Optimal allocation method and system based on contract electric quantity and carbon quota

Abstract

The invention discloses an optimal distribution method and system based on contract electric quantity and carbon quota, and belongs to the technical field of electric power. The method comprises a multi-time-scale production benefit model and an environmental benefit model. Data preprocessing: determining parameters of the model, including electricity price, renewable energy generating capacity, thermal power generation capacity, contract electric quantity, carbon market and other related parameters; based on a multi-time scale production benefit model, a monthly, daily and real-time user production benefit model and a user electricity purchase cost model are respectively established; based on a multi-time-scale environmental benefit model, monthly and daily user environmental benefit models are respectively established, and a free carbon quota optimal distribution mode is adopted; the model can help a high-energy-consumption user to formulate an optimal contract electric quantity and carbon quota decomposition strategy, the energy consumption cost is reduced, and the economic benefit is improved.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and specifically relates to an optimization allocation method and system based on contracted electricity and carbon quota. Background Art

[0002] As the construction of the carbon market becomes increasingly complete, many market players are currently trading in both the carbon market and the electricity market. The addition of the carbon market requires market players to consider carbon costs in the production process, further promoting the low-carbon development of the industry. At the same time, in the electricity market, after corporate users sign an annual contract for electricity, they decompose the annual contract for electricity into months, days or shorter time scales according to actual production needs. In addition to purchasing medium- and long-term contract electricity, corporate users also need to purchase additional electricity to meet actual needs. This part of electricity is mainly determined by the electricity prices at different time scales. The cost of purchasing electricity directly affects the economic benefits of corporate users, so users need to design a reasonable multi-time scale contract electricity decomposition plan to reduce electricity costs. Summary of the invention

[0003] Purpose of the invention: Under the coordinated development of the electricity-carbon market, seasonal fluctuations in renewable energy output and regional economic development will affect changes in carbon prices. Many users participate in carbon market and electricity market transactions at the same time. When formulating multi-time scale contract electricity and carbon quota decomposition plans, users need to consider both electricity costs and carbon costs. An optimization allocation method based on contract electricity and carbon quotas is proposed to help high-energy-consuming users formulate optimal contract electricity and carbon quota decomposition strategies, reduce energy costs and improve economic benefits. The present invention also provides an optimization allocation system based on contract electricity and carbon quotas.

[0004] Technical solution: In the first aspect, the present invention provides an optimization allocation method based on contracted electricity and carbon quota, comprising the following steps:

[0005] Data preprocessing, determining the model parameters including electricity price, renewable energy power generation, thermal power generation, contracted power, carbon market and other related parameters.

[0006] Based on the multi-time scale production benefit model, monthly, daily and real-time user production revenue model and user electricity purchase cost model were established respectively;

[0007] Based on the multi-time scale environmental benefit model, monthly and daily user environmental benefit models were established respectively, and the free carbon quota allocation method was optimized allocation.

[0008] Further, the input data of the model is determined, the steps include:

[0009] S11, considering that high-energy-consuming enterprise users are steel users, the selected steel is rebar, and the steel consumption, profit margin, annual contract total electricity, monthly steel price, and daily steel price are determined.

[0010] S12, considering the regional resource endowment, choose wind power and solar power as renewable energy, and determine the power generation costs of wind power and solar power.

[0011] S13, considering the low carbon emission characteristics of renewable energy, assuming the carbon emission coefficient of renewable energy is 0, determine the carbon emission coefficient of thermal power, monthly and daily carbon prices.

[0012] Furthermore, the production benefit model includes a user production income model and a user electricity purchase cost model.

[0013] Furthermore, the user production revenue model includes:

[0014] S21, the calculation formula for user monthly production revenue is as follows:

[0015]

[0016] The constraints satisfied by the production revenue model are as follows:

[0017]

[0018] In the formula, The production income of the user in month y; The power consumption of the user in month y; is the marginal income obtained by the user in month y, that is, the economic benefit brought by consuming 1 kWh of electricity; α is the electricity consumed per unit of commodity produced; is the market price of the product in month y; ε is the profit margin of the user selling the product; is the maximum value of the user's electricity consumption in month y; It is the minimum value of user electricity consumption in month y.

[0019] S22, the calculation formula for user daily production revenue is as follows:

[0020]

[0021] The constraints satisfied by the production revenue model are as follows:

[0022]

[0023] In the formula, is the production income of the user on day d; is the electricity consumption of the user on day d; is the marginal income obtained by the user on day d; is the market price of the product on day d; is the maximum value of the user's electricity consumption on day d; is the minimum value of the user's electricity consumption on day d.

[0024] S23, the calculation formula for user real-time production revenue is as follows:

[0025]

[0026] The constraints satisfied by the production revenue model are as follows:

[0027]

[0028] In the formula, is the production income of the user at time h; is the power consumption of the user at time h; is the marginal income obtained by the user at time h; is the market price of the product at time h; is the maximum value of the user's electricity consumption at the hth time; It is the minimum value of the user's power consumption at the hth hour.

[0029] Furthermore, the user electricity purchase cost model includes:

[0030] S31, the monthly electricity purchase cost of the user is as follows:

[0031]

[0032] The constraints satisfied by the electricity purchase cost are as follows:

[0033]

[0034] In the formula, The electricity purchase cost of the user in month y; is the clearing price of electricity in the electricity market in month y; Allocate the contracted electricity for the user in the yth month; It is the negotiated electricity price when the electricity buyer and seller sign an annual contract. A year is 12 months; The total amount of annual contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user in the yth month.

[0035] S32, the user's daily electricity purchase cost is as follows:

[0036]

[0037] The constraints satisfied by the electricity purchase cost are as follows:

[0038]

[0039] In the formula, is the electricity purchase cost of the user on day d; is the clearing price of electricity in the electricity market on day d; Allocate the contractual electricity for the user on day d; It is the negotiated electricity price when the electricity buyer and seller sign a monthly contract. Month is the number of days in a month; The total amount of monthly contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user on day d.

[0040] S33, the user's real-time electricity purchase cost is as follows:

[0041]

[0042] The constraints satisfied by the electricity purchase cost are as follows:

[0043]

[0044] In the formula, is the electricity purchase cost of the user at time h; is the clearing price of the electricity market at time h; The contracted electricity quantity allocated to the user at time h; It is the negotiated electricity price when the electricity buyer and seller sign a daily contract. Day is the number of hours per day; The total amount of daily contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user at the hth time.

[0045] Furthermore, in S31, the calculation formula of the monthly electricity price is:

[0046]

[0047] In the formula, a coal and b coal are the linear coefficient and quadratic coefficient of marginal fuel cost; C re,i is the electricity generation cost of renewable energy generator i; is the thermal power generation in month y; is the power generation of renewable energy generator i in month y.

[0048] Furthermore, establishing an environmental benefit model includes:

[0049] S41, the initial carbon quota model is:

[0050] Q user,free =P user,average E(1-α)

[0051] In the formula, Q user,free is the user's initial carbon emissions; E is the user's reference carbon emission intensity; P user,average is the average annual electricity consumption of users, and its value is the average electricity consumption of users in the previous 2 to 4 years; α is the reduction rate of carbon emission intensity of users.

[0052] S42, the monthly environmental benefit model obtained by high energy consumption enterprise users in the carbon market is:

[0053]

[0054] The constraints that the carbon quota meets are as follows:

[0055]

[0056] In the formula, The environmental benefits obtained by users in month y; is the carbon price in month y; y is the carbon emission factor of month y; Allocate free carbon quotas for the user in month y; The minimum free carbon quota allocated to the user in month y; The maximum free carbon quota allocated to the user in month y. S42, the daily environmental benefit model obtained by high energy-consuming enterprise users in the carbon market is:

[0057]

[0058] The constraints that the carbon quota meets are as follows:

[0059]

[0060] In the formula, is the environmental benefit obtained by the user on day d; is the carbon price on day d; Allocate free carbon quota to the user on day d, ω d is the carbon emission factor on day d; The minimum free carbon quota allocated to the user on day d; The maximum free carbon quota allocated to the user on day d. S43, the total amount of carbon quota owned at the end of the year is calculated as:

[0061]

[0062] Q user =Q user,free +Q user,hold

[0063] In the formula, C carbon,penaltyCarbon penalties that market entities need to pay if they fail to fulfill their obligations at the end of the year; is the penalty coefficient; p c,max is the maximum carbon price, Q total The actual total carbon emissions of the market entity during the compliance period; Q user Q is the total amount of carbon quotas owned by market entities at the end of the year; user,hold It is the net holdings of carbon quotas bought and sold by market entities.

[0064] Furthermore, in S42, the calculation formula of the monthly electricity price is:

[0065]

[0066] In the formula, β is the coefficient of the first-order term; is the actual value of carbon price in month y; is the trend cycle component of carbon price in month y; is the random component of carbon price in month y.

[0067] Furthermore, in S42, the calculation formula for the monthly carbon emission factor taking into account the renewable energy power generation is:

[0068]

[0069] In the formula, ω y is the carbon emission factor of month y; e is the carbon emission coefficient of thermal power generators; is the power generation of thermal power generators in month y; is the electricity generated by renewable energy generator i in month y.

[0070] Furthermore, the decomposition model of contracted electricity and carbon quota is as follows:

[0071] S51, the objective function with year-month as the time scale is

[0072]

[0073] In the formula, ε 1 and ε 2 is the benefit weight coefficient.

[0074] S52, the month-day decomposition model is based on the year-month decomposition model, which is further divided into a decomposition model with a monthly time scale. Its objective function is

[0075]

[0076] S53, the day-hour decomposition model is further divided into a decomposition model with hourly time scale based on the month-day decomposition model, and its objective function is:

[0077]

[0078] On the other hand, the present invention also provides a system for establishing a contract electricity quantity and carbon quota decomposition model, comprising:

[0079] Data processing module: used to determine the parameters of the model, including electricity price, renewable energy power generation, thermal power generation, contracted power, carbon market and other related parameters;

[0080] Multi-time scale production benefit module: used to establish monthly, daily and real-time user production benefit models and user electricity purchase cost models;

[0081] Multi-time scale environmental benefit module: used to establish monthly and daily user environmental benefit models, and the free carbon quota allocation method is optimized allocation.

[0082] In a third aspect, the present invention further provides a device for constructing a contract electricity quantity and carbon quota decomposition model, comprising:

[0083] one or more processors;

[0084] A memory for storing one or more programs;

[0085] When the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0086] In a fourth aspect, a storage medium comprising computer executable instructions is provided, wherein when the computer executable instructions are executed by a processor, the processor executes the above method.

[0087] Beneficial effects: Compared with the prior art, the advantages of the present invention are:

[0088] The present invention constructs a production benefit model based on multiple time scales through multiple parameters, and establishes monthly, daily and real-time user production benefit models and user electricity purchase cost models respectively; based on the multi-time scale environmental benefit model, monthly and daily user environmental benefit models are established respectively, and a contract electricity and carbon quota decomposition model is constructed on the basis of the above multiple models. Through the decomposition model, the environmental effect of emission reduction and carbon reduction can be achieved by adopting an optimized allocation of free carbon quota allocation method in the electricity-carbon coupling market environment. Therefore, by optimizing the allocation of free carbon quotas, users can control carbon emissions more flexibly and adjust them according to market demand and price changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0090] Figure 1 is a flow chart of a model building method according to Embodiment 1 of the present invention;

[0091] Figure 2 is a schematic diagram of the structure of a model building system according to Embodiment 2 of the present invention;

[0092] Figure 3 is a schematic diagram of the structure of a model building device according to Embodiment 3 of the present invention;

[0093] Figure 4 This is a schematic diagram of the decomposition results of the annual contract electricity in a certain area of ​​Guangdong obtained in Example 1 of the present invention

[0094] Figure 5 This is a schematic diagram of the decomposition results of the monthly contract electricity volume in a certain area of ​​Guangdong according to Example 1 of the present invention;

[0095] Figure 6 This is a schematic diagram of the decomposition results of the daily contract electricity quantity in a certain area of ​​Guangdong according to Example 1 of the present invention;

[0096] Figure 7 This is a schematic diagram of the decomposition results of the annual free carbon quota in a certain area of ​​Guangdong according to Example 1 of the present invention;

[0097] Figure 8 This is a schematic diagram of the decomposition results of the monthly free carbon quota in a certain area of ​​Guangdong described in Example 1 of the present invention. DETAILED DESCRIPTION

[0098] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0099] Example 1

[0100] like Figure 1 As shown, an embodiment of the present invention provides a method for decomposing contracted electricity and carbon quota, comprising the following steps:

[0101] S1, determine the parameters of the model including electricity price, renewable energy power generation, thermal power generation, contracted power and carbon market and other related parameters;

[0102] S11, considering that high-energy-consuming enterprise users are steel users, the selected steel is rebar, and the steel consumption, profit margin, annual contract total electricity, monthly steel price, and daily steel price are determined.

[0103] S12, considering the regional resource endowment, choose wind power and solar power as renewable energy, and determine the power generation costs of wind power and solar power.

[0104] S13, considering the low carbon emission characteristics of renewable energy, assuming the carbon emission coefficient of renewable energy is 0, determine the carbon emission coefficient of thermal power, monthly and daily carbon prices.

[0105] S2, based on the multi-time scale production benefit model, established monthly, daily and real-time user production revenue model and user electricity purchase cost model respectively;

[0106] The specific steps include:

[0107] S21, the calculation formula for user monthly production revenue is as follows:

[0108]

[0109] The constraints satisfied by the production revenue model are as follows:

[0110]

[0111] In the formula, The production income of the user in month y; The power consumption of the user in month y; is the marginal income obtained by the user in month y, that is, the economic benefit brought by consuming 1 kWh of electricity; α is the electricity consumed per unit of commodity produced; is the market price of the product in month y; ε is the profit margin of the user selling the product; is the maximum value of the user's electricity consumption in month y; It is the minimum value of user electricity consumption in month y.

[0112] S22, the calculation formula for user daily production revenue is as follows:

[0113]

[0114] The constraints satisfied by the production revenue model are as follows:

[0115]

[0116] In the formula, is the production income of the user on day d; is the electricity consumption of the user on day d; is the marginal income obtained by the user on day d; is the market price of the product on day d; is the maximum value of the user's electricity consumption on day d; is the minimum value of the user's electricity consumption on day d.

[0117] S23, the calculation formula for user real-time production revenue is as follows:

[0118]

[0119] The constraints satisfied by the production revenue model are as follows:

[0120]

[0121] In the formula, is the production income of the user at time h; is the power consumption of the user at time h; is the marginal income obtained by the user at time h; is the market price of the product at time h; is the maximum value of the user's electricity consumption at the hth time; It is the minimum value of the user's power consumption at the hth hour.

[0122] S24, the monthly electricity purchase cost of the user is as follows:

[0123]

[0124] The constraints satisfied by the electricity purchase cost are as follows:

[0125]

[0126] In the formula, The electricity purchase cost of the user in month y; is the clearing price of electricity in the electricity market in month y; Allocate the contracted electricity for the user in the yth month; It is the negotiated electricity price when the electricity buyer and seller sign an annual contract. A year is 12 months; The total amount of annual contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user in the yth month.

[0127] S25, the user's daily electricity purchase cost is as follows:

[0128]

[0129] The constraints satisfied by the electricity purchase cost are as follows:

[0130]

[0131] In the formula, is the electricity purchase cost of the user on day d; is the clearing price of electricity in the electricity market on day d; Allocate the contractual electricity for the user on day d; It is the negotiated electricity price when the electricity buyer and seller sign a monthly contract. Month is the number of days in a month; The total amount of monthly contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user on day d.

[0132] S26, the user's real-time electricity purchase cost is as follows:

[0133]

[0134] The constraints satisfied by the electricity purchase cost are as follows:

[0135]

[0136] In the formula, is the electricity purchase cost of the user at time h; is the clearing price of the electricity market at time h; The contracted electricity quantity allocated to the user at time h; It is the negotiated electricity price when the electricity buyer and seller sign a daily contract. Day is the number of hours per day; The total amount of daily contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user at the hth time.

[0137] S3, based on the multi-time scale environmental benefit model, established monthly and daily user environmental benefit models respectively, and the free carbon quota allocation method was optimized allocation;

[0138] The specific steps are:

[0139] S31, the initial carbon quota model is:

[0140] Q user,free =P user,average E(1-α)

[0141] In the formula, Q user,free is the user's initial carbon emissions; E is the user's reference carbon emission intensity; P user,average is the average annual electricity consumption of users, and its value is the average electricity consumption of users in the previous 2 to 4 years; α is the reduction rate of carbon emission intensity of users.

[0142] S32, the monthly environmental benefit model obtained by high energy consumption enterprise users in the carbon market is:

[0143]

[0144] The constraints that the carbon quota meets are as follows:

[0145]

[0146] In the formula, The environmental benefits obtained by users in month y; is the carbon price in month y; y is the carbon emission factor of month y; Allocate free carbon quotas for the user in month y; The minimum free carbon quota allocated to the user in month y; The maximum amount of free carbon quota allocated to the user in month y.

[0147] S33, the daily environmental benefit model obtained by high energy consumption enterprise users in the carbon market is:

[0148]

[0149] The constraints that the carbon quota meets are as follows:

[0150]

[0151] In the formula, is the environmental benefit obtained by the user on day d; is the carbon price on day d; Allocate free carbon quota to the user on day d, ω d is the carbon emission factor on day d; The minimum free carbon quota allocated to the user on day d; The maximum amount of free carbon quota allocated to the user on day d.

[0152] S34, the total amount of carbon quotas at the end of the year is calculated as follows:

[0153]

[0154] Q user =Q user,free +Q user,hold

[0155] In the formula, C carbon,penalty Carbon penalties that market entities need to pay if they fail to fulfill their obligations at the end of the year; is the penalty coefficient; p c,max is the maximum carbon price, Q total The actual total carbon emissions of the market entity during the compliance period; Q user Q is the total amount of carbon quotas owned by market entities at the end of the year; user,hold It is the net holdings of carbon quotas bought and sold by market entities.

[0156] In this embodiment, the decomposition model of contracted electricity and carbon quota is as follows:

[0157] S51, the objective function with year-month as the time scale is:

[0158]

[0159] In the formula, ε 1 and ε 2 is the benefit weight coefficient.

[0160] S52, the month-day decomposition model is based on the year-month decomposition model, which is further divided into a decomposition model with a monthly time scale. Its objective function is:

[0161]

[0162] S53, the day-hour decomposition model is further divided into a decomposition model with hourly time scale based on the month-day decomposition model, and its objective function is:

[0163]

[0164] In this embodiment, the training and testing processes of the above-mentioned model are not described any more, and the conventional methods used by those skilled in the art can be used.

[0165] The decomposition of contracted electricity and carbon quotas on the user side needs to consider multiple factors such as the user's electricity consumption behavior and energy structure. Unlike the power generation side, the decomposition of contracted electricity and carbon quotas on the user side requires more flexible and personalized solutions to meet the electricity needs and economic benefits of different users. By in-depth research on the models and methods of contracted electricity and carbon quota decomposition on the user side, the needs of users can be better met and the efficiency and stability of the electricity market can be improved. The present invention mainly introduces the multi-time scale decomposition of contracted electricity and carbon quotas in an electricity-carbon coupled market environment.

[0166] In the electricity-carbon coupling market environment, the use of optimized allocation of free carbon quotas can achieve the environmental effect of reducing emissions and carbon emissions. By optimizing the allocation of free carbon quotas, users can control carbon emissions more flexibly. And adjust according to market demand and price changes. The principles of free carbon quota allocation and contract electricity allocation follow the principle that when the market price is high, users allocate more quotas and contract electricity, and reduce the purchase of shortfalls in the market. When the price is low, users allocate less free quotas and contract electricity, and choose to go to the market to purchase shortfalls.

[0167] In order to more clearly illustrate the technical details of this solution, this embodiment provides the following specific cases:

[0168] 1. Example parameter setting

[0169] (1) Enterprise user data

[0170] The high-energy-consuming enterprise users selected in this application are steel users, and the products they produce are rebar. This paper assumes that the steel consumption α is 800kwh / t. The profit margin ε is set to 0.2, and the annual contract electricity is 21.98 billion kWh.

[0171] (2) Energy data

[0172] The renewable energy selected in this application is wind energy and hydropower. It is assumed that the cost of wind power generation is 325 yuan / MWh and the cost of hydropower generation is 300 yuan / MWh. The carbon emission coefficient of renewable energy is 0 and the carbon emission coefficient of thermal power is 0.875tCO 2 / MWh.

[0173] 2. Analysis of the decomposition results of market players’ contracted electricity and carbon quotas based on multiple time scales

[0174] This application compares and analyzes the differences in the decomposition results of contracted electricity and carbon quotas of market entities in the electricity-carbon market. The scenario considered is a contracted electricity and carbon quota decomposition model based on multiple time scales in an electricity-carbon coupled market environment, and the free carbon quota allocation method is optimized allocation.

[0175] 2.1 Analysis of the decomposition results of multi-time scale contract electricity and carbon quota in the steel industry

[0176] The annual, monthly and daily contract electricity decomposition results for a certain area in Guangdong are as follows: Figure 4 , Figure 5 , Figure 6 As shown, the following conclusions are drawn:

[0177] (1) The relationship between the contracted electricity allocation method and electricity prices. Steel users allocate as much contracted electricity as possible during periods of high electricity prices. Users use contracted electricity to meet their electricity needs, thereby reducing the need to purchase electricity from the electricity market, reducing sensitivity to high electricity prices, and thus reducing electricity purchase costs. During periods of low electricity prices, steel users will reduce the allocation of contracted electricity and instead purchase the required electricity from the electricity market.

[0178] In summary, the decomposition of contracted electricity is closely related to electricity prices. The contracted electricity allocation method can flexibly adjust the allocation of contracted electricity according to the fluctuation of electricity prices to minimize the cost of purchasing electricity.

[0179] (2) Characteristics of seasonal distribution of contracted electricity. The power generation of renewable energy has seasonal variations, and the power generation of renewable energy has a significant impact on the supply and demand relationship and price fluctuations in the electricity market. When the power generation of renewable energy is large, the electricity price is low, and when the power generation of renewable energy is small, the electricity price is high. Therefore, the electricity price also shows seasonal changes. The distribution of contracted electricity is closely related to the fluctuation of electricity prices. The contracted electricity distribution method of steel users will be greatly affected by the endowment of renewable energy resources. The specific distribution method will vary according to the seasonal variation characteristics of renewable energy resources in the region, so as to maximize the use of renewable energy to reduce electricity costs.

[0180] The annual and monthly free carbon quota decomposition results of Guangdong are as follows: Figure 7 , Figure 8 As shown, the following conclusions are drawn:

[0181] (1) The relationship between the free carbon quota allocation method and carbon price. The free carbon quota allocation method is closely related to the carbon price. Steel users allocate as many free carbon quotas as possible during periods of high carbon prices to reduce the purchase of quotas from the carbon market. During periods of low carbon prices, steel users will reduce the allocation of free carbon quotas and instead purchase the required quotas from the carbon market. The free carbon quota allocation method can flexibly adjust the allocation amount of free carbon quotas according to the fluctuation of carbon prices to minimize carbon emission costs.

[0182] (2) Seasonal distribution characteristics of free carbon quota allocation. Carbon price is related to the seasonal variation of renewable energy. When the power generation of renewable energy is large, the carbon price is low, and when the power generation of renewable energy is small, the carbon price is high. Therefore, the carbon price shows a seasonal variation pattern. The annual free carbon quota allocation method for steel users will be greatly affected by the endowment of renewable energy resources. The specific allocation method will vary according to the seasonal variation characteristics of renewable energy resources in the region, so as to maximize the use of renewable energy to reduce carbon emission costs.

[0183] Example 2

[0184] like Figure 2 As shown, a system for establishing a contract electricity and carbon quota decomposition model includes:

[0185] Data processing module: used to determine the parameters of the model, including electricity price, renewable energy power generation, thermal power generation, contracted power, carbon market and other related parameters;

[0186] Multi-time scale production benefit module: used to establish monthly, daily and real-time user production benefit models and user electricity purchase cost models;

[0187] Multi-time scale environmental benefit module: used to establish monthly and daily user environmental benefit models, and the free carbon quota allocation method is optimized allocation.

[0188] The data processing module includes:

[0189] Data unit for high energy consumption enterprise users: The steel material is rebar, steel consumption, profit margin, annual contract total electricity, monthly steel price, and daily steel price are determined;

[0190] Renewable energy data unit: determine the type of renewable energy and the cost of generating electricity from renewable energy;

[0191] Carbon emission data unit: determine the carbon emission coefficient of renewable energy, the carbon emission coefficient of thermal power, and collect monthly and daily carbon prices. ;

[0192] The multi-timescale production efficiency module includes:

[0193] Monthly production revenue unit of users:

[0194]

[0195] The constraints satisfied by the production revenue model are as follows:

[0196]

[0197] In the formula, The production income of the user in month y; The power consumption of the user in month y; is the marginal income obtained by the user in month y, that is, the economic benefit brought by consuming 1 kWh of electricity; α is the electricity consumed per unit of commodity produced; is the market price of the product in month y; ε is the profit margin of the user selling the product; is the maximum value of the user's electricity consumption in month y; It is the minimum value of user electricity consumption in month y.

[0198] User daily production revenue unit:

[0199]

[0200] The constraints satisfied by the production revenue model are as follows:

[0201]

[0202] In the formula, is the production income of the user on day d; is the electricity consumption of the user on day d; is the marginal income obtained by the user on day d; is the market price of the product on day d; is the maximum value of the user's electricity consumption on day d; is the minimum value of the user's electricity consumption on day d.

[0203] Monthly electricity purchase cost unit for users:

[0204]

[0205] The constraints satisfied by the electricity purchase cost are as follows:

[0206]

[0207] In the formula, The electricity purchase cost of the user in month y; is the clearing price of electricity in the electricity market in month y; Allocate the contracted electricity for the user in the yth month; It is the negotiated electricity price when the electricity buyer and seller sign an annual contract. A year is 12 months; The total amount of annual contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user in the yth month.

[0208] The user's daily electricity purchase cost unit:

[0209]

[0210] The constraints satisfied by the electricity purchase cost are as follows:

[0211]

[0212] In the formula, is the electricity purchase cost of the user on day d; is the clearing price of electricity in the electricity market on day d; Allocate the contractual electricity for the user on day d; It is the negotiated electricity price when the electricity buyer and seller sign a monthly contract. Month is the number of days in a month; The total amount of monthly contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user on day d.

[0213] The user's real-time electricity purchase cost unit:

[0214]

[0215] The constraints satisfied by the electricity purchase cost are as follows:

[0216]

[0217] In the formula, is the electricity purchase cost of the user at time h; is the clearing price of the electricity market at time h; The contracted electricity quantity allocated to the user at time h; It is the negotiated electricity price when the electricity buyer and seller sign a daily contract. Day is the number of hours per day; The total amount of daily contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user at the hth time.

[0218] The multi-time scale environmental benefits module includes:

[0219] Initial carbon quota unit:

[0220] Q user,free =P user,average E(1-α)

[0221] In the formula, Q user,free is the user's initial carbon emissions; E is the user's reference carbon emission intensity; P user，average is the average annual electricity consumption of users, and its value is the average electricity consumption of users in the previous 2 to 4 years; α is the reduction rate of carbon emission intensity of users.

[0222] Monthly environmental benefit units obtained by high energy-consuming corporate users in the carbon market:

[0223]

[0224] The constraints that the carbon quota meets are as follows:

[0225]

[0226] In the formula, The environmental benefits obtained by users in month y; is the carbon price in month y; y is the carbon emission factor of month y; Allocate free carbon quotas for the user in month y; The minimum free carbon quota allocated to the user in month y; The maximum amount of free carbon quota allocated to the user in month y.

[0227] Daily environmental benefit units obtained by high energy-consuming corporate users in the carbon market:

[0228]

[0229] The constraints that the carbon quota meets are as follows:

[0230]

[0231] In the formula, is the environmental benefit obtained by the user on day d; is the carbon price on day d; Allocate free carbon quota to the user on day d, ω d is the carbon emission factor on day d; The minimum free carbon quota allocated to the user on day d; The maximum amount of free carbon quota allocated to the user on day d.

[0232] Total carbon quota units owned at the end of the year:

[0233]

[0234] Q user =Q user,free +Q user,hold

[0235] In the formula, C carbon,penalty Carbon penalties that market entities need to pay if they fail to fulfill their obligations at the end of the year; is the penalty coefficient; p c,max is the maximum carbon price, Q total The actual total carbon emissions of the market entity during the compliance period; Q user Q is the total amount of carbon quotas owned by market entities at the end of the year; user,hold It is the net holdings of carbon quotas bought and sold by market entities.

[0236] Example 3

[0237] like Figure 3 As shown, a schematic diagram of the structure of a device for establishing a contract electricity quantity and carbon quota decomposition model provided by the present invention, this embodiment provides services for the implementation of the establishment method of the above-mentioned embodiment 1 of the present invention, and the model building method in the above-mentioned embodiment 1 can be configured; Figure 3 A block diagram of an exemplary device 12 suitable for implementing embodiments of the present invention is shown. Figure 3 The device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0238] like Figure 3 As shown, the device 12 is in the form of a general-purpose computing device, and the components of the device 12 include but are not limited to: one or more processors or processing units 16, system memory 28; and a bus 18 connecting different system components including: system memory 28 and processing unit 16.

[0239] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0240] Device 12 typically includes a variety of computer system readable media; such media may be any available media that can be accessed by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

[0241] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, usually called a "hard drive"). Although Figure 3 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0242] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0243] The device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the device 12, and / or any device that enables the device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. Figure 3 As shown, the network adapter 20 communicates with other modules of the device 12 via the bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0244] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the contract electricity and carbon quota decomposition model method provided in the embodiment of the present invention.

[0245] The above equipment provides a reference for establishing the contract electricity and carbon quota decomposition model.

[0246] Example 4

[0247] In this embodiment, a storage medium containing computer executable instructions is provided. When the computer executable instructions are executed by a computer processor, they are used to execute the method of embodiment 1.

[0248] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0249] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0250] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0251] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0252] Of course, the storage medium containing computer executable instructions provided in an embodiment of the present invention is not limited to the above method operations, but can also execute related operations in the multi-time scale contract electricity and carbon quota decomposition of market entities in the electricity-carbon coupling market provided in any embodiment of the present invention.

[0253] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0254] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An optimization allocation method based on contracted electricity and carbon quota, characterized in that: The method includes: Collect relevant data from various aspects, including: parameter data of high-energy-consuming enterprise users, parameter data of renewable energy power generation costs, and parameter data of carbon emissions based on thermal power generation; Constructing a multi-time-scale production benefit model based on the high-energy-consuming enterprise user parameter data, wherein the multi-time-scale production benefit model includes a monthly, daily and real-time user production benefit model and a monthly, daily and real-time user electricity purchase cost model; Constructing a multi-time scale environmental benefit model based on the user parameter data of the high energy consumption enterprise, the power generation cost parameter data of renewable energy, and the carbon emission parameter data based on thermal power generation, wherein the multi-time scale environmental benefit model includes a monthly and daily user environmental benefit model; According to the monthly, daily and real-time user production revenue models, the monthly, daily and real-time user electricity purchase cost models and the monthly and daily user environmental benefit models, a year-month decomposition model, a month-day decomposition model and a day-time decomposition model are constructed, and the optimal allocation method of carbon quotas is obtained according to the above decomposition models.

2. The optimization allocation method based on contracted electricity and carbon quota according to claim 1 is characterized in that: The collection of various related data includes: Considering that the high-energy-consuming enterprise users are steel users, the selected steel is rebar, and the steel consumption, profit margin, annual contract total electricity, monthly steel price, and daily steel price are determined; Considering the regional resource endowment, wind and solar energy are selected as renewable energy, and the power generation costs of wind and solar energy are determined; Taking into account the low carbon emission characteristics of renewable energy, the carbon emission coefficient of renewable energy is set to zero, thereby determining the carbon emission coefficient of thermal power, monthly and daily carbon prices.

3. The optimization allocation method based on contracted electricity and carbon quota according to claim 2 is characterized in that: The monthly user production revenue model includes the representation of the user's monthly production revenue and corresponding constraints; Among them, the formula for expressing the user's monthly production income is: The corresponding constraints are expressed as: in, The production income of the user in month y; The power consumption of the user in month y; is the marginal income obtained by the user in month y, that is, the economic benefit brought by consuming 1 kWh of electricity; α is the electricity consumed per unit of commodity produced; is the market price of the product in month y; ε is the profit margin of the user selling the product; is the maximum value of the user's electricity consumption in month y; is the minimum value of the user's electricity consumption in month y; The daily user production revenue model includes the representation of the user's daily production revenue and corresponding constraints; The calculation formula for user daily production revenue is as follows: The corresponding constraints are as follows: In the formula, is the production income of the user on day d; is the electricity consumption of the user on day d; is the marginal income obtained by the user on day d; is the market price of the product on day d; is the maximum value of the user's electricity consumption on day d; is the minimum value of the user's electricity consumption on day d; The real-time user production revenue model includes the representation of the user's real-time production revenue and corresponding constraints; The calculation formula for the real-time production income of a household is as follows: The corresponding constraints are as follows: In the formula, is the production income of the user at time h; is the power consumption of the user at time h; is the marginal income obtained by the user at time h; is the market price of the product at time h; is the maximum value of the user's electricity consumption at the hth time; It is the minimum value of the user's power consumption at the hth hour.

4. The optimization allocation method based on contracted electricity and carbon quota according to claim 3 is characterized in that: The monthly user electricity purchase cost model includes the monthly electricity purchase cost representation of the user and the constraints satisfied by the electricity purchase cost, specifically: The monthly electricity purchase cost for users is as follows: The constraints satisfied by the electricity purchase cost are as follows: In the formula, The electricity purchase cost of the user in month y; is the clearing price of electricity in the electricity market in month y; Allocate the contracted electricity for the user in the yth month; It is the negotiated electricity price when the electricity buyer and seller sign an annual contract, and a year is 12 months; The total amount of annual contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user in the yth month; The daily user electricity purchase cost model includes the user's daily electricity purchase cost representation and the constraints that the electricity purchase cost satisfies, specifically: The daily electricity purchase cost of users is as follows: The constraints satisfied by the electricity purchase cost are as follows: In the formula, is the electricity purchase cost of the user on day d; is the clearing price of electricity in the electricity market on day d; Allocate the contractual electricity for the user on day d; It is the negotiated electricity price when the electricity buyer and seller sign a monthly contract, and month is the number of days in a month; The total amount of monthly contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user on day d; The real-time user electricity purchase cost model includes the user's real-time electricity purchase cost representation and the constraints satisfied by the electricity purchase cost, specifically: The real-time electricity purchase cost of the user is as follows: The constraints satisfied by the electricity purchase cost are as follows: In the formula, is the electricity purchase cost of the user at time h; is the clearing price of the electricity market at time h; The contracted electricity quantity allocated to the user at time h; It is the negotiated electricity price when the electricity buyer and seller sign a daily contract, and day is the number of hours per day; The total amount of daily contracted electricity signed for the user; The minimum value of the contracted electricity allocated to the user at the hth time.

5. The optimization allocation method based on contracted electricity and carbon quota according to claim 4 is characterized in that: The clearing price of the electricity market in month y is It is expressed as: Among them, a coal and b coal are the linear coefficient and quadratic coefficient of marginal fuel cost; C re,i is the electricity generation cost of renewable energy generator i; is the thermal power generation in month y; is the power generation of renewable energy generator i in month y, and n is the total number of renewable energy generators.

6. The optimization allocation method based on contracted electricity and carbon quota according to claim 5 is characterized in that: The multi-time scale environmental benefit model includes an initial carbon quota model, which is expressed as: Q user,free =P user,average E(1-α); (20) In the formula, Q user,free is the user's initial carbon emissions; E is the user's reference carbon emission intensity; P user,average is the average annual electricity consumption of users, and its value is the average electricity consumption of users in the previous 2 to 4 years; α is the reduction rate of carbon emission intensity of users.

7. The optimization allocation method based on contracted electricity and carbon quota according to claim 6 is characterized in that: The monthly user environmental benefit model includes: the monthly environmental benefit model obtained by high-energy-consuming enterprise users in the carbon market and the corresponding constraints, specifically: The monthly environmental benefit model obtained by energy-consuming corporate users in the carbon market is: The constraints that the carbon quota meets are as follows: In the formula, The environmental benefits obtained by users in month y; is the carbon price in month y; y is the carbon emission factor of month y; Allocate free carbon quotas for the user in month y; The minimum free carbon quota allocated to the user in month y; The maximum amount of free carbon quota allocated to the user in month y; The daily user environmental benefit model includes: the daily environmental benefit model obtained by high-energy-consuming enterprise users in the carbon market and the corresponding constraints, specifically: The daily environmental benefit model obtained by high energy-consuming enterprise users in the carbon market is: The constraints that the carbon quota meets are as follows: In the formula, is the environmental benefit obtained by the user on day d; is the carbon price on day d; Allocate free carbon quota to the user on day d, ω d is the carbon emission factor on day d; The minimum free carbon quota allocated to the user on day d; The maximum amount of free carbon quota allocated to the user on day d.

8. The optimization allocation method based on contracted electricity and carbon quota according to claim 7 is characterized in that: The carbon price in month y It is expressed as; In the formula, β is the coefficient of the first-order term; is the actual value of carbon price in month y; is the trend cycle component of carbon price in month y; is the random component of carbon price in month y.

9. The optimization allocation method based on contracted electricity and carbon quota according to claim 7 is characterized in that: The carbon emission factor ω of the yth month y It is expressed as: Where, e is the carbon emission coefficient of thermal power generators; is the power generation of thermal power generators in month y; is the electricity generated by renewable energy generator i in month y.

10. The optimization allocation method based on contracted electricity and carbon quota according to claim 7 is characterized in that: The year-month decomposition model includes an objective function with year-month as the time scale, which is expressed as: Among them, ε1 and ε2 are benefit weight coefficients, C carbon,penalty is the total amount of carbon quotas owned at the end of the year; The month-day decomposition model is based on the year-month decomposition model, which is further divided into a decomposition model with a month as the time scale, and its objective function is: The day-time decomposition model is based on the month-day decomposition model, which is further divided into a decomposition model with hours as the time scale, and its objective function is:

11. The optimization allocation method based on contracted electricity and carbon quota according to claim 10 is characterized in that: The total amount of carbon quotas owned at the end of the year C carbon,penalty It is expressed as: Q user =Q user,free +Q user,hold ;(33) In the formula, C carbon,penalty Carbon penalties that market entities need to pay if they fail to fulfill their obligations at the end of the year; is the penalty coefficient; p c,max is the maximum carbon price, Q total The actual total carbon emissions of the market entity during the compliance period; Q user The total amount of carbon quotas owned by market entities at the end of the year; Q user,hold It is the net holdings of carbon quotas bought and sold by market entities.

12. An optimization allocation system based on contracted electricity and carbon quota, characterized in that: The system includes: The data collection module is used to collect various relevant data, including: parameter data of high-energy-consuming enterprise users, parameter data of renewable energy power generation costs, and parameter data of carbon emissions based on thermal power generation; A production benefit model construction module is used to construct a multi-time scale production benefit model based on the user parameter data of the high energy consumption enterprise, and the multi-time scale production benefit model includes a monthly, daily and real-time user production income model and a monthly, daily and real-time user electricity purchase cost model; An environmental benefit model construction module is used to construct a multi-time scale environmental benefit model based on the user parameter data of the high-energy-consuming enterprise, the power generation cost parameter data of renewable energy, and the carbon emission parameter data based on thermal power generation. The multi-time scale environmental benefit model includes monthly and daily user environmental benefit models; The decomposition model construction module is used to construct a year-month decomposition model, a month-day decomposition model and a day-time decomposition model according to the monthly, daily and real-time user production revenue model, the monthly, daily and real-time user electricity purchase cost model and the monthly and daily user environmental benefit model, and obtain the optimal allocation method of carbon quotas according to the above decomposition models.

13. An optimization allocation device based on contracted electricity and carbon quota, characterized in that: include: one or more processors; A memory for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 11.

14. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a processor, the processor performs the method according to any one of claims 1 to 11.

Images