A method and device for predicting carbon emission costs with carbon price changes

Abstract

The present invention discloses a method and device for predicting carbon emission costs with changes in carbon prices. After establishing an initial electricity model based on the carbon emission cost, the initial carbon price electricity model is processed by an autocorrelation function and a partial autocorrelation function, which can effectively simulate the changes in carbon prices with market supply and demand, that is, the changes in carbon prices over time are simulated to further obtain an autoregressive moving average model, and historical carbon emission data are input into the autoregressive moving average model in a preset data form, ensuring the consistency between the form of the data to be predicted and the model, thereby improving the accuracy of carbon emission cost prediction after carbon price changes.

Description

Technical Field

[0001] The present invention relates to the technical field of power analysis, and in particular to a method and device for predicting carbon emission costs with changing carbon prices. Background Art

[0002] The carbon market is an effective market mechanism for addressing climate change. It is a valuable policy tool for optimizing the allocation of carbon emission resources, reducing the overall cost of carbon reduction for society, achieving greenhouse gas emissions control, and promoting the transition of the supply-side structure to a green and low-carbon economy. In 2020, my country abolished the coal-electricity price linkage mechanism and replaced the benchmark on-grid electricity price mechanism for coal-fired power generation with a market-based electricity pricing mechanism consisting of a "base price plus a floating range." In 2021, the price fluctuation range was expanded to, in principle, no more than 20%. High-energy-consuming enterprises were exempted from the 20% upward price limit, further deepening the marketization of electricity pricing.

[0003] As the domestic governance system continues to deepen and improve, the carbon market and the electricity market will achieve coordinated and coupled mechanisms. From the power generation side, the level of carbon emission costs in the carbon market will, to a certain extent, affect the order in which generators are cleared from the electricity market, further consolidating the competitiveness of high-efficiency, low-emission units in the market and promoting low-carbon development in the power industry.

[0004] In the short term, carbon pricing will drive natural gas to replace coal-fired power generation. In a liberalized electricity market with economic dispatch, generators are ranked by marginal cost, with marginal cost determining the clearing of units. The carbon emission coefficient per kilowatt-hour of natural gas-fired power generation is half that of coal-fired power generation. Given the same carbon price, the carbon emission cost of natural gas-fired power generation is lower than that of coal-fired power generation. The introduction of a carbon price in the electricity market will increase the marginal cost of high-emission coal-fired units more significantly than that of gas-fired units. Once the carbon price reaches a certain level, gas-fired power generation will become more competitive, replacing coal-fired units in the electricity spot market as a marginal treatment mechanism, achieving fuel switching. However, under different endowment markets and supply and demand conditions, fuel costs can also shift this output order. In the long term, rising carbon prices will promote the advantages of clean energy, shift investment directions, and accelerate decarbonization in the power sector. The carbon market's total emissions cap will decline more rapidly, reducing the supply of allowances and gradually tightening carbon allowances in the power sector. Carbon market participants will need to consider more expensive emission reduction measures. With advances in renewable energy technology, rising carbon prices, and a larger proportion of allowances auctioned, the economic viability of wind and solar power projects has become increasingly prominent, the advantages of clean energy generation have gradually increased, and the power industry's power generation structure has become cleaner. For end users, the transmission of carbon emission costs through electricity prices has invisibly encouraged end users to conserve electricity, driving carbon markets to achieve emission reduction effects on a larger scale. This has highlighted the market's decisive role in optimizing resource allocation and accelerated low-carbon development across the industry. However, existing research rarely involves quantitative analysis of the linkages between multiple markets, and most studies focus on the national level, with few examining interprovincial power grid perspectives. Summary of the Invention

[0005] The technical problem to be solved by the present invention is: a method and device for predicting carbon emission costs with changing carbon prices, so as to improve the accuracy of carbon emission cost prediction.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for predicting carbon emission costs with changing carbon prices, comprising the steps of:

[0008] Establish an initial carbon price electricity model based on carbon emission costs;

[0009] Identifying and determining the order of the initial carbon price and electricity quantity model through an autocorrelation function and a partial autocorrelation function, and obtaining the order corresponding to the carbon price and electricity quantity model;

[0010] According to the order corresponding to the carbon price and electricity quantity model, the autoregressive parameter and the moving average parameter are obtained respectively;

[0011] Generate an autoregressive moving average model according to the autoregressive parameters and the moving average parameters;

[0012] Historical carbon emission data is obtained, and the carbon emission data is input into the autoregressive moving average model in a preset data format to obtain a predicted carbon emission cost.

[0013] A device for predicting emission costs with changes in carbon prices, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above-mentioned method for predicting carbon emission costs with changes in carbon prices is implemented.

[0014] The beneficial effects of the present invention are: after establishing the initial electricity model based on the carbon emission cost, the initial carbon price electricity model is processed by the autocorrelation function and the partial autocorrelation function, which can effectively simulate the changes in carbon price with the market supply and demand relationship, that is, the simulation of the change of carbon price over time is further obtained to obtain the autoregressive moving average model, and the historical carbon emission data is input into the autoregressive moving average model in a preset data form, ensuring the consistency between the form of the data to be predicted and the model, thereby improving the accuracy of carbon emission cost prediction after carbon price changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of the steps of a method for predicting carbon emission costs with carbon price changes in an embodiment of the present invention;

[0016] Figure 2 This is a typical load curve for working days in Fujian Province;

[0017] Figure 3 This is a schematic diagram of the average carbon price in major carbon markets in 2021;

[0018] Figure 4 The calculation results of the levelized electricity cost of seven types of power generation technologies at different carbon price levels corresponding to a carbon emission cost prediction method with carbon price changes in an embodiment of the present invention;

[0019] Figure 5 The power supply curve of a carbon emission cost prediction method with carbon price changes according to an embodiment of the present invention when the carbon price is 10 yuan / ton;

[0020] Figure 6 An electricity supply curve in the absence of a carbon price in a carbon emission cost prediction method with carbon price changes according to an embodiment of the present invention;

[0021] Figure 7 This is an electricity supply curve for a carbon price of 40 yuan / ton in a carbon emission cost prediction method with carbon price changes according to an embodiment of the present invention;

[0022] Figure 8 The power supply curve of a carbon emission cost prediction method with carbon price changes according to an embodiment of the present invention when the carbon price is 60 yuan / ton;

[0023] Figure 9 This is an electricity supply curve for a carbon price of 120 yuan / ton in a carbon emission cost prediction method with carbon price changes according to an embodiment of the present invention;

[0024] Figure 10 Schematic diagram of the structure of a device for predicting emission costs based on carbon price changes in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0026] Please refer to Figure 1 A method for predicting carbon emission costs with carbon price changes comprises the following steps:

[0027] Establish an initial carbon price electricity model based on carbon emission costs;

[0028] Identifying and determining the order of the initial carbon price and electricity quantity model through an autocorrelation function and a partial autocorrelation function, and obtaining the order corresponding to the carbon price and electricity quantity model;

[0029] According to the order corresponding to the carbon price and electricity quantity model, the autoregressive parameter and the moving average parameter are obtained respectively;

[0030] Generate an autoregressive moving average model according to the autoregressive parameters and the moving average parameters;

[0031] Historical carbon emission data is obtained, and the carbon emission data is input into the autoregressive moving average model in a preset data format to obtain a predicted carbon emission cost.

[0032] From the above description, it can be seen that the beneficial effect of the present invention is that after the initial electricity model is established based on the carbon emission cost, the initial carbon price electricity model is processed by the autocorrelation function and the partial autocorrelation function, which can effectively simulate the changes in carbon price with the market supply and demand relationship, that is, the simulation of the change of carbon price over time is further obtained to obtain the autoregressive moving average model, and the historical carbon emission data is input into the autoregressive moving average model in a preset data form to ensure the consistency between the form of the data to be predicted and the model, thereby improving the accuracy of the carbon emission cost prediction after the carbon price changes.

[0033] Furthermore, establishing an initial carbon price electricity model based on carbon emission costs includes:

[0034]

[0035] Where CO2fix represents the carbon emission reduction cost; Represents the carbon price; the carbon price includes a time-varying component. represents the carbon price; Indicates the amount of carbon.

[0036] From the above description, it can be seen that the initial carbon price electricity model is constructed based on the carbon emission reduction cost and carbon price, which fully considers the impact of carbon emission reduction cost on carbon emission cost, and takes the carbon price part as a time-varying feature to simulate the change of carbon price over time, thereby improving the prediction accuracy of carbon emission cost.

[0037] Furthermore, generating an autoregressive moving average model according to the autoregressive parameters and the moving average parameters includes:

[0038]

[0039] Where, Represented as white noise, and is the autoregressive parameter, and is the partial autoregressive parameter.

[0040] From the above description, we can know that the carbon price model and carbon usage model are obtained by the autoregressive parameter and the moving average parameter respectively, that is, by replace Thus, accurate carbon emission cost data can be obtained based on the output carbon price and carbon usage.

[0041] Furthermore, the initial carbon price and electricity quantity model is identified and ordered by the autocorrelation function and the partial autocorrelation function to obtain the order corresponding to the carbon price and electricity quantity model, which includes:

[0042] Performing model identification and order determination on the initial carbon price and electricity quantity model according to the autocorrelation function to obtain a first-order numerical upper limit value;

[0043] Performing model identification and order determination on the initial carbon price and electricity quantity model according to the partial autocorrelation function to obtain a second-order numerical upper limit value;

[0044] The order corresponding to the carbon price electricity model is obtained according to the first-order numerical upper limit value and the second-order numerical upper limit value.

[0045] From the above description, it can be seen that the corresponding first-order numerical upper limit and second-order numerical upper limit are obtained through the autocorrelation function and the partial autocorrelation function respectively, so that the carbon price and electricity model can be described according to the corresponding first-order numerical upper limit and second-order numerical upper limit, thereby improving the prediction accuracy of the final carbon emission cost.

[0046] Furthermore, the order corresponding to the carbon price electricity model obtained according to the first-order numerical upper limit and the second-order numerical upper limit includes:

[0047] The first order upper limit value and the second order numerical upper limit value are optimized by Akaike information criterion or Bayesian information criterion.

[0048] From the above description, it can be seen that the order corresponding to the carbon price electricity model is obtained by optimizing the first order upper limit value and the second order numerical upper limit value through the Akaike information criterion or the Bayesian information criterion.

[0049] Furthermore, the initial carbon price electricity model is subjected to model identification and order determination based on the autocorrelation function, and the first-order numerical upper limit value obtained includes:

[0050]

[0051] Where, ρ p Expressed as a function of the first order upper limit value p.

[0052] From the above description, it can be seen that the initial carbon price electricity model is processed by the autocorrelation function and the first-order numerical upper limit is obtained, so that the accurate autoregressive parameter value can be obtained based on the first-order numerical upper limit.

[0053] Furthermore, the initial carbon price and electricity quantity model is subjected to model identification and order determination based on the partial autocorrelation function to obtain a second-order numerical upper limit value, which includes:

[0054]

[0055]

[0056] Where, φ q is x t-q The coefficient of PACF(q) = φ q ξ t is the error term.

[0057] From the above description, it can be seen that the initial carbon price electricity model is processed by the partial autocorrelation function and the second-order numerical upper limit is obtained, so that the accurate partial autoregressive parameter value can be obtained based on the second-order numerical upper limit.

[0058] Furthermore, the acquiring of historical carbon emission data and inputting the carbon emission data into the autoregressive moving average model in a preset data format to obtain the predicted carbon emission cost includes:

[0059] Determining whether the historical carbon emission data is a stationary non-pure random sequence; if not, converting the historical carbon emission data into the stationary non-pure random sequence through differential processing;

[0060] The stationary non-pure random sequence is input into the autoregressive moving average model to obtain the predicted carbon emission cost.

[0061] From the above description, it can be seen that by performing differential processing on the historical carbon emission data which is a non-stationary pure random sequence, the historical carbon emission data can meet the data input form of the autoregressive moving average model, thereby effectively realizing the prediction based on the input historical carbon emission data and outputting accurate carbon emission cost results.

[0062] Furthermore, the converting of the historical carbon emission data into the stationary non-pure random sequence by differential processing includes:

[0063] ΔX t =X t+1 -X t ;

[0064] Wherein, X represents the historical carbon emission data; ΔX represents the historical carbon emission data after differential processing.

[0065] From the above description, it can be seen that by performing differential processing on historical carbon emission data through the above formula, the historical carbon emission data can be effectively converted into a stationary pure random sequence, thereby meeting the input form of the autoregressive moving average model.

[0066] Another embodiment of the present invention provides an emission cost prediction device for carbon price changes, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the carbon emission cost prediction method for carbon price changes as described above is implemented.

[0067] The above-mentioned carbon price change prediction method and device of the present invention can predict and output the corresponding carbon emission cost based on the change of carbon price, which is described below through specific implementation methods:

[0068] Example 1

[0069] Please refer to Figure 1 A method for predicting carbon emission costs with carbon price changes comprises the following steps:

[0070] S1. Establish an initial carbon price electricity model based on carbon emission costs:

[0071]

[0072] Where CO2fix represents the carbon emission reduction cost, which includes carbon emission reduction investment and emission reduction equipment operating costs; represents the carbon price, which includes a part that changes with time, that is, the carbon price part is the part with time-varying characteristics;

[0073] S2. Identifying and determining the order of the initial carbon price and electricity quantity model through an auto-correlation function (ACF) and a partial auto-correlation function (PACF) to obtain the order corresponding to the carbon price and electricity quantity model;

[0074] S21. Perform model identification and order determination on the initial carbon price and electricity quantity model according to the autocorrelation function to obtain a first-order numerical upper limit value:

[0075]

[0076] Where, ρ p Expressed as a function of the first order upper limit value p;

[0077] S22. Perform model identification and order determination on the initial carbon price and electricity quantity model according to the partial autocorrelation function to obtain a second-order numerical upper limit value:

[0078]

[0079] Where, φ q is x t-q The coefficient of PACF(q) = φ q ξ tis the error term;

[0080] S23. Obtaining the order of the carbon price and electricity quantity model based on the upper limit of the first-order value and the upper limit of the second-order value. Since the orders of the first-order value p and the second-order value q cannot be accurately determined when the ACF and PACF truncation are used to determine whether the model is an ARMA model,

[0081] In an optional embodiment, in order to more accurately determine the orders of the first-order value p and the second-order value q, the first-order upper limit value and the second-order upper limit value are further optimized according to the Akaike information criterion (AIC) or the Bayesian Information Criterion (BIC) to obtain the order corresponding to the carbon price electricity model;

[0082] Let L be the likelihood function of the model parameters, that is, given a set of model parameters, L is the probability of obtaining sample data under this set of parameters; the larger L is, the more accurate the description of the model by the given parameters; for larger orders, L is also larger; in addition, more parameters are prone to overfitting; the AIC criterion and the BIC criterion are the trade-offs between the likelihood function and the number of parameters. Let k be the number of parameters, and we get the definitions of AIC and BIC:

[0083] AIC = -2ln(L) + 2k;

[0084] BIC = -2ln(L) + kln(n);

[0085] In the above formula, n is the sequence width. When the order p, q increases, 2ln(L) will become larger, but at the same time 2k will also become larger; therefore, there is an optimal value for AIC and BIC; the optimal order is the order that makes AIC and BIC take the maximum value (p * ,q * );

[0086] S3. Obtain autoregressive parameters and moving average parameters according to the order corresponding to the carbon price and electricity quantity model; if the autoregressive parameters are obtained: and And the partial autoregressive parameter and

[0087]

[0088] S4. Generate an autoregressive moving average model according to the autoregressive parameters and the moving average parameters:

[0089]

[0090] Where, Represented as white noise, and is the autoregressive parameter, and is the partial autoregressive parameter;

[0091] S5. Obtain historical carbon emission data, and input the carbon emission data into the autoregressive moving average model in a preset data format to obtain a predicted carbon cost;

[0092] S51, determining whether the historical carbon emission data is a stationary non-pure random sequence; if not, converting the historical carbon emission data into the stationary non-pure random sequence through differential processing; the historical carbon emission data includes carbon emission amount and carbon price data;

[0093] The test method of the stationary non-pure random sequence is as follows:

[0094] For example, the time series of carbon emissions and carbon price data meet the following requirements: (1) for any time t, the mean of the input data is always constant; (2) for any time t1 and t2, the correlation coefficient of this time series is determined by the time period between the two time points. If the starting points of the two time points do not cause any influence, then the input data is a stationary non-pure random series;

[0095] The differential processing includes:

[0096] ΔX t =X t+1 -X t ;

[0097] Wherein, X represents the historical carbon emission data; ΔX represents the historical carbon emission data after differential processing;

[0098] S52: Input the stationary non-pure random sequence into the autoregressive moving average model to obtain the predicted carbon cost.

[0099] Example 2

[0100] This embodiment differs from the first embodiment in that an electricity cost model is established based on the carbon price change prediction method in the first embodiment;

[0101] The method further includes the following steps before step S1:

[0102] S01. Establish a power generation cost model for coal-fired power plants. For coal-fired power plants, the power generation cost can be divided into capacity cost and electricity cost. Capacity cost mainly includes salary and financial costs, equipment depreciation costs, and management costs. Electricity cost includes operation and maintenance and fuel costs, and other variable costs (including production consumable materials, other pollutant emission costs, water charges, etc.). Among them, fuel cost is the main component of electricity cost, and the specific relationship is as follows:

[0103] C 发电 =C 容量 +C 电量 ;

[0104] C 容量 =C 折旧 +C 工资财务 +C 管理 ;

[0105] C 电量 =C 总燃料 +C 其他可变成本 ;

[0106] For thermal power plants, the capacity cost C 容量 The proportion is relatively low and is relatively fixed during the actual operation of power companies, so it can be ignored in the cost components of bidding strategies. The power generation cost is mainly composed of the electricity cost, and the levelized cost of electricity (LCOE) model is used. The levelized cost of electricity (LCOE) refers to the power generation cost per kilowatt-hour (kW·h) of a power generation project during its construction and operation life. It is a widely recognized and highly transparent method for calculating power generation costs.

[0107] The definition of LCOE comes from the identity that the net present value of revenues equals the net present value of costs:

[0108]

[0109] In the formula, LCOE n With E n The product of is the total income of the system during its life cycle, in 100 million yuan; E n is the power generation, in billion kWh; n is the operating life of the power plant; r is the discount rate; Cost n is the total cost including electricity cost and construction cost, etc.; the above formula is transformed into the same formula:

[0110]

[0111] In electricity market quotations, determining real-time electricity prices based on marginal costs can maximize social benefits. Electricity costs are the basis for setting real-time and long-term electricity prices. Currently, the principle of marginal cost quotation is adopted, whereby quotations are made based on the electricity costs of generators from smallest to largest, with the cost of the marginal unit being used as the cost for completing electricity transactions.

[0112] S02. The carbon emission cost prediction model constructed in Example 1 is introduced into the above model. After the introduction of the carbon emission trading system, the increase of carbon emission reduction costs in electricity market transactions will change the electricity cost component, that is, affect the marginal bids of coal-fired units and natural gas generators. Therefore, in the process of calculating the electricity cost of the generator units, the impact of carbon emission reduction costs on the electricity market is focused on. The levelized electricity cost is redefined. The electricity cost includes fuel cost, carbon emission cost, and other variable costs. The electricity cost is expressed by the formula:

[0113]

[0114] represents the annual fuel cost of the power plant; Represents other variable costs, including production consumable material costs, other pollutant emission costs (including carbon oxides, nitrogen oxides, sulfur oxides, etc.) and water charges; represents the cost of carbon emissions;

[0115] That is, the levelized cost of electricity model based on carbon price is obtained:

[0116]

[0117] After the steps in the first embodiment, the method further includes:

[0118] S6. Build a mathematical model for power production simulation: The mathematical model for power production simulation provides a specific formula for its objective function and constraints. The optimization goal of the power system economic dispatch problem is to minimize the levelized cost of electricity while satisfying various operating constraints. The decision variable is the output of the unit during each dispatch period.

[0119] S6a. Objective function

[0120] minLCOE t

[0121]

[0122]

[0123]

[0124] Among them, C fuel,t is the power system operating cost at time t, is the carbon trading cost of the power system at time t. N is the total number of power generation units, P I,t is the power generation of the i-th type power unit at time t, δ i is the unit operating cost of the i-th type power unit, where the unit operating cost of the thermal power unit includes fuel costs and operation and maintenance costs, and the unit operating cost of other units is only the operation and maintenance cost; k i is the carbon emission intensity of the i-th type thermal power unit, η i is the carbon emission intensity quota of the i-th type thermal power unit; is the carbon trading price corresponding to time t predicted by the historical carbon trading price. p and β q Represents the autoregressive parameter and moving average parameter in the carbon trading value prediction equation;

[0125] S6b. Constraints

[0126] S6b1, System power real-time balance constraints:

[0127]

[0128] Among them, P i,t is the power generation of the i-th type power unit at time t, D t is the total electricity demand at time t, without considering the power transmission network losses;

[0129] S6b2, System Spinning Reserve Constraints:

[0130] The power system power supply needs to adjust its output in real time to meet the real-time changes in power load. Thermal power units are used for spinning reserve due to their flexible scheduling characteristics. The following conditions are met:

[0131]

[0132]

[0133] Where R ut is the upper spinning reserve constraint, R dt is the lower spinning reserve constraint, P i,max is the maximum output limit of thermal power unit i, P i,min is the minimum possible output of thermal power unit i;

[0134] S6b3, thermal power unit climbing constraints and output upper and lower limit constraints:

[0135] |P i,t+1 -P i,t |≤P i,v

[0136] P i,min ≤P i,t ≤Pi,max

[0137] Where, P i,v is the maximum range of output variation of thermal power unit i within a unit time;

[0138] S6c, Transaction Clearing Mathematical Model

[0139] Assume that the market is based on the quotation curves of units and node loads, and the quotation curve of unit i is λ i (p i )=α i +β i p i , is a monotonically rising straight line, and the quoted load curve λ of node j j (p j )=α j +β j p j It is a monotonically decreasing straight line. The trading market matches transactions with the objective function of maximizing social welfare. The mathematical description of the spot market transaction clearing process is:

[0140]

[0141] S6c1, the constraints are:

[0142]

[0143]

[0144]

[0145] Where, the transaction volume p of node j load is j , It is p j The upper and lower limits of unit i’s transaction output p i , It is p i The upper and lower limits of node j load quotation strategy coefficient α j , β j , the bidding strategy coefficient u of unit i i 、v i ΔP is the system loss, which depends on node load, unit output, and network topology. System losses are generally ignored during the trade clearing process, and active power balance is achieved through the real-time balancing market and automatic generation control unit auxiliary services. The clearing process uses the Lagrange multiplier method to solve the following system of equations:

[0146]

[0147] Where λ is the Lagrange multiplier of the constraint in c1. i 、zi Inequality constraints The upper and lower bounds of the Lagrange multiplier, y i 、x i is the corresponding slack variable; m j 、n j Inequality constraints The upper and lower bounds of the Lagrange multiplier, γ j is the corresponding slack variable.

[0148] Example 3

[0149] This example provides a specific example to verify and calculate the power production simulation mathematical model obtained in Example 2;

[0150] P1. Collect data on the target objects; take Fujian Province's carbon market and electricity market as the research objects, obtain relevant basic data of Fujian Province, including electricity demand, installed capacity of various power sources, technical and economic parameters of various power sources, and fuel costs and consumption rates, etc., and set scenarios based on the current status of Fujian Province's carbon market transactions, and simulate each scenario; evaluate and analyze the model calculation results after the introduction of the carbon market;

[0151] 1) Electricity demand data

[0152] Please refer to Figure 2 , which is a typical load curve for a working day in Fujian Province. The peak load occurs at 11:00 a.m., the power load trough is between 2:00 a.m. and 6:00 a.m., with the lowest point at 4:00 a.m., after which it begins to climb. The load is high in the afternoon and night, basically around 34,000MW. After 5:00 p.m., the load drops slightly compared to the afternoon, and the power load is relatively low.

[0153] 2) Installed capacity of various power supplies

[0154] Generator unit types include coal-fired power generation, gas-fired power generation, hydropower, nuclear power, wind power, photovoltaic power generation, and biomass power generation. Specific indicators include installed capacity, capacity share, comprehensive plant power consumption rate, actual power generation of generator units, full-load power generation of generator units, actual load rate, actual power supply, load rate upper limit, and power supply upper limit. Among them, coal-fired power generation is further divided into conventional coal-fired power and cogeneration units. The data are shown in Tables 1 and 2.

[0155] Table 1 Composition of power generation equipment in Fujian Province

[0156]

[0157] Table 2: Breakdown of coal-fired generating units in Fujian Province

[0158]

[0159] 3) Technical and economic parameters of various power supplies

[0160] The main technical and economic parameters of various power sources include power construction investment cost, expected life, annual operating time, operation and maintenance cost, power fuel consumption rate, and fuel price, as shown in Table 3. In addition, the fuel carbon emission intensity is relatively constant for each fuel. The calculation example sets the fuel carbon emission intensity with reference to the prevailing standard. Specifically, the average carbon emission intensity of coal-fired units is 2.78kg-C02 / kg-coal; the average carbon emission intensity of gas-fired units is 2.19kg-C02 / Nm3-ng.

[0161] Table 3 Expected lifespan and economic parameters of various power sources in Fujian Province

[0162]

[0163] Table 4 shows the average power generation coal consumption of various coal-fired power units in Fujian Province at different load rates. There is a negative correlation between load rate and average power generation coal consumption, and as the load rate increases, the average power generation coal consumption decreases.

[0164] Table 4 Average coal consumption of coal-fired power units in Fujian Province at various load rates

[0165]

[0166] 4) Comparison of carbon prices in different carbon markets

[0167] Please refer to Figure 3 , is the average carbon price of major carbon markets in 2021, including 8 carbon markets such as Fujian, Beijing, and Shanghai, as well as the United States, Europe, Japan, and South Korea (the average carbon price in Europe is about 350 yuan / ton, not listed in the figure); affected by factors such as supply and demand levels, economic operation, industry development and policy guidance, the average carbon price levels of various carbon markets are different; compared with the world's major carbon markets, China's average carbon price level is relatively low; in 2021, the average carbon prices of domestic carbon markets can be divided into three levels, among which the average carbon price levels in Fujian and Shenzhen are relatively low, about 10 yuan per ton; the average carbon prices in Shanghai, Tianjin, Chongqing and Hubei are between 20 and 30 yuan per ton; the average carbon price in the carbon markets of Beijing and Guangdong is relatively high, about 40 yuan per ton;

[0168] P2, output simulation results;

[0169] P21, please refer to Figure 4, which is the result of calculating the levelized electricity cost of seven types of power generation technologies under different carbon price levels; with the continuous increase in carbon emission prices, the electricity cost shows a linear growth trend according to the increase in carbon price, that is, the increase in carbon emission prices will cause the increase in power generation costs and decrease in profits of coal-fired and gas-fired units; among them, the carbon emission cost of coal-fired power generation units is higher, and the levelized cost of electricity increases faster under the influence of carbon price; when the carbon price is 20 yuan, the economic efficiency of gas-fired power generation has surpassed that of coal-fired power generation; in addition, the levelized electricity cost of clean energy is less affected by carbon price; when the carbon price is 70 yuan, the economic efficiency of hydropower exceeds that of coal-fired power generation, and the electricity cost of hydropower is already lower than the comprehensive electricity cost; when the carbon price is 90 yuan, the economic efficiency of nuclear power is The economic efficiency of biomass power generation is close to that of coal-fired power generation when the carbon price is 120 yuan, and the cost of nuclear power is lower than the comprehensive electricity cost to achieve grid parity. Due to the different carbon emission intensities of different power generation technologies, the carbon price attached to different carbon emission costs of different power generation technologies is different. High-carbon emission power generation technologies (such as coal-fired power generation) have higher carbon emission costs, and the power generation cost increases faster when the carbon price rises. Gas-fired power generation with relatively lower carbon emissions is less affected by the increase in carbon prices. Other new energy power generation technologies that basically do not produce carbon emissions are basically affected by the carbon price at zero. The differentiated impact of carbon prices on different power generation technologies will lead to differentiated carbon emission costs, thereby affecting the electricity costs of various power generation technologies.

[0170] P22, calculation results of output curve of each unit;

[0171] P221, please refer to Figure 5 , is to calculate the output curve of each unit under the current carbon price of the target object; according to the current (2021) carbon price of 10 yuan / ton in the Fujian carbon market, the output curve of each unit in Fujian Province under the current carbon price is obtained; Figure 5 Under the current carbon price, the main power source in Fujian Province is coal-fired power generation, while other power generation units such as nuclear power, hydropower and wind power have relatively low output;

[0172] P222, taking the current carbon price of 10 yuan / ton as the benchmark curve, calculate the output curves of each unit under different carbon prices by adjusting the carbon price range;

[0173] Please refer to Figure 6-9 , referring to the carbon price levels of various domestic and international carbon markets, the carbon price parameters were adjusted to 0, 40 (higher domestic level), 60 (US), and 120 (higher foreign level) yuan / ton, and the output curves of various units in Fujian Province were simulated under different carbon price conditions; in the scenario of no carbon price, coal-fired power generation bears more than 60% of the power load; under the current carbon price (10 yuan / ton), compared with the scenario of no carbon price, the output share of coal-fired power generation units decreases, while the output share of nuclear power and gas power generation units increases;

[0174] When the carbon price increases by 40 yuan / ton, the cost of electricity from gas-fired power generation is much lower than that from coal-fired power generation at full load;

[0175] When the carbon price is 60 yuan / ton, the output share of clean power generation units further increases, accounting for about 40% of the total load. Among them, the cost of hydropower electricity is close to the comprehensive electricity cost, and the output is close to full load;

[0176] When the carbon price is 120 yuan / ton, due to the high carbon price and carbon emissions, the output of coal-fired power generation units will further drop significantly; Fujian Province's main power sources will become clean power generation units such as nuclear power, hydropower, and wind power, and the cost of nuclear power is lower than the comprehensive power generation component, and it can output at full load; due to the different carbon emission intensities of different power generation technologies, the carbon price attached to the carbon emission costs of different power generation technologies will also vary; coal-fired power generation produces more carbon emissions, while gas-fired power generation produces relatively less, and other new energy power generation technologies basically do not produce carbon emissions; therefore, after the carbon price rises, the power generation cost of coal-fired power generation will rise rapidly, while the power generation cost of gas-fired power generation will rise more slowly, and the power generation cost of other new energy power generation technologies will basically not be affected; when the carbon price rises to a certain level, the power generation cost of low-carbon emission technologies such as gas-fired power generation will be lower than that of coal-fired power generation, and will be cleared out first;

[0177] It can be seen that there is a mutually restrictive relationship between the carbon market and the electricity market. For the carbon market, the increase in total carbon emissions and the rise in carbon prices in carbon trading will have a counter-effect on thermal power units in the electricity market (especially coal-fired power units), increasing the power generation costs of thermal power units, suppressing the market competitiveness of thermal power units, and prompting an increase in the proportion of new energy power generation; in the future, as the marketization process of the electricity market and the carbon market advances, factors such as carbon prices in the new environment will affect the costs of different power generation technologies, and thus affect their economic feasibility, which will be the focus of attention when evaluating the economic feasibility of different power generation technologies in the future; the carbon market internalizes the external costs of carbon emissions. While increasing the cost of fossil energy, it also provides additional economic incentives and strong price signals for non-fossil energy power generation, which can divert funds from fossil fuels to cleaner and more efficient energy use.

[0178] Example 4

[0179] Please refer to Figure 10 , a device for predicting emission costs with changes in carbon prices, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in a method for predicting carbon emission costs with changes in carbon prices as described in embodiments one and two is implemented.

[0180] In summary, the present invention provides a method and device for predicting carbon emission costs under carbon price changes. After establishing an initial electricity model based on the carbon emission cost, the initial carbon price electricity model is processed by autocorrelation function and partial autocorrelation function, which can effectively simulate the changes in carbon price with market supply and demand, that is, simulate the changes in carbon price over time to further obtain an autoregressive moving average model, and input historical carbon emission data into the autoregressive moving average model in a preset data form to ensure the consistency between the form of the data to be predicted and the model, thereby improving the accuracy of carbon emission cost prediction after carbon price changes, and then can help adjust the power structure according to the mutual constraints between the carbon market and the electricity market, and make greater contributions to reducing carbon emissions.

[0181] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for predicting carbon emission costs with carbon price changes, characterized in that: Including steps: Establish an initial carbon price electricity model based on carbon emission costs; Identifying and determining the order of the initial carbon price and electricity quantity model through an autocorrelation function and a partial autocorrelation function, and obtaining the order corresponding to the carbon price and electricity quantity model; According to the order corresponding to the carbon price and electricity quantity model, the autoregressive parameter and the moving average parameter are obtained respectively; Generate an autoregressive moving average model according to the autoregressive parameters and the moving average parameters; Obtaining historical carbon emission data, and inputting the carbon emission data into the autoregressive moving average model in a preset data format to obtain a predicted carbon emission cost; The establishment of an initial carbon price electricity model based on carbon emission costs includes: Where, represents the carbon reduction cost; represents the carbon price; the carbon price includes a time-varying component; Generating the autoregressive moving average model according to the autoregressive parameter and the moving average parameter includes: Where, Represented as white noise, and is the autoregressive parameter, and is the partial autoregressive parameter; The initial carbon price and electricity quantity model is identified and ordered by the autocorrelation function and the partial autocorrelation function to obtain the order corresponding to the carbon price and electricity quantity model. Performing model identification and order determination on the initial carbon price and electricity quantity model according to the autocorrelation function to obtain a first-order numerical upper limit value; Performing model identification and order determination on the initial carbon price and electricity quantity model according to the partial autocorrelation function to obtain a second-order numerical upper limit value; The order corresponding to the carbon price electricity model is obtained according to the first-order numerical upper limit and the second-order numerical upper limit, including: Optimizing the first-order numerical upper limit and the second-order numerical upper limit by Akaike Information Criterion or Bayesian Information Criterion to obtain the order corresponding to the carbon price and electricity quantity model; The initial carbon price electricity model is identified and ordered according to the autocorrelation function, and the first-order numerical upper limit value obtained includes: ； Where, Expressed as a function of the first-order numerical upper limit p; The performing model identification and order determination on the initial carbon price and electricity quantity model according to the partial autocorrelation function to obtain the second-order numerical upper limit value includes: ... ； Where, for The coefficient of ; is the error term.

2. The method for predicting carbon emission costs according to carbon price changes according to claim 1, characterized in that: The step of obtaining historical carbon emission data and inputting the carbon emission data into the autoregressive moving average model in a preset data format to obtain a predicted carbon emission cost includes: Determining whether the historical carbon emission data is a stationary non-pure random sequence; if not, converting the historical carbon emission data into the stationary non-pure random sequence through differential processing; The stationary non-pure random sequence is input into the autoregressive moving average model to obtain the predicted carbon emission cost.

3. The method for predicting carbon emission costs according to carbon price changes according to claim 2, characterized in that: The converting of the historical carbon emission data into the stationary non-pure random sequence by differential processing includes: ； Wherein, X represents the historical carbon emission data; represents the historical carbon emission data after differential processing.

4. A device for predicting emission costs of carbon price changes, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for predicting carbon emission costs with carbon price changes as described in any one of claims 1 to 3 is implemented.

Images