Park controllable resource response optimization method and device considering carbon emission reduction benefits under differential working conditions, electronic equipment and storage medium

Abstract

The invention discloses a park controllable resource response optimization method and device considering carbon emission reduction benefits under differential working conditions, electronic equipment and a storage medium, and relates to the technical field of resource control optimization. According to the method, a park adjustable resource response optimization model considering the carbon emission under the differentiated working conditions, a carbon emission measurement and calculation model considering the differentiated working conditions and a response optimization model of adjustable resources of a high-energy-consumption park are constructed. In the carbon emission measurement and calculation model, the high-energy-consumption park is divided into four areas including a building area, a traffic area, a waste treatment area and an industrial area, and the carbon emission of each area is measured and calculated based on an emission factor method. And in the adjustable resource response optimization model, considering the carbon emission of each region of the high-energy-consumption park, and constructing a day-ahead and intra-day scheduling optimization model, thereby realizing optimal scheduling of adjustable resources of the park.

Description

TECHNICAL FIELD

[0001] The application discloses a park controllable resource response optimization method and device considering carbon emission reduction benefits under differentiated working conditions, electronic equipment and storage medium, and relates to the technical field of resource regulation and optimization. BACKGROUND

[0002] With high proportion of new energy access to power grid, power grid faces the challenge of new energy output uncertainty and volatility. Due to the lack of flexible regulation resources in regional power grid, it can only rely on public thermal power peak shaving, and cannot fully meet the regulation needs of future development of power grid in terms of scale and time scale. Therefore, in the face of the dual challenges of large-scale clean energy access and the increasing load demand for the smooth operation of power grid, it is crucial to tap the carbon emission reduction potential and adjustable resources within high energy consumption parks.

[0003] Currently, the research on carbon emission reduction estimation within high energy consumption parks usually uses a unified static carbon emission factor to account for the carbon emissions of the entire park. These studies regard the park as a whole or simply divide the park into zones. The existing research uses a unified static carbon emission factor to account for the carbon emissions of the park, and only makes a simple division or regards the park as a whole, resulting in two core problems:

[0004] Carbon emission estimation and emission reduction decision-making are disconnected: the unified factor cannot reflect the actual emission characteristics of different zones (divided according to carbon emission subjects), and simple zoning or overall accounting can hide the energy consumption differences and emission reduction potential of each subject, resulting in insufficient accuracy of carbon emission data, and further leading to a disconnect between the emission reduction targets (such as carbon emission control) based on this and actual production needs, making it difficult to achieve targeted carbon emission regulation and control.

[0005] Resource regulation potential is not fully tapped: as the zones are not refined according to the subjects, the regulation characteristics of each adjustable resource (such as equipment of different enterprises, energy storage, etc.) cannot be accurately identified, making it difficult to develop fine day-ahead plans (such as start-stop, scheduling) and intra-day rolling correction strategies when dealing with new energy output fluctuations, and the flexibility and economy of resource regulation are insufficient, which not only fails to efficiently respond to the demand of power balance and reserve capacity of the power grid, but also makes it difficult to balance the multi-objective optimization of comprehensive energy cost, energy efficiency and carbon emissions. SUMMARY

[0006] The present application provides a park controllable resource response optimization method and device considering carbon emission reduction benefits under differentiated working conditions, electronic equipment and storage medium, which solves the problems of the prior art.

[0007] In a first aspect, the present application provides a park controllable resource response optimization method considering carbon emission reduction benefits under differentiated working conditions, which includes:

[0008] S1, the high energy consumption park is divided into zones according to the subject of carbon emission, and the carbon emission of each zone is calculated by the emission factor method, and the total carbon emission of the high energy consumption park includes the sum of the carbon emission of each zone of the high energy consumption park;

[0009] S2, according to the minimum comprehensive energy cost, the maximum comprehensive energy efficiency and the minimum total carbon emission as the day-ahead stage objective function, the minimum cost as the optimization target of the day-ahead stage, a response optimization model of the adjustable resources of the high energy consumption park is established;

[0010] S3, the response optimization model is solved, and the adjustable resources of the park are optimized according to the solving result.

[0011] In some implementations, the zones of the high energy consumption park include: building area, traffic area, waste treatment area, industrial area; the total carbon emission of the high energy consumption park in S1 includes the carbon emission of the building area, the carbon emission of the traffic area, the carbon emission of the waste treatment area and the carbon emission of the industrial area:

[0012] S11, the carbon emission of the building area E bldg As shown in formula (2):

[0013]

[0014] Wherein, m1 is the energy type consumed by the building area; C bldg,i is the carbon emission coefficient of the i-th energy of the building area; EF bldg,i is the consumption of the i-th energy of the building area; B bldg is the carbon recycling rate of the building area;

[0015] S12, the carbon emission of the traffic area E trans As shown in formula (3):

[0016]

[0017] Wherein, m2 is the energy type consumed by the traffic area; C trans,i is the carbon emission coefficient of the i-th energy of the traffic area; EF trans,i is the consumption of the i-th energy of the traffic area; B trans is the carbon recycling rate of the traffic area;

[0018] S13, the carbon emission of the waste treatment area E deal As shown in formula (4):

[0019] E deal = E s + E l (4)

[0020] E s is the carbon emission of the solid waste treatment process; E l is the carbon emission of the wastewater treatment process;

[0021] S14, the carbon emission of the industrial zone, is classified and calculated by the isolated network operation condition, the external energy purchase condition, and the grid-connected operation condition, as shown in equation (7):

[0022] E ind = E energy + E steel + E AL (7)

[0023] E ind is the total carbon emission of the industrial zone; E energy is the carbon emission of the energy consumption part; E steel is the carbon emission of the steel enterprise production process; E AL is the carbon emission of the electrolytic aluminum enterprise production process.

[0024] In some implementations, S13 includes:

[0025] S131, the carbon emission E s of the solid waste treatment process is calculated as shown in equation (5):

[0026]

[0027] where m3 is the type of solid waste treatment; EF s,i is the treatment amount of the i-th type of solid waste; K s,i is the carbon content ratio in the i-th type of solid waste; is the conversion coefficient of carbon converted into carbon dioxide;

[0028] S132, the carbon emission E l of the wastewater treatment process is calculated as shown in equation (6):

[0029]

[0030] where m4 is the type of wastewater treatment; D i is the chemical oxygen demand (COD) content of the i-th type of wastewater; D is the COD content of surface water; V i is the discharge amount of the i-th type of wastewater; is the conversion coefficient of carbon converted into carbon dioxide.

[0031] In some implementations, in S2, the response optimization model of the controllable resources of the high-energy consumption park includes a day-ahead scheduling optimization model and an intra-day scheduling optimization model, which include:

[0032] In S21, the optimization objective of the day-ahead scheduling optimization model includes minimum comprehensive energy consumption cost, maximum comprehensive energy efficiency, and minimum total carbon emission.

[0033] In S22, the optimization objective of the intra-day scheduling optimization model includes minimum adjustment cost.

[0034] In some implementations, in S22, the optimization objective of the intra-day scheduling optimization model includes minimum adjustment cost, as shown in formulas (51) and (52):

[0035]

[0036] In the formula, c θ is the adjustment cost of the device θ unit power change; ΔP θ,t is the power change value of the device θ at time t; is the output of the device θ at time t in the intra-day stage; is the planned output of the device θ at time t in the day-ahead.

[0037] In some implementations, in S3, the following steps are included:

[0038] In S31, according to the multi-time scale optimization model, the intra-day scheduling optimization model is processed, and the production plan of the park is optimized according to the processing result;

[0039] In S32, according to the park enterprise demand and the overall operation demand of the park, the constraint conditions of the response optimization model of the controllable resources of the high-energy consumption park are determined through line constraints, adjustable resource constraints, and production constraints;

[0040] In S33, according to the objective function, the de-dimensioning processing is performed through the descending semi-gradient membership function, and the multi-objective model is converted into a single-objective model through the weighting function.

[0041] In a second aspect, an embodiment of the present application provides a park controllable resource response optimization device considering carbon emission reduction benefits under different working conditions, which includes:

[0042] A measurement model construction module is configured to perform partition processing on a high-energy consumption park according to a carbon emission subject, measure the carbon emission of each partition through an emission factor method, and calculate the total carbon emission of the high-energy consumption park as the sum of the carbon emissions of each partition.

[0043] An optimization model construction module is configured to construct a response optimization model of the controllable resources in the high-energy consumption park by adjusting a minimum cost as an optimization target in an intra-day stage according to a minimum comprehensive energy consumption cost, a maximum comprehensive energy efficiency and a minimum total carbon emission as a day-ahead stage objective function.

[0044] A model solution module is configured to solve the response optimization model and optimize the response of the controllable resources in the park according to a solution result.

[0045] In some implementations, the sub-zones of the high-energy consumption park include a building zone, a transportation zone, a waste treatment zone and an industrial zone; and the total carbon emission of the high-energy consumption park in the calculation model construction module includes carbon emissions of the building zone, the transportation zone, the waste treatment zone and the industrial zone.

[0046] A building zone carbon emission calculation unit is configured to calculate the carbon emission E bldg As shown in formula (2):

[0047]

[0048] wherein m1 is a type of energy consumed by the building zone; C bldg,i is a carbon emission coefficient of the i-th type of energy in the building zone; EF bldg,i is a consumption amount of the i-th type of energy in the building zone; B bldg is a carbon recycling rate of the building zone.

[0049] A transportation zone carbon emission calculation unit is configured to calculate the carbon emission E trans As shown in formula (3):

[0050]

[0051] wherein m2 is a type of energy consumed by the transportation zone; C trans,i is a carbon emission coefficient of the i-th type of energy in the transportation zone; EF trans,i is a consumption amount of the i-th type of energy in the transportation zone; B trans is a carbon recycling rate of the transportation zone.

[0052] A waste treatment zone carbon emission calculation unit is configured to calculate the carbon emission E deal As shown in formula (4):

[0053] E deal = E s + E l (4)

[0054] wherein E s is a carbon emission of a solid waste treatment process; E l is a carbon emission of a waste water treatment process.

[0055] The waste treatment area carbon emission calculation unit is used for calculating the carbon emission of the industrial area through isolated network operation conditions, external energy purchase conditions and grid-connected operation conditions, as shown in formula (7):

[0056] E ind = E energy + E steel + E AL (7)

[0057] In the formula, E ind is the total carbon emission of the industrial area; E energy is the carbon emission of the energy consumption part; E steel is the carbon emission of the steel enterprise production process; and E AL is the carbon emission of the electrolytic aluminum enterprise production process.

[0058] In some implementations, the waste treatment area carbon emission calculation unit includes:

[0059] A solid waste calculation sub-unit is used for calculating the carbon emission E s of the solid waste treatment process, as shown in formula (5):

[0060]

[0061] In the formula, m3 is the type of solid waste treatment; EF s,i is the treatment amount of the i-th type of solid waste; K s,i is the carbon content ratio in the i-th type of solid waste; K is the conversion coefficient of carbon into carbon dioxide; and K

[0062] A wastewater calculation sub-unit is used for calculating the carbon emission E l of the wastewater treatment process, as shown in formula (6):

[0063]

[0064] In the formula, m4 is the type of wastewater treatment; D i is the chemical oxygen demand (COD) content of the i-th type of wastewater; D is the COD content of surface water; V i is the discharge amount of the i-th type of wastewater; and K is the conversion coefficient of carbon into carbon dioxide.

[0065] In some implementations, in the optimization model construction module, the response optimization model of the high-energy consumption park controllable resources includes a day-ahead scheduling optimization model and an intra-day scheduling optimization model, including:

[0066] The day-ahead scheduling unit, an optimization objective of the day-ahead scheduling optimization model includes minimum comprehensive energy consumption cost, maximum comprehensive energy efficiency, and minimum total carbon emission;

[0067] The intra-day scheduling unit, an optimization objective of the intra-day scheduling optimization model includes minimum adjustment cost.

[0068] In some implementations, in the intra-day scheduling unit, the optimization objective of the intra-day scheduling optimization model includes minimum adjustment cost, as shown in formulas (51) and (52):

[0069]

[0070] In the formula, c θ is the adjustment cost of the device θ unit power change; ΔP θ,t is the power change value of the device θ at time t; is the output of the device θ at time t in the intra-day stage; is the planned output of the device θ at time t in the day-ahead.

[0071] In some implementations, the model solving module includes:

[0072] The scheduling optimization unit is configured to process the intra-day scheduling optimization model according to the multi-time scale optimization model, and perform scheduling optimization on the park production plan according to a processing result.

[0073] The conditional optimization unit is configured to determine constraint conditions of the response optimization model of the controllable resource of the high-energy consumption park according to park enterprise demand and park overall operation demand, through line constraints, adjustable resource constraints, and production constraints.

[0074] The model decomposition unit is configured to perform dimensionless processing through a descending semi-gradient membership function according to the objective function, and convert the multi-objective model into a single-objective model through a weighting function.

[0075] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method in the first aspect.

[0076] In a fourth aspect, an embodiment of the present application provides a computer storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method in the first aspect.

[0077] One or more embodiments of the present application can at least bring the following beneficial effects:

[0078] The beneficial effects of the present application are: the method of the present application partitions the park according to the carbon emission subject, and based on the emission factor method, the carbon emission of each region is calculated, which breaks through the limitation of unified static factor and simple partitioning: by refining the partition and accurately calculating the emission of each region, the energy consumption difference and emission reduction potential of different subjects can be truly reflected, and accurate data support can be provided for carbon emission reduction targets (such as minimum total carbon emission), so that the emission reduction decision is closely combined with the actual production of each region, and targeted carbon emission reduction regulation is realized.

[0079] The method of the present application constructs a response optimization model of the day-ahead stage (minimum comprehensive energy consumption cost, maximum comprehensive energy efficiency, minimum total carbon emission) and the day-ahead stage (minimum regulation cost), combines multi-time scale optimization logic, can make global scheduling plan (such as start-stop, scheduling) of controllable resources based on predicted data in day-ahead, and can dynamically correct based on measured data in day-ahead, realize the refinement and flexibility of resource regulation; at the same time, through the dimensionless of the descent semi-gradient membership function, the multi-objective is converted into a single objective by the weighted function, which ensures that the multi-objective model can be solved efficiently, so that the controllable resources can meet the requirements of power balance, standby capacity and other requirements in the response process, and maximize the comprehensive benefits of resource regulation.

[0080] The present application lays a data foundation by accurate calculation of carbon emission in different regions, and realizes the refinement and synergy of resource regulation by means of multi-stage multi-objective model, which can solve the problems of inaccurate carbon emission calculation and inefficient resource regulation in the background technology, and realize the precision, efficiency and multi-objective collaborative optimization of controllable resource response in high-energy consumption park. BRIEF DESCRIPTION OF DRAWINGS

[0081] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0082] Figure 1 It is a response optimization model framework of a differentiated working condition considering carbon emission reduction benefit of park controllable resource response optimization method provided by the embodiment of the present application. DETAILED DESCRIPTION

[0083] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0084] Embodiment one:

[0085] The park controllable resource response optimization method considering carbon emission reduction benefit under different working conditions provided in this embodiment comprises:

[0086] Firstly, according to S1, the high energy consumption park is divided into building area, traffic area, waste treatment area and industrial area according to the main body of carbon emission of high energy consumption park, and the carbon emission of each area is calculated based on emission factor method (IPCC coefficient method), and a carbon emission calculation model considering different working conditions is constructed.

[0087] The total carbon emission calculation model of the high energy consumption park is as follows:

[0088] E = E bldg +E trans +E deal +E ind (1)

[0089] In the formula, E represents the total carbon emission of the high energy consumption park; E bldg represents the carbon emission of the building area; E trans represents the carbon emission of the traffic area; E deal represents the carbon emission of the waste treatment area; E ind represents the carbon emission of the industrial area.

[0090] Further, the step S1 specifically comprises:

[0091] S11, building area carbon emission calculation

[0092] The carbon emission of the building area mainly comes from the carbon emission generated by the energy consumption for realizing the functions of heating, cooling, ventilation and lighting in the process of using commercial buildings and residential buildings. The carbon emission accounting method of the building area is as follows:

[0093]

[0094] In the formula, m1 is the type of energy consumed in the building area; Cbldg,i is the carbon emission factor of the i-th energy in the building area; EF bldg,i is the consumption of the i-th energy in the building area; B bldg is the carbon recycling rate of the building area.

[0095] S12, carbon emission calculation of the transportation area

[0096] Passenger cars and trucks in the transportation area are the hub of transportation between the park and the outside world. The carbon dioxide generated by the energy consumption in the transportation process is the source of carbon emissions in the transportation area. The carbon emission calculation method of the transportation area is as follows:

[0097]

[0098] In the formula, m2 is the type of energy consumed in the transportation area; C trans,i is the carbon emission factor of the i-th energy in the transportation area; EF trans,i is the consumption of the i-th energy in the transportation area; B trans is the carbon recycling rate of the transportation area.

[0099] S13, carbon emission calculation of the waste disposal area

[0100] The carbon dioxide generated in the process of solid waste and wastewater treatment is also an important source of carbon dioxide emissions in the high-energy consumption park.

[0101] E deal = E s + E l (4)

[0102] In the formula, E s is the carbon emission of the solid waste treatment process; E l is the carbon emission of the wastewater treatment process.

[0103] Further, the S13 step specifically includes:

[0104] S131, solid waste

[0105] The solid waste in the high-energy consumption park includes general solid waste, hazardous waste, etc. The estimation of the carbon dioxide emissions generated by the solid waste is as follows:

[0106]

[0107] In the formula, m3 is the type of solid waste treatment; EF s,i is the treatment amount of the i-th solid waste; K s,i is the carbon content ratio in the i-th solid waste; is the conversion factor of carbon to carbon dioxide.

[0108] S132, wastewater

[0109] The types of wastewater in high energy consumption park include wastewater generated in daily life of residential areas and industrial wastewater, etc. Through the understanding of the degradation process of microorganisms to organic matter in the sewage treatment process, it is found that 87% of the organic matter is degraded into carbon dioxide, so the carbon dioxide emissions generated by wastewater treatment can be calculated according to 87% of the organic carbon source. The formula for estimating the carbon dioxide emissions of wastewater treatment is as follows:

[0110]

[0111] In the formula, m4 is the type of wastewater treatment; D i is the chemical oxygen demand (COD) content of the i-th type of wastewater; D is the COD content of surface water; V i is the discharge amount of the i-th type of wastewater; is the conversion coefficient of carbon into carbon dioxide.

[0112] S14, carbon emission estimation of industrial area

[0113] The different parts of the carbon emission model under different working conditions are concentrated in the energy consumption part of the industrial area. According to the three different working conditions of isolated network operation, external energy purchase and grid-connected operation, the carbon emissions of the industrial area under each working condition are calculated.

[0114] Further, the S14 step specifically includes:

[0115] S141, isolated network operation condition

[0116] The carbon dioxide emissions generated by the industrial area under the isolated network operation condition mainly include the carbon dioxide emissions generated by energy consumption and the carbon dioxide emissions generated by the industrial production process. Among them, the carbon dioxide emissions generated by the energy consumption part come from the energy consumption to meet the electricity and heat load of the enterprises in the park; the carbon emissions of the industrial production part are determined by the production process of the enterprise, which come from the carbon emissions caused by the consumption of production raw materials in the enterprise.

[0117] The total carbon emissions of the industrial area under the isolated network operation condition are calculated by the following formula:

[0118] E ind = E energy + E steel + E AL (7)

[0119] In the formula, E ind is the total carbon emissions of the industrial area; E energy is the carbon emissions of the energy consumption part; E steel is the carbon emissions of the steel enterprise production process; EAL Carbon emissions for the production process of electrolytic aluminum enterprises.

[0120] Further, the step S141 specifically comprises:

[0121] S1411, energy consumption

[0122] The carbon emissions generated by energy consumption are mainly calculated according to the energy consumption in the industrial zone. Referring to the carbon emission coefficient under various types of energy consumption, the carbon dioxide emissions of the industrial zone in each year are accounted. Referring to the carbon emission accounting method, the carbon emission accounting method of the industrial zone is shown as follows:

[0123]

[0124] In the formula, m5 is the type of energy consumed by the industrial zone; EF energy,i is the consumption of the i-th type of energy in the industrial zone; B ind is the carbon recycling rate of the industrial zone.

[0125] S1412, industrial production

[0126] Since the polysilicon production process only involves the carbon emission process caused by the use of electricity, the carbon emission amount is calculated by using the carbon emission model of energy consumption. The carbon emission amount of only two types of enterprises, i.e., steel and electrolytic aluminum, is considered in the industrial production part:

[0127] When the method of the application is applied to a steel enterprise: in the steel production process, the consumption of flux, electrode, raw material, fossil energy (such as coke, coal, etc.) and other raw materials will cause direct carbon emissions. Among them, coke, coal and other substances will be partially converted into by-product coal gas in the production process, and the storage characteristics of the by-product coal gas will cause the time-varying nature of carbon emissions. The carbon emission amount of other raw materials in the production process is mainly related to the process yield, and the total amount is relatively fixed. The specific model is shown as follows:

[0128]

[0129] In the formula, is the carbon emission amount of the steel enterprise at t period; ε coke , are the carbon content coefficients of coke and coal gas, respectively; ε i is the fixed carbon emission coefficient of the i-th fuel-independent process; are the coke consumption and the i-th process output in the t period, respectively; is the demand of process i for coal gas j; is the output of the j-th coal gas.

[0130] In addition, the steel production process also involves the carbon emission amount caused by the consumption of flux, electrode and raw material, and the specific calculation formula is as follows:

[0131]

[0132] In the formula, Q is the carbon dioxide emission amount generated in the steel production process; Q i Q is the carbon dioxide emission amount generated in the steel production process; Q ano Q is the carbon dioxide emission amount generated in the steel production process; Q j Q is the carbon dioxide emission amount generated in the steel production process; Q i Q is the carbon dioxide emission amount generated in the steel production process; Q ano Q is the carbon dioxide emission amount generated in the steel production process; Q j Q is the carbon dioxide emission amount generated in the steel production process; Q

[0133] In the steel production process, in addition to the carbon emission part, there is also a carbon fixation process, and the amount of fixed carbon should be deducted from the carbon emission amount, and the specific amount can be obtained by the following formula:

[0134]

[0135] In the formula, R COO Q is the carbon dioxide emission amount generated in the steel production process; Q k,COO Q is the carbon dioxide emission amount generated in the steel production process; Q k,COO Q is the carbon dioxide emission amount generated in the steel production process; Q ano Q is the carbon dioxide emission amount generated in the steel production process; Q Cano Q is the carbon dioxide emission amount generated in the steel production process; Q Al Q is the carbon dioxide emission amount generated in the steel production process; Q

[0136] According to the above formula, the total carbon emission amount of the steel enterprise is calculated as:

[0137]

[0138] When the method of the application is applied to an electrolytic aluminum enterprise: in the electrolytic aluminum production process, the carbon emission process caused by the use of electricity, the carbon emission process caused by the anode consumption and the carbon emission process caused by the anode effect are involved. Among them, the carbon emission amount caused by the use of electricity is calculated by using the carbon emission model of energy consumption, and the carbon emission caused by the anode consumption is calculated according to the following formula:

[0139] E ano = EF Cano × Q Al (13)

[0140] In the formula, E ano Q is the carbon dioxide emission amount generated in the steel production process; QCano Carbon dioxide emission factor of carbon anode consumption; Q Al Production of aluminum.

[0141] wherein the carbon dioxide emission factor of carbon anode consumption is calculated according to the following formula:

[0142]

[0143] In the formula, NC Cano Net consumption of carbon anode per ton of aluminum, which can adopt the recommended value of 0.42 tC / t-Al of China Nonferrous Metals Industry Association; S Cano Average sulfur content of carbon anode, which can adopt the recommended value of 2% of China Nonferrous Metals Industry Association; A Cano Average ash content of carbon anode, which can adopt the recommended value of 0.4% of China Nonferrous Metals Industry Association.

[0144] When anode effect occurs in an electrolytic aluminum enterprise, two kinds of perfluorocarbons, carbon tetrafluoride (CF4) and hexafluorodio carbon (C2F6), are discharged, so the carbon emission amount caused by anode consumption can be calculated according to the following formula:

[0145]

[0146] In the formula, E FCs Anode effect perfluorocarbon emission amount; CF4 emission factor of anode effect; C2F6 emission factor of anode effect. Among them, the emission factor of anode effect is closely related to the technical type of electrolytic cell. At present, the point type prebaked cell technology (PFPB) is mainly adopted in the production of electrolytic aluminum in China, and the recommended emission factor values of China Nonferrous Metals Industry Association are 0.034 kg CF4 / t-Al and 0.0034 kg C2F6 / t-Al.

[0147] In addition, if there is a calcined limestone process in the electrolytic aluminum enterprise, the carbon dioxide emission amount of the calcined decomposition process of limestone also needs to be considered, and the specific calculation formula is as follows:

[0148] E Ca = EF Ca × Q Ca (16)

[0149] In the formula, E Ca Carbon dioxide emission amount of limestone calcination decomposition; EF Ca Carbon dioxide emission factor of calcined limestone, which can adopt the recommended value of 0.405 tons of carbon dioxide per ton of limestone of China Nonferrous Metals Industry Association; Q Ca Consumption of limestone raw materials.

[0150] According to the above formula, the total carbon emissions of the aluminum electrolysis enterprise are calculated as follows:

[0151] E AL = E ano + E FCs + E Ca (17) S142, external energy purchasing condition

[0152] The carbon dioxide emissions generated by the industrial zone under the external energy purchasing condition mainly include the carbon dioxide generated by energy consumption and the carbon dioxide emissions generated by industrial production process. Among them, the carbon dioxide emissions generated by energy consumption are indirect carbon emissions generated when the park purchases external energy to meet the demand of enterprises in the park; and the carbon emissions of the industrial production part are determined by the production process of the enterprise, which comes from the carbon emissions caused by the consumption of production raw materials in the enterprise. The total carbon emissions are the same as formula (7).

[0153] Further, the S142 step further includes:

[0154] S1421, energy consumption

[0155] The carbon emissions generated by energy consumption are mainly calculated according to the energy consumption situation in the high-energy consumption park, and the carbon emission coefficients under various energy consumption situations are referred to for the accounting of the carbon dioxide emissions of the industrial zone in each year. According to the carbon emission accounting method, the carbon emission accounting method of the industrial zone is as follows:

[0156] E energy = ε el E e,buy + ε h E h,buy (18)

[0157] In the formula, ε el , ε h are the carbon content coefficients of electricity and heat respectively; E e,buy , E h,buy are the electricity and heat purchased from the outside of the park.

[0158] S1422, industrial production

[0159] The carbon emissions of the industrial production part are only related to the carbon emissions generated by the consumption of raw materials per unit output of the enterprise, and are irrelevant to the working condition, and the calculation method of the carbon emissions is consistent with the isolated network operation condition.

[0160] S143, grid-connected operation condition

[0161] The carbon dioxide emissions generated by the industrial area in the park under grid-connected operation conditions mainly include carbon dioxide generated by energy consumption and carbon dioxide generated by industrial production process. Among them, the carbon dioxide generated by the energy consumption part of the industrial area comes from the energy consumption to meet the electricity and heat load of the enterprises in the park, the indirect carbon emissions generated when the park purchases external energy to meet the demand of the enterprises in the park, and the indirectly reduced carbon emissions caused by the sale of excess electricity and heat energy; and the carbon emissions of the industrial production part are determined by the production process of the enterprise, and come from the carbon emissions caused by the consumption of production raw materials in the enterprise. The total carbon emissions are shown in formula (7).

[0162] Further, the S143 step further comprises:

[0163] S1431, energy consumption

[0164] The carbon emissions generated by energy consumption are mainly calculated according to the energy consumption situation in the high-energy-consuming park. Referring to the carbon emission accounting method, the carbon emission accounting method of the industrial department is as follows:

[0165]

[0166] In the formula, N ele , N hot are the types of power supply and heat supply energy in the park; Q ele , Q hot are the power and heat production of the park; Q ele,out , Q hot,out are the sold electricity and heat; E out is the reduced carbon emissions of the sold electricity and heat.

[0167] S1432, industrial production

[0168] The carbon emissions of the industrial production part are only related to the carbon emissions generated by the consumption of raw materials caused by the unit output of the enterprise, and are irrelevant to the working conditions. The carbon emission measurement method is consistent with the isolated network operation condition.

[0169] Next, according to S2, the response optimization model of the controllable resources in the high-energy-consuming park is constructed. As shown in the response optimization model framework diagram shown in Figure 1 , the response optimization of the adjustable resources in the high-energy-consuming park needs to be carried out around multi-time scale, multi-resource coordination and multi-objective optimization, combined with power supply unit constraints, power system flexibility requirements and enterprise production constraints, and considering the production and power supply risks of the park. Therefore, the present application constructs the response optimization model of the controllable resources in the high-energy-consuming park, and the minimum comprehensive energy cost, the maximum comprehensive energy efficiency and the minimum carbon emissions are taken as the optimization objectives in the day-ahead stage, and the minimum adjustment cost is taken as the optimization objective in the day-ahead stage.

[0170] Further, the S2 step comprises:

[0171] S21, a day-ahead scheduling optimization model. The day-ahead scheduling optimization model of the high-energy-consumption park comprises three optimization objectives of minimum comprehensive energy consumption cost, maximum comprehensive energy efficiency and minimum carbon emission.

[0172] Further, the S21 step comprises:

[0173] S211, comprehensive energy consumption cost

[0174] The minimum comprehensive energy consumption cost objective function is as follows:

[0175] minF1=C ope +C energy +C adj (21)

[0176] In the formula, C ope is the operation and maintenance cost of the park energy supply equipment; C energy is the external interaction cost of the park; C adj is the cost of calling load-side adjustable resources of the park.

[0177] The operation and maintenance cost of the park energy supply equipment comprises the operation and maintenance cost of the energy supply equipment and the fuel cost, and is specifically as follows:

[0178]

[0179] In the formula, l is the lth raw material consumed; ω l is the price coefficient of the lth raw material consumed; P l is the consumption of the lth raw material; is the maintenance price coefficient of the kth energy supply equipment of the park; P k,t is the operation power of the kth equipment at the t period; T is the scheduling period; Ω Eq is the set of energy supply equipment of the high-energy-consumption park.

[0180] The park can purchase energy from the outside, which includes three parts of purchasing electricity, purchasing heat and purchasing gas, and can also sell the generated excess to the power grid. The external interaction cost of the park includes the expenditure cost of purchasing corresponding energy from the outside when the power grid, heat grid and gas grid are insufficient, and the excess transaction income is deducted, and is specifically as follows:

[0181]

[0182] In the formula, are the purchase and sale electricity prices of the power grid at the t period respectively; are the purchase and sale electricity of the power grid at the t period respectively; ω buy,h is the heat price coefficient; ω is the purchase efficiency; ω buy,gas ω is the natural gas price; ω is the amount of natural gas purchased in the t period.

[0183] The park call load side adjustable resource cost includes the compensation cost required by the steel, polysilicon, and electrolytic aluminum production load when accepting the park dispatching, and is specifically as follows:

[0184]

[0185] In the formula, ω adj,i ω is the unit load compensation coefficient of the i th load; ω is the load power adjustment change amount of the i th load in the t period; ω CL ω is the set of adjustable loads.

[0186] S212, comprehensive energy efficiency

[0187] The comprehensive energy efficiency maximum target function is as follows:

[0188]

[0189] In the formula, N is the number of energy subsystems; k j , k m , k z ω is the standard coal conversion coefficient corresponding to the j th type of energy, the m th energy storage, and the z th type of consumed energy; ω z ω is the z th type of energy consumption; ω is the energy storage amount of the m th energy storage device and the energy output amount considering the energy release loss; M is the total number of energy storage devices.

[0190] S213, carbon emission

[0191] The carbon emissions of the industrial park under the isolated network operation condition, the external purchase energy condition, and the grid-connected operation condition are different, which also leads to different carbon emissions of the entire park. The calculation of the total carbon emission E under each condition is shown in formula (1). The minimum carbon emission target function is as follows:

[0192] min F3 = E (26)

[0193] Further, while performing S213, the day-ahead dispatching optimization model needs to meet the following constraint conditions:

[0194] S2131, power balance constraint

[0195] The power constraint includes the power balance constraint, the heat balance constraint, and the natural gas balance constraint, and is specifically as follows:

[0196]

[0197]

[0198] wherein, are the total electric, thermal and gas load in the park, respectively.

[0199] S2132, thermal power constraints

[0200] Thermal power constraints include maximum and minimum power constraints, unit ramping constraints and unit start-up and shut-down constraints.

[0201]

[0202]

[0203] wherein, P TP,t is the power output of the thermal unit; is the rated capacity of the thermal unit; U i,t is the start-up state of the i-th thermal unit at time period t; is the up-ramp rate of the i-th thermal unit; is the down-ramp rate of the i-th thermal unit; is the minimum time for start-up of the i-th thermal unit; is the minimum time for shut-down of the i-th thermal unit; is the time for continuous start-up of the i-th thermal unit until time period t-1; is the time for continuous shut-down of the i-th thermal unit until time period t-1.

[0204] S2133, system reserve constraints

[0205] Based on the load and wind power prediction, it is generally not less than 3% of the predicted load plus 5% of the predicted wind power output.

[0206]

[0207] 0≤R i,t ≤R i,max ,i∈Ω CL (34)

[0208] 0≤R i,t ≤P i,max -P i,t ,i∈Ω CL (35)

[0209] wherein, R i,t is the reserve capacity of the i-th adjustable unit at time t; R i,max is the maximum reserve capacity of the i-th adjustable unit; P i,max is the maximum power of the i-th adjustable unit; P i,tP (t) is the power of the ith adjustable unit at time t.

[0210] S2134, wind and solar power output uncertainty

[0211] In actual operation, many random factors affect the day-ahead scheduling optimization model, and the scheme obtained by the model is difficult to meet the actual scheduling requirements, so the influence of uncertainty needs to be fully considered in the model. Considering that the fluctuation range of wind and solar power output is within the box-type uncertainty set U constructed as follows:

[0212]

[0213] In the formula, P WPP,t , P PV,t are the wind power and photovoltaic power considering uncertainty, respectively; are the maximum fluctuation deviation allowed by wind power and photovoltaic power, respectively.

[0214] S2135, equipment operation constraints

[0215] S21351, steel enterprises

[0216] According to the energy consumption behavior of steel enterprises, the steel industry load can be divided into three categories according to the fluctuation characteristics: sustained impact load, intermittent impact load, and stable load.

[0217] First, sustained impact load

[0218] Sustained impact load is mainly for rolling mills. Sustained impact load is not suitable for regulation and control due to its high requirements for temperature, speed, and pressure in the production process, but it can be regulated and controlled by shifting the process flow time.

[0219]

[0220]

[0221] In the formula, P roll (t, t0, Δt, a) is the power generated by a piece of steel billet through a rolling mill; t0 is the time when the steel billet just enters the rolling mill; Δt is the time required for the steel billet to pass through the rolling mill; a is the average power of the rolling mill within Δt; t Rij is the time when the steel billet enters the ith rough rolling mill for the jth time; Δt Rij is the time length of the steel billet passing through the ith rough rolling mill for the jth time; a Rij is the time length of the steel billet passing through the ith rough rolling mill for the jth time; n rough is the number of rough rolling mills; k i is the rolling pass of the ith rough rolling mill; t Fi is the time when the steel billet enters the ith finishing rolling mill; Δt Fiis the time length spent by the billet on the i-th finishing mill;a Fi is the average power of the billet on the i-th finishing mill; ΔT Fi is the time interval between the first and the i-th billet entering the finishing process; n finish is the number of finishing mills; n steel is the number of billets.

[0222] Second, intermittent impact load

[0223] The intermittent impact load in the steel industry is mainly the electric arc furnace. Its essence belongs to the electric heating load. The thermal load usually has certain thermal inertia, and the steelmaking production link of the electric arc furnace is usually provided with a buffer storage area between the next production link. The interruptible and transferable characteristics of the electric arc furnace can be utilized to appropriately shut down or delay the start of the electric arc furnace, which is a method of participating in the demand side regulation.

[0224]

[0225] In the formula, t on is the moment when the electric arc furnace is powered on and arcing; Δt up is the time length required for the moment when the electric arc furnace is arcing to reach the moment when the stable rated power is required, which is usually 5-10 s; Δt down is the time length required for the electric arc furnace power to be 0 from the order of the operator to shut down the electric arc furnace, which is usually not more than 10 s; t off is the moment when the electric arc furnace is completely powered off; P rated is the rated power of the electric arc furnace when it is running; δ(t) is a random value between (-δ max , δ max ), which is used to represent the random power fluctuation of the electric arc furnace when it is running stably, and is usually set to 5%-20% according to the actual working condition.

[0226] Third, stable load

[0227] The stable load mainly has two types. One is the load with large power but maintaining stable for a long time, and its fluctuation characteristics depend on the production plan, such as the electric arc furnace for continuous operation, the load oxygen generator, etc. The other is the load with frequent fluctuations but small power, such as the conveyor matched with the rolling load, the water pump, etc.

[0228] P others (t) = (1 + δ other (t)) P otherrated (40)

[0229] In the formula, P otherrated is the sum of the rated powers of other types of loads; δ other (t) is a random value between (-δ othermax , δ othermaxrandom value of the other class load, representing the random fluctuation power size of other class load running, since the other class load power is stable in medium and short term, can be set as 5% othermax .

[0230] Fourth, the total power characteristics

[0231] The power of a single rolling line and a single electric arc furnace is expanded to obtain the total power of the entire steel enterprise over time.

[0232]

[0233] P (t) =∑P enterprise (t) is the total power of a certain steel enterprise; P RFtotali (t) is the power of the i-th rolling line; P LFi (t) is the power of the i-th electric arc furnace; N R is the number of rolling lines; N L is the number of electric arc furnaces.

[0234] S21352, polysilicon enterprise

[0235] According to the load power characteristics, changing the end voltage U va of the silicon rod can realize the adjustment of the polysilicon load power. The power characteristics of a polysilicon rod with a radius of r can be expressed by the following formula:

[0236]

[0237] I (t) =∑I PCS is the effective value of the single-phase current of the polysilicon load; A PCS , B PCS , C PCS , D PCS , G PCS , H PCS are constants, which can be fitted from the production data of actual rated operation.

[0238] Based on this, the equivalent resistance when the radius is r can be obtained:

[0239]

[0240] In the production process of polysilicon, there is an energy conversion relationship as follows:

[0241]

[0242] Δt is the time change; ΔQ out is the heat required for heating the reaction gas; L Si , K, η hotThe total length of the silicon rod, the heat transfer coefficient of the silicon rod and the mixed gas, and the proportion of the reaction endotherm are all constants; T out The equivalent temperature of the chassis and the furnace wall surface is a constant; T x The surface temperature of the silicon rod is generally 1000℃≤T x ≤1100℃, which can ensure production, T x =1080℃ is the most suitable temperature for production, i.e., the rated temperature. When participating in power regulation, the polysilicon generally participates in downward power regulation, and its adjustable range is 1000℃≤T x ≤1080℃.

[0243] When regulating the polysilicon load power, the load power can be equivalent to the power consumed by the heating and cooling water. Because the polysilicon production will close the gas inlet valve for a short time at low load power, ΔQ out is small and can be ignored. At this time, the polysilicon load power can be regulated by adjusting the flow rate of the cooling water. Let the cooling water flow rate regulation rate be α wat (0≤α wat ≤1), and its regulation range is 90% to 100%.

[0244] Therefore, the regulation range of the effective value U va of the polysilicon load single-phase voltage is:

[0245]

[0246] S21353, electrolytic aluminum enterprise

[0247] The electrolytic aluminum load part generally uses diode rectification. The relationship between the direct current voltage V dc and the high-voltage bus voltage V LO-V connected to the load side can be expressed as follows:

[0248]

[0249] In the formula, k AL is the regulation transformer ratio; and V SR is the saturation reactor voltage drop.

[0250] The electrolytic aluminum load is connected to a load-regulated transformer. Changing the ratio k AL can achieve m-level regulation of the electrolytic aluminum load:

[0251] k AL ={k AL1 ,k AL2 ,…,k ALm} (47)

[0252] Finally, the regulation range of the electrolytic aluminum load is:

[0253]

[0254] wherein, are the minimum and maximum values of the high-voltage bus voltage, respectively; are the minimum and maximum values of the saturable reactor when meeting the production requirements, respectively.

[0255] According to S22, the high-energy consumption park will be affected by the uncertainty of wind and light output in the daily scheduling stage, and the adjustable resources need to be called to ensure the supply and demand balance constraint in the park. Therefore, the daily scheduling optimization model takes the minimum adjustment cost as the optimization objective.

[0256]

[0257]

[0258] wherein, c θ is the adjustment cost of the device θ unit power change; ΔP θ,t is the power change value of the device θ at time t; is the output of the device θ at time t in the daily stage; is the planned output of the device θ at time t in the day-ahead stage.

[0259] The constraint conditions that the daily scheduling optimization model needs to meet are consistent with those of the day-ahead stage.

[0260] Next, according to S3, the response optimization model of the adjustable resources of the high-energy consumption park is solved. First, the multi-time scale optimization model is used to optimize the park production plan. In the day-ahead stage, based on the new energy output prediction and load demand prediction, the start-stop plan, production scheduling and energy storage charging and discharging strategy of the adjustable resources in the park are made. In the daily rolling optimization stage, based on the latest new energy output prediction, load measurement data and device state, the scheduling plan is rolling corrected to respond to real-time fluctuations. Then the model objective and constraint conditions are determined. Based on the park enterprise demand and park overall operation demand, in the day-ahead stage, the model objective is selected as the minimum comprehensive energy cost, the maximum energy efficiency and the minimum carbon emission from the economic, energy efficiency and carbon emission angles. In the daily rolling optimization stage, the minimum adjustment cost is taken as the objective function. Considering the types of adjustable resources, the operation constraints of various types of adjustable resources and adjustable resource constraints are determined. At the same time, the node power balance equation of the power grid, the rotating reserve capacity demand, and the production constraints such as process continuity are combined to determine the constraint conditions of the overall model. Finally, in the solving process, the energy efficiency objective function and nonlinear constraints are linearized, the original problem is converted into a mixed integer programming problem, and the three objective functions are de-dimensioned using the reduced semi-gradient membership function. Then, the multi-objective model is converted into a single-objective model for solving by using the weighting function.

[0261] Example two:

[0262] The embodiment of the present application provides a park adjustable resource response optimization device considering carbon emission reduction benefits under differentiated working conditions, comprising:

[0263] A measurement model construction module is configured to divide a high energy consumption park according to a carbon emission subject, measure carbon emissions of the divided zones by an emission factor method, and construct a carbon emission measurement model according to the carbon emissions, wherein the total carbon emissions of the high energy consumption park include the sum of carbon emissions of a building zone, carbon emissions of a traffic zone, carbon emissions of a waste treatment zone and carbon emissions of an industrial zone.

[0264] An optimization model construction module is configured to take minimum comprehensive energy consumption cost, maximum comprehensive energy efficiency and minimum carbon emissions as a day-ahead stage objective function, take minimum adjustment cost as an optimization objective of a day-in stage, and establish a response optimization model of adjustable resources of the high energy consumption park, wherein the response optimization model of the adjustable resources of the high energy consumption park includes a day-ahead scheduling optimization model and a day-in scheduling optimization model, and comprises:

[0265] A day-ahead scheduling unit is configured to take minimum comprehensive energy consumption cost, maximum comprehensive energy efficiency and minimum carbon emissions as the optimization objective of the day-ahead scheduling optimization model.

[0266] A day-in scheduling unit is configured to take minimum adjustment cost as the optimization objective of the day-in scheduling optimization model.

[0267] A model solution module is configured to optimize according to the response optimization model of the adjustable resources of the high energy consumption park, and comprises:

[0268] A scheduling optimization unit is configured to process the day-in scheduling optimization model according to a multi-time scale optimization model, and schedule and optimize a park production plan.

[0269] A conditional optimization unit is configured to determine constraint conditions of the response optimization model of the adjustable resources of the high energy consumption park according to park enterprise demand and park overall operation demand, through line constraints, adjustable resource constraints and production constraints.

[0270] A model decomposition unit is configured to perform dimensionless processing through a descending semi-gradient membership function according to the objective function, and convert a multi-objective model into a single-objective model through a weighting function.

[0271] In some implementations, the measurement model construction module comprises:

[0272] A building zone carbon emission measurement unit is configured to measure the carbon emissions E bldg As shown in formula (2):

[0273]

[0274] wherein m1 is the energy type consumed by the construction area; C bldg,i is the carbon emission factor of the i-th energy type in the construction area; EF bldg,i is the consumption of the i-th energy type in the construction area; B bldg is the carbon recycling rate of the construction area;

[0275] a carbon emission calculation unit for the transportation area, for calculating the carbon emission E trans as shown in equation (3):

[0276]

[0277] wherein m2 is the energy type consumed by the transportation area; C trans,i is the carbon emission factor of the i-th energy type in the transportation area; EF trans,i is the consumption of the i-th energy type in the transportation area; B trans is the carbon recycling rate of the transportation area;

[0278] a carbon emission calculation unit for the waste treatment area, for calculating the carbon emission E deal as shown in equation (4):

[0279] E deal = E s + E l (4)

[0280] wherein E s is the carbon emission of the solid waste treatment process; E l is the carbon emission of the wastewater treatment process;

[0281] a carbon emission calculation unit for the waste treatment area, for calculating the carbon emission of the industrial area by classifying the calculation of the carbon emission of the industrial area by the isolated network operation condition, the external energy purchase condition, and the grid-connected operation condition, as shown in equation (7):

[0282] E ind = E energy + E steel + E AL (7)

[0283] wherein E ind is the total carbon emission of the industrial area; E energy is the carbon emission of the energy consumption part; E steel is the carbon emission of the steel enterprise production process; E AL is the carbon emission of the electrolytic aluminum enterprise production process.

[0284] In some implementations, the carbon emission calculation unit for the waste treatment area comprises:

[0285] a solid waste measurement unit for calculating carbon emissions E of the solid waste treatment process s as shown in equation (5):

[0286]

[0287] wherein m3 is the type of solid waste treatment; EF s,i is the treatment amount of the i-th type of solid waste; K s,i is the carbon content ratio in the i-th type of solid waste; is the conversion coefficient of carbon into carbon dioxide;

[0288] a wastewater measurement unit for calculating carbon emissions E of the wastewater treatment process l as shown in equation (6):

[0289]

[0290] wherein m4 is the type of wastewater treatment; D i is the Chemical Oxygen Demand (COD) content of the i-th type of wastewater; D is the COD content of surface water; V i is the discharge amount of the i-th type of wastewater; is the conversion coefficient of carbon into carbon dioxide.

[0291] In some implementations, in the intra-day scheduling unit, the optimization objective of the intra-day scheduling optimization model includes minimizing adjustment cost, as shown in equations (51) and (52):

[0292]

[0293] wherein c θ is the adjustment cost of a unit power change of the device θ; ΔP θ,t is the power change value of the device θ at time t; is the output of the device θ at time t in the intra-day stage; is the planned output of the device θ at time t in the day-ahead stage.

[0294] Embodiment Three:

[0295] The embodiment also provides an electronic device including a memory and a processor, wherein the memory is configured to store one or more computer instructions, and the one or more computer instructions are configured to be executed by the processor to implement the method of embodiment one.

[0296] In practical applications, the processor can be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller (MCU), a microprocessor, or other electronic elements, which are used to execute the methods in the above embodiments.

[0297] The method implemented by the embodiment is as shown in the content of Embodiment One.

[0298] Embodiment Four

[0299] The embodiment also provides a computer readable storage medium, and the computer readable storage medium stores a computer program. When the computer program is executed by one or more processors, the method of Embodiment One is implemented.

[0300] The computer readable storage medium can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk.

[0301] The method implemented by the embodiment is as shown in the content of Embodiment One.

[0302] In several embodiments provided by the present application, it should be understood that the disclosed system and method can also be implemented in other ways. The system and method embodiments described above are only illustrative.

[0303] It should be noted that, in the present document, the terms "first", "second", and the like, in the description and in the claims of the present application, as well as above-mentioned figures, are intended to distinguish similar objects and not to describe a particular chronological or hierarchical order. The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus including the element.

[0304] Although the present application has been disclosed with reference to the above embodiments, the content described is merely for the purpose of facilitating understanding of the present application and is not intended to limit the present application. Any person skilled in the art of the present application can make any modification and change in the form and details without departing from the spirit and scope of the present application, and the patent protection scope of the present application shall be subject to the scope defined by the appended claims.

Claims

1. A method for optimizing the response of adjustable resources in a park under differentiated working conditions, taking into account the benefits of carbon emission reduction, characterized in that: include: S1. Divide the high-energy-consuming parks into different zones according to the main body of carbon emissions, and calculate the carbon emissions of each zone using the emission factor method. The total carbon emissions of the high-energy-consuming parks include the sum of the carbon emissions of each zone of the high-energy-consuming parks; S2, based on minimizing comprehensive energy cost, maximizing comprehensive energy efficiency and minimizing total carbon emissions as the objective function of the day-ahead phase, and minimizing adjustment cost as the optimization objective of the intraday phase, a response optimization model of the adjustable resources of the high-energy-consuming park is established; S3, solving the response optimization model, and optimizing the response of the park's adjustable resources according to the solution results.

2. The method according to claim 1, characterized in that The high-energy-consuming park is divided into: construction area, transportation area, waste treatment area, and industrial area. The total carbon emissions of the high-energy-consuming park in S1 include the carbon emissions of the construction area, transportation area, waste treatment area, and industrial area: S11, carbon emissions E of the building area bldg , as shown in formula (2): Among them, m1 is the type of energy consumed in the building area; C bldg,i is the carbon emission coefficient of the i-th energy in the building area; EF bldg,i is the consumption of the i-th energy in the building area; B bldg is the carbon recycling rate of the building area; S12, carbon emissions E of the traffic area trans , as shown in formula (3): Where m2 is the type of energy consumed in the traffic area; C trans,i is the carbon emission coefficient of the i-th energy in the transportation area; EF trans,i is the consumption of the i-th energy in the traffic area; B trans is the carbon recycling rate of the transportation area; S13, carbon emissions E of the waste treatment area deal , as shown in formula (4): AND deal =And s +E l (4) Where, E s is the carbon emission from solid waste treatment process; E l is the carbon emissions from the wastewater treatment process; S14, the carbon emissions of the industrial zone are calculated by classification based on isolated grid operation conditions, external energy purchase conditions, and grid-connected operation conditions, as shown in formula (7): AND ind =And energy +E steel +E AL (7) Where, E ind is the total carbon emissions of the industrial zone; E energy E is the carbon emission of energy consumption; steel E is the carbon emission of the steel enterprise’s production process; AL It is the carbon emissions from the production process of electrolytic aluminum enterprises.

3. The method according to claim 2, characterized in that S13 includes: S131, carbon emissions E of the solid waste treatment process s , as shown in formula (5): Where m3 is the type of solid waste treated; EF s,i is the treatment capacity of the i-th type of solid waste; K s,i is the carbon content ratio in the i-th type of solid waste; is the conversion factor of carbon into carbon dioxide; S132, carbon emissions E of the wastewater treatment process l , as shown in formula (6): Where m4 is the type of wastewater treatment; D i is the chemical oxygen demand (COD) content of the i-th type of wastewater; D is the COD content of surface water; V i is the discharge volume of Category i wastewater; is the conversion factor of carbon to carbon dioxide.

4. The method according to claim 1, wherein In S2, the response optimization model of the adjustable resources of the high-energy-consuming park includes a day-ahead scheduling optimization model and an intraday scheduling optimization model, including: S21, the optimization objectives of the day-ahead scheduling optimization model include minimizing comprehensive energy costs, maximizing comprehensive energy efficiency, and minimizing total carbon emissions; S22, the optimization objective of the intraday scheduling optimization model includes minimizing the adjustment cost.

5. The method according to claim 4, characterized in that S22, the optimization objective of the intraday scheduling optimization model includes minimizing the adjustment cost, as shown in formulas (51) and (52): Where c θ is the adjustment cost of the unit power change of the equipment θ; ΔP θ,t is the power change value of the device θ at time t; is the output of the device θ at time t within the day; is the planned output of device θ at time t the day before.

6. The method according to claim 1, characterized in that S3, including: S31, processing the intraday scheduling optimization model according to the multi-time scale optimization model, and optimizing the scheduling of the park production plan according to the processing result; S32, determining the constraint conditions of the response optimization model of the adjustable resources of the high-energy-consuming park through row constraints, adjustable resource constraints, and production constraints based on the needs of the park enterprises and the overall operation needs of the park; S33: According to the objective function, de-dimensionalization is performed by reducing the semi-gradient membership function, and the multi-objective model is converted into a single-objective model by using a weighting function.

7. A park-adjustable resource response optimization device that considers carbon emission reduction benefits under differentiated working conditions, characterized in that: include: A calculation model construction module is used to divide high-energy-consuming parks into zones according to the main body of carbon emissions, and calculate the carbon emissions of each zone using the emission factor method. The total carbon emissions of high-energy-consuming parks include the sum of the carbon emissions of each zone of the high-energy-consuming parks; An optimization model construction module is used to establish a response optimization model for the adjustable resources of high-energy-consuming parks by setting the minimum comprehensive energy cost, the maximum comprehensive energy efficiency and the minimum total carbon emissions as the day-ahead objective function and setting the minimum adjustment cost as the optimization goal for the intraday phase; The model solving module is used to solve the response optimization model and optimize the response of the park's adjustable resources according to the solution results.

8. The device according to claim 7, characterized in that The high-energy-consuming park is divided into: construction area, transportation area, waste treatment area, and industrial area; the total carbon emissions of the high-energy-consuming park in the calculation model construction module include the carbon emissions of the construction area, the carbon emissions of the transportation area, the carbon emissions of the waste treatment area, and the carbon emissions of the industrial area: Building area carbon emission calculation unit, used for the carbon emission E of the building area bldg , as shown in formula (2): Among them, m1 is the type of energy consumed in the building area; C bldg,i is the carbon emission coefficient of the i-th energy in the building area; EF bldg,i is the consumption of the i-th energy in the building area; B bldg is the carbon recycling rate of the building area; Traffic zone carbon emission calculation unit, used for the carbon emission E of the traffic zone trans , as shown in formula (3): Where m2 is the type of energy consumed in the traffic area; C trans,i is the carbon emission coefficient of the i-th energy in the transportation area; EF trans,i is the consumption of the i-th energy in the traffic area; B trans is the carbon recycling rate of the transportation area; S13, carbon emissions E of the waste treatment area deal , as shown in formula (4): AND deal =And s +E l (4) Where, E s is the carbon emission from solid waste treatment process; E l is the carbon emissions from the wastewater treatment process; The waste treatment area carbon emission calculation unit is used to calculate the carbon emissions of the industrial area by classification based on isolated grid operation conditions, external energy purchase conditions, and grid-connected operation conditions, as shown in formula (7): AND ind =And energy +E steel +E AL (7) Where, E ind is the total carbon emissions of the industrial zone; E energy E is the carbon emission of energy consumption; steel E is the carbon emission of the steel enterprise’s production process; AL It is the carbon emissions from the production process of electrolytic aluminum enterprises.

9. The device according to claim 8, characterized in that The waste treatment area carbon emission measurement unit includes: Solid waste calculation subunit, used for carbon emissions E of the solid waste treatment process s , as shown in formula (5): Where m3 is the type of solid waste treated; EF s,i is the treatment capacity of the i-th type of solid waste; K s,i is the carbon content ratio in the i-th type of solid waste; is the conversion factor of carbon into carbon dioxide; Wastewater calculation subunit, used for carbon emissions E of the wastewater treatment process l , as shown in formula (6): Where m4 is the type of wastewater treatment; D i is the chemical oxygen demand (COD) content of the i-th type of wastewater; D is the COD content of surface water; V i is the discharge volume of the i-th type of wastewater; Conversion factor for carbon to carbon dioxide.

10. The device according to claim 7, characterized in that In the optimization model construction module, the response optimization model of the adjustable resources of the high-energy-consuming park includes a day-ahead scheduling optimization model and an intraday scheduling optimization model, including: a day-ahead scheduling unit, wherein the optimization objectives of the day-ahead scheduling optimization model include minimizing comprehensive energy costs, maximizing comprehensive energy efficiency, and minimizing total carbon emissions; The intraday scheduling unit is used for optimizing the intraday scheduling optimization model, wherein the optimization objective includes minimizing the adjustment cost.

11. The device according to claim 10, characterized in that In the intraday scheduling unit, the optimization objective of the intraday scheduling optimization model includes minimizing the adjustment cost, as shown in formulas (51) and (52): Where c θ is the adjustment cost of the unit power change of the equipment θ; ΔP θ,t is the power change value of the device θ at time t; is the output of the device θ at time t within the day; is the planned output of device θ at time t the day before.

12. The device according to claim 7, characterized in that Model solving module, including: A scheduling optimization unit, configured to process the intraday scheduling optimization model according to a multi-time-scale optimization model, and perform scheduling optimization on the park production plan according to the processing result; A condition optimization unit is used to determine the constraint conditions of the response optimization model of the adjustable resources of the high-energy-consuming park through row constraints, adjustable resource constraints and production constraints according to the needs of the park enterprises and the overall operation needs of the park; The model disassembly unit is used to perform dimensionless processing by reducing the semi-gradient membership function according to the objective function, and convert the multi-objective model into a single-objective model through a weighting function.

13. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method as described in any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is used to implement the method according to any one of claims 1 to 6.

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