolus method; Step S6: Set peak-shaving cost allocation constraints using the nucleolus method; Step S7: Allocate peak-shaving costs using a carbon-electricity synergistic peak-shaving dual-layer optimization algorithm; wherein, the carbon-electricity synergistic peak-shaving dual-layer optimization algorithm constructs a cost allocation model in the upper layer and constructs power output strategies for thermal power, energy storage, and new energy sources in the lower layer;

[0021] Step S7 specifically includes: Step S7.1: Input the initial peak-shaving alliance allocation ratio and initialize the sharing weight Π. (0) Set the convergence threshold ε and the maximum number of iterations K. max To achieve upper-level initialization; Step S7.2: Based on the current apportionment weight Π (k) It calls upon a mixed-integer nonlinear programming solver and optimizes power output strategies for thermal power, energy storage, and new energy sources. Then calculate the real-time contribution C contribution To achieve lower-level scheduling optimization; Step S7.3: Pass the real-time contribution C from the lower layer to the upper layer. contribution The cost deviation ΔC and the updated alliance excess value; Step S7.4: Iterate and determine convergence using the kernel solution; If max|Π (k+0.1) -Π (k) |<ε or k≥K max If the result is positive, proceed to step S7.5; otherwise, proceed to step S7.6. Step S7.5: Output the optimal solution; Step S7.6: Generate a new allocation scheme Π. (k+0.1) Then return to step S7.2 until step S7.5 is executed.

[0022] Preferably, step S1 specifically includes: Step S1.1: Calculating the deviation of the load of any power user relative to its mean; the deviation d of the load of any power user j (j∈J) relative to its mean. j,t The corresponding expression is:

[0023]

[0024] In the formula, j∈J, t∈T; Let J be the load amount of load j in time period t; T is the set of peak-shaving periods; This represents the average load J within time period T;

[0025] Step S1.2: Calculate the deviation of the output of any renewable energy source relative to its mean; any renewable energy source new (new∈G) new The deviation d of the output power from its mean. new,t The corresponding expression is:

[0026]

[0027] In the formula, new∈G new ,t∈T;G new A collection of fluctuating photovoltaic and wind power; P new,t For the output of photovoltaic and wind power in time period t; This represents the average value of new photovoltaic and wind power generation within time period T.

[0028] Step S1.3: Calculate the deviation of the output of any stored energy from its mean; any stored energy es (es∈G) es The deviation d of the output power from its mean. es,t The corresponding expression is:

[0029]

[0030] In the formula, es∈Ges, t∈T; G es For energy storage systems participating in peak shaving; The charging power of the energy storage system es during time period t; Let es be the discharge power of the energy storage system during time period t;

[0031] For indicator functions, when hour, otherwise

[0032] For indicator functions, when hour, otherwise

[0033] Step S1.4: Calculate the system net load; System net load L t The corresponding expression is:

[0034]

[0035] In the formula, t∈T; The output of the energy storage system during time period t; the net load of the system L t The corresponding expression indicates that the total electricity consumption of any peak-shaving demand subject and any alternative scenario of the peak-shaving demand subject is equal;

[0036] Step S1.5: Set N = J∪G new ∪G es Given the set of peak-shaving demand subjects, and setting the sum of the contributions of any peak-shaving demand subject n (n∈N) to the upward and downward fluctuations of net load within time period T to 0, the corresponding expression for this process is:

[0037]

[0038] In the formula, d n,t The deviation of peak-shaving demand subject n from its mean in time period t;

[0039] Step S1.6: When the output of all loads and fluctuating power sources is constant under ideal conditions, set the net load that the conventional unit can meet. If the process remains constant during time period T, the corresponding expression is:

[0040]

[0041] In the formula, L is the system net load vector; d n It is the deviation vector of the load or output of the peak demand subject n relative to its mean.

[0042] Preferably, step S2 specifically includes: Step S2.1: Building a thermal power unit operating cost model; the expressions corresponding to the operating costs of thermal power units under conventional peak shaving and deep peak shaving are:

[0043]

[0044] In the formula, For the conventional peak-shaving operation cost of thermal power units; f i,t For unit loss costs;

[0045] Among them, the conventional peak-shaving operation cost of thermal power units The corresponding expression is:

[0046]

[0047] In the formula, a i b i and c i These are the consumption coefficients of thermal power unit i, respectively; To provide power to thermal power units;

[0048] Among them, unit loss cost f i,t The corresponding expression is:

[0049] f i,t =βS unit,i / (2N f (P));

[0050] In the formula, β is the operating influence coefficient of the thermal power unit; S unit,i N represents the purchase cost of the i-th thermal power unit; f (P) represents the number of rotor cracking cycles determined by the rotor low-cycle fatigue curve;

[0051] Step S2.2: Establish a peak-shaving profit model for deep-shaving thermal power units; the expression corresponding to the peak-shaving profit model for deep-shaving thermal power units is:

[0052]

[0053] In the formula, For peak-shaving compensation costs of thermal power units with deep regulation; The peak-shaving compensation price for thermal power units; For paid peak-shaving power of thermal power units; To increase the profits of thermal power units participating in peak shaving;

[0054] Step S2.3: Construct a wind power profit model; the expression corresponding to the wind power profit model is:

[0055]

[0056] In the formula, To generate profits from wind turbine participation in peak shaving; The effective on-grid tariff for wind power; The wind turbine provides power; θ represents the wind curtailment penalty. This represents the amount of wind power curtailed at wind farm l during time period t;

[0057] Step S2.4: Construct a photovoltaic profit model; the expression corresponding to the photovoltaic profit model is:

[0058]

[0059] In the formula, The effective feed-in tariff for photovoltaic power; Powering the photovoltaic units; ε represents the peak-shaving cost of photovoltaic units; ε represents the curtailment penalty. This represents the amount of solar power curtailed by photovoltaic power station m during time period t.

[0060] Preferably, step S4 further includes: step S4.2: setting the objective function C. all The constraints of the objective function in step S4.2 include: system balance constraints, deep peak shaving constraints of thermal power units, ramping constraints of thermal power units, output constraints of photovoltaic and wind power, and energy storage constraints.

[0061] The expression corresponding to the system equilibrium constraint is:

[0062]

[0063] In the formula, To provide power to thermal power units; To contribute to the power generation of new energy units; Contribute to energy storage; Total load;

[0064] The expression corresponding to the deep peak shaving constraint of the thermal power unit is:

[0065]

[0066] In the formula, u i,t The variable is a 0-1 representing the operating status of the unit. A value of 1 indicates that unit i is in the on state during time period t, and a value of 0 indicates that it is in the off state. Constraints on the upward and downward ramp speeds of unit i; Minimize the basic peak-shaving technical output of unit i during time period t;

[0067] The expression corresponding to the ramping constraint of the thermal power unit is:

[0068] P i,t -P i,(t-1) ≥-R i,down

[0069] P i,t -P i,(t-1) ≤R i,up ;

[0070] In the formula, P i,t R represents the output of unit i during time period t; i,down and R i,up These represent the positive and negative reserve requirements of the system during time period t, respectively.

[0071] The expressions corresponding to the photovoltaic and wind power output constraints are:

[0072]

[0073] In the formula, Represents photovoltaic power generation; Represents wind power output; Represents the total output of new energy sources;

[0074] The expression corresponding to the energy storage constraint is:

[0075]

[0076] In the formula, S es,t S represents the system's energy storage capacity during time period t; min and S max These represent the maximum and minimum energy storage capacities, respectively; α is a correction factor. and These are the energy storage charging power and energy storage discharging power, respectively; η a and η b These are energy storage charging efficiency and energy storage discharging efficiency, respectively; E s For energy storage.

[0077] Preferably, step S5 specifically includes: Step S5.1: Defining the set of peak-shaving demand entities as N = {thermal power, energy storage, wind power, photovoltaic}; and defining any alliance This represents a combination of topics participating in peak shaving; and for each alliance S, the objective function C established in step S4.1 is invoked. all Calculate the total system cost C(S) when only the entities within S undertake the peak-shaving task; Step S5.2: Use the marginal contribution pre-allocation method to allocate the total revenue or cost according to the marginal contribution of each peak-shaving entity to the overall goal, so as to realize the initial cost allocation scheme π. (0) ;

[0078] The initial cost allocation scheme π (0) The corresponding expression is:

[0079]

[0080] In the formula, This indicates that thermal power accounts for 60% of the total cost; This indicates that new energy sources account for 20% of the total cost; This indicates that energy storage accounts for 10% of the total cost;

[0081] Step S5.3: Iteratively optimize the excess value; Step S5.3 includes: Step S5.31: Calculate the excess value of all alliances under the current allocation scheme:

[0082]

[0083] In the formula, e(S,π) (k) ) indicates the league's excess value; Indicates the percentage of a particular alliance; This indicates that all alliances are seeking a sum;

[0084] Step S5.32: Identify the alliance S corresponding to the maximum excess value. max Alliance S max The corresponding expression is:

[0085]

[0086] Step S5.33: Adjust the allocation scheme to reduce S max The excess value, the corresponding expression for this process is:

[0087] min∈ste(S,π (k+0.1) )≤ε;

[0088] In the formula,

[0089] Step S5.34: Repeat steps S5.31 to S5.33 until the maximum excess value converges to 0, thus obtaining the nucleolar solution π. * .

[0090] Preferably, step S6 specifically includes: step S6.1: adding a coalition cost consistency constraint; the expression corresponding to the coalition cost consistency constraint is:

[0091]

[0092] In the formula, ε represents the allowable excess deviation, used to balance calculation efficiency and accuracy;

[0093] Step S6.2: Add individual rationality constraints; the expression corresponding to the individual rationality constraints is:

[0094] π n ≤C({n});

[0095] In the formula,

[0096] Preferably, in step S7, the carbon-electricity synergistic peak-shaving dual-layer optimization algorithm constructs a cost-sharing model at the upper layer and expressions corresponding to the output strategies of thermal power, energy storage, and new energy sources at the lower layer, as follows:

[0097]

[0098] In step S7.2, the real-time contribution C contribution The corresponding expression is:

[0099]

[0100] In step S7.3, the expression corresponding to the cost deviation ΔC is:

[0101] ΔC=||C actual -C estimated ||;

[0102] In the formula, C actual Indicates actual cost; C estimated ||·|| represents the estimated cost; ||·|| represents the norm;

[0103] The expression for updating the league's excess value is:

[0104]

[0105] The beneficial effects of this invention are:

[0106] (1) The carbon capture power plant collaborative peak-shaving cost allocation optimization method based on multidimensional quantification and nucleolus method provided by this invention achieves accurate cost quantification. Specifically, by using a spatiotemporal two-dimensional peak-shaving cost model, carbon capture energy consumption and peak-shaving depth are dynamically coupled, reducing the total peak-shaving cost by 6.5%;

[0107] (2) The carbon capture power plant collaborative peak-shaving cost allocation optimization method based on multidimensional quantification and nucleolus method provided by this invention achieves the goal of fair cost allocation. Specifically, the nucleolus method is used to optimize the alliance reorganization mechanism, thereby increasing the allocation fairness coefficient to 0.91;

[0108] (3) The carbon capture power plant collaborative peak-shaving cost allocation optimization method based on multidimensional quantification and nucleolus method provided by this invention has produced beneficial technical effects of low carbon economy. Specifically, when the carbon price is 150-250 yuan / ton, carbon emissions are reduced by 40%-50%, and the total cost increase is ≤5%.

[0109] Additional aspects and advantages of the invention will become apparent from the description which follows, or may be learned by practice of the invention. Attached Figure Description

[0110] Figure 1 A schematic flowchart of an embodiment of the present invention is shown, illustrating a method for optimizing cost allocation in collaborative peak shaving of carbon capture power plants based on multidimensional quantization and nucleolus method.

[0111] Figure 2 The diagram shows a simulation of the 24-hour output of a conventional thermal power unit under deep adjustment (without considering carbon price) in scenario 1 of an embodiment of the present invention, without considering energy storage.

[0112] Figure 3The diagram shows a simulation of the 24-hour output of a conventional thermal power unit under deep adjustment (without considering carbon price) in scenario 2 of an embodiment of the present invention, considering energy storage during wind and solar combined operation.

[0113] Figure 4 A schematic diagram illustrating the relationship between carbon price, carbon emissions, and peak-shaving costs according to an embodiment of the present invention is shown. Detailed Implementation

[0114] To better understand the above-mentioned objects, features, and advantages of the present invention, such as Figures 1 to 4 As shown in the accompanying drawings and specific embodiments, the present invention will be further described in detail below. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0115] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0116] In one embodiment of the present invention, a method for optimizing cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method includes:

[0117] Step S1: Quantify the peak-shaving costs of wind, solar, and energy storage using the scenario substitution method; Step S2: Set proactive peak-shaving constraints for wind, solar, and thermal power plants; Step S3: Construct a peak-shaving coupling model for carbon capture thermal power plants.

[0118] Step S3 specifically includes: Step S3.1: Calculating the carbon capture efficiency to obtain the net available peak-shaving output of the deep-tunneling thermal power unit; the expression corresponding to the net available peak-shaving output of the deep-tunneling thermal power unit i is:

[0119]

[0120] In the formula, To optimize the net available peak-shaving output of thermal power units; To determine the maximum available peak-shaving output of thermal power units; d i d is the carbon capture energy consumption coefficient. i η represents the percentage of electricity generated per unit mass of CO2 captured; CCUS For carbon capture efficiency; Benchmark carbon emission intensity;

[0121] Step S3.2: Set the carbon capture system to operate in FC mode and FlexC mode, and introduce a binary decision variable to characterize the switching between FC mode and FlexC mode, and establish a penalty coefficient for FlexC mode to accurately calculate the cost of different carbon capture operation modes; wherein, in FC mode, the carbon capture rate η CCUS The peak-shaving depth of the unit is kept constant at a certain design value, but is limited by the minimum technical output. In FlexC mode, the capture rate is dynamically adjusted to release unit output during peak demand periods, but this incurs additional carbon emission costs.

[0122] Step S3.3: Based on steps S3.1 and S3.2, construct a joint optimization objective function for carbon-electricity costs to obtain the total cost of carbon capture and peak shaving for deep-tuning thermal power units; the total cost of carbon capture and peak shaving for deep-tuning thermal power units. The corresponding expression is:

[0123]

[0124] In the formula, For carbon price; γ represents the real-time carbon emission intensity of unit i at time t; γ is the FlexC mode switching penalty coefficient. The additional carbon emissions resulting from the reduced capture rate; Represents a binary decision variable. Indicates FC mode, Indicates FlexC mode;

[0125] Step S4: Construct a peak-shaving cost optimization model;

[0126] Step S4 specifically includes:

[0127] Step S4.1: Minimize the total system peak-shaving cost as the objective function C all Objective function C all This is the peak-shaving cost optimization model; the objective function C all The corresponding expression is:

[0128]

[0129] In the formula, Unit output cost for wind and solar power; C represents the output of wind and solar power at time t; a The cost of charging a unit of energy storage; C b Cost per unit of energy storage for discharge;

[0130] Step S5: Allocate peak-shaving costs using the nucleolus method; Step S6: Set peak-shaving cost allocation constraints using the nucleolus method; Step S7: Allocate peak-shaving costs using a carbon-electricity synergistic peak-shaving dual-layer optimization algorithm; wherein, the carbon-electricity synergistic peak-shaving dual-layer optimization algorithm constructs a cost allocation model at the upper layer and constructs power output strategies for thermal power, energy storage, and new energy sources at the lower layer; Step S7 specifically includes: Step S7.1: Input the initial peak-shaving alliance ratio and initialize the allocation weight Π (0) Set the convergence threshold ε and the maximum number of iterations K. max In order to achieve

[0131] Upper-level initialization; Step S7.2: Based on the current allocated weight Π (k) It calls upon a mixed-integer nonlinear programming solver and optimizes power output strategies for thermal power, energy storage, and new energy sources. Then calculate the real-time contribution C contribution To achieve lower-level scheduling optimization; Step S7.3: Pass the real-time contribution C from the lower layer to the upper layer. contribution The cost deviation ΔC and the updated alliance excess value; Step S7.4: Iterate and determine convergence using the kernel solution; If max|Π (k+0.1) -Π (k) |<ε or k≥K max If the result is positive, proceed to step S7.5; otherwise, proceed to step S7.6. Step S7.5: Output the optimal solution; Step S7.6: Generate a new allocation scheme Π. (k+0.1) Then return to step S7.2 until step S7.5 is executed.

[0132] In this embodiment, the carbon capture power plant collaborative peak-shaving cost allocation optimization method based on multidimensional quantification and the nucleolus method provided by the present invention achieves accurate cost quantification. Specifically, by dynamically coupling carbon capture energy consumption and peak-shaving depth through a spatiotemporal two-dimensional peak-shaving cost model, the total peak-shaving cost is reduced by 6.5%. The carbon capture power plant collaborative peak-shaving cost allocation optimization method based on multidimensional quantification and the nucleolus method provided by the present invention achieves the goal of fair cost allocation. Specifically, by using the nucleolus method to optimize the alliance reorganization mechanism, the allocation fairness coefficient is increased to 0.91. The carbon capture power plant collaborative peak-shaving cost allocation optimization method based on multidimensional quantification and the nucleolus method provided by the present invention produces beneficial technical effects of low-carbon economy. Specifically, when the carbon price is 150-250 yuan / ton, carbon emissions are reduced by 40%-50%, and the total cost increase is ≤5%.

[0133] In one embodiment of the present invention, step S1 specifically includes: Step S1.1: Calculating the deviation of the load of any power user relative to its mean; the deviation d of the load of any power user j (j∈J) relative to its mean. j,t The corresponding expression is:

[0134]

[0135] In the formula, j∈J, t∈T; Let J be the load amount of load j in time period t; T is the set of peak-shaving periods; This represents the average load J within time period T;

[0136] Step S1.2: Calculate the deviation of the output of any renewable energy source relative to its mean; any renewable energy source new (new∈G) new The deviation d of the output power from its mean. new,t The corresponding expression is:

[0137]

[0138] In the formula, new∈G new ,t∈T;G new A collection of fluctuating photovoltaic and wind power; P new,t For the output of photovoltaic and wind power in time period t; This represents the average value of new photovoltaic and wind power generation within time period T.

[0139] Step S1.3: Calculate the deviation of the output of any stored energy from its mean; any stored energy es (es∈G) es The deviation d of the output power from its mean. es,t The corresponding expression is:

[0140]

[0141] In the formula, es∈Ges, t∈T; G es For energy storage systems participating in peak shaving; The charging power of the energy storage system es during time period t; Let es be the discharge power of the energy storage system during time period t;

[0142] For indicator functions, when hour, otherwise

[0143] For indicator functions, when hour, otherwise

[0144] Step S1.4: Calculate the system net load; System net load L t The corresponding expression is:

[0145]

[0146] In the formula, t∈T; The output of the energy storage system during time period t; the net load of the system L t The corresponding expression indicates that the total electricity consumption of any peak-shaving demand subject and any alternative scenario of the peak-shaving demand subject is equal;

[0147] Step S1.5: Set N = J∪G new ∪G es Given the set of peak-shaving demand subjects, and setting the sum of the contributions of any peak-shaving demand subject n (n∈N) to the upward and downward fluctuations of net load within time period T to 0, the corresponding expression for this process is:

[0148]

[0149] In the formula, d n,t The deviation of peak-shaving demand subject n from its mean in time period t;

[0150] Step S1.6: When the output of all loads and fluctuating power sources is constant under ideal conditions, set the net load that the conventional unit can meet. If the process remains constant during time period T, the corresponding expression is:

[0151]

[0152] In the formula, L is the system net load vector; d n It is the deviation vector of the load or output of the peak demand subject n relative to its mean.

[0153] In one embodiment of the present invention, step S2 specifically includes: Step S2.1: Building a thermal power unit operating cost model; the expressions corresponding to the operating costs of thermal power units under conventional peak shaving and deep peak shaving are:

[0154]

[0155] In the formula, For the conventional peak-shaving operation cost of thermal power units; f i,t For unit loss costs;

[0156] Among them, the conventional peak-shaving operation cost of thermal power units The corresponding expression is:

[0157]

[0158] In the formula, a i b i and c i These are the consumption coefficients of thermal power unit i, respectively; To provide power to thermal power units;

[0159] Among them, unit loss cost f i,t The corresponding expression is:

[0160] f i,t =βS unit,i / (2N f (P));

[0161] In the formula, β is the operating influence coefficient of the thermal power unit; S unit,i N represents the purchase cost of the i-th thermal power unit; f (P) represents the number of rotor cracking cycles determined by the rotor low-cycle fatigue curve;

[0162] Step S2.2: Establish a peak-shaving profit model for deep-shaving thermal power units; the expression corresponding to the peak-shaving profit model for deep-shaving thermal power units is:

[0163]

[0164] In the formula, For peak-shaving compensation costs of thermal power units with deep regulation; The peak-shaving compensation price for thermal power units; For paid peak-shaving power of thermal power units; To increase the profits of thermal power units participating in peak shaving;

[0165] Step S2.3: Construct a wind power profit model; the expression corresponding to the wind power profit model is:

[0166]

[0167] In the formula, To generate profits from wind turbine participation in peak shaving; The effective on-grid tariff for wind power; The wind turbine provides power; θ represents the wind curtailment penalty. This represents the amount of wind power curtailed at wind farm l during time period t;

[0168] Step S2.4: Construct a photovoltaic profit model; the expression corresponding to the photovoltaic profit model is:

[0169]

[0170] In the formula, The effective feed-in tariff for photovoltaic power; Powering the photovoltaic units; ε represents the peak-shaving cost of photovoltaic units; ε represents the curtailment penalty. This represents the amount of solar power curtailed by photovoltaic power station m during time period t.

[0171] In one embodiment of the present invention, step S4 further includes: step S4.2: setting the objective function C. all The constraints of the objective function in step S4.2 include: system balance constraints, deep peak shaving constraints of thermal power units, ramping constraints of thermal power units, output constraints of photovoltaic and wind power, and energy storage constraints.

[0172] The expression corresponding to the system equilibrium constraint is:

[0173]

[0174] In the formula, To provide power to thermal power units; To contribute to the power generation of new energy units; Contribute to energy storage; Total load;

[0175] The expression corresponding to the deep peak shaving constraint of the thermal power unit is:

[0176]

[0177] In the formula, u i,t The variable is a 0-1 representing the operating status of the unit. A value of 1 indicates that unit i is in the on state during time period t, and a value of 0 indicates that it is in the off state. Constraints on the upward and downward ramp speeds of unit i; Minimize the basic peak-shaving technical output of unit i during time period t;

[0178] The expression corresponding to the ramping constraint of the thermal power unit is:

[0179] P i,t -P i,(t-1) ≥-R i,down

[0180] P i,t -P i,(t-1) ≤R i,up ;

[0181] In the formula, P i,t R represents the output of unit i during time period t; i,down and R i,up These represent the positive and negative reserve requirements of the system during time period t, respectively.

[0182] The expressions corresponding to the photovoltaic and wind power output constraints are:

[0183]

[0184] In the formula, Represents photovoltaic power generation; Represents wind power output; Represents the total output of new energy sources;

[0185] The expression corresponding to the energy storage constraint is:

[0186]

[0187] In the formula, S es,t S represents the system's energy storage capacity during time period t; min and S max These represent the maximum and minimum energy storage capacities, respectively; α is a correction factor. and These are the energy storage charging power and energy storage discharging power, respectively; η a and η b These are energy storage charging efficiency and energy storage discharging efficiency, respectively; E s For energy storage.

[0188] In one embodiment of the present invention, step S5 specifically includes: step S5.1: defining the set of peak-shaving demand subjects as N = {thermal power, energy storage, wind power, photovoltaic}; and defining any alliance. This represents a combination of topics participating in peak shaving; and for each alliance S, the objective function C established in step S4.1 is invoked. all Calculate the total system cost C(S) when only the entities within S undertake the peak-shaving task; Step S5.2: Use the marginal contribution pre-allocation method to allocate the total revenue or cost according to the marginal contribution of each peak-shaving entity to the overall goal, so as to realize the initial cost allocation scheme π. (0) ;

[0189] The initial cost allocation scheme π (0) The corresponding expression is:

[0190]

[0191] In the formula, This indicates that thermal power accounts for 60% of the total cost; This indicates that new energy sources account for 20% of the total cost; This indicates that energy storage accounts for 10% of the total cost;

[0192] Step S5.3: Iteratively optimize the excess value; Step S5.3 includes: Step S5.31: Calculate the excess value of all alliances under the current allocation scheme:

[0193]

[0194] In the formula, e(S,π) (k) ) indicates the league's excess value; Indicates the percentage of a particular alliance; This indicates that all alliances are seeking a sum;

[0195] Step S5.32: Identify the alliance S corresponding to the maximum excess value. max Alliance S max The corresponding expression is:

[0196]

[0197] Step S5.33: Adjust the allocation scheme to reduce S max The excess value, the corresponding expression for this process is:

[0198] min∈ste(S,π (k+0.1) )≤ε;

[0199] In the formula,

[0200] Step S5.34: Repeat steps S5.31 to S5.33 until the maximum excess value converges to 0, thus obtaining the nucleolar solution π. * .

[0201] In one embodiment of the present invention, step S6 specifically includes: step S6.1: adding a coalition cost consistency constraint; the expression corresponding to the coalition cost consistency constraint is:

[0202]

[0203] In the formula, ε represents the allowable excess deviation, used to balance calculation efficiency and accuracy;

[0204] Step S6.2: Add individual rationality constraints; the expression corresponding to the individual rationality constraints is:

[0205] π n ≤C({n});

[0206] In the formula,

[0207] In one embodiment of the present invention, in step S7, the carbon-electricity synergistic peak-shaving dual-layer optimization algorithm constructs a cost-sharing model at the upper layer and constructs expressions corresponding to the output strategies of thermal power, energy storage, and new energy sources at the lower layer as follows:

[0208]

[0209] In step S7.2, the real-time contribution C contribution The corresponding expression is:

[0210]

[0211] In step S7.3, the expression corresponding to the cost deviation ΔC is:

[0212] ΔC=||C actual -C estimated ||;

[0213] In the formula, C actual Indicates actual cost; C estimated ||·|| represents the estimated cost; ||·|| represents the norm;

[0214] The expression for updating the league's excess value is:

[0215]

[0216] The technical solution of the present invention will be illustrated below with a specific embodiment. For example... Figure 1 As shown in the figure, the optimization method for cost sharing of collaborative peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method in this specific embodiment is achieved through the following steps:

[0217] 1) Step S1: Use the scenario substitution method to quantify the peak-shaving costs of wind, solar and energy storage respectively, so as to serve the subsequent calculation of peak-shaving costs;

[0218] The quantification of peak-shaving costs is fundamental to peak-shaving cost calculation and allocation. The diversified power sources in new power systems bring diverse peak-shaving resources, and these nonlinear output peak-shaving resources pose a significant challenge to the calculation of system peak-shaving costs. Step S1 analyzes alternative scenarios for wind power fluctuation costs and constructs a multi-dimensional peak-shaving cost quantification framework, aiming to address the nonlinearity of peak-shaving costs in power systems containing carbon capture power plants.

[0219] Further, step S1 specifically includes: Step S1.1: Calculate the deviation of the load of any power user relative to its mean; the deviation d of the load of any power user j (j∈J) relative to its mean. j,t The corresponding expression is:

[0220]

[0221] In the formula, j∈J, t∈T; Let J be the load amount of load j in time period t; T is the set of peak-shaving periods; This represents the average load J within time period T;

[0222] Step S1.2: Calculate the deviation of the output of any renewable energy source relative to its mean; any renewable energy source new (new∈G) new The deviation d of the output power from its mean. new,t The corresponding expression is:

[0223]

[0224] In the formula, new∈G new ,t∈T;G new A collection of fluctuating photovoltaic and wind power; P new,t For the output of photovoltaic and wind power in time period t; This represents the average value of new photovoltaic and wind power generation within time period T.

[0225] Step S1.3: Calculate the deviation of the output of any stored energy from its mean; any stored energy es (es∈G) es The deviation d of the output power from its mean. es,t The corresponding expression is:

[0226]

[0227] In the formula, es∈Ges, t∈T; G es For energy storage systems participating in peak shaving; The charging power of the energy storage system es during time period t; Let es be the discharge power of the energy storage system during time period t;

[0228] For indicator functions, when hour, otherwise

[0229] For indicator functions, when hour, otherwise

[0230] Step S1.4: Calculate the system net load; System net load L t The corresponding expression is:

[0231]

[0232] In the formula, t∈T; The output of the energy storage system during time period t; the net load of the system L t The corresponding expression indicates that the total electricity consumption of any peak-shaving demand subject and any alternative scenario of the peak-shaving demand subject is equal;

[0233] Step S1.5: Set N = J∪G new ∪G es Given the set of peak-shaving demand subjects, and setting the sum of the contributions of any peak-shaving demand subject n (n∈N) to the upward and downward fluctuations of net load within time period T to 0, the corresponding expression for this process is:

[0234]

[0235] In the formula, d n,t The deviation of peak-shaving demand subject n from its mean in time period t;

[0236] Step S1.6: When the output of all loads and fluctuating power sources is constant under ideal conditions, set the net load that the conventional unit can meet. If the process remains constant during time period T, the corresponding expression is:

[0237]

[0238] In the formula, L is the system net load vector; d n It is the deviation vector of the load or output of the peak demand subject n relative to its mean.

[0239] When the system net load is constant, there is no need for peak-valley load regulation. In this situation, the dispatching agency can directly allocate the most cost-effective conventional generating units to meet power demand. This avoids costs associated with load fluctuations, such as deep peak shaving, start-up and shutdown peak shaving, and unit ramp-up. In this ideal scenario, the system's peak-shaving cost is minimized, providing a benchmark for measuring the peak-shaving costs incurred by different entities with varying peak-shaving needs. This benchmark allows for a more precise quantification of the impact of each entity on peak-shaving costs, laying a crucial foundation for subsequent peak-shaving cost analysis, allocation, and related decision-making.

[0240] 2) Step S2: Set proactive constraints for peak shaving of wind, solar, and thermal power;

[0241] In the process of power system peak shaving, the enthusiasm of various stakeholders participating in peak shaving mainly depends on whether they can gain benefits from peak shaving services. Taking conventional units as an example, coal consumption costs, start-up and shutdown costs, and oil injection costs generated by deep peak shaving jointly determine their optimal output. When the compensation received by conventional thermal power units for participating in peak shaving exceeds their own costs, their enthusiasm for participating in peak shaving will significantly increase. Step S2 establishes a reasonable peak shaving compensation mechanism, aiming to incentivize thermal power units to actively participate in grid peak shaving work.

[0242] Further, step S2 specifically includes: Step S2.1: Establishing an operating cost model for thermal power units; the operating costs of thermal power units under conventional peak shaving and deep peak shaving are respectively:

[0243]

[0244] In the formula, For the conventional peak-shaving operation cost of thermal power units; f i,t For unit loss costs;

[0245] To simplify calculations, this specific embodiment ignores start-up and shutdown costs for thermal power units, only calculating coal consumption costs; therefore, the conventional peak-shaving operation cost of thermal power units is... The corresponding expression is:

[0246]

[0247] In the formula, a i b i and c i These are the consumption coefficients of thermal power unit i, respectively; To provide power to thermal power units;

[0248] When the generating unit undergoes deep peak shaving, its operating state deviates significantly from the design value, resulting in a substantial decrease in power generation efficiency and additional unit loss costs and oil injection costs. This specific embodiment roughly calculates the unit loss cost using the Manson-Coffin formula; therefore, the unit loss cost f... i,t The corresponding expression is:

[0249] f i,t =βS unit,i / (2N f (P));

[0250] In the formula, β is the operating influence coefficient of the thermal power unit; S unit,i N represents the purchase cost of the i-th thermal power unit; f (P) represents the number of rotor cracking cycles determined by the rotor low-cycle fatigue curve;

[0251] Step S2.2: Establish a peak-shaving profit model for deep-shaving thermal power units; the expression corresponding to the peak-shaving profit model for deep-shaving thermal power units is:

[0252]

[0253] In the formula, For peak-shaving compensation costs of thermal power units with deep regulation; The peak-shaving compensation price for thermal power units; For paid peak-shaving power of thermal power units; To increase the profits of thermal power units participating in peak shaving;

[0254] Step S2.3: Construct a wind power profit model; the expression corresponding to the wind power profit model is:

[0255]

[0256] In the formula, To generate profits from wind turbine participation in peak shaving; The effective on-grid tariff for wind power; The wind turbine provides power; θ represents the wind curtailment penalty. This represents the amount of wind power curtailed at wind farm l during time period t;

[0257] Step S2.4: Construct a photovoltaic profit model; the expression corresponding to the photovoltaic profit model is:

[0258]

[0259] In the formula, The effective feed-in tariff for photovoltaic power; Powering the photovoltaic units; ε represents the peak-shaving cost of photovoltaic units; ε represents the curtailment penalty. This represents the amount of solar power curtailed by photovoltaic power station m during time period t.

[0260] When wind turbine profits Time or profit of photovoltaic power station When the cost of peak shaving for wind farms or solar power plants exceeds the benefits of increased power generation due to deep peak shaving, the wind farms or solar power plants will withdraw from peak shaving cooperation to protect their individual interests; when profits... If the compensation provided to thermal power units is insufficient to cover the peak-shaving costs, the thermal power units will withdraw from the peak-shaving cooperation first.

[0261] 3) Step S3: Construct a peak-shaving coupling model for carbon capture thermal power plants;

[0262] In the field of power system peak-shaving cost research, incorporating the operation and maintenance costs of carbon capture equipment and carbon market trading costs into the objective function is of significant theoretical importance. This is based on the completeness of cost accounting, cost causality, and a comprehensive consideration of the economic operation of the system. To quantitatively characterize the dynamic coupling constraint mechanism of the carbon capture system on the peak-shaving capacity of thermal power units, step S3 constructs a dynamic model of thermal power unit peak-shaving considering electricity-carbon interaction characteristics. By introducing key state variables such as the energy consumption coefficient of the carbon capture system, the dynamic carbon capture operation mode, the carbon capture operation mode penalty coefficient, and carbon emission intensity, the system analyzes the composite nonlinear impact of the carbon capture process on the unit's peak-shaving capacity margin and marginal operating cost.

[0263] Further, step S3 specifically includes: Step S3.1: Calculating the carbon capture efficiency to obtain the net available peak-shaving output of the deep-tuning thermal power unit; the expression corresponding to the net available peak-shaving output of the deep-tuning thermal power unit i is:

[0264]

[0265] In the formula, To optimize the net available peak-shaving output of thermal power units; To determine the maximum available peak-shaving output of thermal power units; di d is the carbon capture energy consumption coefficient. i η represents the percentage of electricity generated per unit mass of CO2 captured; CCUS Carbon capture efficiency, expressed as a percentage (%). The baseline carbon emission intensity is expressed in (tCO2 / MWh).

[0266] Step S3.2: Set the carbon capture system to operate in FC mode and FlexC mode, and introduce a binary decision variable to characterize the switching between FC mode and FlexC mode, and establish a penalty coefficient for FlexC mode to accurately calculate the cost of different carbon capture operation modes; wherein, in FC mode, the carbon capture rate η CCUS The peak-shaving depth of the unit is kept constant at a certain design value, which is 85%, but the peak-shaving depth of the unit is limited by the minimum technical output. In FlexC mode, the capture rate is dynamically adjusted to release unit output during peak demand periods, but this incurs additional carbon emission costs.

[0267] Step S3.3: Based on steps S3.1 and S3.2, construct a joint optimization objective function for carbon-electricity costs to obtain the total cost of carbon capture and peak shaving for deep-tuning thermal power units; the total cost of carbon capture and peak shaving for deep-tuning thermal power units. The corresponding expression is:

[0268]

[0269] In the formula, The price is the carbon price, expressed in yuan per ton of CO2. γ represents the real-time carbon emission intensity of unit i at time t, expressed in (tCO2 / MWh); γ is the FlexC mode switching penalty coefficient. The additional carbon emissions resulting from the reduced capture rate; Represents a binary decision variable. Indicates FC mode, Indicates FlexC mode;

[0270] Specifically, carbon capture systems operate in two modes: Full Capture (FC) and Flexible Capture (FlexC). In FC mode, the carbon capture rate η... CCUS The peak load is kept constant at 85% of the design value, but the peak load depth of the unit is limited by the minimum technical output. In FlexC mode, the capture rate is dynamically adjusted to release unit output during peak demand periods, but this incurs additional carbon emission costs. The model introduces binary decision variables. The system characterizes the switching of operating modes and establishes γ as the penalty coefficient for the FlexC mode to accurately calculate the cost of different carbon capture operation modes.

[0271] 4) Step S4: Construct a peak-shaving cost optimization model;

[0272] Currently, the main entities participating in paid peak shaving include thermal power units, wind farms, photovoltaic power plants, and energy storage facilities. This paper constructs a scheduling optimization model. This model covers deep peak shaving thermal power units equipped with CCUS, conventional thermal power units, and wind, solar, and energy storage units.

[0273] Further, step S4 specifically includes: Step S4.1: Minimizing the total system peak-shaving cost as the objective function C all Objective function C all The corresponding expression is:

[0274]

[0275] In the formula, Unit output cost for wind and solar power; C represents the output of wind and solar power at time t; a The cost of charging a unit of energy storage; C b Cost per unit of energy storage for discharge;

[0276] Step S4.2: Set the constraints of the objective function; in step S4.2, the constraints of the objective function include: system balance constraints, deep peak shaving constraints of thermal power units, ramp-up constraints of thermal power units, output constraints of photovoltaic and wind power, and energy storage constraints; the expression corresponding to the system balance constraints is:

[0277]

[0278] In the formula, To provide power to thermal power units; To contribute to the power generation of new energy units; Contribute to energy storage; Total load;

[0279] The expression corresponding to the deep peak shaving constraint of the thermal power unit is:

[0280]

[0281] In the formula, u i,t The variable is a 0-1 representing the operating status of the unit. A value of 1 indicates that unit i is in the on state during time period t, and a value of 0 indicates that it is in the off state. Constraints on the upward and downward ramp speeds of unit i; Minimize the basic peak-shaving technical output of unit i during time period t;

[0282] The expression corresponding to the ramping constraint of the thermal power unit is:

[0283] P i,t -P i,(t-1) ≥-R i,down

[0284] P i,t -P i,(t-1) ≤R i,up ;

[0285] In the formula, P i,t R represents the output of unit i during time period t; i,down and R i,up These represent the positive and negative reserve requirements of the system during time period t, respectively.

[0286] The expressions corresponding to the photovoltaic and wind power output constraints are:

[0287]

[0288] In the formula, Represents photovoltaic power generation; Represents wind power output; Represents the total output of new energy sources;

[0289] The expression corresponding to the energy storage constraint is:

[0290]

[0291] In the formula, S es,t S represents the system's energy storage capacity during time period t; min and S max These represent the maximum and minimum energy storage capacities, respectively; α is a correction factor. and These are the energy storage charging power and energy storage discharging power, respectively; η a and η b These are energy storage charging efficiency and energy storage discharging efficiency, respectively; E s For energy storage.

[0292] 5) Step S5: Allocate peak-shaving costs using the nucleolus method. The nucleolus method is an effective method in cooperative game theory for solving cost allocation problems. Unlike the Shapley value method, the nucleolus method is based on the concept of excess value in a coalition. By continuously optimizing the allocation among coalition members, it makes the allocation results for each participating entity more in line with the principle of fairness, and it has unique advantages in handling complex cost allocation problems. To reflect that peak-shaving costs in a fair allocation system originate from different peak-shaving entities, the nucleolus method is used to calculate the peak-shaving costs caused by different peak-shaving demand entities, and the paid peak-shaving costs are allocated based on this.

[0293] Further, step S5 specifically includes: Step S5.1: Defining the set of peak-shaving demand entities as N = {thermal power, energy storage, wind power, photovoltaic}; and defining any alliance This represents a combination of topics participating in peak shaving; and for each alliance S, the objective function C established in step S4.1 is invoked. all Calculate the total system cost C(S) when only the entities within S undertake the peak-shaving task; Step S5.2: Use the marginal contribution pre-allocation method to allocate the total revenue or cost according to the marginal contribution of each peak-shaving entity to the overall goal, so as to realize the initial cost allocation scheme π. (0) The initial cost allocation scheme π (0) The corresponding expression is:

[0294]

[0295] In the formula, This indicates that thermal power accounts for 60% of the total cost; This indicates that new energy sources account for 20% of the total cost; This indicates that energy storage accounts for 10% of the total cost;

[0296] Step S5.3: Iteratively optimize the excess value; Step S5.3 includes: Step S5.31: Calculate the excess value of all alliances under the current allocation scheme:

[0297]

[0298] In the formula, e(S,π) (k) ) indicates the league's excess value; Indicates the percentage of a particular alliance; This indicates that all alliances are seeking a sum;

[0299] Step S5.32: Identify the alliance S corresponding to the maximum excess value. max Alliance S max The corresponding expression is:

[0300]

[0301] Step S5.33: Adjust the allocation scheme to reduce S max The excess value, the corresponding expression for this process is:

[0302] min∈ste(S,π (k+0.1) )≤ε;

[0303] In the formula,

[0304] Step S5.34: Repeat steps S5.31 to S5.33 until the maximum excess value converges to 0, thus obtaining the nucleolar solution π.* .

[0305] 6) Step S6: Driven by the nucleolus method, set the peak-shaving cost allocation constraints; to embed the nucleolus method into the optimization model, add the following constraints: alliance cost consistency constraint and individual rationality constraint.

[0306] Further, step S6 specifically includes: Step S6.1: Adding a coalition cost consistency constraint; the expression corresponding to the coalition cost consistency constraint is:

[0307]

[0308] In the formula, ε represents the allowable excess deviation, used to balance calculation efficiency and accuracy;

[0309] Step S6.2: Add individual rationality constraints; the expression corresponding to the individual rationality constraints is:

[0310] π n ≤C({n});

[0311] In the formula,

[0312] Ensure that the cost allocated to each individual entity does not exceed its independent peak-shaving cost, and avoid "free-riding" behavior.

[0313] 7) Step S7: Use the carbon-electricity synergistic peak shaving dual-layer optimization algorithm to allocate peak shaving costs; wherein, the carbon-electricity synergistic peak shaving dual-layer optimization algorithm constructs a cost allocation model in the upper layer and constructs power output strategies for thermal power, energy storage and new energy in the lower layer;

[0314] To systematically address the cost quantification and allocation issues of multi-entity peak shaving under CCUS technology, step S7 proposes a carbon-peak shaving bi-level optimization algorithm (CPS-BOA). This algorithm achieves global optimization of peak shaving economy, fairness, and low carbon emissions through a bidirectional iterative mechanism of upper-level nucleolus-based dynamic allocation and lower-level multi-resource collaborative scheduling. Its mathematical framework is as follows:

[0315]

[0316] Further, step S7 specifically includes: Step S7.1: Input the initial peak-shaving alliance allocation ratio and initialize the sharing weight Π. (0) Set the convergence threshold ε and the maximum number of iterations K. max This is to achieve upper-level initialization; specifically, the peak-shaving alliance set N = {thermal power, energy storage, new energy} is input, and the convergence threshold ε = 10 is set.-3 With the maximum number of iterations K max =50; Step S7.2: Based on the current allocation weight Π (k) It calls upon a mixed-integer nonlinear programming solver and optimizes power output strategies for thermal power, energy storage, and new energy sources. Then calculate the real-time contribution C contribution To achieve lower-level scheduling optimization; Step S7.3: Pass the real-time contribution C from the lower layer to the upper layer. contribution The cost deviation ΔC and the updated alliance excess value; Step S7.4: Iterate and determine convergence using the kernel solution; If max|Π (k+0.1) -Π (k) |<ε or k≥K max If the result is positive, proceed to step S7.5; otherwise, proceed to step S7.6. Step S7.5: Output the optimal solution; Step S7.6: Generate a new allocation scheme Π. (k+0.1) Then return to step S7.2 until step S7.5 is executed.

[0317] Further, in step S7.2, the real-time contribution C contribution The corresponding expression is:

[0318]

[0319] In step S7.3, the expression corresponding to the cost deviation ΔC is:

[0320] ΔC=||C actual -C estimated ||;

[0321] In the formula, C actual Indicates actual cost; C estimated ||·|| represents the estimated cost; ||·|| represents the norm;

[0322] The expression for updating the league's excess value is:

[0323]

[0324] In terms of peak-shaving cost quantification, a multi-dimensional quantification system is constructed. The time dimension analyzes the dynamic coupling characteristics of load demand and peak-shaving power sources; the spatial dimension introduces the unit's proactive peak-shaving indicator as a key constraint, and a CCUS thermal power peak-shaving cost model considering both carbon emissions and carbon costs is established. Combining these three points, a hybrid peak-shaving resource model is established, encompassing deep peak-shaving by conventional thermal power, deep peak-shaving by CCUS thermal power units for wind power, photovoltaic power, and new energy storage. Regarding peak-shaving cost allocation, based on cooperative game theory, a nucleolus method is used to construct a peak-shaving cost allocation model, and the fairness of the allocation scheme is verified using Shapley values. A two-layer peak-shaving optimization algorithm is used to transfer the lower-layer hybrid peak-shaving resource costs to the upper layer for nucleolus peak-shaving cost allocation, yielding the results.

[0325] To verify the effectiveness and advancement of the cost allocation optimization method for coordinated peak shaving in carbon capture power plants based on multidimensional quantification and the nucleolus method in this specific embodiment, and the superiority of the nucleolus method in cost allocation, this specific embodiment conducts a comparative analysis based on four scheduling scenarios. The scenarios are set as follows: Scenario 1: Wind and solar joint operation. Without considering energy storage, deep scheduling of conventional thermal power units (without considering carbon price), such as... Figure 2 As shown. Scenario 2: Combined wind and solar operation. Considering energy storage, deep regulation of conventional thermal power units (not considering carbon price), such as... Figure 3 As shown. Scenario 3: Wind and solar combined operation. Considering energy storage, deep regulation of conventional thermal power units (considering carbon price). Scenario 4: Wind and solar combined operation. Considering energy storage, deep regulation of CCUS thermal power units (considering carbon price). For example... Figure 2 and Figure 3 As shown in the figure, the carbon capture power plant collaborative peak shaving cost allocation optimization method based on multidimensional quantification and nucleolus method in this specific embodiment has superior advancement in cost calculation, and the nucleolus method has obvious advantages in cost allocation.

[0326] Furthermore, such as Figure 4 As shown, after considering the impact of carbon prices on peak-shaving costs, the peak-shaving cost change rate of CCUS units is lower than that of traditional thermal power units when calculating peak-shaving costs. Figure 4 This indicates that considering the peak-shaving cost of CCUS units is closer to the actual cost.

[0327] This invention provides a method for optimizing the cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantification and the nucleolus method, achieving precise cost quantification. Specifically, by dynamically coupling carbon capture energy consumption and peak shaving depth through a spatiotemporal two-dimensional peak shaving cost model, the total peak shaving cost is reduced by 6.5%. This method also achieves fair cost allocation. Specifically, by employing the nucleolus method to optimize the alliance reorganization mechanism, the allocation fairness coefficient is increased to 0.91. Furthermore, this method produces beneficial technical effects in terms of low-carbon economy. Specifically, when the carbon price is 150-250 yuan / ton, carbon emissions are reduced by 40%-50%, and the total cost increase is ≤5%.

[0328] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing cost allocation in collaborative peak shaving of carbon capture power plants based on multidimensional quantization and nucleolus method, characterized in that, include: Step S1: Quantify the peak-shaving costs of wind, solar and energy storage using the scenario substitution method; Step S2: Set proactive constraints for peak shaving of wind, solar, and thermal power. Step S3: Construct a peak-shaving coupling model for carbon capture thermal power plants; Step S3 specifically includes: Step S3.1: Calculate the carbon capture efficiency to obtain the net available peak-shaving output of the deep-regulation thermal power unit; deep-regulation thermal power unit The expression corresponding to the net available peak-shaving output is: ； In the formula, To optimize the net available peak-shaving output of thermal power units; To adjust the maximum available peak-shaving output of thermal power units; The carbon capture energy consumption coefficient; For carbon capture efficiency; As a benchmark carbon emission intensity; Step S3.2: Set the carbon capture system to operate in FC mode and FlexC mode, and introduce binary decision variables to characterize the switching between FC mode and FlexC mode, and establish a penalty coefficient for FlexC mode to accurately calculate the cost of different carbon capture operation modes; among them, in FC mode, carbon capture efficiency The peak-shaving depth of the unit is kept constant at a certain design value, but is limited by the minimum technical output. In FlexC mode, the capture rate is dynamically adjusted to release unit output during peak demand periods, but this incurs additional carbon emission costs. Step S3.3: Based on steps S3.1 and S3.2, construct a joint optimization objective function for carbon-electricity costs to obtain the total cost of carbon capture and peak shaving for deep-tuning thermal power units; the total cost of carbon capture and peak shaving for deep-tuning thermal power units. The corresponding expression is: ； In the formula, For carbon price; For the unit exist Real-time carbon emission intensity; Switch the penalty coefficient for FlexC mode; The additional carbon emissions resulting from the reduced capture rate; Represents a binary decision variable. Indicates FC mode, Indicates FlexC mode; A collection of peak-shaving periods; Step S4: Construct a peak-shaving cost optimization model; Step S4 specifically includes: Step S4.1: Minimize the total system peak-shaving cost as the objective function. Objective function This is the peak-shaving cost optimization model; the objective function is... The corresponding expression is: ； In the formula, Unit output cost for wind and solar power; For the output of wind power and photovoltaic power during time period t; The cost of charging energy storage units; Cost per unit of energy storage for discharge; For energy storage systems The charging power during time period t; For energy storage systems The discharge power at time t; G es For energy storage systems participating in peak shaving; new represents any renewable energy source; G new It is a combination of fluctuating photovoltaic and wind power; Step S5: Allocate peak-shaving costs using the nucleolus method; Step S6: Set peak-shaving cost allocation constraints using the nucleolus method; Step S7: Use a carbon-electricity synergistic peak shaving dual-layer optimization algorithm to allocate peak shaving costs; wherein, the carbon-electricity synergistic peak shaving dual-layer optimization algorithm constructs a cost allocation model in the upper layer and constructs power output strategies for thermal power, energy storage, and new energy sources in the lower layer; Step S7 specifically includes: Step S7.1: Input the initial peak-shaving alliance allocation ratio and initialize the sharing weight. Set a convergence threshold And set the maximum number of iterations. This is to achieve upper-level initialization; Step S7.2: Based on the current allocation weight It calls upon a mixed-integer nonlinear programming solver and optimizes power output strategies for thermal power, energy storage, and new energy sources. Then calculate the real-time contribution. To achieve lower-level scheduling optimization; Step S7.3: Pass the real-time contribution value from the lower layer to the upper layer. Cost deviation ∆C, and updated alliance excess value; Step S7.4: Employ nucleolar solution iteration and convergence determination; if If yes, then proceed to step S7.5; otherwise, proceed to step S7.

6. Step S7.5: Output the optimal solution; Step S7.6: Generate a new allocation scheme Then return to step S7.2 until step S7.5 is executed.

2. The method for optimizing cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method according to claim 1, characterized in that, Step S1 specifically includes: Step S1.1: Calculate the deviation of any electricity user's load from its mean; any electricity user The deviation of the load from its mean The corresponding expression is: ； In the formula, ; For users The load during the period The load capacity; Indicates user The average load within the set T of peak-shaving periods; Step S1.2: Calculate the deviation of the output of any renewable energy source from its mean; any renewable energy source The deviation of the output from its mean The corresponding expression is: ； In the formula, ; It is a combination of fluctuating photovoltaic and wind power; The output of photovoltaic and wind power during time period t; This represents the average output of photovoltaic and wind power within the set T of peak-shaving periods; Step S1.3: Calculate the deviation of the output of any energy storage unit from its mean; (Any energy storage unit) The deviation of the output from its mean The corresponding expression is: ； In the formula, ; Step S1.4: Calculate the system net load; System net load The corresponding expression is: ； In the formula, ; The output of the energy storage system during time period t; the net load of the system. The corresponding expression indicates that the total electricity consumption of any peak-shaving demand subject and any alternative scenario of the peak-shaving demand subject is equal; Step S1.5: Setting For the set of peak-shaving demand entities, and for setting any peak-shaving demand entity. The sum of the contributions of the upward and downward fluctuations in net load during time period t is 0. The corresponding expression for this process is: ； In the formula, The deviation of peak-shaving demand subject n from its mean in time period t; Step S1.6: When the output of all loads and fluctuating power sources is constant under ideal conditions, set the net load that the conventional unit can meet. If the process remains constant during time interval t, the corresponding expression is: ； In the formula, This represents the system net load vector; It is the deviation vector of the load or output of the peak demand subject n relative to its mean.

3. The method for optimizing cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method according to claim 2, characterized in that, Step S2 specifically includes: Step S2.1: Establish an operating cost model for thermal power units; the expressions for the operating costs of thermal power units under conventional peak shaving and deep peak shaving are as follows: ； In the formula, Costs of routine peak-shaving operation of thermal power units; For unit loss costs; Among them, the conventional peak-shaving operation cost of thermal power units The corresponding expression is: ； In the formula, thermal power units Consumption coefficient; Among them, unit loss cost The corresponding expression is: ； In the formula, This is the influence coefficient on the operation of thermal power units; For the first The purchase cost of a thermal power unit; This indicates the number of rotor cracking cycles determined by the rotor's low-cycle fatigue curve. Step S2.2: Establish a peak-shaving profit model for deep-shaving thermal power units; the expression corresponding to the peak-shaving profit model for deep-shaving thermal power units is: ； In the formula, For peak-shaving compensation costs of thermal power units with deep regulation; The peak-shaving compensation price for thermal power units; For paid peak-shaving power of thermal power units; To increase the profits of thermal power units participating in peak shaving; Step S2.3: Construct a wind power profit model; the expression corresponding to the wind power profit model is: ； In the formula, To generate profits from wind turbine participation in peak shaving; The effective on-grid tariff for wind power; To provide power to wind turbines; This indicates a punishment for abandoning the wind; Indicates wind farm During the period The generated wind curtailment power; Step S2.4: Construct a photovoltaic profit model; the expression corresponding to the photovoltaic profit model is: ； In the formula, The effective feed-in tariff for photovoltaic power; Powering the photovoltaic units; Peak shaving costs for photovoltaic units; This indicates a punishment for abandoning light; Indicates photovoltaic power station During the period The amount of light wasted.

4. The method for optimizing cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method according to claim 3, characterized in that, Step S4 further includes: Step S4.2: Set the objective function Constraints; In step S4.2, the constraints of the objective function include: system balance constraints, deep peak shaving constraints of thermal power units, ramping constraints of thermal power units, output constraints of photovoltaic and wind power, and energy storage constraints. The expression corresponding to the system equilibrium constraint is: ； In the formula, This represents the sum of the outputs of photovoltaic and wind power during time period t. Contribute to energy storage; For users The total load within the set T of peak-shaving periods; The expression corresponding to the deep peak shaving constraint of the thermal power unit is: ； In the formula, The variable is a 0-1 representing the operating status of the unit. A value of 1 indicates that unit i is in the on state during time period t, and a value of 0 indicates that it is in the off state. Constraints on the upward and downward ramp speeds of unit i; Minimize the basic peak-shaving technical output of unit i during time period t; The expression corresponding to the ramping constraint of the thermal power unit is: ； In the formula, Let i be the output of unit i during time period t; and These represent the positive and negative reserve requirements of the system during time period t, respectively. The expressions corresponding to the photovoltaic and wind power output constraints are: ； In the formula, Represents photovoltaic power generation; Represents wind power output; Represents the total output of new energy sources; The expression corresponding to the energy storage constraint is: ； In the formula, This represents the energy storage capacity of the system during time period t. and These are the maximum energy storage capacity and the minimum energy storage capacity, respectively. This is a correction factor; and These are the energy storage charging power and the energy storage discharging power, respectively. and These are energy storage charging efficiency and energy storage discharging efficiency, respectively. For energy storage.

5. The method for optimizing cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method according to claim 4, characterized in that, Step S5 specifically includes: Step S5.1: Define the set of peak-shaving demand entities as N = {thermal power, energy storage, wind power, photovoltaic}; and define any alliance. This refers to combinations of topics participating in peak shaving; and for each alliance Call the target function established in step S4.

1. Calculation only by Total system cost when the main body undertakes peak shaving tasks ; Step S5.2: Using a pre-allocation method based on marginal contribution, the total revenue or cost is allocated according to the marginal contribution of each peak-shaving entity to the overall objective, in order to achieve the initial cost allocation scheme. ; The initial cost allocation scheme The corresponding expression is: ； In the formula, This indicates that thermal power accounts for 60% of the total cost; This indicates that new energy sources account for 20% of the total cost; This indicates that energy storage accounts for 10% of the total cost; Step S5.3: Iteratively optimize the excess value; Step S5.3 includes: Step S5.31: Calculate the excess value of all alliances under the current allocation scheme: ； In the formula, Indicates the league's excess value; Indicates the percentage of a particular alliance; This indicates that all alliances are seeking a sum. Step S5.32: Identify the alliance corresponding to the maximum excess value ;alliance The corresponding expression is: ； Step S5.33: Adjust the allocation scheme to reduce The excess value, the corresponding expression for this process is: ； In the formula, ; Step S5.34: Repeat steps S5.31 to S5.33 until the maximum excess value converges to 0, thus obtaining the nucleolar solution. .

6. The method for optimizing cost allocation of collaborative peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method according to claim 5, characterized in that, Step S6 specifically includes: Step S6.1: Add a coalition cost consistency constraint; the expression corresponding to the coalition cost consistency constraint is: ； In the formula, ; The allowable excess value deviation is used to balance calculation efficiency and accuracy; Step S6.2: Add individual rationality constraints; the expression corresponding to the individual rationality constraints is: ； In the formula, .

7. The cost sharing of coordinated peak shaving in carbon capture power plants based on multidimensional quantization and nucleolus method as described in claim 6. The optimization method is characterized by, In step S7, the carbon-electricity synergistic peak-shaving dual-layer optimization algorithm constructs a cost-sharing model at the upper layer and expressions corresponding to the output strategies of thermal power, energy storage, and new energy sources at the lower layer, as follows: ； In step S7.2, the real-time contribution... The corresponding expression is: ； In step S7.3, the expression corresponding to the cost deviation ∆C is: ； In the formula, Indicates actual cost; Indicates estimated cost; Represents the norm; The expression for updating the league's excess value is: 。

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