Power plant multi-stage risk scheduling method considering carbon-electricity multi-dimensional correlation

Abstract

The invention discloses a power plant multi-stage risk scheduling method considering carbon-electricity multi-dimensional correlation. The method comprises the following steps: establishing a multi-scale uncertainty scene tree model of a carbon-electricity system, and further determining a carbon-electricity monthly long-term cost parameter, and daily generating capacity and power load capacity of renewable energy sources; establishing annual, monthly and daily carbon-electricity joint decision models of the power plant; establishing a clearing model of the carbon-electricity system; and an alternating random dual dynamic programming algorithm is used for solving to obtain a carbon quota cost parameter and a carbon quota clearing amount of a carbon system and an electric power cost parameter and an electric power clearing amount of an electric system, so that the output of a power plant is optimized and dispatched, and multi-stage risk dispatching is realized. According to the method, by introducing the carbon-electricity multi-dimensional correlation, the problem of complex decision making of a power plant in a carbon system and an electric power system is effectively solved, and the optimal composition of the electric power and carbon emission permissions in each system can be determined through optimal combination, so that the scheduling accuracy and benefits are improved.

Description

Technical Field

[0001] The present invention relates to a multi-stage risk scheduling method for a power plant, and in particular to a multi-stage risk scheduling method for a power plant that takes into account the multi-dimensional correlation between carbon and electricity. Background Art

[0002] In the carbon system, power plants will encounter the settlement problem of equivalent quotas corresponding to the cumulative carbon emissions and participate in the forward system. At the same time, power plants need to interact in multi-period power systems including the forward system and arrange their output according to the interaction. In the power and carbon system environment, power plants can maximize input costs by optimizing the combination in multiple interactive options. According to the optimization results, power plants can determine the optimal composition of electricity and carbon emission rights in their respective forward systems and arrange their own dispatch accordingly. However, traditional methods have not yet considered the multiple correlations between carbon and electricity in the multi-stage optimal dispatch of power plants, resulting in a lack of consideration of carbon emissions when power plants set power generation plans, which is not conducive to carbon reduction in power plants. Summary of the invention

[0003] In order to solve the problems existing in the background technology, the present invention provides a multi-stage risk scheduling method for power plants considering the multi-dimensional correlation between carbon and electricity.

[0004] The technical solution adopted by the present invention is:

[0005] The multi-stage risk scheduling method of a power plant considering the multi-dimensional correlation between carbon and electricity of the present invention comprises:

[0006] Step 1) A scenario tree is established as a multi-scale uncertainty scenario tree model of the carbon-electricity system based on the historical data of the carbon-electricity monthly forward cost parameters, renewable energy daily data, and load daily data of the carbon-electricity system, and the carbon-electricity monthly forward cost parameters, daily power generation of renewable energy, and daily power load of the carbon-electricity system under typical scenarios are determined based on the multi-scale uncertainty scenario tree model.

[0007] Step 2) Establish annual carbon-electricity joint decision-making model, monthly carbon-electricity joint decision-making model and daily carbon-electricity joint decision-making model for power plants; interact carbon quota and electricity between carbon-electricity system and power plants.

[0008] Step 3) Establish a clearing model for the carbon-electricity system.

[0009] Step 4) Input the monthly forward carbon-electricity cost parameters, renewable energy generation and power load under the typical scenario of the carbon-electricity system into the annual carbon-electricity joint decision model, the monthly carbon-electricity joint decision model, the daily carbon-electricity joint decision model and the carbon-electricity system clearing model for processing, and use the alternating random dual dynamic programming algorithm to solve. After the daily carbon-electricity joint decision model is processed, the carbon quota cost parameters and power cost parameters of the power plant are output and then input into the carbon-electricity system clearing model for processing. After the carbon-electricity system clearing model is processed, the carbon quota cost parameters and carbon quota clearing amount of the carbon system and the power cost parameters and power clearing amount of the power system are output, and then the output of the power plant is optimized and dispatched to realize the multi-stage risk dispatch of the power plant. The cost can be measured in electricity or carbon quota.

[0010] In step 2), the annual carbon-electricity joint decision-making model of the power plant is as follows:

[0011]

[0012] in, represents the annual input cost of the power plant; and They represent the annual electricity input cost and annual carbon quota output cost of the ith power plant respectively; p τ represents the probability of the τth monthly phase in the current year; represents the input cost of the power plant in the τth monthly period in the current year; λ HE and μ HC They represent the preset annual electricity cost parameter and the preset annual carbon quota cost parameter of the power plant respectively; and They represent the annual electricity and annual carbon quota of the i-th power plant respectively.

[0013] The annual carbon-electricity joint decision-making model of the power plant meets the following constraints:

[0014]

[0015] in, and Represents the annual carbon quota of the i-th power plant The allocation to the τth monthly period and the annual power of the jth generator unit in the ith power plant The amount allocated to the τth monthly period; p ω represents the probability of the ωth daily stage in the current month, ω∈τ means that the daily stage ω belongs to the monthly stage τ; Ω i represents the set of generator sets of the i-th power plant; represents the active output of the jth generator set in the ωth day stage; represents the carbon emission intensity of the jth generator unit in the power plant; represents the preset carbon quota of the ith power plant in the τth monthly period; represents the carbon quota clearance of the i-th power plant in the ω-day period; VaR H represents the risk value parameter of the annual stage; α represents the confidence level; represents the preset auxiliary variable for the τth monthly period, and represents the net input cost under the monthly period τ and the annual risk value parameter VaR H The left deviation of H,min Represents the minimum conditional VaR parameter allowed for the annual period.

[0016] In annual decision-making, the annual allocation strategy should be considered. This means that the year should be allocated to each month and combined with the monthly decision. In addition, the annual decision should also consider the carbon emission cap limit, that is, the cumulative carbon emissions from electricity production in that year should not exceed the sum of the carbon quotas net input from the annual, monthly and daily carbon systems, and at the same time, the conditional risk value constraints of the annual stage need to be met.

[0017] The default annual electricity cost parameter λ of the power plant HE and the preset annual carbon quota cost parameter μ HC and the annual electricity consumption of the i-th power plant and annual carbon quota The annual carbon-electricity joint decision-making model of the power plant is input for processing, and the annual carbon-electricity joint decision-making model outputs the carbon quota of the i-th power plant after processing. Allocation to the τth monthly period and the annual power generation of the jth generator unit in the i-th power plant Allocation to the τth monthly period The data is then input into the power plant's monthly carbon-electricity joint decision-making model for processing.

[0018] In step 2), the monthly carbon-electricity joint decision-making model of the power plant is as follows:

[0019]

[0020] in, represents the input cost of the power plant in the τth monthly period in the current year; and They represent the preset electricity input price and carbon quota output price of the ith power plant in the τth monthly period; p ω Represents the probability of the ω-th day stage in the current month; represents the input cost of the power plant at the ωth day stage in the current month; Ωi represents the set of generator sets of the i-th power plant; and represents the preset electricity price parameter and the preset carbon quota price parameter of the power plant in the τth monthly period; and They respectively represent the preset carbon quota of the ith power plant in the τth monthly period and the preset electricity quantity of the jth generating unit in the ith power plant in the τth monthly period.

[0021] The monthly carbon-electricity joint decision-making model of the power plant meets the following constraints:

[0022]

[0023] in, and Represents the monthly carbon quota of the i-th power plant The allocation to the ω-th daily period and the monthly electricity consumption of the j-th generator unit in the i-th power plant The amount allocated to the ω-day stage; and Represents the annual carbon quota of the i-th power plant The allocation to the τth monthly period and the annual power of the jth generator unit in the ith power plant Allocation to the τth monthly period; Represents the preset auxiliary variable of the ωth daily stage, representing the net input cost under the daily stage ω and monthly risk value parameter VaR M Left skewness of the variable; VaR M represents the monthly risk value parameter; α represents the confidence level; R M,min represents the minimum conditional VaR parameter allowed in the monthly period; It represents the input cost of the power plant in the ωth day period in the current month.

[0024] The default power price parameter of the power plant in the τth monthly period is and preset carbon quota cost parameters and the carbon quota of the i-th power plant Allocation to the τth monthly period and the annual electricity consumption of the jth generator unit in the i-th power plant Allocation to the τth monthly period The monthly carbon-electricity joint decision-making model is used to process the monthly carbon-electricity joint decision-making model, which then outputs the monthly carbon quota of the i-th power plant. Allocation to the ω-day stage and the monthly power of the jth generator unit in the i-th power plant Allocation to the ω-day stage The data is then input into the power plant's daily carbon-electricity joint decision-making model for processing.

[0025] The carbon-electricity monthly forward price parameters include the preset electricity price parameters of the power plant in the τth monthly period and preset carbon quota cost parameters

[0026] In the monthly stage, the power plant needs to use the result of the annual allocation to the monthly as the boundary condition to determine the preset monthly electricity and monthly carbon quota in the monthly electricity and carbon system. In addition, it is also necessary to decide how to further allocate the monthly preset amount to the daily strategy. In the monthly decision, the power plant receives the annual electricity allocation result and the carbon quota allocation result, and then uses this as the boundary to determine the signing amount of the monthly contract, and further allocates the annual allocation to the month and the signing amount of the monthly contract to the daily stage, that is, the annual contract is allocated to the month, and the monthly contract is allocated to the daily stage.

[0027] In step 2), the daily carbon-electricity joint decision-making model of the power plant is as follows:

[0028]

[0029] in, represents the input cost of the power plant at the daily stage; p ω Represents the probability of the ω-th day stage in the current month; and represents the preset electricity input price and carbon quota output price of the i-th power plant in the ω-day period; Ω i represents the set of generator sets of the i-th power plant; and They represent the clearing electricity of the j-th generating unit and the r-th renewable energy unit of the power plant in the ω-th daily period respectively; and They represent the electricity cost parameter and carbon quota cost parameter of the i-th power plant in the ω-th day stage respectively; and They represent the operating costs of the j-th generating unit and the r-th renewable energy unit of the power plant respectively; and They represent the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth daily period respectively; It represents the carbon quota clearance of the i-th power plant in the ω-th daily period.

[0030] The daily carbon-electricity joint decision-making model of the power plant meets the following constraints:

[0031]

[0032]

[0033] in, and They represent the power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period, and They represent the upper and lower limits of the power cost parameter of the j-th generator unit in the power plant at the ω-th day stage, and They represent the upper and lower limits of the power cost parameters of the r-th renewable energy unit of the power plant in the ω-th daily stage; and They represent the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day stage, and They represent the upper and lower limits of the input cost parameters of the carbon quota of the ith power plant in the ωth day period, and They represent the upper and lower limits of the output cost parameters of the carbon quota of the ith power plant in the ωth daily period; and Represents the monthly carbon quota of the i-th power plant The allocation to the ω-th daily period and the monthly electricity consumption of the j-th generator unit in the i-th power plant The amount allocated to the ω-day stage; and They represent the carbon quota input and output clearance of the i-th power plant in the ω-th daily period respectively; represents the carbon emission intensity of the jth generator unit in the power plant; and They respectively represent the power parameter set of the power system and the carbon quota parameter set of the carbon system.

[0034] The monthly carbon quota of the i-th power plant Allocation to the ω-day stage and the monthly power of the jth generator unit in the i-th power plant Allocation to the ω-day stage The daily carbon-electricity joint decision-making model of the power plant is input for processing. After processing, the daily carbon-electricity joint decision-making model outputs the power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth daily stage. and And the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day period and

[0035] The carbon quota cost parameters of the power plant include the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day stage: and The power cost parameters of the power plant include the power cost parameters of the jth generator unit and the rth renewable energy unit in the ωth day stage: and

[0036] In step 3), the clearing model of the carbon-electricity system is as follows:

[0037]

[0038] in, and They represent the power parameter set of the power system and the carbon quota parameter set of the carbon system respectively; and They represent the power cost parameter and clearing amount of the d-th power load in the ω-th day stage respectively; and They represent the power cost parameters of the j-th generator unit and the r-th renewable energy unit of the power plant in the ω-th daily stage respectively; and They represent the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period respectively; Ω n represents the set of units and loads at the nth node in the carbon-electric system, j / r / d∈Ω n Indicates that the unit / load is located at node n, Θ n Represents the upstream node set of the nth node; B nm represents the inductance of the line between the nth node and the mth node; and Represent the phase angles of the nth node and the mth node respectively; represents the electricity cost parameter of the i-th power plant in the ω-th day stage; represents the maximum power flow through the line between the nth node and the mth node; and denote the maximum and minimum dual variables of the line power flow between the nth node and the mth node in the ωth daily period, respectively; and They represent the maximum active power output of the jth generator set and the rth renewable energy unit in the ωth day period, It represents the maximum power consumption of the dth power load in the ωth daily period; and denote the maximum and minimum dual variables of the active power output of the j-th generator unit in the power plant at the ω-th day, and denote the maximum and minimum dual variables of the active power output of the rth renewable energy unit in the power plant at the ωth daily stage, and They represent the maximum and minimum dual variables of the power consumption of the d-th power load in the ω-th daily period; Indicates the maximum phase angle amplitude of the nth node; and They represent the maximum and minimum dual variables of the phase angle amplitude of the nth node in the ωth day stage respectively; and They represent the phase angle and its dual variable of the first node in the ω-th day stage respectively; and They represent the carbon quota input cost parameter and output cost parameter of other power plants o in the ω-day period respectively; and They represent the carbon quota input and output clearance of other power plants o in the ω-day period respectively; and They represent the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th daily stage respectively; and They represent the carbon quota input and output clearance of the i-th power plant in the ω-th daily period respectively; The dual variable representing the carbon quota clearance amount in the ω-th day period; and They represent the maximum carbon quota input and output of the ith power plant, and They represent the maximum and minimum dual variables of the carbon quota input of the ith power plant in the ωth day period, and They represent the maximum and minimum dual variables of the carbon quota output of the ith power plant in the ωth daily period; and denote the maximum carbon quota input and output of other power plants o, and are the maximum and minimum dual variables representing the maximum carbon quota input of other power plants o, and They represent the maximum and minimum dual variables of the maximum carbon quota output of other power plant o respectively; the variables after the colon are the dual variables of the equations before their respective colons.

[0039] The power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period are and The input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th daily period and The maximum active power output of the jth generator set in the ωth daily period and the maximum power consumption of the dth power load in the ωth day period Input into the carbon-electricity system clearing model for processing, and the carbon-electricity system clearing model outputs the electricity cost parameter of the i-th power plant in the ω-day stage after processing The power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period and The dual variable of carbon quota clearance in the ωth day and the carbon quota input and output clearance of the i-th power plant in the ω-day period and

[0040] The carbon quota cost parameter of the carbon system is the dual variable of the carbon quota clearance amount in the ωth day stage The carbon quota clearance of the carbon system is the carbon quota input clearance of the i-th power plant in the ω-th daily period. and output clearance The power cost parameter of the power system is the power cost parameter of the i-th power plant in the ω-th day stage The power output of the power system is the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period. and

[0041] The daily power generation of renewable energy is the maximum active power output of the jth generator set in the ωth daily stage. The daily power load is the maximum power consumption of the dth power load in the ωth daily stage.

[0042] In the step 4), an alternating stochastic dual dynamic programming algorithm is used for solving the problem. Specifically, the nonlinear terms in the daily carbon-electricity joint decision-making model and the clearing model are linearized, and then the optimization theory KKT (Karush-Kuhn-Tucker) conditions are used for solving the problem in the forward process, and the strong duality theory SDT (Strong Duality Theorem) conditions are used for solving the problem in the backward process.

[0043] The electronic device of the present invention comprises: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method as described above.

[0044] The computer-readable storage medium of the present invention stores program data thereon, and is characterized in that the program data implements the method described above when executed by a processor.

[0045] The beneficial effects of the present invention are:

[0046] The method of the present invention introduces the multi-dimensional correlation between carbon and electricity, establishes a multi-scale uncertainty scenario tree model, combines the annual, monthly and daily carbon-electricity joint decision-making systems, and uses the alternating random dual dynamic programming algorithm to solve the problem. The method effectively solves the complex decision-making problems of power plants in the carbon system and the power system. The method can determine the optimal composition of electricity and carbon emission rights in each system through optimization combination, thereby improving the accuracy and efficiency of scheduling and realizing multi-stage risk scheduling of power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a logic block diagram of the method of the present invention. DETAILED DESCRIPTION

[0048] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] like Figure 1 As shown, the multi-stage risk scheduling method of power plants considering the multi-dimensional correlation between carbon and electricity of the present invention is specifically as follows:

[0050] Step 1) A scenario tree is established as a multi-scale uncertainty scenario tree model of the carbon-electricity system based on the historical data of the carbon-electricity monthly forward cost parameters, renewable energy daily data, and load daily data of the carbon-electricity system, and the carbon-electricity monthly forward cost parameters, daily power generation of renewable energy, and daily power load of the carbon-electricity system under typical scenarios are determined based on the multi-scale uncertainty scenario tree model.

[0051] Since the multi-stage dispatch process spans a whole year, the multi-scale uncertainty mainly includes two parts: monthly uncertainty and daily uncertainty. Monthly uncertainty mainly considers the uncertainty of the monthly forward cost parameters in the carbon-electricity system; daily uncertainty includes the uncertainty of renewable energy and load, which can affect the clearing results of the power and carbon systems. Based on historical data, the monthly power and carbon quota forward cost parameters and daily renewable energy and load data are generated. On this basis, a scenario tree is constructed for multi-stage dispatch optimization. The scenario tree is a tree structure model for decision analysis. It describes and analyzes complex decision environments by branching uncertainty factors into various possible scenarios at different time points. The structure of the scenario tree is a tree structure, which is divided into three layers. The decision time point of the first layer is consistent with the annual decision, which is a deterministic scenario (the root of the scenario tree). The decision time point of the second layer is consistent with the monthly decision, and can be divided into multiple different nodes (leaves of the scenario tree) according to the price of the monthly forward contract. The decision time point of the third layer is consistent with the daily decision, and can be divided into multiple different nodes (leaves of the scenario tree) according to the uncertainty of renewable energy and load. The root is connected to the second-level leaves and the third-level leaves through branches, together forming a scene tree.

[0052] Step 2) Establish the annual carbon-electricity joint decision-making model, monthly carbon-electricity joint decision-making model and daily carbon-electricity joint decision-making model of the power plant; the carbon-electricity system and the power plant interact with each other on carbon quota and electricity. The annual carbon-electricity joint decision-making model of the power plant is as follows:

[0053]

[0054] in, represents the annual input cost of the power plant; and They represent the annual electricity input cost and annual carbon quota output cost of the ith power plant respectively; p τ represents the probability of the τth monthly phase in the current year; represents the input cost of the power plant in the τth monthly period in the current year; λ HE and μ HC They represent the preset annual electricity cost parameter and the preset annual carbon quota cost parameter of the power plant respectively; and They represent the annual electricity and annual carbon quota of the i-th power plant respectively.

[0055] The annual carbon-electricity joint decision-making model of the power plant meets the following constraints:

[0056]

[0057] in, and Represents the annual carbon quota of the i-th power plant The allocation to the τth monthly period and the annual power of the jth generator unit in the ith power plant The amount allocated to the τth monthly period; p ω represents the probability of the ωth daily stage in the current month, ω∈τ means that the daily stage ω belongs to the monthly stage τ; Ω i represents the set of generator sets of the i-th power plant; represents the active output of the jth generator set in the ωth day stage; represents the carbon emission intensity of the jth generator unit in the power plant; represents the preset carbon quota of the ith power plant in the τth monthly period; represents the carbon quota clearance of the i-th power plant in the ω-day period; VaR H represents the risk value parameter of the annual stage; α represents the confidence level; represents the preset auxiliary variable for the τth monthly period, and represents the net input cost under the monthly period τ and the annual risk value parameter VaR H The left deviation of H,min Represents the minimum conditional VaR parameter allowed for the annual period.

[0058] In annual decision-making, the annual allocation strategy should be considered. This means that the year should be allocated to each month and combined with the monthly decision. In addition, the annual decision should also consider the carbon emission cap limit, that is, the cumulative carbon emissions from electricity production in that year should not exceed the sum of the carbon quotas net input from the annual, monthly and daily carbon systems, and at the same time, the conditional risk value constraints of the annual stage need to be met.

[0059] The default annual electricity cost parameter λ of the power plant HE and the preset annual carbon quota cost parameter μ HC and the annual electricity consumption of the i-th power plant and annual carbon quota The annual carbon-electricity joint decision-making model of the power plant is input for processing, and the annual carbon-electricity joint decision-making model outputs the carbon quota of the i-th power plant after processing. Allocation to the τth monthly period and the annual power generation of the jth generator unit in the i-th power plant Allocation to the τth monthly period The data is then input into the power plant's monthly carbon-electricity joint decision-making model for processing.

[0060] The monthly carbon-electricity joint decision-making model of power plants is as follows:

[0061]

[0062] in, represents the input cost of the power plant in the τth monthly period in the current year; and They represent the preset electricity input price and carbon quota output price of the ith power plant in the τth monthly period; p ω Represents the probability of the ω-th day stage in the current month; represents the input cost of the power plant at the ωth day stage in the current month; Ω i represents the set of generator sets of the i-th power plant; and represents the preset electricity price parameter and the preset carbon quota price parameter of the power plant in the τth monthly period; and They respectively represent the preset carbon quota of the ith power plant in the τth monthly period and the preset electricity quantity of the jth generating unit in the ith power plant in the τth monthly period.

[0063] The monthly carbon-electricity joint decision-making model of the power plant meets the following constraints:

[0064]

[0065]

[0066] in, and Represents the monthly carbon quota of the i-th power plant The allocation to the ω-th daily period and the monthly electricity consumption of the j-th generator unit in the i-th power plant The amount allocated to the ω-day stage; and Represents the annual carbon quota of the i-th power plant The allocation to the τth monthly period and the annual power of the jth generator unit in the ith power plant Allocation to the τth monthly period; Represents the preset auxiliary variable of the ωth daily stage, representing the net input cost under the daily stage ω and monthly risk value parameter VaR M Left skewness of the variable; VaR M represents the monthly risk value parameter; α represents the confidence level; R M,min represents the minimum conditional VaR parameter allowed in the monthly period; It represents the input cost of the power plant in the ωth day period in the current month.

[0067] The default power price parameter of the power plant in the τth monthly period is and preset carbon quota cost parameters and the carbon quota of the i-th power plant Allocation to the τth monthly period and the annual electricity consumption of the jth generator unit in the i-th power plant Allocation to the τth monthly period The monthly carbon-electricity joint decision-making model is used to process the monthly carbon-electricity joint decision-making model, which then outputs the monthly carbon quota of the i-th power plant. Allocation to the ω-day stage and the monthly power of the jth generator unit in the i-th power plant Allocation to the ω-day stage The data is then input into the power plant's daily carbon-electricity joint decision-making model for processing.

[0068] The carbon-electricity monthly forward price parameters include the preset electricity price parameters of the power plant in the τth monthly period and preset carbon quota cost parameters

[0069] In the monthly stage, the power plant needs to use the result of the annual allocation to the monthly as the boundary condition to determine the preset monthly electricity and monthly carbon quota in the monthly electricity and carbon system. In addition, it is also necessary to decide how to further allocate the monthly preset amount to the daily strategy. In the monthly decision, the power plant receives the annual electricity allocation result and the carbon quota allocation result, and then uses this as the boundary to determine the signing amount of the monthly contract, and further allocates the annual allocation to the month and the signing amount of the monthly contract to the daily stage, that is, the annual contract is allocated to the month, and the monthly contract is allocated to the daily stage.

[0070] The daily carbon-electricity joint decision-making model of the power plant is as follows:

[0071]

[0072] in, represents the input cost of the power plant at the daily stage; p ω Represents the probability of the ω-th day stage in the current month; and represents the preset electricity input price and carbon quota output price of the i-th power plant in the ω-day period; Ω i represents the set of generator sets of the i-th power plant; and They represent the clearing electricity of the j-th generating unit and the r-th renewable energy unit of the power plant in the ω-th daily period respectively; and They represent the electricity cost parameter and carbon quota cost parameter of the i-th power plant in the ω-th day stage respectively; and They represent the operating costs of the j-th generating unit and the r-th renewable energy unit of the power plant respectively; and They represent the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth daily period respectively; It represents the carbon quota clearance of the i-th power plant in the ω-th daily period.

[0073] The daily carbon-electricity joint decision-making model of the power plant meets the following constraints:

[0074]

[0075] in, and They represent the power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period, and They represent the upper and lower limits of the power cost parameter of the j-th generator unit in the power plant at the ω-th day stage, and They represent the upper and lower limits of the power cost parameters of the r-th renewable energy unit of the power plant in the ω-th daily stage; and They represent the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day stage, and They represent the upper and lower limits of the input cost parameters of the carbon quota of the ith power plant in the ωth day period, and They represent the upper and lower limits of the output cost parameters of the carbon quota of the ith power plant in the ωth daily period; and Represents the monthly carbon quota of the i-th power plant The allocation to the ω-th daily period and the monthly electricity consumption of the j-th generator unit in the i-th power plant The amount allocated to the ω-day stage; and They represent the carbon quota input and output clearance of the i-th power plant in the ω-th daily period respectively; represents the carbon emission intensity of the jth generator unit in the power plant; and They respectively represent the power parameter set of the power system and the carbon quota parameter set of the carbon system.

[0076] The monthly carbon quota of the i-th power plant Allocation to the ω-day stage and the monthly power of the jth generator unit in the i-th power plant Allocation to the ω-day stage The daily carbon-electricity joint decision-making model of the power plant is input for processing. After processing, the daily carbon-electricity joint decision-making model outputs the power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth daily stage. and And the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day period and

[0077] The carbon quota cost parameters of the power plant include the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day stage: and The power cost parameters of the power plant include the power cost parameters of the jth generator unit and the rth renewable energy unit in the ωth day stage: and

[0078] Step 3) Establish a clearing model for the carbon-electricity system, as follows:

[0079]

[0080] in, and They represent the power parameter set of the power system and the carbon quota parameter set of the carbon system respectively; and They represent the power cost parameter and clearing amount of the d-th power load in the ω-th day stage respectively; and They represent the power cost parameters of the j-th generator unit and the r-th renewable energy unit of the power plant in the ω-th daily stage respectively; and They represent the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period respectively; Ω n represents the set of units and loads at the nth node in the carbon-electric system, j / r / d∈Ω n Indicates that the unit / load is located at node n, Θ n Represents the upstream node set of the nth node; B nm represents the inductance of the line between the nth node and the mth node; and Represent the phase angles of the nth node and the mth node respectively; represents the electricity cost parameter of the i-th power plant in the ω-th day stage; represents the maximum power flow through the line between the nth node and the mth node; and denote the maximum and minimum dual variables of the line power flow between the nth node and the mth node in the ωth daily period, respectively; and They represent the maximum active power output of the jth generator set and the rth renewable energy unit in the ωth day period, It represents the maximum power consumption of the dth power load in the ωth daily period; and denote the maximum and minimum dual variables of the active power output of the j-th generator unit in the power plant at the ω-th day, and denote the maximum and minimum dual variables of the active power output of the rth renewable energy unit in the power plant at the ωth daily stage, and They represent the maximum and minimum dual variables of the power consumption of the d-th power load in the ω-th daily period; Indicates the maximum phase angle amplitude of the nth node; and They represent the maximum and minimum dual variables of the phase angle amplitude of the nth node in the ωth day stage respectively; and They represent the phase angle and its dual variable of the first node in the ω-th day stage respectively; and They represent the carbon quota input cost parameter and output cost parameter of other power plants o in the ω-day period respectively; and They represent the carbon quota input and output clearance of other power plants o in the ω-day period respectively; and They represent the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th daily stage respectively; and They represent the carbon quota input and output clearance of the i-th power plant in the ω-th daily period respectively; The dual variable representing the carbon quota clearance amount in the ω-th day period; and They represent the maximum carbon quota input and output of the ith power plant, and They represent the maximum and minimum dual variables of the carbon quota input of the ith power plant in the ωth day period, and They represent the maximum and minimum dual variables of the carbon quota output of the ith power plant in the ωth daily period; and denote the maximum carbon quota input and output of other power plants o, and are the maximum and minimum dual variables representing the maximum carbon quota input of other power plants o, and They represent the maximum and minimum dual variables of the maximum carbon quota output of other power plant o respectively; the variables after the colon are the dual variables of the equations before their respective colons.

[0081] The power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period are and The input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th daily period and The maximum active power output of the jth generator set in the ωth daily period and the maximum power consumption of the dth power load in the ωth day period Input into the carbon-electricity system clearing model for processing, and the carbon-electricity system clearing model outputs the electricity cost parameter of the i-th power plant in the ω-day stage after processing The power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period and The dual variable of carbon quota clearance in the ωth day and the carbon quota input and output clearance of the i-th power plant in the ω-day period and

[0082] The carbon quota cost parameter of the carbon system is the dual variable of the carbon quota clearance amount in the ωth day stage The carbon quota clearance of the carbon system is the carbon quota input clearance of the i-th power plant in the ω-th daily period. and output clearance The power cost parameter of the power system is the power cost parameter of the i-th power plant in the ω-th day stage The power output of the power system is the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period. and

[0083] The daily power generation of renewable energy is the maximum active power output of the jth generator set in the ωth daily stage. The daily power load is the maximum power consumption of the dth power load in the ωth daily stage.

[0084] Step 4) Input the monthly forward carbon-electricity cost parameters, renewable energy generation and power load under the typical scenario of the carbon-electricity system into the annual carbon-electricity joint decision model, the monthly carbon-electricity joint decision model, the daily carbon-electricity joint decision model and the carbon-electricity system clearing model for processing, and use the alternating random dual dynamic programming algorithm to solve. After the daily carbon-electricity joint decision model is processed, the carbon quota cost parameters and power cost parameters of the power plant are output and then input into the carbon-electricity system clearing model for processing. After the carbon-electricity system clearing model is processed, the carbon quota cost parameters and carbon quota clearing amount of the carbon system and the power cost parameters and power clearing amount of the power system are output, and then the output of the power plant is optimized and dispatched to realize the multi-stage risk dispatch of the power plant. The cost can be measured in electricity or carbon quota.

[0085] The alternating stochastic dual dynamic programming algorithm is used to solve the problem. Specifically, the nonlinear terms in the daily carbon-electricity joint decision-making model and the clearing model are linearized. Then, the optimization theory KKT conditions are used to solve the problem in the forward process, and the strong duality theory SDT conditions are used to solve the problem in the backward process.

[0086] In the model of the present invention, due to the nonlinear terms in the objective functions of the daily carbon-electricity joint decision-making model and the clearing model, the double-layer daily decision-making problem and huge scenes, which makes this problem a large-scale nonlinear optimization problem that is difficult to solve directly.

[0087] Traditional stochastic dual dynamic programming (SDDP) is solved through a two-stage process of forward and backward. In the forward process, the uncertainty information is sampled to generate test scenarios, and the decision variables of each stage are calculated according to the test scenarios, and the statistical upper bound of the first stage problem is calculated; in the backward process, the optimal cutting plane that approximates the value function of each stage is generated, and the cutting plane is continuously added to the forward problem. However, the traditional stochastic dual dynamic programming problem requires that the problem of each stage must be a linear model. Since this model is a large-scale nonlinear optimization problem, the traditional stochastic dual dynamic programming algorithm is not suitable for solving this nonlinear model.

[0088] To this end, the method of the present invention proposes an alternating stochastic dual dynamic programming (A-SDDP) algorithm. First, the nonlinear terms need to be linearized, including the linearization of the bilinear terms of the objective function and the conversion of the two-layer problem into a single-layer problem. There are two methods to convert a two-layer problem into a single-layer problem, one is the optimization theory KKT condition, and the other is the strong duality theory SDT condition. The KKT condition can obtain an exact solution, but it will introduce binary variables, which is difficult to solve in multi-stage stochastic optimization, while SDT will introduce bilinear terms. Alternating stochastic dual dynamic programming obtains an exact solution by using KKT in the forward process, and uses SDT conditions in the backward process. The method is as follows:

[0089] First, the model is linearized. The nonlinear terms of the model include two parts, namely the nonlinear terms in the objective function of the daily carbon-electricity joint decision-making model and the nonlinear terms introduced in the clearing model (together forming a two-layer problem). and According to the strong duality theory, it can be equivalently transformed into a linear constraint; for the nonlinear term (two-layer problem) introduced by the clearing model, the strong duality theory SDT and KKT conditions are used to replace and The problem is that the third-stage model consisting of two models can be transformed into a single-layer model as shown below:

[0090]

[0091]

[0092] st(15)-(23)

[0093]

[0094]

[0095] In the following formula:

[0096]

[0097] There is a bilinear term in the SDT constraint It can be linearized by McCormick's envelope method and thus can be Similarly, and The bilinear term can also be expressed as and Therefore, the third stage problem can be transformed into a linear programming as follows:

[0098]

[0099] Then, in the forward process, the KKT-constrained optimization problem can be used to obtain the exact solution of the two-layer problem, so that the upper and lower limits required for envelope relaxation can be obtained within an appropriate range. Then, in the backward process, the SDT-constrained optimization problem is solved, and then the optimal cutting plane can be obtained. The other steps are the same as the traditional stochastic dual dynamic programming method. The solution obtained by envelope relaxation in the forward process is a valid solution, that is, the lower bound of the original minimum problem. By continuously introducing cutting planes, this lower bound will continue to rise. The sample average approximation result of the stochastic dual dynamic programming is the statistical upper bound of the original problem. As the upper and lower bounds keep approaching, the problem will eventually converge.

[0100] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages. The application is described according to the flowchart of the method, system and computer program product of the embodiments of the present application.

[0101] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the present invention is intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0102] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of equivalent technologies of the present invention, the present application is also intended to include these modifications and variations.

Claims

1. A multi-stage risk scheduling method for power plants considering the multi-dimensional correlation between carbon and electricity, characterized in that: include: Step 1) Establish a scenario tree as a multi-scale uncertainty scenario tree model of the carbon-electricity system based on the historical data of the carbon-electricity monthly forward cost parameters, renewable energy daily data, and load daily data of the carbon-electricity system, and determine the carbon-electricity monthly forward cost parameters, daily power generation of renewable energy, and daily power load of the carbon-electricity system based on the multi-scale uncertainty scenario tree model; Step 2) Establishing an annual carbon-electricity joint decision-making model, a monthly carbon-electricity joint decision-making model and a daily carbon-electricity joint decision-making model for the power plant; Step 3) Establishing a clearing model for the carbon-electricity system; Step 4) The carbon-electricity monthly forward cost parameters of the carbon-electricity system, the power generation and power load of renewable energy are input into the annual carbon-electricity joint decision-making model, the monthly carbon-electricity joint decision-making model, the daily carbon-electricity joint decision-making model and the carbon-electricity system clearing model for processing, and solved using the alternating random dual dynamic programming algorithm. After processing, the daily carbon-electricity joint decision-making model outputs the carbon quota cost parameters and power cost parameters of the power plant, which are then input into the carbon-electricity system clearing model for processing. After processing, the carbon-electricity system clearing model outputs the carbon quota cost parameters and carbon quota clearing amount of the carbon system and the power cost parameters and power clearing amount of the power system, thereby optimizing the output of the power plant and realizing multi-stage risk scheduling of the power plant.

2. The multi-stage risk scheduling method for power plants considering the multi-dimensional correlation between carbon and electricity according to claim 1 is characterized by: In step 2), the annual carbon-electricity joint decision-making model of the power plant is as follows: in, represents the annual input cost of the power plant; and They represent the annual electricity input cost and annual carbon quota output cost of the ith power plant respectively; p τ represents the probability of the τth monthly phase in the current year; represents the input cost of the power plant in the τth monthly period in the current year; λ HE and μ HC They represent the preset annual electricity cost parameter and the preset annual carbon quota cost parameter of the power plant respectively; and They represent the annual electricity and annual carbon quota of the i-th power plant respectively; The annual carbon-electricity joint decision-making model of the power plant meets the following constraints: in, and Represents the annual carbon quota of the i-th power plant The allocation to the τth monthly period and the annual power of the jth generator unit in the ith power plant The amount allocated to the τth monthly period; p ω Indicates the probability of the ω-th day stage in the current month; Ω i represents the set of generator sets of the i-th power plant; represents the active output of the jth generator set in the ωth day stage; represents the carbon emission intensity of the jth generator unit in the power plant; represents the preset carbon quota of the ith power plant in the τth monthly period; represents the carbon quota clearance of the i-th power plant in the ω-day period; VaR H represents the risk value parameter of the annual stage; α represents the confidence level; represents the preset auxiliary variable in the τth monthly period; R H,min represents the minimum conditional VaR parameter allowed during the annual period; The default annual electricity cost parameter λ of the power plant HE and the preset annual carbon quota cost parameter μ HC and the annual electricity consumption of the i-th power plant and annual carbon quota The annual carbon-electricity joint decision-making model of the power plant is input for processing, and the annual carbon-electricity joint decision-making model outputs the carbon quota of the i-th power plant after processing. Allocation to the τth monthly period and the annual power generation of the jth generator unit in the i-th power plant Allocation to the τth monthly period The data is then input into the power plant's monthly carbon-electricity joint decision-making model for processing.

3. The multi-stage risk scheduling method for power plants considering the multi-dimensional correlation between carbon and electricity according to claim 1 is characterized by: In step 2), the monthly carbon-electricity joint decision-making model of the power plant is as follows: in, represents the input cost of the power plant in the τth monthly period in the current year; and They represent the preset electricity input price and carbon quota output price of the ith power plant in the τth monthly period; p ω Represents the probability of the ω-th day stage in the current month; represents the input cost of the power plant at the ωth day stage in the current month; Ω i represents the set of generator sets of the i-th power plant; and represents the preset electricity price parameter and the preset carbon quota price parameter of the power plant in the τth monthly period; and They represent the preset carbon quota of the ith power plant in the τth monthly period and the preset electricity quantity of the jth generating unit in the ith power plant in the τth monthly period respectively; The monthly carbon-electricity joint decision-making model of the power plant meets the following constraints: in, and Represents the monthly carbon quota of the i-th power plant The allocation to the ω-th daily period and the monthly electricity consumption of the j-th generator unit in the i-th power plant The amount allocated to the ω-day stage; and Represents the annual carbon quota of the i-th power plant The allocation to the τth monthly period and the annual power of the jth generator unit in the ith power plant Allocation to the τth monthly period; Represents the preset auxiliary variable of the ω-day stage; VaR M represents the monthly risk value parameter; α represents the confidence level; R M,min represents the minimum conditional VaR parameter allowed in the monthly period; represents the input cost of the power plant at the ω-th day stage in the current month; The default power price parameter of the power plant in the τth monthly period is and preset carbon quota cost parameters and the carbon quota of the i-th power plant Allocation to the τth monthly period and the annual electricity consumption of the jth generator unit in the i-th power plant Allocation to the τth monthly period The monthly carbon-electricity joint decision-making model is used to process the monthly carbon-electricity joint decision-making model, which then outputs the monthly carbon quota of the i-th power plant. Allocation to the ω-day stage and the monthly power of the jth generator unit in the i-th power plant Allocation to the ω-day stage Then it is input into the daily carbon-electricity joint decision-making model of the power plant for processing; The carbon-electricity monthly forward price parameters include the preset electricity price parameters of the power plant in the τth monthly period and preset carbon quota cost parameters 4. The multi-stage risk scheduling method for power plants considering the multi-dimensional correlation between carbon and electricity according to claim 1 is characterized by: In step 2), the daily carbon-electricity joint decision-making model of the power plant is as follows: in, represents the input cost of the power plant at the daily stage; p ω Represents the probability of the ω-th day stage in the current month; and represents the preset electricity input price and carbon quota output price of the i-th power plant in the ω-day period; Ω i represents the set of generator sets of the i-th power plant; and They represent the clearing electricity of the j-th generating unit and the r-th renewable energy unit of the power plant in the ω-th daily period respectively; and They represent the electricity cost parameter and carbon quota cost parameter of the i-th power plant in the ω-th day stage respectively; and They represent the operating costs of the j-th generating unit and the r-th renewable energy unit of the power plant respectively; and They represent the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth daily period respectively; represents the carbon quota clearance of the i-th power plant in the ω-th daily period; The daily carbon-electricity joint decision-making model of the power plant meets the following constraints: in, and They represent the power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period, and They represent the upper and lower limits of the power cost parameter of the j-th generator unit in the power plant at the ω-th day stage, and They represent the upper and lower limits of the power cost parameters of the r-th renewable energy unit of the power plant in the ω-th daily stage; and They represent the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day stage, and They represent the upper and lower limits of the input cost parameters of the carbon quota of the ith power plant in the ωth day period, and They represent the upper and lower limits of the output cost parameters of the carbon quota of the ith power plant in the ωth daily period; and Represents the monthly carbon quota of the i-th power plant The allocation to the ω-th daily period and the monthly electricity consumption of the j-th generator unit in the i-th power plant The amount allocated to the ω-day stage; and They represent the carbon quota input and output clearance of the i-th power plant in the ω-th daily period respectively; represents the carbon emission intensity of the jth generator unit in the power plant; and They represent the power parameter set of the power system and the carbon quota parameter set of the carbon system respectively; The monthly carbon quota of the i-th power plant Allocation to the ω-day stage and the monthly power of the jth generator unit in the i-th power plant Allocation to the ω-day stage The daily carbon-electricity joint decision-making model of the power plant is input for processing. After processing, the daily carbon-electricity joint decision-making model outputs the power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth daily stage. and And the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day period and The carbon quota cost parameters of the power plant include the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th day stage: and The power cost parameters of the power plant include the power cost parameters of the jth generator unit and the rth renewable energy unit in the ωth day stage: and 5. The multi-stage risk scheduling method for power plants considering the multi-dimensional correlation between carbon and electricity according to claim 1 is characterized by: In step 3), the clearing model of the carbon-electricity system is as follows: in, and They represent the power parameter set of the power system and the carbon quota parameter set of the carbon system respectively; and They represent the power cost parameter and clearing amount of the d-th power load in the ω-th day stage respectively; and They represent the power cost parameters of the j-th generator unit and the r-th renewable energy unit of the power plant in the ω-th daily stage respectively; and They represent the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period respectively; Ω n represents the set of units and loads at the nth node in the carbon-electric system, Θ n Represents the upstream node set of the nth node; B nm represents the inductance of the line between the nth node and the mth node; and Represent the phase angles of the nth node and the mth node respectively; represents the electricity cost parameter of the i-th power plant in the ω-th day stage; represents the maximum power flow through the line between the nth node and the mth node; and denote the maximum and minimum dual variables of the line power flow between the nth node and the mth node in the ωth daily period, respectively; and They represent the maximum active power output of the jth generator set and the rth renewable energy unit in the ωth day period, It represents the maximum power consumption of the dth power load in the ωth daily period; and denote the maximum and minimum dual variables of the active power output of the j-th generator unit in the power plant at the ω-th day, and denote the maximum and minimum dual variables of the active power output of the rth renewable energy unit in the power plant at the ωth daily stage, and They represent the maximum and minimum dual variables of the power consumption of the d-th power load in the ω-th daily period; Indicates the maximum phase angle amplitude of the nth node; and They represent the maximum and minimum dual variables of the phase angle amplitude of the nth node in the ωth day stage respectively; and They represent the phase angle and its dual variable of the first node in the ω-th day stage respectively; and They represent the carbon quota input cost parameter and output cost parameter of other power plants o in the ω-day period respectively; and They represent the carbon quota input and output clearance of other power plants o in the ω-day period respectively; and They represent the input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th daily stage respectively; and They represent the carbon quota input and output clearance of the i-th power plant in the ω-th daily period respectively; The dual variable representing the carbon quota clearance amount in the ω-th day period; and They represent the maximum carbon quota input and output of the ith power plant, and They represent the maximum and minimum dual variables of the carbon quota input of the ith power plant in the ωth day period, and They represent the maximum and minimum dual variables of the carbon quota output of the ith power plant in the ωth daily period; and denote the maximum carbon quota input and output of other power plants o, and are the maximum and minimum dual variables representing the maximum carbon quota input of other power plants o, and are the maximum and minimum dual variables representing the maximum carbon quota output of other power plants o, respectively; The power cost parameters of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period are and The input cost parameter and output cost parameter of the carbon quota of the i-th power plant in the ω-th daily period and The maximum active power output of the jth generator set in the ωth daily period and the maximum power consumption of the dth power load in the ωth day period Input into the carbon-electricity system clearing model for processing, and the carbon-electricity system clearing model outputs the electricity cost parameter of the i-th power plant in the ω-day stage after processing The power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period and The dual variable of carbon quota clearance in the ωth day and the carbon quota input and output clearance of the i-th power plant in the ω-day period and The carbon quota cost parameter of the carbon system is the dual variable of the carbon quota clearance amount in the ωth day stage The carbon quota clearance of the carbon system is the carbon quota input clearance of the i-th power plant in the ω-th daily period. and output clearance The power cost parameter of the power system is the power cost parameter of the i-th power plant in the ω-th day stage The power output of the power system is the power generation of the jth generator unit and the rth renewable energy unit of the power plant in the ωth day period. and The daily power generation of renewable energy is the maximum active power output of the jth generator set in the ωth daily stage. The daily power load is the maximum power consumption of the dth power load in the ωth daily stage.

6. The multi-stage risk scheduling method for power plants considering the multi-dimensional correlation between carbon and electricity according to claim 1 is characterized by: In the step 4), an alternating stochastic dual dynamic programming algorithm is used for solving the problem. Specifically, the nonlinear terms in the daily carbon-electricity joint decision-making model and the clearing model are linearized, and then the optimization theory KKT conditions are used for solving the problem in the forward process, and the strong dual theory SDT conditions are used for solving the problem in the backward process.

7. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having program data stored thereon, characterized in that: When the program data is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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